Eras Tour vs. Soccer’s Big Tournament: At MetLife Stadium Are We Ever, Ever Getting &#8230;. Out of This Parking Lot?

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How to Optimize Electric Vehicle Charging Stations

ev charging station with person charging car

Electric vehicles (EVs) are a key lever in the effort to reduce transportation emissions and build a more resilient transportation system. And despite market uncertainty, used EV sales reached a new record high in March 2026.¹ But to encourage widespread EV adoption, drivers need access to reliable, convenient, and affordable EV charging stations.

For public agencies; utilities; and real estate, retail, and fueling companies working on expanding EV charging infrastructure, finding the right locations for new chargers and monitoring traffic to existing sites is critical, especially when limited funding is available to cover installation costs. While the right locations can drive positive return on investment (ROI), foster public trust in EVs, and help drive down vehicle emissions, the wrong locations could result in largely unused stations and poor ROI that dissuades future investment.

To optimize private and public EV charging infrastructure, businesses and agencies both need a strategic approach that balances several key goals:

Charger utilization
Reliability (uptime)
Coverage (access)
Equity (fair distribution)
Grid/cost performance (capacity and operating costs)

Each of these variables impacts ROI and public trust, which are key to driving long-term EV adoption and emissions reduction.

In this article, we’ll discuss five core considerations for effective EV charging infrastructure and the key metrics you can use to make the most of charging infrastructure investments.

1. Define Charging Demand and the Right Charger Mix (Level 2 vs DC Fast Chargers)

Charging demand can differ based on location and use case, so before you can identify optimal locations for chargers and what type of chargers you need, it’s important to define the type and level of charging demand you’re hoping to meet.

For example, the type of charging infrastructure (Level 2 vs. DC fast charging stations) and number of chargers needed along a major travel corridor may be very different from what you’d need to support EV charging at a residential multi-unit building, or an electric truck fleet charging hub.

To understand the unique charging mix needed to meet your goals, a data-driven approach is key. Many vehicle traffic metrics can help you understand where and which type(s) of chargers you need, including:

Vehicle volumes by time of day
Origin-Destination and routing patterns
Dwell time and trip distances
EV Activity (relative activity of EV trips vs. hybrid and gas vehicle trips)
Aggregated driver demographics (to support equitable charger deployment)

Below, we explore how each of these metrics can inform where and what type of charging infrastructure you may want to invest in.

Match charger speed to dwell time

Dwell time is a particularly useful metric to understand what type(s) of chargers you need to meet charging demand in a particular area. Dwell time measures how long a vehicle dwells (or stops) at a given location before it leaves.

Different charger levels have different charging capacities and take different amounts of time to charge a vehicle. For example, Level 2 chargers are typically more affordable to install, but offer less kilowatt output and use AC (rather than DC) current. These factors mean that a Level 2 charger takes longer to fully charge a vehicle’s battery – often between 2 and 8 hours.

Meanwhile, a DC fast charger (sometimes called a level 3 charger) can handle more kilowatts and also converts AC current from the grid to DC current before it reaches the electric vehicle. This results in a much faster charge – often between 15 and 30 minutes.

Understanding vehicle dwell time can help you determine what mix of chargers makes sense at a given location. If vehicles tend to stop for hours at a time (like at residences, workplaces, hotels, and certain entertainment venues), Level 2 chargers may be appropriate. However, to support charging in places where people only tend to stop for 10-30 minutes (like highway rest stops), DC fast chargers may be needed. Mismatching charging speed and dwell time is one of the easiest ways to create underutilized charging infrastructure.

Additionally, because personal vehicles often have different charging needs and different opportunities for electrification than commercial vehicles/trucks, segmenting dwell times by vehicle type can further inform the appropriate charger mix at a specific location. With StreetLight, customers can segment vehicle activity data by personal vs. commercial vehicles and further drill down into light-, medium-, and heavy-duty trends for additional clarity on charging demand.

Size your charging infrastructure for today + expansion

To fully optimize your charging infrastructure, it’s important to design for growth from day one. Deploying charging infrastructure that only meets the demands of today can lead to costly redevelopment as EV adoption rates increase and charging demand outstrips available supply.

A phased expansion plan can help you ensure a right-sized investment both now and in the future. Making a large up-front investment to build maximum capacity charging infrastructure can result in early underutilization and low ROI for development and utility partners. Instead, consider a deployment plan that expands to include more sites, power pathways, and port add-ons as future demand and capacity grow.

To design a phased approach that consistently meets the needs of EV users, you need to determine which potential charging station locations will be most utilized based on today’s driving patterns (more on that below).

2. Find High-Value EV Charging Station Locations Using Real Mobility Patterns

Identifying the highest value EV charging locations is more complex than knowing which roads are busiest or where chargers already exist (although these are helpful factors to consider). To build a prioritized list of sites for EV chargers, you need clear insights into real travel behavior.

Here are some data-driven ways you can identify strong locations for EV chargers and inform effective phasing for long-term deployment.

Identify where vehicles already stop long enough to charge

In addition to being useful in identifying the right charger mix, as discussed above, dwell time is also a key metric for identifying the most impactful locations for EV charger deployment.

Use the dwell time metric to understand where vehicles are stopping long enough to charge. You’re looking for average dwell times around 10-30 minutes for DC fast chargers and a few hours or more for Level 2 chargers.

Likewise, trip length can be a useful metric in identifying where drivers are most likely to need a charge at a given location. The farther a vehicle has traveled before it stops at a given location, the more likely it is to need a charge at that location. Look for locations where both dwell time and trip lengths are relatively high.

Similarly, Origin-Destination and routing patterns can provide additional clarity on charging demand. Identifying common origins and destinations as well as the top routes drivers take between locations can help identify potential corridors or regions where drivers are likely to need a charge.

By prioritizing locations where dwell time and trip patterns already align with charging needs, the chargers you deploy are more likely to see high utilization rates and ROI, which can support expansion efforts and further boost EV adoption.

Do a gap analysis of existing charging locations

Understanding where EV chargers currently exist is another key factor in prioritizing where to invest in additional charging infrastructure. Identifying gaps in your existing charging network helps you ensure that drivers always have a convenient place to charge, and no two charging hubs are competing for the same demand (i.e., cannibalizing each other).

When identifying optimal potential locations for new chargers, always compare these locations to a map of existing infrastructure. If one site looks like a strong candidate for new chargers based on metrics like dwell time and trip length, but it’s less than a mile away from an existing charging hub, that could warrant de-prioritizing that location or saving it for later phases of expansion.

Validate “sticky” demand vs. one-off traffic

To prioritize locations with consistent charging demand, it’s also important to understand seasonal and event-based fluctuations in travel behavior. The most strategic placements for charging infrastructure tend to have “sticky” or consistent demand (e.g., due to commuting patterns or regular errands) rather than seasonal or one-time spikes.

Prioritizing locations with consistent rather than spiky demand also helps ensure charging locations always have ample grid capacity and will drive reliable long-term ROI.

To identify stick demand, it’s helpful to use a traffic data source that has high temporal coverage rather than basing decisions on temporary counts that may capture only one or two days or traffic activity.

StreetLight’s repository of traffic data offers high temporal coverage and granularity that can help users separate seasonal and one-time spikes from consistent travel patterns. With the ability to measure any hour of the day and any day of the week, down to 15-minute granularity, this also enables you to understand how charging demand might shift over the course of a day or week.

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3. Screen Charging Locations for Grid Capacity and Site Feasibility

The best EV charging locations can still fail if the site can’t reliably deliver the necessary power or has land use constraints that create inconvenience for drivers.

Grid constraints that impact ROI

Grid capacity and utility rates can make or break the economic feasibility of your deployment plan. Drivers will be less likely to use charging locations that are unreliable or overly expensive.

Coordinating with local electric utilities can help agencies and developers ensure the chosen locations can support increased grid demand at an affordable rate.

To learn more about protecting grid capacity and coordinating long-term electrification planning, see how New England’s largest utility uses StreetLight data to get ahead of EV charging demand.

Site constraints that kill performance

Certain practical land use constraints can also impact site performance. Consider factors like:

Visibility – Can drivers clearly see the site from the road and navigate the site safely and confidently, even at night?
Access points – Are they clear and convenient for drivers?
Space – Is it easy for vehicles of different sizes to maneuver and park safely?

Permitting + stakeholders (reduce project friction)

Coordination with utilities, cities, and property owners is essential for any charger deployment project. Starting this process early can help prevent permitting delays and ensure positive stakeholder relationships that will support future expansion efforts.

4. Optimize EV Charging Locations for Equity and Network Coverage

To build a charging network that meets the needs of all road users, an equitable distribution of charging station locations is key. Consider who has the most access to current charging locations and how you can fill gaps for drivers who lack convenient access.

For example, it’s common to find charging network gaps in communities that are commonly underserved, while wealthier districts may already have more available charging options.

Expanding charging access to more communities also helps you avoid deploying chargers that go underutilized because EV drivers who might visit that location already have convenient access to charging elsewhere, such as at their home or workplace.

Aggregated traveler demographics can help you go beyond simple geographic spread when identifying coverage gaps in your local charging network. For example, StreetLight can help you understand the mix of travelers at a given location based on factors like household income, family size, and more.

5. Forecast Utilization and Prioritize a Build-Out Portfolio

After analyzing all the above factors, you’ll need to rank your list of candidate sites by priority in order to phase your deployment into short-, medium-, and long-term expansion goals.

Your specific funding sources, existing coverage, stakeholder goals, and local travel patterns can impact how you choose to optimize your charger deployment plan. Consider creating a weighted ranking system for factors like:

Overall vehicle volumes
Dwell times and trip lengths
Origin-Destination and routing patterns
Charger mix needed (Level 2 vs. DC fast chargers)
Demand stickiness
Equity
Grid capacity
Site feasibility
Distance from existing charging infrastructure

Once you’ve created a ranking system that matches your set of regional priorities, give each potential site a score in each category. You can then multiply that score by the weight assigned to that category, and repeat the process for each other category, adding the values together to create a total score for each site. This can help you prioritize the highest-scored sites for early deployment and slate lower-scoring sites for future phases of expansion.

You might also choose to qualitatively review sites that score highly and select a mix of sites that optimize for a variety of different criteria. Again, your specific approach may depend on the factors that are most important for your specific region, project goals, or stakeholder partners.

Turn Mobility Analytics Into Better EV Charging Locations With StreetLight

Many factors go into identifying your area’s highest priority locations for new EV chargers, so making an informed, defensible decision requires having reliable insights into how, where, and when people drive and park.

StreetLight has the most trusted repository of mobility data available on the market, offering insights that are rigorously validated and trusted by agencies, researchers, and businesses across North America.

Industry peers are already using StreetLight to forecast charging demand and choose optimal charger locations. For example, experts in the Silicon Valley used StreetLight data to identify 400+ locations for new public chargers and support California’s goal of having five million EVs on state roads by 2030.

Likewise, Eversource, New England’s largest utility, used data from StreetLight to model regional EV charging demand and design appropriate charging rates and energy management strategies to protect grid capacity.

Learn more about how StreetLight supports EV charger optimization on our location intelligence for EV charging and fueling page.

To see if StreetLight’s mobility data can help optimize your own EV charging infrastructure planning, reach out to a team member today.

FAQs

What data do you need to optimize EV charging locations?

Vehicle volumes, dwell times, trip lengths, traveler demographics, Origin-Destination patterns, routing, personal vehicle vs. truck activity, and other metrics can be useful in optimizing EV charging locations. While the specific metrics you need may vary based on your goals, a data-driven deployment strategy is key to avoiding underutilized stations that deliver poor ROI.

How do you choose between Level 2 and DC fast chargers?

Level 2 chargers are more affordable to install but take longer to deliver a full charge, while DC fast chargers can be more expensive but also more efficient. Determining the appropriate mix of charger types for your project depends on factors like:

Your budget
Your development partner(s)
Grid capacity
Existing infrastructure mix
Existing vehicle mix (e.g., how much traffic is trucks vs. personal vehicles)
How long vehicles typically stop at potential charging locations (dwell time)
The location (e.g., along a highway vs. at a retail hub)

For more information on choosing the right charger mix, see Section 1 above.

How do you improve EV charging station utilization after launch?

Optimal site selection is key to building highly utilized charging stations, but there are also strategies you can use after deployment to boost utilization rates.

Consider strategies like:

Improving the visibility of the charging stations. Clear signage, good lighting, and consistent map integration (e.g., on Google Maps or charging-specific platforms like ChargeHub) can all help increase discoverability for drivers.
Implement pricing incentives. Tactics like dynamic pricing for peak vs. off-peak hours and idle fees for vehicles that remain plugged in after fully charged can help encourage more visits throughout the day and increase availability of chargers during peak demand hours.
Invest in reliability. Ensure chargers are in good working condition with reliable up-time to support local charging demand. Regular, proactive maintenance and strategic energy management strategies can help with this.
Improve customer experience. Small details can make or break the customer experience and likelihood of repeat visitors. Consider adding amenities like shade canopies, lighting, trash cans, and flexible payment options.

References

Levin, Tim. “What EV Slowdown? Used Electric Car Sales Hit A Record High In March.” Inside EVs. April 20, 2026. https://insideevs.com/news/793503/used-ev-market-us-march-sales-2026/

Latest Results: Who’s Winning the Traffic Game During Soccer’s Biggest Tournament?

StreetLight is analzing notable traffic trends around three U.S. stadiums during the 2026 World Cup to understand how congestion is evolving in L.A., New York/New Jersey, and Kansas City.

During the World Cup, game play isn’t exclusively on the field. Outside the walls of the stadiums, transportation agencies in host cities have been running drills for months, deciding how to handle an extraordinary and prolonged traffic surge day-of-the-event.

The World Cup poses a unique challenge for traffic management for a few reasons:

quantity of games
popularity of fan gatherings
differences in fan behaviors
outcomes of matches as tournaments progress
and an influx of international visitors unfamiliar with local systems.

(Watch our recent webinar to see how traffic is developing at a World Cup host city airport given the expected surge in air travel.)

As with any mega-event, no historical event provides an exact match for travel behavior at the next event. With this in mind, StreetLight is using our real-time Traffic Monitor to capture how traffic is developing at three of the U.S. cities hosting matches.

Get more data-driven event management strategies in our Traffic Intelligence for Mega-Events eBook

Fan fests have been a particularly important aspect of World Cup games, allowing fans to celebrate their teams and watch the game throughout the city, whether or not they have a ticket. But they also add an additional nuance to traffic management.

In Kansas City, on Thursday June 25, the Netherlands faced off against Tunisia, and the game day celebrations commenced with a one-mile plus walk to the official FIFA Fan Fest. The march shut down traffic on two main corridors for about 2.5 hours, creating additional traffic volume on nearby roads.

*Atypical volumes on nearby roadways in the hours before, during, and after the fan walk.*

Then when it came to traveling from Fan Fest to the game drop off location, travel times peaked at 42 minutes, compared to typical travel times of just under 18 minutes. Peak congestion occurred between 4:30-5:15 ahead of the 6PM kick off.

Queuing was significant, and for the last 4.5 miles into the stadium along I-70, queues backed up along 3.4 miles of it, adding 23+ minutes of travel time.

*3.4 miles of queuing along I-70 heading into Arrowhead Stadium*.

In the case of L.A., a sprawled city with a complex transportation network, transportation agencies are directing much of their resources to transit as the action plan for managing congestion chaos. With SoFi stadium located in Inglewood near some of Southern California’s most traffic-snarled freeways and limited parking on-site, agencies are investing in express buses around the city and enhanced Metro service to the game, among other strategies. Transit agencies are also providing service to fan events and watch parties throughout the city. Locals and those traveling for other purposes have been warned to expect heavy traffic, with road restrictions and parking controls put in place to help manage flow.

L.A., which will host the 2028 Olympics, has been vocal about treating the World Cup as a dress rehearsal for managing traffic during the upcoming international multi-week event.

So how did traffic flow before, during, and after Friday’s game, and what can L.A. and other host cities learn from the first U.S. match up?

*Time lapse shows traffic within about 1.5 miles of SoFi Stadium in the 12 hours before, during, and after the game.*

Traffic was significant, of course. Congestion began building around noon, 6 hours ahead of the game, which likely played a role in an opening ceremony that looked only half-attended. Fans reported additional security challenges getting inside the stadium ahead of kick off.

Still, traffic was considerably worse in the hours after the game ended. Traffic appeared largely resolved by 11PM, although the game was over by 7:50PM.

*Heat map shows how queuing evolved before, during, and after the game on West Century Boulevard East. The road is a major arterial and access route to nearby freeways.*

After the U.S. Men’s National Team game on Friday, June 12th, travel times along West Century Boulevard and Manchester Avenue near SoFi showed 1 hour+ travel times to go about 5 miles. Compare that to a week earlier at the same time on Friday, June 5th, when travel times were only 21 minutes, while typical free flow is only 11 minutes on that same corridor.

We also looked at MetLife Stadium in New York/New Jersey to understand how traffic at a recent World Cup game, where travel times were 7x longer than typical, compared to traffic during the Eras Tour, finding that the World Cup delays far surpassed those experienced during a 2023 Eras Tour concert at the same stadium.

Agencies and organizations in cities including L.A., Dallas, Philadelphia, Kansas City, and more are using Traffic Monitor to manage World Cup traffic and its broader impacts on the roadway network before, during, and after the games. Agencies across North America can leverage the real-time data feed alongside historical data to:

get a bird’s eye view of traffic as it develops
zero in on any areas of concern
identify surprise disruptions and their impact on the broader network
conduct after-action reporting
and iterate on management strategies as new insights emerge.

To leverage real-time traffic insights for special events in your region, reach out to our team here.

Learn more ways to manage and monitor special event traffic with real-time data

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How to Measure and Calculate Foot Traffic

The amount of pedestrian traffic (AKA “foot traffic”) at a given location can have a big impact on business success. Whether you want to open a new store, run offline ads, or tailor your open hours to optimize walk-ins, knowing where people walk and when is key.

But foot traffic patterns can be difficult to measure. Many businesses are forced to rely on incomplete data sources such as consumer behavior surveys or counters that only capture in-store traffic to inform decisions that can greatly impact their bottom line. Inferring actionable takeaways from these sources can be risky due to limited geographical and temporal data coverage or responder bias that can skew results.

So how can businesses get reliable insight into real foot traffic patterns to make data-driven decisions that ultimately drive revenue? In this article, we’ll explore some of the common ways to measure foot traffic, including:

Manual counts
Automated people counters (e.g., on-location sensors and cameras)
Consumer surveys
On-demand foot traffic data software (like StreetLight)

You can use this guide to determine which method works best for you, whether you need basic entry counts, customer behavior insights, or multi-location coverage. We’ll also explore how advancements in foot traffic analytics are helping businesses fill key data gaps left behind by other common methods.

How to Set Up Your Foot Traffic Measurement for Accurate Data

The collection method you choose to gather foot traffic data can have a major impact on how reliable that data is for different use cases.

For example, manual counts are limited to a specific time frame and subject to human counting errors. That means you may not get data that reflects seasonal differences or off-peak hours, depending on the collection window(s) you choose.

Additionally, automated people counters like Wi-Fi sensors or thermal cameras have a limited spatial scope, typically only capturing foot traffic data based on those who enter a location or pass directly outside it. While this can be useful for tracking visitation patterns, it’s less useful in understanding the broader scope of foot traffic around a given location.

For use cases like site selection and advertising, among others, visibility into this broader scope of foot traffic is key to help decision-makers understand potential customer demand and how your customers move beyond the range of a single point of interest. Measuring store visits alone can’t provide insight into potential demand and how to capture it.

When using these methods, defining clear parameters is crucial to gathering accurate data. Oftentimes, foot traffic inaccuracies stem from unclear definitions. Take care to:

Clearly define your place boundary (e.g., a single door, your full site, or a specific road segment)
Select your reporting window (e.g., a specific hour, a day, or a full week. Consider how activity may change over the course of a day, week, or year when choosing your reporting window, as this can impact how representative your counts are)
Decide how you want to treat repeat visits and pass-throughs (i.e., do you want to understand the total number of visits, or is it more important to know how many unique visitors stopped in? What about people who pass through the place boundary without stopping to browse or purchase anything?)

Additionally, be aware of common counting errors with these methods:

Shared entrances or poorly defined place boundaries can lead to misattributed foot traffic counts (e.g., mistakenly counting a neighbor’s visitor as your own)
Passersby can mistakenly be counted as visitors, especially when they pass closely to a door sensor or camera
Double-counting can occur when you have multiple sensors throughout your site, or simply due to human error, in the case of manual counts

On-demand foot traffic analytics such as those offered by StreetLight can help avoid these common counting errors while offering increased spatial and temporal coverage that may not be possible with other methods. To see how StreetLight rigorously validates its foot traffic counts for every road and trail in the U.S., check out this white paper.

Foot Traffic Calculator: KPIs to Consider (With Simple Formulas)

Once foot traffic data has been collected, the next step is to use that data to derive actionable insights that drive profitable business decisions. Understanding your Key Performance Indicators (or KPIs) is key to this part of the process.

There are many KPIs you might want to consider when translating foot traffic data into performance-driving decisions. Let’s explore some of the top KPIs that inform site selection, marketing efforts, and business operations.

Key Metrics to Consider

Total visits or total trips

Total visits is a measurement of how many times people visited a specific location within a given time period. This measurement includes visits from people who may or may not take a desired action such as making a purchase. It can also include repeat visits from the same individual.

Total visits is a useful metric in understanding overall consumer demand, measuring the success of certain marketing strategies, and optimizing operational decisions such as open hours and staffing plans.

Similarly, total trips is a measurement of how many trips were made on foot to or through a particular location. While total visits specifically looks at how many times people visited a location (i.e., stopped at or entered within the location you define), total trips may additionally include people who passed by or through your location.

Understanding total trips can be especially useful in identifying high-traffic areas that could support a new store location, expanded operating hours, or additional staffing or marketing efforts. Likewise, total trips can be a helpful metric to analyze alongside store visitation patterns, helping illuminate areas where it may be possible to increase foot traffic into a particular location based on foot traffic patterns nearby that location.

Total trips can often be further broken down into pass-through trips vs. trips that end in your location. This means total visits can often be derived from a larger total trip count. This can be useful to measuring not just actual visits but also potential visits, helping decision-makers understand how much total foot traffic could translate into visits. We discuss this further in the section on capture rate below.

Conversion rate

Conversion rate is a measure of how often foot traffic actually converts into a desired action, as defined by you. Often, that desired action is making a purchase, though conversion actions could also include other desired behaviors such as signing up for a membership program, inquiring about a promotion, picking up a brochure, or simply entering your location.

Whatever conversion action you want to measure, the formula is basically the same. Divide the total number of conversion actions taken within a given time period by the total number of visits (or trips) in that same time period. The resulting number is your conversion rate.

This metric is particularly useful in understanding the impact that marketing efforts, customer service, and other factors may have on the likelihood that visitors take a specific action (such as making a purchase) within your location.

A low conversion rate can signal that, even though people are interested, they are ultimately not persuaded to take action during their visit. Meanwhile, a high conversion rate can signal that visitors’ experiences at the location are successfully driving them to take the actions you want them to take.

Capture rate

Capture rate is similar to conversion rate, and the two terms are often used interchangeably, though there is a subtle difference. While a “conversion” can be any type of desired action defined by you, a “capture” is typically a specific type of action—namely, entering a particular location.

Capture rate can be useful in understanding how often foot traffic near your physical location translates into actual visits.

To calculate capture rate, divide the number of total visits by the number of total trips.

Want to turn foot traffic into store visits?

How to Use Foot Traffic Data in Retail Stores

Once you have your KPIs measured, how do you connect these metrics to actions? We’ve discussed above what types of decisions common KPIs can be useful for. Now let’s cover a few common scenarios faced by retailers and dive deeper on how you might use foot traffic analytics to get a competitive edge.

Scenario 1: Staffing based on peak hour analysis

To ensure you have the right number of staff working at any given time, you need to understand how customer demand ebbs and flows during the day and week. Identifying your peak hours of foot traffic is a great first step to optimizing staffing.

To perform a peak hour analysis, separate foot traffic counts for your store into hourly bins and note the hour(s) when the counts are highest. You may want to run a peak hour analysis for each day of the week, or at least analyze a typical weekday vs. weekend day to understand how optimal staffing may shift over the course of a week.

These analyses can also help you identify optimal operating hours for your store, avoiding times when traffic is especially low and prioritizing hours where traffic is especially high.

Scenario 2: Site selection based on foot traffic with customer profiles

Understanding pedestrian and vehicle traffic at a particular location is key to identifying the most advantageous site for a new store — but not every traveler is necessarily a potential customer. Pairing total counts with data on traveler demographics can help you understand where traffic is most likely to translate into store visits and sales.

Some foot traffic analytics providers, like StreetLight, can pair foot traffic counts with aggregated demographics derived from the most recent census data to help you understand not just where consumers travel, but specifically where your target customers frequent. For example, StreetLight allows you to measure the overall breakdown of traffic by household income, family size, and other factors.

Why StreetLight Is the Gold Standard for Measuring Pedestrian Mobility

StreetLight has been a leading provider of transportation data solutions for over a decade, earning the trust of businesses, agencies, and firms across North America. We navigate the landscape of available data sources and rigorously validate our metrics so our customers can trust they’re getting reliable insights that power smarter decisions.

Businesses trust StreetLight to deliver the most reliable, up-to-date, and granular metrics available on the market. StreetLight helps businesses leverage foot traffic and vehicle activity metrics to:

Identify growing (and shrinking) markets and sites for your business
Choose the most advantageous locations for real estate investments
Optimize store operations like open hours and staffing
Measure the success of marketing, merchandising, and layout decisions
And more

For example, Motionworks uses StreetLight to measure the impact of billboard advertising and understand how specific customer profiles move around a given location to help their clients make data-driven marketing decisions.

Similarly, NaviRetail leverages StreetLight’s traffic count data to help retailers evaluate potential new store locations and identify where high consumer traffic can support those real estate investments.

Additionally, unlike other foot traffic data providers, StreetLight also offers bike activity metrics at the road segment level. When businesses have visibility into multiple modes of active transportation (i.e., both walking and biking), they are able to leverage a more complete picture of how potential customers move, what they’re likely to buy, how they encounter ads, and more.

On top of biking and walking data, StreetLight also offers the most comprehensive repository of vehicle activity data in the marketplace. This demo video explores how retailers can use vehicle traffic data for site selection, operational decisions, and more.

To learn more about how businesses like yours are using StreetLight today, visit our page on traffic data analytics for business.

To see if StreetLight’s foot traffic analytics could support your business goals, reach out to a team member today.

FAQs

1. What is considered foot traffic?

“Foot traffic” can mean a few different things depending on the context. Sometimes, when businesses talk about foot traffic, they are referencing the number of people who walk into a particular store or other point of interest.

Additionally, foot traffic may be used more broadly to refer to the number of pedestrians who walk to or through a particular area. This can include people walking along a particular road or intersection, not just those who enter a store.

This broader definition of foot traffic can be useful for businesses who want to measure how nearby pedestrian travel may translate into store visits at a potential new retail location, or event planners and venue managers who want to understand how event-goers and other pedestrians may arrive, depart, or route around a venue during an event.

2. How is foot traffic calculated?

Foot traffic is calculated by counting the number of pedestrians that travel to or through a particular location within a particular time frame.

For example, foot traffic can be measured by calculating Annual Average Daily Pedestrian Volumes (AADPT), which sums up the total number of pedestrians traveling at a particular location each year, divided by 364 days, to arrive at a daily average number of pedestrian trips for that location.

To learn more about how StreetLight calculates and validates its foot traffic counts, see our AADPT and AADBT Methodology and Validation white paper.

3. What is the best way to track foot traffic?

There are multiple ways to track foot traffic, including sensors installed at retail entrances, exterior cameras, and manual counts. Each of these methods can be useful for measuring visits to a store or point of interest. However, they each have limitations: sensors and cameras may only capture a narrow view of foot traffic entering or passing by a location, while manual counts are often resource intensive and only capture a snapshot in time.

Foot traffic analytics derived from big data can help fill in the gaps left by these other methods. Foot traffic analytics based on location data can offer more geographic and temporal coverage, which is critical to making well-informed site selection decisions as well as event management, retail operations, and other business decisions that are impacted by customer demand.

The Traffic Operations Toolkit: Address congestion, closures, and more with real-time and historical data

Road conditions can change rapidly. A fallen tree or flooded bridge might force cars to detour onto adjacent streets. Lane closures during road work can create bumper to bumper traffic. A crash may block lanes and require immediate response.

For agencies managing many miles of roadway and moving thousands (or even tens of thousands) of vehicles hourly, keeping traffic flowing smoothly and safely can be difficult enough during typical conditions. So, disruptions – planned, unplanned, or recurring – can quickly stall traffic or create unsafe conditions for everyone on the road. And the longer it takes for Traffic Operators, Engineers, and Planners to respond to disruptions, the worse conditions may get.

That’s why Traffic Operations teams are turning to transportation data to achieve smoother closures and detours, faster incident detection and emergency response, and proactive communication.

In the past, operators have had limited visibility into their road networks. While some major thoroughfares and intersections may have permanent sensors installed that can help operators spot unusual conditions, many roadways lack these sensors, forcing operators to send staff into the field for a first-hand look at conditions or wait on complaints from road users or reports from incident responders before they’re aware of an issue.

But today, access to transportation big data enables operators to monitor what’s happening on their entire road network in real time and investigate historical traffic patterns in minutes to get ahead of future disruptions, contextualize what’s happening now, or learn from past disruptions.

In this report, we’ll explore how you can leverage the latest advancements in transportation data for:

Daily roadway operational performance and congestion mitigation
Construction management and lane closure/detour planning
Special events management
Evacuation scenarios
Effective incident response
Clear communication with the public (e.g., on travel times, closures, detours)

1. Address Congestions and Speeding on Key Routes

Vehicle registrations are on the rise¹ and despite a brief lull during COVID, congestion is back with a vengeance across the U.S. This makes identifying and addressing bottlenecks a persistent challenge for operators. How do you mitigate slowdowns before cars become gridlocked and stop congestion from recurring?

Meanwhile, fatal crashes reached a 16-year high in 2023, with nearly a third of them involving speeding — and the situation hasn’t improved much since.² How can operations teams address speeding before the next crash and ensure improvements remain effective?

Analyzing real-time and historical traffic data is key to answering all these questions. Solutions like StreetLight’s Traffic Monitor product can help alert you to atypical volumes and speeds happening right now across your road network and compare current conditions to historical baselines to contextualize their severity.

*StreetLight’s Traffic Monitor here shows a timeline of average speeds on a specific road segment.*

Because congestion and speeding may have many causes, analyzing historical traffic patterns is key to diagnosing the ‘why’ behind persistent hazards and identifying effective solutions. Here are just a few examples:

Are commuting patterns a main cause of congestion?
- Analyze vehicle volumes, speeds, and travel times by time of day, day of week, and direction to see if there are consistent peaks and dips.
Does congestion ebb and flow during the year?
- Compare seasonal traffic trends to understand how tourism, recreation, academic calendars, or other events may impact traffic.
Could other routes help alleviate slowdowns on your most congested corridors?
- Investigate where traffic spills over during congestion to inform effective detours and communicate them to the public.
Are drivers complying with detour routes?
- Investigate atypical volumes to understand which alternate routes drivers are actually taking and how it’s impacting congestion.
Do specific destinations drive the bulk of traffic?
- Analyze common origins and destinations to determine where transit routes or multimodal infrastructure can help reduce the number of vehicles on the road.
Where are the speeding hotspots in your road network?
- Analyze average vehicle speeds to see where driving patterns don’t align with posted speed limits and evaluate potential solutions like speed feedback signs or temporary road diets.

Data in Action: Identifying recurring bottlenecks in Downtown LA

Los Angeles, California is notorious for traffic congestion due to multiple factors, including commuters crowding freeways during rush hours and major events at venues like SoFi Stadium or the Hollywood Bowl. This creates recurring bottlenecks throughout the city that can cause driver frustration, slow down emergency response, and delay goods from reaching their destination.

Using its Traffic Monitor product, StreetLight went back in time to investigate historical vehicle speeds and quickly zero in on the most severely congested corridors. Using Wednesday, July 23, 2025 as an example of typical weekday traffic, StreetLight then used the timeline feature to see how vehicle speeds change throughout the Downtown LA road network over the course of the day.

*Here, Traffic Monitor shows vehicle speeds throughout the Downtown LA road network at 5 p.m. on a Wednesday in July 2025.*

Actionable takeaways:

Morning slowdowns are common on key routes, but evening congestion is more severe and begins as early as 3pm.
By 5pm, congestion doesn’t just impact major corridors but also spills over to local roads throughout downtown.
Northbound traffic is especially congested in the evening on corridors like the Santa Ana and Santa Monica Freeways as commuters return home to the suburbs.

🎞️ Watch the full analysis here.

2. Keep Traffic Flowing During Construction and Events

Road construction and special events are among the most predictable causes of traffic disruptions — and among the most costly and frustrating, too. But by learning from past projects and monitoring current conditions, you can minimize disruptions — and the complaints that come with them.

There are many ways to use historical traffic data to prepare for upcoming construction and events:

Need to close one or more lanes?
- Review historical vehicle volumes by time of day and day of week to identify optimal closure windows and understand how a partial vs. full closure may impact nearby roads.
Closing an entire road?
- Choose the right detours based on historical routing patterns and capacity on alternate roadways.
Want to ensure safe work zones?
- Analyze historical vehicle speeds and queuing patterns to meet CFR requirements and determine where safety measures like barriers or variable message signs could mitigate risks.
Planning traffic management for an upcoming event?
- Review traffic patterns on past event dates and times to see if adjusting signal timings near venues or deploying resources like signage or traffic controllers could help.
Looking to streamline future event planning and operations?
- Evaluate traffic management outcomes on previous event dates to identify effective strategies and create repeatable event traffic management playbooks.

Real-time data can also help you ensure safe, flowing traffic during construction and events, and highlight opportunities to quickly deploy congestion mitigation or safety interventions:

Queues forming ahead of work zones?
- Measure queue lengths to meet federal regulations for work zone performance³ and determine where queue warning signage or other end-of-queue crash prevention measures may be needed.
Event egress causing gridlock?
- Identify and communicate where the bottlenecks are to improve travel experiences and see where adjusting signal timings or deploying traffic controllers could help.
Travel times increasing considerably during ongoing road work?
- See how current travel times compare to recent fluctuations to determine whether you need to adjust road work windows, implement congestion mitigation measures, or notify the public.
Road work having minimal impacts on traffic flow?
- Review fluctuations over the course of the day and week to see if road work windows could be safely expanded to complete the project faster.

Data in Action: Improving football fan experiences at Northwest Stadium

Northwest Stadium, the 70k-seat home of the Washington Commanders, has a congestion problem. The 2024 NFL Voice of the Fan survey reported that game day experiences at the stadium were among the lowest rated in the league, with only 15% of attendees finding it easy to arrive and depart from the stadium.

*Traffic Monitor’s Route Monitoring feature shows conditions on routes to and from the stadium on a non-game weekday in September.*

To shed light on where, when, and why game day congestion occurs, StreetLight used its Traffic Monitor product to compare two game days where road conditions differed:

Traffic patterns shift during a blowout vs. close game

December 1, 2024 – Sure Win

As the game’s end neared, it was clear the Commanders would defeat the Titans. Some fans left the stadium early, resulting in fewer vehicles exiting at once and less congestion on I-95.

DELAYS: Up to 30 minutes
QUEUING: Up to 48% of the route queued

*Traffic Monitor’s Queuing module shows moderate congestion on Capital Beltway North around 4:30 p.m. on December 1st, 2024, as some fans began an early egress.*

December 22, 2024 – Close Game

Commanders pull off a win against the Eagles on the final play of the game. Many fans stayed in their seats until these final exciting moments, resulting in a surge of exiting vehicles on I-95 and a huge peak in travel time.

DELAYS: Up to 52 minutes
QUEUING: Up to 76% of the route queued

These insights can help operators prepare for a variety of scenarios and evaluate potential improvements.

Additionally, operators can monitor real-time conditions on these key routes on game days to spot whether temporary interventions are needed and communicate current travel times and incidents with the public.

🎞️ See what else you can do with the Queuing feature in this video.

Get more data-driven event management strategies in our Traffic Intelligence for Mega-Events eBook

Data in Action: Minimizing construction impacts in Des Moines, Iowa

To resolve operational issues at the I-35/80 interchange in Des Moines, Iowa DOT needed to add lanes, install a new interchange, and widen multiple ramps. But this project area sees the highest traffic volumes in the state alongside high incident rates, and overlapped with other construction projects happening during the same time frame.

To minimize disruptions, Iowa DOT used StreetLight’s Traffic Monitor during construction to answer questions like:

When are vehicle volumes low enough to accommodate lane closures?
Can the local street network handle additional traffic during closures?
Are people avoiding this section of the interstate?
What other routes are people taking? Are detours working?
Which ramps are being used while ramp closures are in place?
How do work zone crashes impact the transportation system?

Iowa traffic specialists used StreetLight’s Traffic Monitor to see how delays fluctuate on streets near the impacted interchange and determine whether the local road network could handle additional traffic during construction-related closures. Sections with atypically high delays show up in red (more severe) and yellow (less severe).

*Iowa traffic specialists also used Traffic Monitor to evaluate different detour scenarios and find the routes most likely to cause minimal congestion.*

Traffic Monitor’s real-time insights enabled Iowa operators to plan effective lane closures and detours and share up-to-date information on delays, volumes, and speeds with first responders, city officials, and businesses that are impacted by the project.

See how Virginia measures the impact of seasonal tourism

3. Respond Faster to Unplanned Disruptions

To detect and address incidents like crashes, objects in the road, broken traffic lights, inclement weather, and more, agencies typically either need sensors installed wherever the incident occurs or staff in the field to report on conditions. But neither sensors nor staff can be present on all roadways at
all times due to high installation costs, dangers to staff, weather conditions, and other limitations. That
means incident detection and response is often subject to inevitable delays, further compounding congestion and safety.

But today, real-time transportation data can help fill these coverage gaps, enabling operators to detect incidents just moments after they occur and rapidly deploy the necessary resources to address them. Likewise, with information in their hands more quickly, officials can communicate clearly with
the public in a timely manner, updating drivers on travel time delays, temporary closures and detours, and more.

Plus, by comparing real-time conditions to historical baselines, operators can quickly contextualize the severity of an incident. But even with this wealth of data, keeping an eye on what’s happening across your entire road network at all times would be a colossal task. How do you quickly sift through current
conditions on your roads and zero in on issues that may need your attention?

Tools like the Real-Time Incident Feed in StreetLight’s Traffic Monitor product can help alert operators to atypical conditions, their severity, and potential causes. Traffic Monitor’s Real-Time Incident Feed displays up-to-date information on:

Full and partial road closures
Ongoing road works
Crashes

This helps shed light on not only where atypical traffic patterns are occurring but why and how severe their impacts are so you can prioritize effective solutions where they’re needed.

The Real-Time Incident Feed shows real-time road closures and other incidents in Washington, D.C. at the time of analysis on June 10, 2025. Here, you can see several total road closures in red and partial closures in yellow, with the icon colors corresponding to the relative severity of the incident.

Data in Action: Improving Future Evacuations with Data from Hurricane Ian

Extreme weather can be volatile, with forecasts shifting quickly. When Hurricane Ian changed course 24 hours before its expected landfall in September 2022, its unpredictable trajectory complicated traffic management efforts for Florida officials.

StreetLight investigated what happened on evacuation routes 48, 24, and 8 hours before the storm’s landfall to understand where travel demand surged and what operators can learn for future evacuation scenarios.

Our Traffic Monitor analysis revealed that:

48 hours before landfall, traffic conditions were relatively normal, with no peaks observed in vehicle volumes in either city.
24 hours before landfall, a surge in vehicle volumes hits key routes out of Fort Myers, especially Palm Beach Blvd, where volumes are 2-3x higher than usual. Around the same time, vehicle volumes are also 2-3x higher than normal on routes entering Fort Lauderdale.
8 hours before landfall, vehicle volumes drop off dramatically, indicating evacuation procedures had completely stopped by this time.

*Fort Lauderdale, 24 hours before landfall*

Before the next evacuation scenario, planners can use historical insights like these to understand:

Did people comply with evacuation instructions? Why or why not?
What routes did evacuees use? Were they the ones they were supposed to use?
When did people start and stop evacuating?
When did vehicle volumes peak, and how long were the traffic delays?
What best practices can I take away from these outcomes to inform future scenario planning?

During an evacuation, operators can also use real-time data to monitor how evacuations are going and coordinate rapid response to keep people safe:

When should the evacuation process begin to get everyone out safely?
Are people complying with evacuation orders and routing instructions?
Are there any crashes or other incidents stalling traffic on key routes?
Where should resources be deployed to support more efficient evacuations?
What do I need to communicate to the public during this critical time frame?

🎞️ Watch the full analysis here.

Data in Action: Understanding the ripple effects of a holiday crash in Colorado

On July 3rd, 2025, a fatal crash closed I-70 just as a surge of visitors was traveling to the Colorado Rocky Mountains for the Fourth of July weekend. As responders worked to address the situation and evacuate stuck vehicles from the roadway, the traffic impacts were being felt throughout the nearby road network and would linger long after the incident.

Real-time data can help operators limit the duration and severity of closures during similar incidents and understand their ripple effects across the road network to inform temporary detours and other traffic control measures.

StreetLight used its Traffic Monitor product to understand what happened before, during, and after the crash, which occurred around 1 p.m.:

That morning, traffic volumes were higher than typical as holiday travelers hit the road.
After the crash, traffic came to a standstill for miles behind the crash location.
Ramps leading up to the crash were clogged as vehicles attempted to escape the traffic jam.
To avoid the impacted section of I-70, travelers rerouted onto US Hwy 6 and US-40, clogging these highways as well.
Even after lanes on I-70 were fully reopened, the build-up of previously stranded vehicles and drivers reentering the interstate combined to create major congestion issues lasting for 4 more hours.

*Compared to typical weekday travel times of approximately 25 minutes for this route, travel times on July 3rd stretched to over 4 hours.*

*Traffic Monitor’s Queuing module shows queues on I-70 persisted even after lanes were reopened to traffic. Around 6:30 p.m., drivers were still experiencing delays of more than 50 mins.*

These ripple effects would be difficult to spot without real-time data on the entire road network. While major roadways like I-70 may have sensors installed, smaller detour routes often do not, making it difficult to understand and address impacts on these roadways.

Understanding the full scope of an incident’s impact on the road network can help officials minimize future dangers and disruptions by:

Identifying where additional resources may be needed
Establishing the best detour routes
Communicating proactively with the public

Citations

https://www.forbes.com/advisor/car-insurance/car-ownership-statistics
https://www.iihs.org/research-areas/fatality-statistics/detail/yearly-snapshot
See 23 CFR Part 630 Subpart J: https://www.ecfr.gov/current/title-23/chapter-I/subchapter-G/part-630/subpart-J

Learn how traffic operations experts are using real-time data to enable faster, more informed decisions

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The Top US Road Trips: Data-Driven Insights Into Travel Trends and Consumer Behavior

ANALYSIS

The Top US Road Trips: Data-Driven Insights Into Travel Trends and Consumer Behavior

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StreetLight analyzes Americans’ long-distance travel—and unpacks how businesses serving tourists can turn granular, precise traffic data into a competitive edge.

Americans famously love their road trips, making nearly 2 billion trips in 2024, according to Road Genius. This vehicle-based travel underpins a large slice of the $2.36T US tourism industry.¹ But for tourist-facing businesses to tap into the sector effectively, they must understand the nuances of common travel patterns. Knowing customers’ typical origins, routes, distance traveled, travel times, and more can help businesses serving tourists better allocate advertising dollars, forecast revenues, optimize resourcing, and make more profitable real estate decisions.

To understand how road trip activity varies among the biggest metros and what that can reveal for businesses catering to tourists, StreetLight analyzed road trip mobility patterns across the eight biggest metros to uncover where Americans travel most in summer and winter and related insights.

*Map highlights the eight metros analyzed for most popular road trip destinations.*

Maps of the top 10 destinations in summer and winter for each metro are below, followed by a table with complete results. Companies needing deeper insights on traffic trends can use StreetLight’s data to measure activity with more refined spatial and temporal granularity, and across an array of additional metrics, including demographics, directionality, drive train, and more.

Key Road Trip Insights

East Coast cities change it up in summer and winter. Southern and West Coast cities keep it stable:

With their varying weather, East Coast cities see a bigger seasonal difference between destinations.
New York City road trippers have the least overlap in their summer vs. winter destinations.
Texas metros and Los Angeles have the most stable road trip destinations across seasons—likely due to favorable year-round driving weather and consistent regional attractions.

Big city folk road trip away from hustle and bustle. Smaller city residents head towards the action:

Road trippers from NYC, the highest-population metro in the country, visit lower population destinations on average than those traveling from any of the other metros.
Boston and Washington D.C. road trippers, hailing from the smallest of the eight high population metros analyzed, head to destinations with the highest average population.

Mileage may vary:

Philadelphians' top road trip destinations are farther flung on average than any of the other metros, despite being in the middle of popular and clustered northeastern destinations.
Houston sees the shortest distance road trips on average at about 200 miles, despite the popular narrative that Texans love the open road.

Gambling plus the beach is a winning combo:

The most road trip destination among those studied is Atlantic City which appears six times.

Texans love Austin:

Austin is the most common destination outside the East Coast, appearing in the top 10 in summer and winter for road trippers from both Houston and Dallas.

Maps & Insights by Metro

New York-Newark-Jersey City, NY-NJ-PA

New York City sees the biggest seasonal difference between summer vs. winter destinations.
Boston and Washington D.C. are the top road trip destinations for New Yorkers in both summer and winter.
New Yorkers visit smaller towns overall compared to the other metros, with destinations in the top 10 averaging a population of about 200K.

Los Angeles-Long Beach-Anaheim, CA

Los Angeles sees significant similarity in summer and winter destinations. Eight out of 10 destinations repeat across seasons.
Popular road trips fall within the 100-350 mile range and are a balance between medium-sized cities and resort towns.

Chicago-Naperville-Elgin, IL-IN-WI

Chicago sees relatively high consistency across seasonal destinations.
Chicago’s pattern is mostly regional road trips within a 125–250 mile band, with Nashville standing out as a farther flung favorite.

Dallas-Fort Worth-Arlington, TX

Dallas has the highest seasonal overlap in destinations among metros: 9 out of 10 destinations are the same in summer and winter.
The single summer-only destination is Amarillo and the single winter-only destination is Round Rock.

Houston-The Woodlands-Sugar Land, TX

Compared to Dallas, Houston’s destinations extend further and include more Gulf Coast trips.
Houston's top road trips are the shortest compared to the other metros, averaging 200 miles as the crow flies.

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

Like nearby New York, Philadelphia sees a relatively high split between summer and winter destinations.
On average, Philadelphians travel farther than road trippers from other metros. Philadelphians travel nearly 500 miles to Myrtle Beach in the summer, which ranks as the longest road trip among any of the top 10 tours identified.

Washington-Arlington-Alexandria, DC-VA-MD-WV

Washingtonians are the most urban-oriented road trippers, with top destinations seeing an average population of 1.29 million, the highest among all the metros.
Washington ranks second for average road trip distance.

Boston-Cambridge-Newton, MA-NH

Boston shows strong consistency in destinations by season, especially compared to other East Coast metros.
Boston is second only to Washington for the average population size of its most popular destinations.

See how Virginia measures the impact of seasonal tourism

Table: The most popular U.S. road trip destinations

Origin Metro	Destination City	Destination State	Season	Destination Rank
New York-Newark-Jersey City, NY-NJ-PA	Boston	Massachusetts	summer	1
New York-Newark-Jersey City, NY-NJ-PA	Washington	District of Columbia	summer	2
New York-Newark-Jersey City, NY-NJ-PA	Saratoga Springs	New York	summer	2
New York-Newark-Jersey City, NY-NJ-PA	Ocean City	Maryland	summer	4
New York-Newark-Jersey City, NY-NJ-PA	Virginia Beach	Virginia	summer	5
New York-Newark-Jersey City, NY-NJ-PA	Niagara Falls	New York	summer	6
New York-Newark-Jersey City, NY-NJ-PA	Lake George	New York	summer	7
New York-Newark-Jersey City, NY-NJ-PA	Bolton Landing	New York	summer	8
New York-Newark-Jersey City, NY-NJ-PA	Atlantic City	New Jersey	summer	9
New York-Newark-Jersey City, NY-NJ-PA	Syracuse	New York	summer	10
New York-Newark-Jersey City, NY-NJ-PA	Boston	Massachusetts	winter	1
New York-Newark-Jersey City, NY-NJ-PA	Washington	District of Columbia	winter	2
New York-Newark-Jersey City, NY-NJ-PA	Stratton Mountain	Vermont	winter	3
New York-Newark-Jersey City, NY-NJ-PA	Killington Village	Vermont	winter	4
New York-Newark-Jersey City, NY-NJ-PA	Saratoga Springs	New York	winter	5
New York-Newark-Jersey City, NY-NJ-PA	Arlington	Virginia	winter	6
New York-Newark-Jersey City, NY-NJ-PA	Lake Placid	New York	winter	7
New York-Newark-Jersey City, NY-NJ-PA	Atlantic City	New Jersey	winter	8
New York-Newark-Jersey City, NY-NJ-PA	Pittsburgh	Pennsylvania	winter	9
New York-Newark-Jersey City, NY-NJ-PA	Syracuse	New York	winter	10
Los Angeles-Long Beach-Anaheim, CA	Paradise	Nevada	summer	1
Los Angeles-Long Beach-Anaheim, CA	Bullhead City	Arizona	summer	2
Los Angeles-Long Beach-Anaheim, CA	Lake Havasu City	Arizona	summer	3
Los Angeles-Long Beach-Anaheim, CA	Laughlin	Nevada	summer	4
Los Angeles-Long Beach-Anaheim, CA	Las Vegas	Nevada	summer	5
Los Angeles-Long Beach-Anaheim, CA	San Diego	California	summer	6
Los Angeles-Long Beach-Anaheim, CA	Enterprise	Nevada	summer	7
Los Angeles-Long Beach-Anaheim, CA	Winchester	Nevada	summer	8
Los Angeles-Long Beach-Anaheim, CA	Mammoth Lakes	California	summer	9
Los Angeles-Long Beach-Anaheim, CA	Fresno	California	summer	10
Los Angeles-Long Beach-Anaheim, CA	Paradise	Nevada	winter	1
Los Angeles-Long Beach-Anaheim, CA	Mammoth Lakes	California	winter	2
Los Angeles-Long Beach-Anaheim, CA	Las Vegas	Nevada	winter	3
Los Angeles-Long Beach-Anaheim, CA	Winchester	Nevada	winter	4
Los Angeles-Long Beach-Anaheim, CA	Enterprise	Nevada	winter	5
Los Angeles-Long Beach-Anaheim, CA	Henderson	Nevada	winter	6
Los Angeles-Long Beach-Anaheim, CA	Phoenix	Arizona	winter	7
Los Angeles-Long Beach-Anaheim, CA	San Diego	California	winter	8
Los Angeles-Long Beach-Anaheim, CA	Bullhead City	Arizona	winter	9
Los Angeles-Long Beach-Anaheim, CA	Lake Havasu City	Arizona	winter	10
Chicago-Naperville-Elgin, IL-IN-WI	Springfield	Illinois	summer	1
Chicago-Naperville-Elgin, IL-IN-WI	Nashville-Davidson metropolitan government (balance)	Tennessee	summer	2
Chicago-Naperville-Elgin, IL-IN-WI	Indianapolis city (balance)	Indiana	summer	3
Chicago-Naperville-Elgin, IL-IN-WI	St. Louis	Missouri	summer	4
Chicago-Naperville-Elgin, IL-IN-WI	Fort Wayne	Indiana	summer	5
Chicago-Naperville-Elgin, IL-IN-WI	Davenport	Iowa	summer	6
Chicago-Naperville-Elgin, IL-IN-WI	Bloomington	Indiana	summer	7
Chicago-Naperville-Elgin, IL-IN-WI	Lexington-Fayette	Kentucky	summer	8
Chicago-Naperville-Elgin, IL-IN-WI	The Galena Territory	Illinois	summer	9
Chicago-Naperville-Elgin, IL-IN-WI	Columbus	Ohio	summer	10
Chicago-Naperville-Elgin, IL-IN-WI	Springfield	Illinois	winter	1
Chicago-Naperville-Elgin, IL-IN-WI	Indianapolis city (balance)	Indiana	winter	2
Chicago-Naperville-Elgin, IL-IN-WI	Fort Wayne	Indiana	winter	3
Chicago-Naperville-Elgin, IL-IN-WI	St. Louis	Missouri	winter	4
Chicago-Naperville-Elgin, IL-IN-WI	Nashville-Davidson metropolitan government (balance)	Tennessee	winter	5
Chicago-Naperville-Elgin, IL-IN-WI	Davenport	Iowa	winter	6
Chicago-Naperville-Elgin, IL-IN-WI	Green Bay	Wisconsin	winter	7
Chicago-Naperville-Elgin, IL-IN-WI	Bloomington	Indiana	winter	8
Chicago-Naperville-Elgin, IL-IN-WI	Iowa City	Iowa	winter	9
Chicago-Naperville-Elgin, IL-IN-WI	Detroit	Michigan	winter	10
Dallas-Fort Worth-Arlington, TX	Austin	Texas	summer	1
Dallas-Fort Worth-Arlington, TX	Houston	Texas	summer	2
Dallas-Fort Worth-Arlington, TX	San Antonio	Texas	summer	3
Dallas-Fort Worth-Arlington, TX	Galveston	Texas	summer	4
Dallas-Fort Worth-Arlington, TX	Oklahoma City	Oklahoma	summer	5
Dallas-Fort Worth-Arlington, TX	College Station	Texas	summer	6
Dallas-Fort Worth-Arlington, TX	Lubbock	Texas	summer	7
Dallas-Fort Worth-Arlington, TX	Abilene	Texas	summer	8
Dallas-Fort Worth-Arlington, TX	Shreveport	Louisiana	summer	9
Dallas-Fort Worth-Arlington, TX	Amarillo	Texas	summer	10
Dallas-Fort Worth-Arlington, TX	Houston	Texas	winter	1
Dallas-Fort Worth-Arlington, TX	Austin	Texas	winter	2
Dallas-Fort Worth-Arlington, TX	San Antonio	Texas	winter	3
Dallas-Fort Worth-Arlington, TX	Oklahoma City	Oklahoma	winter	4
Dallas-Fort Worth-Arlington, TX	Lubbock	Texas	winter	5
Dallas-Fort Worth-Arlington, TX	Galveston	Texas	winter	5
Dallas-Fort Worth-Arlington, TX	Abilene	Texas	winter	7
Dallas-Fort Worth-Arlington, TX	Shreveport	Louisiana	winter	8
Dallas-Fort Worth-Arlington, TX	College Station	Texas	winter	9
Dallas-Fort Worth-Arlington, TX	Round Rock	Texas	winter	10
Houston-The Woodlands-Sugar Land, TX	San Antonio	Texas	summer	1
Houston-The Woodlands-Sugar Land, TX	Austin	Texas	summer	2
Houston-The Woodlands-Sugar Land, TX	Dallas	Texas	summer	3
Houston-The Woodlands-Sugar Land, TX	Corpus Christi	Texas	summer	4
Houston-The Woodlands-Sugar Land, TX	New Braunfels	Texas	summer	5
Houston-The Woodlands-Sugar Land, TX	Canyon Lake	Texas	summer	6
Houston-The Woodlands-Sugar Land, TX	Fort Worth	Texas	summer	7
Houston-The Woodlands-Sugar Land, TX	San Marcos	Texas	summer	8
Houston-The Woodlands-Sugar Land, TX	New Orleans	Louisiana	summer	9
Houston-The Woodlands-Sugar Land, TX	Lake Charles	Louisiana	summer	10
Houston-The Woodlands-Sugar Land, TX	San Antonio	Texas	winter	1
Houston-The Woodlands-Sugar Land, TX	Austin	Texas	winter	2
Houston-The Woodlands-Sugar Land, TX	Dallas	Texas	winter	3
Houston-The Woodlands-Sugar Land, TX	Fort Worth	Texas	winter	4
Houston-The Woodlands-Sugar Land, TX	Corpus Christi	Texas	winter	5
Houston-The Woodlands-Sugar Land, TX	New Orleans	Louisiana	winter	6
Houston-The Woodlands-Sugar Land, TX	Arlington	Texas	winter	7
Houston-The Woodlands-Sugar Land, TX	Lake Charles	Louisiana	winter	8
Houston-The Woodlands-Sugar Land, TX	New Braunfels	Texas	winter	9
Houston-The Woodlands-Sugar Land, TX	Lafayette	Louisiana	winter	10
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Virginia Beach	Virginia	summer	1
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Pittsburgh	Pennsylvania	summer	2
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	State College	Pennsylvania	summer	3
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Chincoteague	Virginia	summer	4
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Myrtle Beach	South Carolina	summer	5
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Boston	Massachusetts	summer	6
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	North Myrtle Beach	South Carolina	summer	7
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Norfolk	Virginia	summer	8
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Chesapeake	Virginia	summer	9
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Niagara Falls	New York	summer	10
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Pittsburgh	Pennsylvania	winter	1
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	State College	Pennsylvania	winter	2
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Virginia Beach	Virginia	winter	3
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Boston	Massachusetts	winter	4
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Richmond	Virginia	winter	5
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	New York	New York	winter	6
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Washington	District of Columbia	winter	7
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Chesapeake	Virginia	winter	8
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Killington Village	Vermont	winter	9
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Columbus	Ohio	winter	10
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	Norfolk	Virginia	winter	10
Washington-Arlington-Alexandria, DC-VA-MD-WV	New York	New York	summer	1
Washington-Arlington-Alexandria, DC-VA-MD-WV	Myrtle Beach	South Carolina	summer	2
Washington-Arlington-Alexandria, DC-VA-MD-WV	Atlantic City	New Jersey	summer	3
Washington-Arlington-Alexandria, DC-VA-MD-WV	Virginia Beach	Virginia	summer	4
Washington-Arlington-Alexandria, DC-VA-MD-WV	Charlotte	North Carolina	summer	5
Washington-Arlington-Alexandria, DC-VA-MD-WV	North Myrtle Beach	South Carolina	summer	6
Washington-Arlington-Alexandria, DC-VA-MD-WV	Raleigh	North Carolina	summer	7
Washington-Arlington-Alexandria, DC-VA-MD-WV	Pittsburgh	Pennsylvania	summer	8
Washington-Arlington-Alexandria, DC-VA-MD-WV	Philadelphia	Pennsylvania	summer	9
Washington-Arlington-Alexandria, DC-VA-MD-WV	Greensboro	North Carolina	summer	10
Washington-Arlington-Alexandria, DC-VA-MD-WV	New York	New York	winter	1
Washington-Arlington-Alexandria, DC-VA-MD-WV	Charlotte	North Carolina	winter	2
Washington-Arlington-Alexandria, DC-VA-MD-WV	Atlantic City	New Jersey	winter	2
Washington-Arlington-Alexandria, DC-VA-MD-WV	Raleigh	North Carolina	winter	4
Washington-Arlington-Alexandria, DC-VA-MD-WV	Pittsburgh	Pennsylvania	winter	4
Washington-Arlington-Alexandria, DC-VA-MD-WV	Durham	North Carolina	winter	6
Washington-Arlington-Alexandria, DC-VA-MD-WV	Blacksburg	Virginia	winter	7
Washington-Arlington-Alexandria, DC-VA-MD-WV	Greensboro	North Carolina	winter	8
Washington-Arlington-Alexandria, DC-VA-MD-WV	Philadelphia	Pennsylvania	winter	9
Washington-Arlington-Alexandria, DC-VA-MD-WV	Fayetteville	North Carolina	winter	10
Boston-Cambridge-Newton, MA-NH	New York	New York	summer	1
Boston-Cambridge-Newton, MA-NH	Saratoga Springs	New York	summer	2
Boston-Cambridge-Newton, MA-NH	Bangor	Maine	summer	3
Boston-Cambridge-Newton, MA-NH	Bar Harbor	Maine	summer	4
Boston-Cambridge-Newton, MA-NH	Burlington	Vermont	summer	5
Boston-Cambridge-Newton, MA-NH	South Burlington	Vermont	summer	6
Boston-Cambridge-Newton, MA-NH	Stamford	Connecticut	summer	7
Boston-Cambridge-Newton, MA-NH	Philadelphia	Pennsylvania	summer	8
Boston-Cambridge-Newton, MA-NH	Atlantic City	New Jersey	summer	9
Boston-Cambridge-Newton, MA-NH	Niagara Falls	New York	summer	10
Boston-Cambridge-Newton, MA-NH	Waterville	Maine	summer	10
Boston-Cambridge-Newton, MA-NH	New York	New York	winter	1
Boston-Cambridge-Newton, MA-NH	South Burlington	Vermont	winter	2
Boston-Cambridge-Newton, MA-NH	Burlington	Vermont	winter	3
Boston-Cambridge-Newton, MA-NH	Atlantic City	New Jersey	winter	4
Boston-Cambridge-Newton, MA-NH	Philadelphia	Pennsylvania	winter	4
Boston-Cambridge-Newton, MA-NH	Bangor	Maine	winter	6
Boston-Cambridge-Newton, MA-NH	Stamford	Connecticut	winter	6
Boston-Cambridge-Newton, MA-NH	Augusta	Maine	winter	8
Boston-Cambridge-Newton, MA-NH	Saratoga Springs	New York	winter	8
Boston-Cambridge-Newton, MA-NH	Lake Placid	New York	winter	10

Methodology

The analysis uses StreetLight’s tours data to connect long-distance journeys that include stops of 1 km or less, with a maximum of a 4-hour dwell time, and a minimum total trip distance of 100 miles, ending outside of the metro region.

Want to explore detailed road trip and travel pattern insights for your region or business? Contact StreetLight Data to learn how mobility analytics can support your tourism and transportation strategies.

Footnotes

World Travel & Tourism Council, "U.S. Remains the World’s Most Powerful Travel & Tourism Market." September 4, 2024.

Learn one way to measure tourism's impact on your regional travel demand and economics

Ready to dive deeper and join the conversation?

Learn more about StreetLight’s solutions for Transportation Management & Operations (TSMO) here.

Top 5 Traffic Management Software in 2025

Today’s traffic managers and operators must manage the movement of growing populations amid ecommerce-boosted truck activity, expanding vehicle sizes, and urgent climate resiliency improvements, among other challenges. These challenges are often exacerbated by limited visibility into the complex road networks through which people and goods move.

To respond to present issues — e.g., a crash, an out-of-service traffic signal, or a bottleneck forming at a road work location — and get ahead of future ones, these transportation professionals need eyes on major arterials and local roads alike.

But traditional data collection methods create blind spots. Manual traffic counts and surveys along with incomplete sensor coverage often leave decision-makers guessing instead of acting. The result can be anything from longer commutes to higher emissions, delayed deliveries, dangerous roadways, and frustrated communities.

The good news: modern traffic management software is helping traffic managers and operators harness real-time insights, predictive analytics, and AI-driven insights to optimize traffic flow, reduce congestion, and plan smarter infrastructure. Here are the top five traffic management solutions for 2025.

Key Takeaways

Traffic management software is essential for reducing congestion and improving mobility across cities, counties, and states.
StreetLight stands out with its real-time insights, granular datasets, and comprehensive coverage for the U.S. and Canada.
Alternatives like Iteris, Cubic TrafficWare, and PTV Optima offer complementary capabilities in signal performance analytics and predictive traffic simulations.

What Is Traffic Management Software?

Traffic management software helps transportation professionals monitor, analyze, and optimize the flow of traffic. These platforms leverage technologies like real-time data collection, road network analytics, and predictive modeling, to help agencies reduce congestion, improve safety, and support sustainable mobility initiatives.

What Are the Key Functionalities of Traffic Management Software?

Traffic management software may include:

Real-time traffic monitoring
Incident detection and alerts
Network performance analytics
Scenario and forecasting tools (e.g., to understand how scenarios like special events or construction may impact traffic)
Data visualization and reporting tools

Top 5 Traffic Management Software

A screenshot of StreetLight's homepage visualizes pings from connected vehicles passing through an intersection

1. StreetLight

StreetLight is a leading transportation analytics platform, trusted by cities, counties, MPOs, DOTs, and other public agencies across the U.S. and Canada. It offers a full suite of transportation data solutions for agencies and their consulting partners working on everything from Transportation Planning and Operations to Climate Resilience and Transportation Modeling.

StreetLight's technological rainbow includes data products for planning, safety, operations, climate, commercial, and mobility data procurement

When it comes to Traffic Management, StreetLight’s strengths lie in comprehensive, detailed real-time data coverage and easy-to-use, 24/7 dashboards that turn data into actionable insights. Unlike some other softwares explored here, StreetLight doesn’t just display customers’ own existing sensor data, but pulls in information from connected vehicles, GPS devices, probe data, and other sources to provide high-coverage, high-resolution information on what’s happening on the road network in real time and how it compares to historical benchmarks. This helps agency professionals more confidently identify both traffic anomalies and persistent problem areas to enable rapid response when needed and create effective maintenance of traffic plans for special events, construction, and more.

Key Features

On-Demand Data Access: Access to billions of data points from connected vehicles, GPS devices, probe data, and other sources.
Robust Traffic Monitoring Tools: Detect disruptions, diagnose potential causes, make adjustments, and evaluate their success quickly.
Real-time Insights & Historical Context: Easily compare current conditions like volumes, queues, speeds, and travel times to historical baselines to understand severity and respond quickly with data-supported strategies.
Incident Feed and Route Monitoring: See active crashes, road works, and other incidents impacting traffic and monitor key routes from a central dashboard to spot problems and implement solutions fast.
Scenario Forecasting: Tools like Closure Impacts help map where traffic exits and enters during lane closures to forecast travel impacts and plan detour routes.
A Complete Traffic Timeline: Travel back in time to track road conditions as they unfolded up to the present moment with the help of historical and real-time data to reveal the whole story.
Easy-to-share data visualizations: Collaborate and win support from government and private stakeholders as well as the public.

StreetLight’s Traffic Monitor product (shown above) can help traffic operations teams monitor real-time conditions and review traffic timelines to pinpoint congestion, slowdowns, and more. See how we used it to investigate the impacts of New York’s congestion pricing at https://www.streetlightdata.com/is-congestion-pricing-working-in-nyc/.

Pros

Unmatched data granularity and comprehensive roadway coverage
Ability to detect traffic anomalies quickly by comparing real-time conditions to historical traffic norms
Network-wide monitoring to assess ripple effects of disruptions, including instant insights before, during, and after disruptions.
Intuitive, self-service interface for traffic management and operations teams
Best-in-class customer service and training resources to maximize your effectiveness
Rigorous data validation and a privacy-first approach that completely anonymizes and aggregates data before it’s transformed into actionable metrics
Trusted and tested by public agencies, researchers, consultants, and businesses across North America

Pricing

StreetLight offers customized pricing based on project scope and data needs. Contact the team for a personalized quote.

Do you need insights for effective traffic plans, event management, signal timing, and more?

2. Iteris

Iteris provides traffic management solutions focused on smart infrastructure management, system monitoring, and predictive analytics.

Like some other solutions explored here, Iteris directly assists in the implementation of smart traffic sensor devices, which it then uses to deliver current traffic information to connected vehicles, operators, and analysts via software platforms or custom data feeds.

Features

Smart traffic sensor system design and implementation
Traffic signal performance monitoring
Incident detection and archives to inform operations and planning efforts
Predictive traffic modeling for congestion reduction
Integration with CVD and IoT sensors

Pros

Combines sensor device implementation with self-serve software to understand current conditions, evaluate performance, and execute solutions
Support for local, regional, and state-wide transportation systems
Solutions for operators, planners, consultants, and businesses

Pricing

Iteris does not public pricing information online, and costs vary based on the scale and functionality needed.

3. Cubic TrafficWare

Cubic TrafficWare specializes in adaptive traffic signal control and intersection management systems, with an emphasis on efficiency and safety. In addition to real-time intersection performance insights, their other softwares also allows users to simulate different traffic scenarios to assist with operations and planning challenges.

Similar to Iteris, Cubic also offers hardware systems that are compatible with their software platforms for intersection management.

screenshot of Cubic TrafficWare homepage

Features

Live status, analysis, reporting, and control of intersection devices
A map view that provides users with critical system information and alerts
Integration with live camera feeds, dynamic message signs, and closure information
Specialized intersection management technologies for safer, more efficient traffic flow
Proprietary hardware for smart intersection management
Integrations with Synchro to work between operations and traffic signal timing tools

Pros

Specialized technology for evaluating and improving intersection efficiency
Helps traffic engineers spot potential reliability issues and view current system performance
Focus on smart cities and urban infrastructure
Comprehensive reporting and analytics tools

Pricing

Cubic TrafficWare does not provide its pricing information directly through its website.

4. PTV Optima

PTV Optima offers traffic monitoring and forecasting tools to assist with traffic management. This platform integrates customers’ sensor data into a visualization dashboard and continuously updates to display live traffic conditions and alert users to issues. It can also be set up to trigger automatic actions when issues arise or simulate potential scenarios to assist in planning for operations.

Features

Real-time traffic performance and incident detection
Integration with live sensor feeds, including from public transit
Scenario simulations for operations planning

Pros

Support for predictive modeling
Integrates and maps customers’ sensor data
Ability to automatically trigger actions when issues arise

Pricing

PTV Optima doesn’t publicize their pricing but offers a modular system, so users can purchase only the modules that suit their needs and budget.

5. INRIX

INRIX provides traffic data and analytics that are not reliant on physical sensors, displaying current traffic conditions and helping forecast potential scenarios.

Similar to StreetLight, INRIX uses big data to deliver live insights and offer a larger suite of products that can assist with other transportation operations and planning efforts. Both StreetLight and INRIX help agencies go beyond sensor insights for a more complete analysis of current and historical traffic patterns. To further explore how INRIX and StreetLight compare, check out our guide on INRIX alternatives.

Features

Traffic flow analysis with historical and real-time insights into factors like speed, volume, and travel times
Incident detection and alerts
Traveler information services

Pros

Global coverage
Low-latency data
Provides emergency alert solutions
API integrations available

Pricing

INRIX’s website does not have explicit pricing information readily available; pricing likely varies based on the metrics and coverage needed.

Why StreetLight Is the Smart Choice for Traffic Management

StreetLight combines unparalleled data coverage, advanced forecasting and monitoring tools, and ease of use. Whether you’re planning infrastructure upgrades, focused on work zone safety, or reducing congestion StreetLight delivers actionable insights that drive results.

Compared to the alternatives explored here, StreetLight stands out for:

Instant, granular, high-coverage insights across your entire road network, from major arterials to local roads
Scalable solutions for cities, counties, and states alike
Ability to assess the ripple effects of disruptions throughout the road network for more complete and informed incident response
A blend of real-time and historical data to help track changes, spot anomalies and recurring issues, and understand the severity of disruptions
Specialized scenario forecasting for closures and detour planning

Ready to transform your traffic management strategy? Book a demo today.

Summer’s ending. Who’s hitting the road? Measuring Labor Day travel trends in the nation’s largest metro areas

Labor Day weekend travel is an American tradition, capping off the traditional summer season. But how much does Labor Day travel actually impact driving patterns? StreetLight analyzed trip patterns in the top 50 U.S. metros to see how Labor Day trip volumes, lengths, and travel times compare to a typical Friday.

Understanding leisure ground travel patterns is critical for public sector agencies managing traffic during major peaks in activity, as well as for private sector businesses dependent on tourism activity. Think rest stops, fueling stations, toll operators, roadside motels, restaurants, and vacation hubs, among the many other businesses that serve road trippers.

To understand how the U.S.’s biggest metros compare when it comes to road trippers fleeing the city for one last hurrah, StreetLight used its industry-leading transportation analytics to analyze vehicle travel trends across the largest metropolitan areas.

StreetLight analyzed the 50 largest Metropolitan Statistical Areas (MSAs) by population to understand how travel activity compared on the Friday before Labor Day in 2024 versus a typical Friday during the fall, analyzing by difference in:

Share of out-of-town trips
Total trips
Average trip length
Average travel time

At the end of the analysis, you can see complete results alphabetized by metro.

Key Takeaways

Labor Day Friday does not see as big an uptick as might be expected in the share of trips leaving cities vs. a typical Friday.

The largest increase occurs in Minneapolis, with the share of leaving trips increasing by 1.5 percentage points, followed by Raleigh and Richmond.

Even more surprising: In nearly every metro studied, fewer total trips take place on Labor Day Friday vs. a typical Friday. Only nine of the top 50 metros see total trips increase on Labor Day Friday as compared to a typical Friday.

The popular tourist metros of Tampa, Jacksonville, and Las Vegas see the biggest increase in total trips on Labor Day Friday.

Overall, Hartford sees the biggest share of trips heading out of town on a typical Friday, accounting for about 15% of trips. Raleigh and Salt Lake City see the next highest shares, at roughly 10% each.

Labor Day Friday vs Typical Friday Travel Trends

For anyone who’s tried to get to get out of town on the Friday before a holiday weekend, it may be a surprise to learn that the share of trips leaving the region actually does not increase substantially. In fact, the largest increase in share of trips that exit the city among the metros is Minneapolis with an increase of 1.5 percentage points, from about 4.5% of trips on a typical Friday to 6% on Labor Day Friday. That said, while a 1.5 percentage point difference might seem small, it still represents just over 100,000 trips.

After Minneapolis, the next metros with the biggest bumps in share of trips leaving the city for the long weekend are Raleigh, NC: Richmond, VA; Grand Rapids, MI; and Philadelphia, PA.

MSA	Rank	Percent Change
Minneapolis-St. Paul-Bloomington, MN-WI	1	1.5%
Raleigh-Cary, NC	2	1.3%
Richmond, VA	3	1.3%
Grand Rapids-Wyoming-Kentwood, MI	4	1.2%
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	5	0.9%

Even more surprising than the relatively small bump in share of travel out-of-town: There are fewer total trips in most metros on Labor Day Friday as compared to a typical Friday. This may reflect people not commuting to work and thus driving down trips in general. Only nine of the top 50 metros see total trips increase on Labor Day Friday as compared to a typical Friday.

Tampa, Jacksonville, and Las Vegas top the rankings for increased trips overall on Labor Day Friday. Each of these cities are car-oriented tourist destinations that may see locals and out-of-towners alike traveling around the metro.

Percent Change in Total Trips on Labor Day Friday vs. Typical Friday

MSA	Rank	Percent Change
Tampa-St. Petersburg-Clearwater, FL	1	3.3%
Jacksonville, FL	2	2.0%
Las Vegas-Henderson-North Las Vegas, NV	3	1.1%
Indianapolis-Carmel-Greenwood, IN	4	1.1%
Denver-Aurora-Centennial, CO	5	1.0%

Hartford is the metro where the biggest slice of trips head out of town on a typical Friday, even as the city does not see a notable bump in share of trips leaving the city ahead of Labor Day. Metros with a large portion of typical Friday “leavers” may have a high proportion of long-distance commuters or a significant outdoor culture taking them farther afield on a typical weekend.

MSA	Rank	Share of Total Friday Trips
Hartford-West Hartford-East Hartford, CT	1	15%
Raleigh-Cary, NC	2	10%
Salt Lake City-Murray, UT	3	10%
Grand Rapids-Wyoming-Kentwood, MI	4	8%
Boston-Cambridge-Newton, MA-NH	5	8%

See how Virginia measures the impact of seasonal tourism

For change in average trip length on Labor Day Friday, Raleigh and Minneapolis rank first and second, with Richmond coming in third. This ranking closely tracks the ranking for increase in share of trips out-out-town. Charlotte and Birmingham are the only cities that appear in the top five for increased avg. trip travel length but not in the list for increased share of trips out of the metro.

Percent Change in Average Trip Length, Labor Day Friday vs. Typical Friday

MSA	Rank	Percent Change
Raleigh-Cary, NC	1	16%
Minneapolis-St. Paul-Bloomington, MN-WI	2	13%
Richmond, VA	3	12%
Charlotte-Concord-Gastonia, NC-SC	4	10%
Birmingham, AL	5	8%

And Atlanta enters the top five for overall increased average travel time on Labor Day Friday. The other metros in the list align closely with the top five for increased trip length. Minneapolis is the only one of these metros where trip length increases by a greater percentage than travel time.

Percent Change in Average Travel Time, Labor Day Friday vs. Typical Friday

MSA	Rank	Percent Change
Raleigh-Cary, NC	1	16%
Charlotte-Concord-Gastonia, NC-SC	2	10%
Minneapolis-St. Paul-Bloomington, MN-WI	3	10%
Birmingham, AL	4	9%
Atlanta-Sandy Springs-Roswell, GA	5	7%

By examining empirical data on vehicle activity, we can see that the typical stories we hear in the media about Labor Day “traffic armageddon” are actually far more nuanced. Total trips on Labor Day Friday, in fact, go down in most cities. And trips leaving the metro region increase by far less than we might expect.

For this analysis, we used StreetLight’s historical trip data to investigate total trip volumes, trip lengths, and travel times across the U.S. To dive deeper on your area’s travel trends and power more informed planning, operations, or business decisions, StreetLight offers unmatched spatial and temporal precision across a wide range of metrics, including origin-destinations, top routes, trip purpose, and more.

Check back in the fall when we’ll use our Vehicle Tours data to understand the most popular road trip destinations among the top metros.

Complete Results by MSA

MSA short	Leaving Share of Trips, Typical Friday	Leaving Share of Trips, Labor Day Friday	Change in Leaving Share of Trips, Labor Day vs. Typical	Change in Trip Start Volume Labor Day vs. Typical	Change in Avg. Travel Time, Labor Day vs. Typical	Change in Avg. Trip Length, Labor Day vs. Typical
Atlanta-Sandy Springs-Roswell, GA	3.6%	4.0%	0.4%	0.2%	7.2%	6.6%
Austin-Round Rock-San Marcos, TX	4.6%	5.0%	0.4%	-2.3%	3.1%	4.0%
Baltimore-Columbia-Towson, MD	7.3%	7.4%	0.2%	-2.6%	0.4%	1.9%
Birmingham, AL	6.7%	7.5%	0.8%	-4.4%	8.9%	8.4%
Boston-Cambridge-Newton, MA-NH	8.1%	8.9%	0.8%	-9.0%	0.8%	5.9%
Charlotte-Concord-Gastonia, NC-SC	4.4%	5.1%	0.7%	-1.3%	10.4%	10.3%
Chicago-Naperville-Elgin, IL-IN	2.1%	2.3%	0.3%	-2.3%	3.7%	4.2%
Cincinnati, OH-KY-IN	4.9%	5.2%	0.3%	-0.4%	4.4%	5.5%
Cleveland, OH	6.0%	6.2%	0.1%	-1.4%	2.5%	2.0%
Columbus, OH	5.2%	5.4%	0.2%	-1.6%	2.5%	2.5%
Dallas-Fort Worth-Arlington, TX	2.3%	2.6%	0.3%	-2.2%	3.1%	5.1%
Denver-Aurora-Centennial, CO	4.4%	4.8%	0.4%	1.0%	4.8%	4.7%
Detroit-Warren-Dearborn, MI	3.4%	3.9%	0.5%	-9.5%	2.9%	6.3%
Fresno, CA	5.2%	5.3%	0.1%	-2.1%	3.7%	3.8%
Grand Rapids-Wyoming-Kentwood, MI	8.2%	9.4%	1.2%	-11.3%	4.3%	8.0%
Hartford-West Hartford-East Hartford, CT	14.8%	15.0%	0.2%	-2.5%	1.2%	2.8%
Houston-Pasadena-The Woodlands, TX	1.7%	1.9%	0.2%	-2.9%	1.5%	2.6%
Indianapolis-Carmel-Greenwood, IN	4.6%	5.0%	0.4%	1.1%	4.8%	5.4%
Jacksonville, FL	3.2%	3.5%	0.3%	2.0%	4.5%	5.0%
Kansas City, MO-KS	3.4%	4.0%	0.7%	-3.5%	3.3%	6.8%
Las Vegas-Henderson-North Las Vegas, NV	1.5%	1.8%	0.3%	1.1%	2.6%	3.2%
Los Angeles-Long Beach-Anaheim, CA	3.5%	3.7%	0.2%	-2.7%	-0.5%	3.4%
Louisville/Jefferson County, KY-IN	5.4%	5.7%	0.3%	0.3%	5.3%	5.7%
Memphis, TN-MS-AR	3.0%	3.4%	0.4%	0.7%	5.4%	6.7%
Miami-Fort Lauderdale-West Palm Beach, FL	1.2%	1.6%	0.3%	-1.2%	3.7%	4.3%
Milwaukee-Waukesha, WI	6.2%	6.9%	0.7%	-2.7%	3.2%	6.9%
Minneapolis-St. Paul-Bloomington, MN-WI	4.5%	6.0%	1.5%	-7.7%	9.7%	13.1%
Nashville-Davidson–Murfreesboro–Franklin, TN	4.6%	5.2%	0.6%	-0.5%	5.2%	6.1%
New York-Newark-Jersey City, NY-NJ	2.2%	2.4%	0.2%	-9.0%	-1.5%	2.2%
Oklahoma City, OK	4.6%	5.1%	0.5%	-3.7%	6.0%	7.3%
Orlando-Kissimmee-Sanford, FL	6.4%	6.7%	0.3%	-2.2%	2.9%	3.5%
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	4.6%	5.5%	0.9%	-10.5%	2.2%	7.6%
Phoenix-Mesa-Chandler, AZ	1.1%	1.5%	0.4%	-2.7%	4.9%	5.6%
Pittsburgh, PA	3.6%	4.0%	0.4%	-1.3%	4.2%	4.1%
Portland-Vancouver-Hillsboro, OR-WA	3.8%	4.3%	0.5%	-4.4%	4.7%	5.3%
Providence-Warwick, RI-MA	8.2%	8.1%	-0.1%	-3.0%	0.6%	2.1%
Raleigh-Cary, NC	10.0%	11.3%	1.3%	-7.9%	16.4%	15.9%
Richmond, VA	5.6%	6.9%	1.3%	-9.5%	6.8%	12.1%
Riverside-San Bernardino-Ontario, CA	5.7%	6.0%	0.2%	0.0%	2.7%	4.5%
Sacramento-Roseville-Folsom, CA	5.8%	6.1%	0.3%	0.2%	1.5%	2.8%
Salt Lake City-Murray, UT	9.5%	10.2%	0.6%	-0.7%	5.1%	7.6%
San Antonio-New Braunfels, TX	3.1%	3.4%	0.3%	-3.0%	4.1%	4.2%
San Diego-Chula Vista-Carlsbad, CA	2.6%	2.7%	0.1%	0.0%	0.7%	1.0%
San Francisco-Oakland-Fremont, CA	6.7%	6.8%	0.1%	-1.3%	1.0%	2.0%
San Jose-Sunnyvale-Santa Clara, CA	8.8%	8.8%	0.0%	-5.2%	0.9%	2.1%
Seattle-Tacoma-Bellevue, WA	2.8%	3.4%	0.5%	-2.5%	4.9%	6.1%
St. Louis, MO-IL	2.5%	3.0%	0.4%	-1.5%	4.9%	4.5%
Tampa-St. Petersburg-Clearwater, FL	3.2%	3.5%	0.3%	3.3%	-1.3%	4.0%
Virginia Beach-Chesapeake-Norfolk, VA-NC	3.2%	3.7%	0.5%	-5.5%	2.1%	5.7%
Washington-Arlington-Alexandria, DC-VA-MD-WV	6.0%	6.7%	0.7%	-5.5%	2.5%	6.9%

Learn one way to measure tourism's impact on your regional travel demand and economics

Ready to dive deeper and join the conversation?

Get Congestion Solutions Guide

Before and After Congestion Pricing: From Staten Island to NJ to Manhattan, How Travel Times Are Changing

Is NYC’s congestion pricing working? StreetLight analyzed travel times on ten key routes to see how traffic conditions have changed during rush hour and beyond, including areas where the tolling program faced some resistance.

time lapse of travel time changes during NYC congestion pricing

On January 5, 2025, New York’s MTA launched the Congestion Relief Zone tolling program, charging drivers a fee to enter the notoriously congested streets below 60th St. in Manhattan, excluding key highways and connector roads. The new toll, which includes peak and off-peak pricing, aims to reduce area congestion, air pollution, and safety risk, while raising revenue for the MTA. The tolling effort has implications not only for congestion in the immediate tolled zone but many surrounding areas, as well. (Federal administrators recently said they were rescinding approval of the tolling program, but as of this writing the tolls remain in effect.)

The MTA released initial data from week one of congestion pricing showing improved speeds on many of the bridges and tunnels entering the zone as well as on key bus routes.¹ Overall, most of the routes studied by the MTA have seen travel times improve.

StreetLight is now using its Traffic Monitor product, which helps planners and engineers monitor recent speed and congestion changes, to deepen the picture on congestion tolling with more data since the fee went into effect.

For a bird eye’s view of how traffic looked on a single day three weeks into the launch of congestion pricing, StreetLight used Traffic Monitor to create the gif below, showing the change in atypical speeds over the course of the day on January 28^th, as compared to similar days in January 2024. Green, thicker lines show improved speeds while red segments indicate decreased (i.e slower) speeds.

Of course, no single day provides a perfect measurement of traffic, as any day can be affected by crashes, weather, tourist activity, construction, and other disruptions.

To further contribute to the public’s understanding, StreetLight analyzed change in travel times over a three-week study period in January on ten distinct routes in the NYC metro area. You can see the map of the routes studied below.

map of 10 NYC metro routes measured for travel time change

StreetLight studied north-south routes, crosstown routes, and routes traversing areas outside the toll zone, in places where some have raised concerns about increased congestion from rerouting vehicles. StreetLight also included trips ending at major hospitals, as improving emergency vehicle travel times has been a stated goal of the program.

StreetLight’s analysis finds that most routes studied did see travel times improve. Six of the ten routes saw travel times decrease during both peak and off-peak tolling hours, including routes through New Jersey and Queens where there has been some resistance to congestion tolling.

Both Manhattan-based hospital routes – from Times Square to NYU Langone and the West Village to Memorial Sloan Kettering – saw peak hour travel times decline by 10% and 6%, respectively, a positive indicator for emergency travel within the zone.

For the routes where travel times worsened, the effect was small. Even during peak hours, the increase in travel times was less than a minute on all negatively impacted routes. This may be expected regardless of policy change as vehicle miles traveled have been steadily rising since 2021.²

Routes from New Jersey to Columbus Circle saw an interesting trend. Travel over the George Washington Bridge from Ridgefield Park, NJ to the northern edge of the congestion tolling zone slowed down by a slight 30 seconds during peak hours, as compared to a year earlier. However, travel via the Lincoln Tunnel from East Rutherford, NJ to Columbus Circle improved significantly, by over 3 minutes during peak hours.

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Analyzing Impact by Time of Day for Targeted Interventions

StreetLight allows planners and engineers to analyze travel at highly granular geographic and spatial scale. For example, if city planners are particularly focused on improving bottlenecks during the weekday AM or PM peak, that analysis is simple and straightforward. The impact of the MTA’s congestion charging will change over time as residents and visitors adjust, and as other trends impacting NYC arise. Many analyses will and should be done! StreetLight’s goal is to enable planners to understand and adapt to the complexities of managing congestion.

In the chart below, StreetLight compares the change in travel time on the Times Square to NYU Langone route by weekday only, looking at weekday all day vs. weekday peak AM and weekday peak PM. Peak AM travel times see the biggest improvement as compared to peak PM and all weekday.

Methodology

The analysis compares travel on select routes between January 5-25, 2025 and the same time of day and day of week for the month of January 2024. Travel times are based on sample count speed data.

Routes selected are not comprehensive of traffic in any one area. They represent travel between major destinations and aim to contribute to the picture of congestion pricing’s impact.

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1. Metropolitan Transportation Authority (MTA). Congestion Relief Zone Tolling: Week One Update. January 13, 2025. https://www.mta.info/document/162396

2. U.S. Federal Highway Administration, Moving 12-Month Total Vehicle Miles Traveled [M12MTVUSM227NFWA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/M12MTVUSM227NFWA, March 10, 2025.

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Swift Streets? Complete Rankings for Traffic Management at Every Stadium in Taylor Swift’s U.S. Eras Tour

In a study of traffic delays across the entire U.S. Eras Tour, StreetLight found delays at least doubled at most of the 23 stadiums where Swift performed — but there were some notable outliers. At one venue, traffic actually improved. This report updates and expands StreetLight’s prior analysis of nine stadiums that hosted Eras Tour concerts in March–May 2023.

When Taylor Swift announced her first live tour since 2018, the rush on tickets by fans made national headlines (and earned a congressional hearing).

For transportation and transit agencies, and stadium operators, a very different challenge emerged: Managing traffic from the legions of fans who would descend on the stadiums for the Eras Tour.

Event operations pose a special challenge as they put a dramatic tax on roadway operations over a narrow time block, which local transportation infrastructure is not built to support during a typical day. As a result, stadium operations groups often work in close coordination with local transportation agencies to manage traffic, as well as ingress and egress from the stadium.

So when it comes to the Eras tour, how have the stadiums and agencies fared at managing fan traffic and keeping the roadways flowing? StreetLight ran the numbers to find out. Then, we look at how transportation and operations professionals can use analytics for more effective events traffic management.

Key Findings:

Vehicle Hours of Delay (VHD) on roadways adjacent to the concert venues at least doubled during most Eras Tour concerts. On average, vehicle delays were 277% higher across all stadiums compared to delay hours at comparable times on non-concert dates.
Only four out of 23 venues saw traffic delays increase by less than 100%: MetLife Stadium in East Rutherford, NJ ; Mercedez-Benz Stadium in Atlanta, GA; Empower Field at Mile High in Denver, CO; and Acrisure Stadium in Pittsburgh, PA.
Traffic around MetLife Stadium, which invested heavily in transit access, actually decreased compared to usual delays. This is the only venue where traffic decreased.
The worst venue for increased traffic delays (based on % change from typical conditions) was Gillette Stadium in Foxborough, MA. This is a location where typical VHD is relatively low compared to many of the other venues studied.

Eras Tour Traffic Winners & Losers

To understand the traffic impacts from the U.S. Eras Tour concerts, StreetLight analyzed Vehicle Hours of Delay (VHD) on all non-local roadway segments within a one-mile radius of each stadium during the peak arrival hour of 5-6 p.m. on each concert date. VHD measures the difference in vehicle travel time on a segment during congested versus free-flowing conditions, multiplied by the number of vehicles traveling on that roadway.

This same process was repeated for the same days of week within that month (concert dates and holidays excluded) to determine a baseline VHD for a typical travel day. You can read more about StreetLight’s data here.

Overall, across all 23 stadiums and 62 concerts, average delay hours were 277% higher than typical. In fact, all but four stadiums saw delay hours at least double on average over the course of the concerts.

traffic management rankings by VHD % change for Taylor Swift's Eras Tour U.S. concerts

Two major success stories emerged, however: Atlanta’s Mercedes-Benz Stadium and New Jersey’s MetLife Stadium saw average delays well under 100%.

Atlanta only saw a 32% increase in traffic delays. But NJ’s MetLife Stadium was the real standout.

VHD actually decreased during the concerts, by 27% on average over the course of the three nights. Notably, both Atlanta and New Jersey’s concert venues were given high marks for their emphasis on public transit options to the concert. Atlanta’s Metropolitan Rapid Transit Authority System (MARTA) reported seeing three times the usual ridership during the concert days at stations near the stadium, according to CBSNews. NJTransit, which ran extra service around the stadium, carried 80,000 riders via train and bus to the concert, according to NJ.com.

Of note, on a normal day, both MetLife Stadium and Mercedez-Benz Stadium see higher baseline congestion than most of the other stadiums studied here (with the sole exception of Vegas’ Allegiant Stadium).

Philadelphia also placed a big emphasis on public transit. This may have paid off for the stadium on two of the concert nights. The Friday and Sunday shows in May 2023 at Philadelphia’s Lincoln Financial Field saw below average increases in delays compared to the other stadiums, with VHD 200% and 186% higher than typical for streets around the stadium, respectively.

However, on Saturday night Philadelphia’s Lincoln Financial Field encountered huge snarls, with a 599% increase in hours of delay. This dragged down the stadium’s average across the three nights. It’s also a signal of how tenuous traffic management at an event like this can be, and how easy it is for delays to compound.

But by far the worst increase in traffic delays occurred at Gillette Stadium in Foxborough, MA, near Boston. It saw delays 1,270% higher than typical on average over three nights in May 2023. Typical VHD near the stadium is low compared to many of the other venues in this study, perhaps because Foxborough, MA is a small town of just over 18k residents as of 2022, though its stadium regularly hosts sold out football games as the home of the New England Patriots, and is the largest stadium in the Greater Boston metro area.

Next highest for percent increase in traffic delays, at 737% higher than typical, was Kansas City, MO’s Geha Field at Arrowhead Stadium. Like Gillette Stadium, this venue also sees relatively low typical VHD.

4 venues saw big differences in VHD % increase by concert day during Taylor Swift's Eras Tour U.S. concerts

Like Philadelphia’s Lincoln Financial Field, several other venues also saw dramatic differences in excess VHD depending on the concert date, including AT&T Stadium in Arlington, TX, Gillette Stadium in Foxborough, MA, and Geha Field at Arrowhead Stadium in Kansas City, MO.

Among these venues, Saturdays and Sundays tended to see the worst increase in delays, with Fridays relatively lower. This could be influenced by commuter traffic on Friday evenings peaking between 5 and 6 p.m., driving up typical VHD on Friday evenings, resulting in lower increases comparatively.

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How Transportation, Events, & Operations Professionals Can Manage Event Traffic Better

Events like the hotly anticipated concerts of Swift’s Eras Tour test the limits of everyday traffic operations, and often demand temporary strategies that reduce congestion, encourage shared transportation modes, and keep concert-goers safe.

But anticipating and mitigating traffic issues from special events is far from simple. To minimize delays, promote smooth traffic flow, and ensure safety, planners and operators need to know which routes attendees will travel, the modes they will use on the way, the intersections where they’ll be turning, and what alternate routes people may take as primary routes become congested.

Complicating these challenges is the time and financial cost of gathering the right data needed to understand all these factors. While certain major arterials may benefit from permanent traffic counters, many roadways lack these counters, such as residential or other local roads that may experience cut-through traffic when larger roadways become gridlocked.

This makes it impossible to get historical data with the granularity needed to understand past events or even average seasonal roadway conditions. Meanwhile, collecting data on complex roundabouts, intersections, or weaving segments can also be difficult, even if manual counts or surveys are deployed in advance of the event.

Big Data and Special Events Traffic Planning

A big data approach to special events planning can help fill crucial data gaps to anticipate their traffic impacts. Whether it’s used to inform broader travel demand models or applied for analysis of traffic operations during specific events, access to on-demand transportation analytics expedites special events planning without needing to put staff in harm’s way for manual counts and surveys that only capture a snapshot of traffic during a short period of time.

This expedited process allows planners and operators to proactively evaluate alternative traffic management strategies and communicate their decisions with the public in advance of special events.

Moreover, analyzing Origin-Destination of traffic, and routing to and from event venues can be particularly difficult when using traditional data collection methods, but it can also be one of the best starting points to understanding where and why congestion hotspots occur while also revealing underutilized road segments that could be used to free up traffic.

top routes analysis for state farm stadium event traffic — *A StreetLight Top Routes analysis shows the most-used routes traveling to State Farm Stadium near Phoenix, AZ. Top-used road segments appear in red.*

Big data makes analyzing top routes quick and simple so that traffic operations managers or planners have the best tools to ensure traffic flows smoothly.

When analyzing historical traffic data for special events planning, the following metrics can be helpful:

Origin-Destination (O-D) and Top Routes – to anticipate where attendees are coming from, which roadways can expect the largest increase in travelers, and which less-used segments could be candidates for traffic rerouting.
Turning Movements – to understand where and when people turn into and near the event venue during typical conditions and special events.
Traffic Volumes – to understand where roadways may reach capacity and identify potential detour routes.
VHD – to anticipate the impact and severity of traffic congestion during special events compared to average conditions.
Speed – to evaluate safety conditions and crash risk near the venue, especially for vulnerable road users like pedestrians and cyclists.
Travel Time – to understand how special events impact not just attendees but other road users and communicate expected delays to the public.
Bike and Pedestrian activity – to identify common walking and cycling routes to and from the venue.
Transit ridership – to understand available capacity for shared transportation modes that can help ease congestion.

Origin-Destination analysis for Raymond James Satdium event traffic — *A StreetLight Origin-Destination analysis shows where trips headed to Tampa’s Raymond James Stadium for the Eras Concert began, with darker blues representing higher concentrations of trip starts.*

Planners and traffic engineers can use these metrics to anticipate how traffic conditions will change during special events and prioritize traffic management strategies that will keep traffic flowing and protect the safety of all road users.

For example, examining turning movements at key intersections leading to the event venue could inform temporary signal retiming on the day(s) of the event to offer more opportunities for attendees to make their turns toward the venue. Likewise, identifying increased traffic volumes on residential or other local streets not suited for high-volume traffic could signal the need for signage directing event attendees to preferred alternate routes toward the venue.

Traffic operations managers can now also leverage real-time or near real-time data to monitor traffic disruptions as they develop and compare current speed and volume conditions to historical data to diagnose slow-downs or safety concerns and how to deploy the best solution quickly. StreetLight’s Traffic Monitor product can equip agencies and firms with real-time insights for any road, even newly constructed roads and other roads without physical counters. The gif below shows an example of atypical volumes around Las Vegas’ Allegiant Stadium during the 2024 Super Bowl.

time lapse of super bowl traffic congestion — StreetLight Traffic Monitor product users can view a time lapse of traffic trends measured by atypical volume, speed, atypical speed, and atypical delay. This Super Bowl time lapse shows atypical volumes. Higher volumes appear in red while lower volumes are in blue.

To learn how you can leverage big data for special event and other traffic operations management, check out our Traffic Engineering and Operations Solutions.

Notice Something Different?

If you read StreetLight’s original analysis, covering the first nine venues of the Eras Tour in March–May of 2023, you may have noticed some differences in the results from the original analysis.

To learn more about the methodological changes driving those differences and why the new data reflected in the above analysis improves upon the reliability of congestion insights, check out our new blog on Data and Methodology updates for February 2025. There you’ll find an in-depth explanation of how StreetLight’s new Network Performance analysis type compares to the Segment Analysis data we used for the original nine-venue analysis — and where stadium rankings differed slightly between the two methodologies. You’ll also find information on other recent reliability improvements to metrics like vehicle volumes and VMT.

Data-driven ways to avoid travel delays, transit "crush loads" and more during events

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