Before and After Congestion Pricing: From Staten Island to NJ to Manhattan, How Travel Times Are Changing
ANALYSIS
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.
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 28th, as compared to similar days in January 2024. Green, thicker lines show improved speeds while red segments indicate decreased (i.e slower) speeds.
Year-over-Year speed changes on January 28th in Manhattan and the surrounding region.
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.
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.
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.
___
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.
Get traffic flowing with nine data-driven congestion solutions
Explore the resources listed above and don’t hesitate to reach out if you have any questions. We’re committed to fostering a collaborative community of transportation professionals dedicated to building a better future for our cities and communities.
This blog is for people who really enjoy getting into the weeds about data methodologies!
With StreetLight’s end-of-year product updates (if you’re an existing customer you can see release notes here) we included even more new data months, and we updated some of our methodologies for processing data. Our methodologies are documented in detail on our white papers page but we thought it would be helpful to summarize the updates here and discuss how the methodology changes might affect results of certain analyses, using some of our own previous blogs and ebooks as examples.
While these changes make our results better, we know methodology updates can be tricky for customers, so we want to be as transparent and helpful as possible, so customers can better navigate and understand any differences they may see. If you have questions about any of your own analyses, please contact our support team by visiting help.streetlightdata.com and selecting “Contact Support” or clicking the “Contact Support” link under the Help menu in your StreetLight InSight® account.
TL;DR: Overall the changes are moderate and the updates mean our results more accurately reflect the real world. The most significant impacts are:
Improved spot speeds and sample sizes for road segments analyzed with Network Performance in lieu of Segment Analysis.
Improved All Vehicles Volume estimates for data periods in 2019 nationally, as well as volume estimates for late 2023 for select states.
Improved differentiation between weekday and weekend vehicle volume metrics.
For Analyzing Speeds and Volumes on a Road Segment – Moving to our New Network Performance Analysis Type
During 2024, many users will have seen a new analysis type called “Network Performance” in StreetLight InSight®. This offers many of the same outputs as “Segment Analysis” but with improved methodology and data inputs, ultimately yielding better results. We are now recommending that clients analyzing vehicular movement on road segments transition to Network Performance, in particular for use cases involving measuring changes across time.
What’s Different: Network Performance relies on Aggregated GPS data (AGPS) as its underlying data source. Segment Analysis relies on a combination of Connected Vehicle Data (CVD) and Location-Based Services (LBS) data sources, both of which have smaller sample sizes than AGPS.
AGPS data has a few benefits, including a very high sample size (18-40% of vehicles on the road) and availability in both the US and Canada. Most notably, AGPS data has been continuously available since 2019, allowing for a better comparison across time since “data source” is no longer a variable that could account for differences measured. In addition, Segment Analysis metrics are not available beyond May 2023.
NOTE – As of January 2025, Network Performance can only be run on OpenStreetMap (OSM) segments. While it cannot currently be run on a customer’s LRS if they’ve uploaded that to the system, we are working on adding this capacity as soon as possible. Additionally, you can always work with our services team for a custom analysis that matches the metrics to your LRS.
Exploring Impact: Taylor Swift’s Eras Tour analysis
To explore the impact of shifting to Network Performance, we reran the results of our all-time-most-popular blog, which originally analyzed the Taylor Swift Eras Tour traffic jams using Segment Analysis (you can read the updated Eras Tour analysis here).
The analysis of congestion on typical days across each of the cities shows almost no difference between the two methodologies (Segment Analysis and Network Performance). The only notable difference where Network Performance shows less delay on a typical day is in New York City. We think this reflects Network Performance’s better differentiation of cars from subways and buses, and thus is an improvement.
On Eras Tour days, for most cities, Network Performance picked up a little more of an impact from the concerts as shown in Figure 1. Again, we consider this a positive reflection of Network Performance, as the data source is showing improved differentiation between typical activity and disruptive activity. This is one of the key benefits of big data — to analyze and react when events do not follow typical patterns.
Notably, these changes aren’t big enough to impact the overall story: Looking at excess VHD, the concerts in Vegas followed by Dallas, then Phoenix and then Tampa had the biggest impact on traffic compared to a typical day. The concert in New York City (with the most transit alternatives to driving) had the smallest. Figure 1 shows the changes.
Figure 1: Scatter plot showing Vehicle Hours of Delay on Concert Dates for the newer Network Performance (X-axis) and Segment Analysis (Y-Axis). A dot that is “below” the line indicates that Network Performance found more delay on these concert dates than Segment Analysis.
Looking at excess VHD % change (i.e., the percentage difference between typical VHD and VHD on the day of the concert), the Boston concert shows the biggest percent change for both methodologies, followed by Dallas-Fort Worth and Phoenix. New York City still shows the smallest change. The table below shows how these rankings vary between Segment Analysis and Network Performance, with venue positions shifting by 1 rank at most.
Metro Area
Segment Analysis Rank How much worse (by percent) was traffic on Eras Tour days?
Network Performance Rank How much worse (by percent) was traffic on Eras Tour days?
Boston (Foxborough, MA)
1 (Biggest impact)
1
Dallas-Fort Worth, TX
2
3
Phoenix, AZ
3
2
Houston, TX
4
5
Philadelphia, PA
5
4
Nashville, TN
6
6
Tampa, FL
7
8
Las Vegas, NV
8
7
Atlanta, GA
9
9
New York City, NY
10 (Least Impact)
10
Network Performance Volume Model Updates
In our end-of-year release, we also updated our U.S. Network Performance Volume model for all road segments in the U.S. for all months starting in 2019.
What’s Different: The volume estimates are derived from a machine learning model trained on over 14,000 unique permanent vehicle counts across all states in the contiguous U.S. The updated model uses more training locations than the first version of the model as more states published 2023 data after our initial release. We also used more historical data from 2019 and 2020 to refine the algorithms for those years. In general, these improvements yield:
Reduced bias and improved error in all years, especially on low volume roads
Improvements to the volume model for 2019
Improved weekday vs. weekend comparisons for all years
Figures 3 and 4 compare MAPE (Mean Absolute Percent Error) for various bins of roads for each data year. Deeper dive white papers are available here.
The new release also includes Network Performance volume estimates for Canada.
Figure 3: Nationwide model improvements in v2 (released November 2024) for 2019 data months. Improvements mainly show up in improved MAPE on smaller roads. This indicates that any given road, when run in v2, is likely to have more accurate estimation especially if that road is smaller. Figure 4: Nationwide model improvements in v2 (released November 2024) for early 2024 data months. The two models are much closer in performance, indicating that any given road is less likely to see big swings in volume estimation, because v1 was already strong.
Exploring Impact: VMT Report
Last fall, we published a report ranking VMT changes from Spring 2019 – Spring 2023 for metro areas in the U.S. VMT relies on our volume model, and when the report was published, we were still using our V1 model. In hindsight, for a metric as critically important as VMT, we should not have developed a report with V1 when we knew V2 was coming soon! It created unnecessary confusion for our customers. This was an error we regret and will not repeat. We may publish a more comprehensive update of that report with v2 metrics in the future.
When we reran the results with our improved volume model, we saw some changes:
A number of metro areas showed increased VMT totals for 2019, while most had similar results for 2024. This means that the percentage change in some metros between 2019 and 2024 was overstated in our initial report (Overall, Spring 2019 was in fact closer to Spring 2024, than initially reported by approximately 4-7 percentage points depending on region).
The increase in 2019 was most often attributable to improvements in low/medium volume road accuracy.
This granularity ensures agencies of all sizes, as well as firms and businesses, can get actionable insights to prioritize projects, evaluate impact, and anticipate future needs. It’s also particularly important for transportation modeling, which requires granular, empirical data to help predict how conditions will change over time, or in response to specific infrastructural and policy changes.
Let’s use a few metros in Connecticut to illustrate the change.
Area
V1 2019-2024 Spring Change in VMT
V2 2019-2024 Spring Change in VMT
Bridgeport-Stamford-Norwalk, CT
6.3%
0.4%
Hartford-West Hartford-East Hartford, CT
3.5%
-0.7%
New Haven-Milford, CT
6.0%
0.4%
Norwich-New London, CT
5.6%
-2.2%
Torrington, CT
13.3%
5.8%
Worcester, MA-CT
-2.2%
-1.1%
Connecticut – Statewide
4%
-0.6%
VMT Musings: How do we know what is “right” or “better”?
For our volume metrics, we can publish very precise estimates of overall accuracy based on thousands of “ground truth” permanent counters, as shown in Figure 5.
But VMT over a large area is trickier — there’s no such thing as ground truth. Instead, there are various methodologies, and thoughtful comparisons can be made based on known strengths and weaknesses of each one.
FHWA publishes two different reports on statewide VMT (and individual states have their own methodologies): the Traffic Volume Trends (TVT) and within the Highways Statistics Series (HSS). These are published at the state level, not MSA.
Method Summary:
The TVT is updated faster than HSS and is based on Continuous Count Stations (CCS), extrapolating changes seen on them to the rest of the roads.
For the Highway Statistics Series (HSS), FHWA “estimates national trends by using State reported Highway Performance and Monitoring System (HPMS) data, fuel consumption data (MF-21), vehicle registration data (MV-1), other data such as the R. L. Polk vehicle data, and a host of modeling techniques” and since HSS hasn’t come out yet for 2023, we can’t compare the most recent data.
Like the TVT, StreetLight uses CCS counters from the state in question as well as from similar roads (similar by volume, rural/urban context, weather patterns, and more) in nearby states to create a machine learning model to scale up a ~25% sample to a full count. We estimate each individual road segment’s volume independently using this method, multiply that volume by road segment length, then sum all road segment VMT values up in a given area to estimate that area’s total VMT.
Sticking with Connecticut to illustrate differences:
Figure 6: FHWA TVT, HSS, and StreetLight Annual Connecticut VMT change compared to 2019. The TVT shows more variability than HSS or StreetLight, particularly between 2022 and 2024. The TVT variability is too high compared to common sense in these years, in our opinion. HSS shows far less year over year variation, which builds confidence in its 21/22 estimates, but we don’t think the 2020 number matches common-sense COVID experience.
Looking at these two FHWA methods, we feel our V2 model performs well based on common sense and COVID experience.
Methodologies like TVT often extrapolate growth in VMT in a region from measured growth on CCS counters (which are most often on busy roads). If the balance of growth between highways and more local roads has changed since the pandemic — and we believe it has — then our industry needs to update the methodology used to estimate region-wide VMT. We believe the big data–driven approach offers just such an opportunity and we will explore this in future publications and posts.
And, as always, the more up-to-date, well-maintained permanent counters that are available (especially on lower volume roads) the better everyone’s estimates will be!
Swift Streets? Complete Rankings for Traffic Management at Every Stadium in Taylor Swift’s U.S. Eras Tour
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 delaysat least doubledat most of the 23 stadiums where Swift performed — but there were some notable outliers.At one venue, trafficactually improved.This reportupdates and expandsStreetLight’s prior analysis ofnine stadiumsthat hosted Eras Tour concertsin 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.
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.
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.
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.
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.
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.
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.
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.
Get traffic flowing with nine data-driven congestion solutions
Explore the resources listed above and don’t hesitate to reach out if you have any questions. We’re committed to fostering a collaborative community of transportation professionals dedicated to building a better future for our cities and communities.
Big data is now a common tool for agencies, firms, and businesses looking to understand traffic conditions and transportation patterns – from analyzing the top routes taken by drivers to anticipating how a bike lane will impact safety and congestion.
Alongside this growing adoption of big transportation data, a host of data providers have also proliferated, presenting users with a complex array of options that can be challenging to navigate.
INRIX is among the commonly used big data providers with solutions aimed at transportation use cases and related land use and business applications.
INRIX Pros
INRIX is a popular choice for:
Real-time analytics
Safety analytics targeted at securing grant funds
Smart Delivery analytics, including data on ride share and food delivery patterns
Parking and curb analytics
INRIX Pricing
Like most INRIX competitors covered here, INRIX’s website does not have explicit pricing information readily available, and pricing likely varies based on the metrics and coverage needed.
So how do INRIX’s competitors and alternatives stack up, and when should you consider other options for big transportation data? Below, we explore some of INRIX’s most common alternatives, and the pros and cons of each choice.
StreetLight
Among the leading big data providers in the transportation landscape, StreetLight is the most widely adopted transportation platform in North America. Backed by a robust set of third-party validations, white papers, and customer testimonials, StreetLight is highly trusted by transportation agencies, firms, and businesses across the U.S. and Canada.
StreetLight has a long history in transportation data, and has built up a 10+ year repository of mobility patterns since its founding in 2011. As data sources have come and gone, StreetLight has continuously updated its methodologies to take advantage of emerging data sources and ensure users get only the most reliable insights based on real-world traffic patterns and ample datasets.
This allows StreetLight to turn the richest, largest, and most secure datasets available today into reliable and granular metrics used by professionals across North America.
Specialized Solutions for Many Use Cases
Because of its long history in the industry, StreetLight also offers the advantage of an entire suite of products that address the most common challenges faced by transportation professionals. From improving pedestrian safety and deploying EV chargers to managing traffic congestion and planning construction detours, StreetLight’s purpose-built solutions make it easy for users to make data-driven decisions about their road network, client projects, or business operations.
Highly Granular Data for any Geography in North America
Streetlight’s data also offers deep granularity across space and time. Thanks to its robust repository of data and diverse set of industry-leading data sources, users can confidently dive deeper into highly granular analyses – even looking at temporal increments as small as 15 minutes, and spatial increments as small as a single road segment. In other words, you can get data on any road, any mode, and for any time period.
That means not only can you use StreetLight to understand high-level historical trends over the past five years, but you can also monitor fast-changing traffic trends over the course of a single morning. Transportation operations experts can even monitor real-time traffic patterns that happened a few seconds ago, to ensure traffic is moving safely and efficiently. Likewise, you can zoom in on individual road segments or get region-wide insights, depending on the scope of your project.
This granularity is a major advantage for transportation modelers, who need granular data based on real, observed traffic patterns to help predict how conditions will change over time, or in response to specific infrastructural and policy changes.
Granular data is also highly valuable for practitioners at public agencies, firms, and businesses, who need actionable insights to prioritize projects, evaluate impact, and anticipate future needs.
Industry Longevity and Reliability
The transportation data landscape has undergone several evolutions since StreetLight’s founding in 2011. But StreetLight’s industry-leading data science engine has stood the test of time, offering stability and transparency to its users during times of uncertainty.
Today, StreetLight is part of Jacobs, a Fortune 500 company with years of industry knowledge, adding to StreetLight’s stability in the market.
Pros of StreetLight Over INRIX
With ample white papers and third-party validations available, StreetLight is a strong choice for users who value data transparency or need to ensure the data they use meets very specific qualifications.
For practitioners looking to analyze all modes in one platform, StreetLight offers tools for planners to access insights for vehicle, truck, bicycle, pedestrian, bus, and rail modes.
Because some of INRIX’s solutions are available on non-integrated 3rd-party platforms, users who prefer an integrated, seamless experience may choose StreetLight.
StreetLight Pricing
StreetLight’s pricing depends on which solution you choose. Custom metrics packages may also be available depending on your goals and budget.
Replica’s tagline is “Data to Drive Decisions about the Built Environment,” and its data offerings are designed to match. Similar to INRIX, many of Replica’s applications cater directly to transportation industry use cases, and related use cases in land use and commercial real estate.
Some of Replica’s applications include:
Active Transportation Analysis
EventSight
GeoSnapshot
Standard Growth Scenarios
Transit Demand and Equity Scores
Replica Pros
Replica’s strengths include:
Dashboard data visualizations
Filtering capabilities
Consumer spending behaviors
Land use insights
Traditional mobility patterns
Replica Cons
Despite Replica’s strengths, data recency and granularity may not be as strong as other options on the market. Transportation modelers or other users who want to analyze synthetic behaviors rather than empirical behaviors may also choose Replica. Users will also have less flexibility in how they use the data, since Replica does not offer API or ESRI integration. These limitations can make it challenging to analyze small time frames and individual roadways or segments.
Replica Pricing
A complete breakdown of Replica’s pricing info is not readily accessible on their website, but Replica offer a pricing breakdown for public agencies based on the population of the city, county, or state the agency serves, as well as prices for a single department vs. multiple departments within an agency.
LOCUS
LOCUS, a subsidiary of the consulting firm Cambridge Systematics, was once a visualization tool used exclusively for Cambridge Systematics clients. Now, it is a standalone, on-demand platform offering performance, safety, freight, EV, and multimodal mobility metrics. Like other INRIX alternatives, it specializes in delivering insights on transportation and travel behavior.
LOCUS is a newer offering on the market with a smaller range of products. However, they emphasize custom solutions paired with consulting services through Cambridge Systematics to help customers accomplish a variety of goals.
LOCUS Pros
One thing that makes LOCUS unique is its strengths in transit data, including:
Real-time transit activity
Ridership trends
Transit performance metrics
LOCUS’s real-time transit dashboard uses farecard or survey data and routing to help customers understand these transit patterns.
LOCUS Cons
LOCUS emphasizes custom-built data solutions paired with consulting services, making it a viable option for customers who want a bespoke approach. However, LOCUS offers fewer out-of-the-box solutions than some INRIX alternatives, and may not be the best fit for those who want a do-it-all platform or a self-driven research experience.
LOCUS Pricing
LOCUS’s pricing info is not featured on their website, and may vary based on the customer’s needs.
AirSage
AirSage offers transportation data “customized” to each customer’s needs, and serves public agencies, firms, and businesses. It uses Big Data to deliver insights on how people and vehicles move.
AirSage offers a few products, including:
Trip Matrix – an Origin-Destination matrix the includes trip attributes and trip purpose information
Activity Density – measuring population movement and density, useful for event-based migration patterns such as emergency evacuations
Pedestrian Activity Density – specifically for understanding pedestrian movements
Target Location Analysis – used to understand visitor activity at points of interest
AirSage Pros
Many of AirSage’s products are delivered in CSV format, making it easy to integrate with:
Your own dashboards
GIS platforms
Power BI
Excel
Other mapping tools
AirSage Cons
However, customers who value data transparency may choose other alternatives due to AirSage’s limited availability of white papers, 3rd-party validations, and case studies. Those who want to analyze bike, bus, rail, or truck modes may also prefer other options, as AirSage places more emphasis on personal vehicle and pedestrian use cases.
AirSage Pricing
AirSage does not offer explicit pricing info on their website, however their Terms of Service state that pricing is available on request from a Sales representative, and may vary based on usage and location.
Looking for an INRIX alternative?
While INRIX may appeal to those who need global transportation insights, or with specific interests in parking, curb, and signal data, StreetLight is a powerful alternative for practitioners across North America who value a seamless experience that allows them to analyze any mode, any road, and any time period within a single integrated platform.
If data transparency is important to you, you may also appreciate StreetLight’s robust variety of 3rd-party validations and white papers, which help make StreetLight one of the industry’s most trusted transportation data platforms, satisfying even the most highly technical users in agencies, firms, and businesses.
Investigating how trucks impact social equity with new freight data
Investigating how trucks impact social equity with new freight data
How doestruck activity impact disadvantaged communitiescompared to their non-disadvantaged neighbors?StreetLight uses new truck data to show how you can investigate inequitable truck impacts in your community.
Truck activity can have a huge impact on local emissions and traffic congestion, but not all communities are equally affected. Now, new truck data from StreetLight helps analysts investigate how truck traffic impacts disadvantaged communities (DACs)across the U.S., and how factors like urban density, vehicle weight class, and industry type contribute – and help diagnose – inequitable impact.
Key Takeaways
In New York state, StreetLight’s data indicatesdisadvantaged communities are impacted by worse truck travel delays than non-disadvantaged communities,potentially exacerbatingnegative outcomes, such as air pollution and emissions.
Communities in the urban coreare the most impacted by truck travel delays and the added emissions and pollution they often generate.
Overall, medium-duty trucks show the most unequal impact on disadvantaged communities. Trucks in this category from the public administration and transportation and warehousing industries may warrant special attention for DACs.
Trucks are some of the worst offenders when it comes to vehicle emissions. Although they are indispensable to today’s freight logistics, they also emit more CO2 and other air pollutants than cars because they typically emit more GHGs per mile in addition to traveling much longer average distances.
Compounding trucks’ oversized climate impact is a dramatic spike in freight activity. A 2021 analysis by USDOT’s Bureau of Transportation Statistics predicted total U.S. freight activity would grow 50% by 2050, with trucks accounting for 65% of that total.1 This proliferation of truck traffic also challenges existing road capacity in many communities, exacerbating rising congestion and safety issues.
And these ramifications often come down hardest on disadvantaged communities — people who live in low-income neighborhoods where high congestion, noise pollution, and poor air quality are common. For this reason, analyzing how truck activity impacts disadvantaged communities is critical to reducing harms.
Now, freight planners, fleet operators, and businesses can use new truck data from StreetLight to understand how freight activity impacts different communities, as well as investigate related questions about who truck activity serves, which industries are most represented, and how travel delays impact logistics, emissions, and equity.
Below, StreetLight analyzes commercial vehicle activity in New York, comparing its impact on DACs vs. non-disadvantaged communities.
How Trucks Impact Disadvantaged Communities in New York
Since 2021, disadvantaged communities (DACs) have been a key focus of efforts to improve transportation equity. These communities are “marginalized by underinvestment and overburdened by pollution,” as defined by Executive Order 14096, which established the Justice40 Initiative, a policy promising to funnel at least 40% of federal investments in clean energy, clean transit, sustainable housing, and similar programs into low-income census tracts that qualify as disadvantaged communities.2
In the state of New York, about 36% of all census tracts — accounting for about 35% of the state population — are considered to be DACs.
While all truck traffic can negatively impact a community’s air quality, traffic congestion, and local emissions levels, analyzing how different weight classes impact DACs vs. non-DACs can add helpful nuance to planning efforts aimed at mitigating this impact and targeting policy interventions.
To unpack the impact of trucks on DACs. vs. non-DACs, StreetLight’s analyzed truck activity in New York state by weight class and how it differed in these community types. Streetlight finds that light-duty and medium-duty truck activity have a disproportionate impact on disadvantaged communities per mile. Meanwhile, heavy-duty activity per roadway mile is slightly higher within non-DACs.
Truck Activity Per Roadway Mile is segmented by commercial vehicle weight class to compare trucks’ impact on DACs vs. non-DACs. See our Methodology section at the end for more details.
Going further and factoring in travel delays among trucks, the picture changes substantially.
The chart below illustrates the inequitable impact of congestion, with DACs encountering significantly more traffic delays per roadway mile, even among truck classes that have a lesser presence in DACs compared to non-DACs. In fact, DACs are faced with more traffic delays across every class of truck, suggesting that DACs may also be exposed to disproportionately high emissions from both gas-powered and diesel-powered trucks as these vehicles linger through delays, contributing to poor health outcomes.
Measuring the average delay from free-flow speed per roadway mile reveals that traffic delays impact disadvantaged communities significantly more, regardless of which classes of truck are driving in their community.
While trucks are not necessarily the cause of these traffic delays, congestion mitigation efforts can still help reduce the impact that trucks — and indeed all vehicles — have on the communities they travel through.
Because these delays are averaged across the entire available roadway network, the per-mile delay impacts appear small. Nevertheless, the fact that average delays are higher for DACs than non-DACs suggests that analyzing congestion is a worthy step when evaluating the impact of truck traffic within disadvantaged communities.
While traffic delays are a nuisance for communities in and of themselves, lengthening commute times and making it harder to access essential goods and services, delays also intensify other negative impacts of vehicle activity like emissions and air pollution.
Where TruckDelays Have the BiggestImpact by Density
For agencies to manage truck activity in a way that improves transportation equity, it’s also important to understand that DACs are diverse in makeup, as are non-DACs – no two communities look exactly the same. For example, urban density can have a significant impact on how communities are impacted by trucks.
To understand these nuances, let’s first establish how DACs are distributed across different urban densities – from rural locations to the urban core.
The map above highlights important nuances to consider when comparing census tracts—firstly, rural census tracts cover far more area, and therefore far more roadway network miles, than urban tracts. For this reason, StreetLight normalizes Truck Activity by roadway mile for this analysis (see Methodology section for more details).
Furthermore, the map shows that DACs are concentrated in urban areas. Zooming in on New York City reveals just how many DACs call the city home compared to rural areas throughout the state. The chart below explores this distribution by population.
Since DACs are concentrated in urban areas, you might expect to find that truck delays are only an issue for DACs in urban and urban core locations.
However, in the chart below, we can see that while delays are worse in urban DACs, even in rural and suburban areas, DACs are impacted by more travel time delays than their non-DAC counterparts, highlighting the importance of targeting congestion mitigation efforts and improving freight planning within disadvantaged communities across all urban densities.
How Different Truck Weight Classes Impact Communities
To further contextualize StreetLight’s truck findings by density, let’szero in on the roles different truck weight classes play in each type of tract.
Truck Weight Class Distribution by Urban Density
How Different Industries Contribute to Truck Impact, by Weight Class
Given the disproportionate impactmedium-duty vehicles have on DACs by overall activity, and especially when factoring in travel delays, it’shelpful to understand what roles these vehicles play in communities, and which industries drive medium-duty truck activity.
As the image above depicts, commercial vehicles range from class 1 to class 8, with classes 1 and 2 considered “light-duty” vehicles, ranging from commercial vans to pickup trucks. Classes 7 and 8 are “heavy-duty” vehicles, including garbage trucks, city transit buses, and traditional semi-trailer trucks. Everything in between (classes 3-6) is considered “medium-duty,” ranging from local delivery trucks to school buses.3
Based on mileage, medium-duty truck activity in New York’s DACs is predominantly comprised of Public Administration and Transportation and Warehousing vehicles, with Real Estate and Rental and Leasing (the front-runner for medium-duty activity in non-DACs) trailing just behind.
Meanwhile, Real Estate and Rental and Leasing and Transportation and Warehousing also make a strong showing within the heavy-duty vehicle activity breakdown.Although heavy-duty vehicles show more truck activity per roadway mile within non-DACs compared to their DAC neighbors, these trucks still have a significant impact on DACs, especially as they contribute more emissions and noise pollution per mile traveled compared to smaller trucks. For this reason, understanding the industry breakdown among heavy-duty trucks could also generate valuable lessons for equity-focused freight planning.
Based on these findings, medium- and heavy-duty trucks serving the transportation and warehousing industrycould warrant special attention from freight planners. These trucksoften travel to and from larger hubs of freight activity, such as distribution warehouses and ports, making communities impacted by these high-traffic freight routes that much more likely to experience air and noise pollution due tonearby truck activity. Moreover, much of this truck traffic may not be ending in these communities, merely passing through. For this reason, the Bipartisan Infrastructure Law (BIL) provides special funding opportunities for decarbonization efforts that target ports.4
Key Takeaways for Managing Truck Traffic Impacts
To improve outcomes for disadvantaged communities, planners must consider the inequitable impacts of truck traffic as they work to mitigate congestion, reduce emissions, and route trucks efficiently.
StreetLight’s analysis highlights that some classes of truck impact DACs more than their non-DAC counterparts, suggesting that analyzing this truck activity and targeting electrification efforts toward these weight classes and industries in particular may help address inequitable impact and target the most impactful improvements.
Additionally, the analysis shows traffic delays are a problem disproportionately impacting DACs across all urban densities, but especially in the urban core. Though these delays are not necessarily caused by trucks themselves, they can exacerbate the impacts trucks have on local emissions and air quality, making efforts to mitigate congestion and route trucks more efficiently in disadvantaged communities especially critical.
Finally, the Transportation and Warehousing industry emerges as a significant contributor to trucks’ impact on disadvantaged communities. This industry could warrant special attention from planners or businesses looking to address freight’s equity impact. And for businesses with large logistics operations, placing emphasis on improved routing and electrification could mitigate congestion and emissions impacts on disadvantaged communities.
This analysis includes truck data for March 2024 within the state of New York, including residential roadways.
To measure Truck Activity, StreetLight analyzes sample Vehicle Miles Traveled (VMT) for commercial vehicles per mile of available roadway network in each census tract.
To analyze traffic delay, StreetLight uses a weighted average of travel time delay per mile of available roadway network in each of the urban density and DAC/non-DAC categories. The weight is the segment truck sample count, so that segments with higher numbers of trips have their delay represented proportionately. In other words, the travel time delay value for each of these categories represents how much delay a driver can expect when travelling one mile within that category.
StreetLight analyzes the urban density of census tracts based on the density of their roadway networks.
Census tracts are labeled “disadvantaged” vs. “non-disadvantaged” based on how they are classified by the Justice40 Initiative. In general, census tracts labeled “disadvantaged” meet a threshold for “environmental, climate, or other burdens” and “an associated socio-economic burden.”5
1. USDOT Bureau of Transportation Statistics. “Freight Activity in the U.S. Expected to Grow Fifty Percent by 2050.” November 22, 2021. https://www.bts.gov/newsroom/freight-activity-us-expected-grow-fifty-percent-2050
2. The White House. “Justice40, a whole-of-government initiative.” https://www.whitehouse.gov/environmentaljustice/justice40/
3. U. S. Department of Energy, Alternative Fuels Data Center. “Maps and Data – Vehicle Weight Classes & Categories.” https://afdc.energy.gov/data/10380
4. Office of Energy Efficiency & Renewable Energy. “Federal Funding Opportunities for Port Low- to Zero-Emissions Technologies.” https://www.energy.gov/eere/federal-funding-opportunities-port-low-zero-emission-technologies
5. Office of Energy Justice and Equity. “Justice40 Initiative.” https://www.energy.gov/justice/justice40-initiative
Ready to investigate trucks' equity impact in your community?
Today, the transportation industry, smart cities, and commercial businesses are all increasingly turning to big transportation data to make informed decisions. Alongside this growing demand, big data providers have also proliferated, making decisions about where to source this data more complex.
StreetLight has long been a frontrunner in the transportation data landscape, and below we’ll explain a few reasons why it remains a popular choice. But we’ll also cover some leading alternatives to StreetLight and how they may be useful for certain use cases and practitioners so you can explore your options and find the best solution.
As you’ll see, while many of these alternatives do certain things well, StreetLight is a strong choice for granular, empirical, reliable, full-coverage metrics across North America.
If you’re a current customer looking to improve upon your experience with StreetLight, or have a project you’re not sure how to accomplish in our platform, reach out to our Support team here! Our best-in-class customer service and full suite of training and support resources are part of what makes StreetLight the leading choice in transportation data analytics.
Why People Choose StreetLight
StreetLight is the most widely adopted transportation software, trusted by transportation agencies across the U.S. and Canada, with third-party validations across North America backing its reliability across a wide range of applications. Due to its strong reputation in the transportation data landscape, you may have already seen StreetLight data at work on some high-profile projects, such as the rebuilding of the collapsed Fern Hollow Bridge, or major industry research like this report revealing VMT and congestion are higher than ever.
So what makes StreetLight such a popular choice for agencies, firms, and businesses?
Comprehensive and Cutting-Edge Data Sources
One key advantage to StreetLight is its long history in the transportation industry. Founded in 2011, StreetLight has a 10+ year repository of mobility patterns and is continuously updating its data sources as new ones emerge. This allows StreetLight to turn the richest, largest, and most secure datasets available today into reliable and granular metrics used by professionals across North America.
Purpose-Built Solutions Supportinga Wide Range of Use Cases
StreetLight’s long history in transportation has evolved into a robust suite of productsthat directlyaddress the needs of transportation professionals. Whether you need to manage traffic congestion, improve safety, or deploy EV chargers, StreetLight has a purpose-built solution to help you make data-driven decisions impacting urban mobility and more.
Highly Granular Data for any Geography in North America
Not only can StreetLight support a wide variety of use cases, our data also offers deep granularity across space and time. In other words, you can get data on any road, any mode, and for any time period. That means you can analyze fast-changing road conditions (in increments as small as 15 minutes), high-level historical trends over the past five years, or real-time traffic patterns that happened a few seconds ago for transportation operations management. Similarly, you can zoom in on individual road segments or get region-wide insights, depending on the scope of your project.
This granularity ensures agencies of all sizes, as well as firms and businesses, can get actionable insights to prioritize projects, evaluate impact, and anticipate future needs. It’s also particularly important for transportation modeling, which requires granular, empirical data to help predict how conditions will change over time, or in response to specific infrastructural and policy changes.
Industry Longevity and Reliability
The transportation data landscape has gotten shaken up more than once since StreetLight’s founding in 2011. But as data sources come and go, StreetLight’s industry-leading data science engine has stood the test of time, offering stability and transparency to its users during times of uncertainty.
Today, StreetLight is part of Jacobs, a Fortune 500 company with years of industry knowledge, adding to StreetLight’s stability in the market.
So, given all the advantages we’ve explored, how does StreetLight compare to other mobility data providers on the market? Next, we’ll explore some of StreetLight’s competitors and alternatives to help you determine if StreetLight or another option is right for you.
Replica
Replica’s tagline is “Data to Drive Decisions about the Built Environment,” and its data offerings are designed to match. Similar to StreetLight, many of Replica’s applications cater directly to transportation industry use cases, and related use cases in land use and commercial real estate.
Some of Replica’s applications include:
Active Transportation Analysis
EventSight
GeoSnapshot
Road Closure Scenarios
Safe Streets Planner
Standard Growth Scenarios
Transit Demand and Equity Scores
When People Might Choose Replica Over StreetLight
Replica is a common choice for users who want to analyze economic activity, including consumer spending trends, or certain nuanced land use scenarios.
Replica’s scenario tools (projecting population growth, employment growth, remote work rates, and other scenarios) also offer insights and data visualizations based on predicted behavior and synthetic populations.
When People Might Choose StreetLight Over Replica
StreetLight is a common choice when highly granular datasets or comprehensive, empirical coverage is needed for all road segments, intersections, or time periods. In addition, for transportation operations professionals, StreetLight data offers real-time and historical vehicle traffic patterns to solve capacity constraints and develop traffic management plans.
Transportation modelers or other users who want to analyze empirical behaviors rather than synthetic behaviors may also choose StreetLight.
With APIs and ESRI integration available, StreetLight may also be well-suited to users who want to use datasets within their own software and third-party tools.
Operations and event traffic managers may also choose StreetLight for its real-time and near real-time data solutions.
INRIX
Like StreetLight and Replica, INRIX offers big data solutions designed for transportation use cases and related land use and business applications. INRIX’s global presence also makes it a leading choice beyond North America.
Among INRIX’s strengths on the market are offerings in real-time analytics and safety analytics targeted at securing grant funds such as those available through the Bipartisan Infrastructure Law (BIL).
INRIX’s products also include Smart Delivery analytics for ride share and food delivery, as well as parking and curb analytics.
When People Might Choose INRIX Over StreetLight
INRIX’s global presence makes it a common choice for practitioners who need to analyze mobility beyond North America (however, StreetLight is beginning to expand its operations beyond North America, including the UK and other countries).
INRIX is also a strong choice for users who want to analyze parking, curb, and signal analytics, as well as real-time data that includes roadway alerts.
When People Might Choose StreetLight Over INRIX
With ample white papers and third-party validations available, StreetLight is a strong choice for users who value data transparency or need to ensure the data they use meets very specific qualifications.
For practitioners looking to analyze all modes in one platform, StreetLight offers tools for planners to access insights for vehicle, truck, bicycle, pedestrian, bus, and rail modes.
Because some of INRIX’s solutions are available on non-integrated 3rd-party platforms, users who prefer an integrated, seamless experience may choose StreetLight.
LOCUS, a subsidiary of the consulting firm Cambridge Systematics, was once a visualization tool used exclusively for Cambridge Systematics clients. Now, it is a standalone, on-demand platform offering performance, safety, freight, EV, and multimodal mobility metrics. Like other StreetLight alternatives, it specializes in delivering insights on transportation and travel behavior.
LOCUS is a newer offering on the market with a smaller range of product offerings. However, they emphasize custom solutions paired with consulting services through Cambridge Systematics to help customers accomplish a variety of goals.
When People Might Choose LOCUS Over StreetLight
Users may choose LOCUS for its transit-focused solutions, including solving for the Transit Redesign use case since you can integrate transit data (farecard and APC) with LOCUS data, providing insights on the transit travel market in your region.
When People Might Choose StreetLight Over LOCUS
StreetLight offers AADT, TMC, VMT, and other core transportation metrics not advertised on LOCUS’s website. While LOCUS users may be able to access these insights through custom solutions, users who prefer self-service access to a wide variety of industry metrics may prefer StreetLight.
In addition, for transportation operations professionals, StreetLight offers real-time and historical vehicle traffic patterns to solve capacity constraints and develop traffic management plans.
As a long-term player in the transportation data space, StreetLight also has the benefit of 10+ years of SaaS development experience, a wealth of validated white papers, dozens of case studies, and proven market stability making it a trusted choice for thousands of professionals.
CATT Lab
The Center for Advanced Transportation Technology Laboratory – AKA CATT Lab – is affiliated with the University of Maryland and offers a few products, including:
RITIS – offering real-time data feeds, real-time situational awareness tools, and archived data analysis tools
Probe Data Analytics Suite – a data visualization platform that uses probe data mixed with other agency transportation data to support operations, planning, analysis, research, and performance measures.
In addition to RITIS and the Probe Data Analytics Suite, CATT Lab offers several other tools for monitoring vehicle and freight traffic, including Work Zone Performance Monitoring, Virtual Weigh Station, Incident Timeline, Explore and Visualize Crashes (EVC), and Sky Graph.
CATT Lab’s strength lies in real-time data visualization tools, which are powered by “data fusion” from dozens of agency data feeds, including emergency operations centers, transportation management centers, sensors, CCTV cameras, and sub-systems across the country.
When People Might Choose CATT Lab Over StreetLight
CATT Lab’s strengths in real-time data visualization make it a good option for operations departments and practitioners within public agencies who may already have access on a non-profit or government-funded basis.
Customers looking for data visualization capabilities for air traffic control, incident analysis, or weigh stations may find additional value in CATT Lab’s unique suite of tools.
When People Might Choose StreetLight Over CATT Lab
For those who need multimodal metrics including bike and pedestrian analytics, StreetLight data may be the preferred choice, as CATT Lab’s offerings focus on vehicle and freight activity.
Additionally, some users may prefer StreetLight’s unified platform with robust onboarding, training, and support resources over CATT Lab’s various standalone tools.
Finally, while CATT Lab offers effective real-time data visualization tools, StreetLight’s repository of historical data may be better suited to certain transportation planning applications.
AirSage
AirSage offers transportation data “customized” to each customer’s needs, and serves public agencies, firms, and businesses. It used Big Data to deliver insights on how people and vehicles move.
AirSage offers a few products, including:
Trip Matrix – an Origin-Destination matrix the includes trip attributes and trip purpose information
Activity Density – measuring population movement and density, useful for event-based migration patterns such as emergency evacuations
Pedestrian Activity Density – specifically for understanding pedestrian movements
Target Location Analysis – used to understand visitor activity at points of interest
Many of AirSage’s products are delivered in CSV format, making it easy to integrate with your own dashboards, GIS platforms, Power BI, excel, and other mapping tools.
When People Might Choose AirSage Over StreetLight
According to the Airsage website, many of the offerings are primarily delivered via CSV. Users who prefer CSV data outputs may not find as much value in StreetLight’s in-platform data visualization tools and on-demand, customizable analyses, however StreetLight also offers CSV downloads for all analyses.
When People Might Choose StreetLight Over AirSage
When users are looking for a self-serve, on-demand analytics software with robust data visualization tools for historical and real-time metrics catered to their specific use case, StreetLight may be the preferred option.
StreetLight also has a wealth of 3rd-party validations, case studies, and white papers that may appeal to users that value data transparency or want to ensure their data provider meets specific technical qualifications.
How StreetLight Stacks Up Against Its Competitors
To summarize, while some StreetLight alternatives offer advantages in specific areas such as analyzing economic activity, parking and curb data, transit competitiveness, or air traffic control, StreetLight’s self-serve software with robust data visualization tools catered to the transportation industry’s top use cases makes it a popular choice for many users across North America.
With highly granular real-time and historical multimode metrics derived from diverse data sources and supported by 3rd-party validations and white papers, StreetLight is trusted by agencies, firms, and businesses to deliver reliable insights that satisfy even their most technical users.
And with the recent disruptions that have impacted the transportation data landscape, StreetLight’s unmatched market stability adds peace of mind for customers looking for a long-term solution that can fill their data gaps for years to come.
7 Key Features of Transportation Analytics Software
7 Key Features of Transportation Analytics Software
For today’s transportation planners, the need for specialized, in-depth data is a given. Planning departments rely on a plethora of important metrics to evaluate traffic patterns and choose the best possible infrastructure upgrades.
Yet access to such data and analytics comes in many forms. Many transportation analytics providers offer Big Data solutions, but not all of these options are created equal. With a variety of choices, how can planners ensure they select the right transportation software for their needs?
The best tools have seven essential features for transportation data analytics, each working together to provide a complete source of transportation insights. We’ll explore each of the following features in turn:
The power of Big Data
Machine-learning algorithms
A wide variety of metrics
Detailed mobility insights
Built for transportation studies
User-friendly formats
Privacy protection
1. The Power of Big Data
While traditional data from surveys and sensors still has a place in modern transportation planning, the depth of insight that Big Data can provide is an indispensable addition. With broader coverage and large datasets that support enhanced granularity, big data helps planners keep up with fast-changing traffic patterns and region-wide trends that traditional methods typically struggle to capture.
Instead of relying solely on limited data collection methods, the best transportation analytics tools draw extensive data from a wide range of sources, such as GPS systems, commercial fleets, census results, Connected Vehicle Data, and more. The larger the sample size, the more accurate and helpful the information it provides.
Additionally, the most informative Big Data in transportation details more than just vehicle traffic patterns. It covers multimodal transportation, pulling in essential numbers from public transportation, bike and pedestrian movement, ride-sharing networks, and more.
As the industry leader in traffic data analysis, StreetLight offers more than a decade of validated mobility data compiled from trillions of data points. The StreetLight InSight® platform includes exhaustively tested recent and historical insights, and it’s constantly adding new data sources as they emerge.
2. Machine-Learning Algorithms
Simply having access to a massive amount of data isn’t enough; the task of extracting and organizing, not to mention analyzing and evaluating these massive data sets, is far beyond the scope of most transportation departments. Machine learning — a fundamental component of artificial intelligence (AI) — accelerates this process and (when used properly) provides more accurate results.
With so many transportation analytics now available, only machine learning can bridge the gap between data and analysis. It enables comprehensive analysis of trips from the moment journeys begin to the moment they end, via any mode, on all roads and paths. By being continually trained with vast amounts of data, these algorithms can more accurately pinpoint the correct data and quickly analyze it.
StreetLight accomplishes this through its Route Science® machine learning algorithm, which transforms trillions of inputs into contextualized, aggregated, and normalized travel patterns. Core StreetLight metrics have been exhaustively validated against external sources, including permanent and temporary sensors, household surveys, and the Census, proving that machine learning can efficiently and effectively capture massive amounts of invaluable traffic data.
3. A Wide Variety of Metrics
The most useful transportation planning software offers a wide range of metrics for thorough and detailed analysis. That involves much more than vehicle traffic count metrics like Annual Average Daily Traffic (AADT) and Turning Movement Counts (TMC), as vital as those metrics are for planners. Other critical data points include:
Origin-Destination (O-D) patterns, to help understand trip purpose, top routes, and source of travel demand
Vehicle Miles Traveled (VMT), to measure emissions, travel demand, and more
Vehicle Hours of Delay (VHD), to quantify the extent and impact of congestion
Vehicle Speed, to evaluate safety concerns and congestion impacts
Bike and Pedestrian Volumes, to understand where Vulnerable Road Users face the greatest safety risks and identify opportunities to encourage active transportation modes over vehicle travel
These are just a few examples of the types of metrics that drive informed decision-making. The best software solutions offer an array of validated metrics for commute planning, safety analyses, intersection studies, and more.
A Top Routes analysis in StreetLight InSight® highlights where drivers tend to go after they leave a particular location (in this case, an underpass along Colonial Drive in Orlando, FL).
4. Detailed Mobility Insights
The greater the breadth and scope of data at planners’ fingertips, the more insights they have access to. But perhaps even more important for deeper analysis and understanding is the level of granularity in the numbers — and the interplay between those various data points.
In other words, planners frequently need more than a regional or high-level overview of traffic patterns. They must see mobility numbers for precise time periods and specific road segments. And they must understand how metrics like VHD and TMC relate to driving patterns and decisions.
To provide this level of precision, StreetLight delivers data for any road, any mode, and any time period. Transportation activity can be broken down into 15-minute bins, and multiple metrics can be overlayed for deeper insights. This empowers more informed, impactful decisions. As planners zero in on precise times and locations where issues occur, they can better anticipate the impact of potential changes, weigh the pros and cons of project proposals, and monitor roadway conditions that are developing, even in real time, as the demo below explores.
5. Built for Transportation Studies
The primary goal of using Big Data in transportation is, of course, to support better transportation studies and enable data-driven decisions. Rather than merely producing a complicated data library, transportation software should offer purpose-built analytics catered to the types of projects planners routinely encounter, from safety studies to corridor planning. Whichever transportation analytics software you consider, the test is the same: Does their data simplify the process of studying traffic patterns and evaluating proposed changes?
For example, in the video below, a Senior Regional Planner for the Southern California Association of Governments (SCAG) explains how his team uses StreetLight’s Safety Prioritize tool to prioritize the most urgent road improvements and evaluate the impact of their projects.
6. User-Friendly Formats
Yet another trademark of the best transportation analytics software is its ability to translate and repurpose data for use in a myriad of different formats. It should be easy for users to move from a graphical overview with city-wide data summaries to a CSV with tabulated data for detailed reports.
When analytics are quickly convertible, it’s that much easier to leverage traffic data for the next important project and share information with stakeholders. Grant proposals, for instance, rely on clear, powerful data presentations to secure funding and project buy-in. Tools like StreetLight InSight® allow planners to toggle between 3D visualizations, Esri ArcGIS maps, spreadsheets, and more in order to make the most compelling case possible for their traffic solutions.
7. Privacy Protection
Privacy is a growing concern in the expanding world of Big Data. As companies collect more information, consumers and citizens are increasingly worried about just what info they have and how they’ll use it. Transportation planners and other stakeholders in this arena should be eager to show that their decisions are data-driven without being powered by privacy violations.
That makes extensive privacy protections a critical feature of any transportation analytics software you consider. The data collected and provided by these companies must be based on composite groups of people and aggregate traffic patterns — never on specific individuals. It goes without saying that transportation data analytics companies should not receive or use personally identifiable information, but instead employ multi-layered technical safeguards to prevent any exposure of personal information.
As a leader in transportation analytics, StreetLight takes its commitment to privacy very seriously. The data available in the StreetLight InSight® platform is aggregated and anonymized so that individuals can’t be identified. You can learn more about StreetLight’s data privacy principles here.
Your Hub for Big Transportation Data
Accurate, detailed, and informative data is integral to meeting today’s transportation challenges — and building a more effective, sustainable transportation infrastructure for tomorrow. Whether you’re conducting a high-level transportation network analysis for your region or digging into the metrics for one problematic corridor, you need numbers that empower intelligent, strategic planning.
Fortunately, you can find these insights and more in one easy-to-use platform. StreetLight InSight® is built on Big Data and advanced machine learning algorithms to provide the most robust, comprehensive set of mobility insights available to transportation planners today. With this set of tools, you can access the analytics you need for effective planning and decision-making.
StreetLight draws together data from a vast array of sources, including census results, GPS data, location-based services, road and vehicle sensors, and Connected Vehicle Data. More importantly, it presents information in easily digestible formats planners can use for project planning, grant writing, and in-depth analysis. With access to this much information, you can be confident in each study you conduct or project you plan.
Ready to explore for yourself? Get started with StreetLight today.
6 Types of Transportation Big Data Every City Needs
6 Types of Transportation Big Data Every City Needs
In a changing transportation landscape, it’s more important than ever for planners to have access to detailed data and analytics. Given our increasingly complex transportation networks, planners must consider where to implement road diets, add EV charging infrastructure, establish tollways, expand access to multimodal transport, and so much more.
While traditional methods of data collection and analysis (think: surveys and sensors) can still offer important insights, today’s cities often seek the help of something bigger — Big Data. In transportation, Big Data involves large, complex data sets collected from numerous sources to provide a complete picture of today’s transportation networks, including various modes of transportation and how they interact. This type of detailed data is collected from trillions of pings from connected vehicles and the Internet of Things, combined with contextual data like the census and maps of the roadway network, which is anonymized, aggregated, and processed by machine learning engines to generate recognizable metrics like Annual Average Daily Traffic (AADT) and Vehicle Miles Traveled (VMT), which transportation planners use every day.
Having access to detailed real-time and historical transportation data empowers planners and transportation departments to develop better strategies, reduce costs, prioritize initiatives, and measure the effectiveness of each change or improvement. But which metrics are most important for smart planning and development? To make a wide range of informed decisions, planners typically rely on the following Big Data for transportation, which we’ll cover in this article:
Annual Average Daily Traffic
Origin-Destination
Turning Movement Counts
Vehicle Miles Traveled
Vehicles Hours of Delay
Vehicle Speeds
1. Annual Average Daily Traffic
Annual Average Daily Traffic (AADT) is perhaps the most foundational metric in transportation analytics. It measures the average daily volume of traffic on a given road during a given year, and it’s critical for evaluating road congestion, spotting safety concerns, and planning infrastructure updates. AADT also plays an integral role in shaping non-transportation decisions, such as developing new retail or investigating accident cases.
Historically, this fundamental piece of traffic data has been expensive and time-consuming to acquire. Traditional collection methods for AADT require working counters that can track traffic counts 24 hours a day, 365 days a year. In many jurisdictions, it’s simply not possible to collect this much data for every road.
Today, however, machine learning allows software like StreetLight InSight® to build models that calculate AADT for more roadways — and much more quickly. Instead of spending time and money installing sensors on every roadway or extrapolating annual numbers from a few days’ worth of manual counts, Big Data leverages large sample sizes and a continuous stream of data to quickly deliver the most reliable, up-to-date data on roadway volumes.
Origin-Destination (O-D) patterns provide another essential piece of the puzzle in transportation data analytics. O-D data helps transportation professionals understand where trips begin and end, shedding light on commute patterns, areas of high travel demand, and locations that generate the most traffic. For these reasons, O-D data is often used by agencies to understand where infrastructure improvements, policy changes, or traffic management strategies can help optimize traffic flow, encourage mode shift to walking, biking, and transit, boost economic activity, and more.
An Origin-Destination analysis in StreetLight InSight® shows top origins and destinations, with a time distribution overlay contextualizing when most trips occur.
However, traditional O-D metrics are often incomplete and inefficient to collect, requiring extensive surveys which are typically costly and suffer from human bias and small sample size issues. Big Data can provide more detailed information — without the survey process — and go beyond typical O-D analyses to identify the most prevalent travel routes and allow planners to easily customize their analysis and contextualize O-D patterns with other data overlays like vehicle speeds or AADT. For instance, planners in Sarasota, Florida, used StreetLight O-D data to prioritize bike routes and route directness and create a stronger multimodal network. Using detailed metrics without the need for surveys, Sarasota connected more bike and pedestrian routes to transit services and key destinations to reduce congestion and create a more equitable transportation system.
3. Turning Movement Counts
Turning Movement Counts (TMC) provide critical safety and congestion information about intersections. In simple terms, they demonstrate the volume of traffic entering and exiting an intersection at a given time. While this data can be collected manually, this process is cumbersome and costly. Since the 1970s, pneumatic tube counters have typically provided these counts every 24 to 48 hours, but such tubes often provide inaccurate data due to vehicle angles in intersections. It’s also expensive to lay down enough counters to collect sufficient data — each single-lane tube costs roughly $5,000, while each four-lane tube costs around $9,000.1
Planners can leverage Big Data to collect more frequent, accurate, and less expensive analytics on any intersection. Big Data can model 15-minute granularity for nearly every intersection — signalized or not — at any hour of the day, without the sample-size challenges of 48-hour counts. With this level of collection and analysis, planners can understand turning patterns and peak turning times for almost every road in the U.S. and Canada.
4. Vehicle Miles Traveled
To understand travel demand, measure emissions, evaluate multimodal infrastructure, and more, Vehicle Miles Traveled (VMT) is another critical metric for city transportation planners. VMT estimates the total mileage traveled by vehicles in the region or along specific corridors. This helps planners monitor changes in travel demand over time, allocate resources where they’re most needed, and understand how vehicle travel impacts road infrastructure and regional emissions.
With Big Data, planners have access to continuous, widespread VMT info for any road or region. This information makes it possible to build travel demand forecasts, plan for congestion relief, and direct regional and corridor traffic studies. VMT is also invaluable for detailed estimates of greenhouse gas emissions, fuel tax impacts, and more.
Big data transportation analytics platforms allow cities like Citrus Heights, California, to get comprehensive VMT metrics for every census block, meeting regional reporting requirements like SB 743, shedding light on city-generated transportation emissions, and guiding infrastructure improvements.
5. Vehicle Hours of Delay
Vehicle Hours of Delay (VHD) is an essential metric for measuring congestion issues and targeting traffic bottlenecks. It provides the total number of hours lost to traffic delays in a given area during a specific time period. Understanding VHD can help planners quantify the severity and investigate the causes of traffic delays, as well as predict how road construction, special events, or bad weather may impact travel times. It is also critical for evaluating the effectiveness of traffic management strategies and more accurately forecasting which road improvements will reduce delays.
With StreetLight InSight®, planners can access historical and monthly delay metrics, providing visibility into fluctuations over time. This type of traffic data is invaluable in highly congested urban areas like Los Angeles or Chicago, where the success of each transportation initiative hinges on understanding the exact sources and nature of each bottleneck or problem area.
6. Vehicle Speeds
Vehicle speed metrics help city planners understand a variety of road conditions, particularly road safety and congestion.
Vehicle speed data is critical to measuring and improving road safety, especially when it comes to protecting Vulnerable Road Users like cyclists and pedestrians. To put this into perspective, data from AAA Foundation shows pedestrians are five times more likely to die from crashes when cars are traveling 40 mph vs. 20 mph. Vehicle speed metrics help city planners understand where high speed vehicles threaten the safety of all road users, and evaluate roadways for potential traffic calming strategies like road diets or speed humps.
A data visualization shows where high vehicle speeds and high pedestrian activity overlap on a dangerous section of Oakland, California’s Grand Avenue.
Inversely, identifying areas with lower-than-expected vehicle speeds can help city planners identify areas of high congestion, and evaluate when that congestion is at its worst. Getting real-time data on vehicle speeds can even enable city traffic managers to quickly deploy congestion mitigation strategies like retiming smart traffic signals or deploying trained traffic controllers where needed. While vehicle speeds can be collected from physical sensors like speed cameras or calculated based on the distance and travel time between two separate traffic counters, getting vehicle speed data for every roadway would require installing these types of sensors throughout a city’s entire road network, which is prohibitively expensive and time-consuming. Big data vehicle speed metrics can fill these gaps for cities that want to improve safety or congestion on city streets, even those without permanent counters installed.
Accessing the Data You Need, When You Need It
Solving regional transportation issues is a problem of massive scale and enormous import. Faced with millions of cars traveling thousands of miles per year and the pressing problems of urban congestion, transportation inequity, a pedestrian safety crisis, and greenhouse gas emissions, planners must have access to digestible, actionable metrics to understand and address the issues their constituents face. Injecting Big Data in transportation planning puts a better city mobility network within reach by delivering more information to demystify these common problems and their potential solutions.
With StreetLight InSight®, planners can access the most comprehensive suite of transportation data analytics on the market. While traditional collection methods and metrics are still useful for providing critical snapshots and verifying broader data sets, StreetLight’s Big Data analytics expand upon what’s possible with these traditional methods with 24/7 access to the metrics planners need to make more informed, data-driven decisions for any road and any mode.
1. Federal Highway Administration. “A Summary of Vehicle Detection and Surveillance Technologies use in Intelligent Transportation Systems.” https://www.fhwa.dot.gov/policyinformation/pubs/vdstits2007/04.cfm
Electric vehicles (EVs) are a critical element of the fight against climate change. Compared to a typical gas-powered vehicle, the average EV produces less than half the amount of carbon pollution over its lifetime. [1] Even at today’s moderate levels of EV sales, electric cars are already reducing crude oil usage by 1.7 million barrels a day. [2]
Yet we have a long way to go to reach the tipping point, when EVs take over as the primary form of individual transportation. In the U.S., 10% of new vehicles registered in 2023 were electric, far from a majority. [3] Tipping the scales requires not only an increase in EV production, but a significant expansion of the nation’s EV charging infrastructure.
As of today, our systems are woefully underprepared for an EV-dominant future. What will it take to catch up? Although federal and state-level funding is essential, building an adequate charging infrastructure largely comes down to smart, data-driven planning.
In this article, we’ll explore what that looks like, covering the following:
How do EV charging stations work?
The current state of EV charging infrastructure
Optimizing charger placement in communities
Promoting sustainable energy sources
How Do EV Charging Stations Work?
Electric vehicle supply equipment (EVSE), as charging stations are commonly called, is fundamentally different from gas pumps. While both types of equipment are designed to refuel vehicles, EV charging stations are far less standardized than gas pumps.
EVSE comes in several different types, from basic (extremely slow) Level 1 chargers to high-speed, Level 3 DC fast chargers. Although the latter can recharge an empty car battery to 80% in less than an hour, most public EVSE consists of Level 2 chargers, which take anywhere from four to 10 hours to achieve a similar charge level. [4]
Not surprisingly, EV charging station costs vary widely based on type. You can plug into your home outlet for Level 1 charging, but it’ll cost anywhere from $1,000 for a basic home Level 2 charger to upwards of $50,000 for a commercial Level 3 charger. [5], [6]
Besides the cost of EV charging stations, planners must consider numerous other factors, including the connection type, interoperability with various vehicles, and the payment network of choice. All of these are critical factors in how to build EV charging station infrastructure.
The Current State of EV Infrastructure
In the U.S., the number of public and private EV charging stations has grown rapidly in recent years, thanks largely to available tax credits and incentives to help reduce upfront costs. However, the nation’s EV charging infrastructure has a long way to go to keep pace with near-term goals for vehicle electrification.
That’s especially true for public EVSE. Recent research by Stanford University sounded the alarm that relying on nighttime, home-based charging would put far too much demand on the electrical grid within the next decade. [7]
Currently, there are almost 10 times more home EV chargers than public ones, and that ratio needs to change quickly. [8] Data from the National Renewable Energy Laboratory (NREL) further supports the need for a massive uptick in public charging station installations. As of 2023, there were just over 168,000 public charging ports available in the U.S., but NREL research calls for nearly 1.2 million public ports to match EV demand by 2030.[9], [10]
A screenshot from StreetLight’s EV Dashboard visualizes the largest EV charging infrastructure gaps across Massachusetts, based on vehicle activity and existing charger locations.
It’s not just a numbers game, either. If electric vehicles are to become the norm, EV charging station infrastructure must be more accessible to everyone. That means charging equipment must become more interoperable with all types of EVs, stations must be available where people can conveniently use them, and EVSE must be reliable and easy to use.
Overall, reaching this level of accessibility requires more investment in both public and private (workplace) charging, along with a commitment to eradicating “charging deserts” in underserved communities. [11]
See what emissions reduction tactics your city needs most
The long-term goal may be accessible, reliable charging for everyone, but we are still many miles from that final destination. Accelerating our progress in the right direction requires near-term prioritization. Where does a given community need EV charging infrastructure next? What locations would make it practical and easy for more existing EV drivers to switch from charging at home overnight to charging at work or while shopping during the day?
To understand that, planners must gather and analyze a host of data. Targeting the ideal location for more EV chargers involves analyzing demographics and demand, accessibility concerns, and mobility patterns in various communities. It also requires an understanding of existing infrastructure and the differing needs of rural and urban locations.
For instance, planners in a dense urban area may need to evaluate traffic patterns in numerous parts of the city to understand how far people commute every day, where they tend to stop and for how long, and where people without driveways tend to park. In a rural area, conversely, data about long-range commutes and the most trafficked freeway corridors may be more relevant. They might also examine data about interstate corridors with heavy commercial freight transit to find ideal hubs for charging heavy-duty vehicles.
In the Silcon Valley, for example, planners used StreetLight InSight® to evaluate Origin-Destination (O-D) data, traveler demographics, and more to help choose locations for 400+ public EV chargers.
Planners must also attack the problem at a broader level, considering factors like how a specific location for EV charging stations will affect electricity demand in a particular area.
To prepare the electric grid for rising EV charging demand, Eversource, New England’s largest utility, used StreetLight to forecast where and when charging demand would be highest to plan substation upgrades and charging rates that would incentivize off-peak charging. Their demand analysis also allowed them to coordinate long-term electrification planning with public agencies.
Promoting Sustainable Energy Sources
Taking a wider scope, examining the best locations to maximize the impact of clean transportation can help planners prioritize where and how to build EV charging infrastructure. Although EVs reduce emissions regardless of where you deploy them, they offer the largest reduction in regions that rely on clean energy sources like wind, solar, or hydroelectric power.
For example, driving an EV in a coal-dependent state like West Virginia results in a 50% reduction in emissions compared to driving a gas car. In Texas, which is a national leader in solar and wind power generation, choosing an EV reduces emissions by over 77%. [12]
Practically, the application here is twofold: On one hand, it may be beneficial to develop more EV charging infrastructure in areas that already rely on clean energy sources. Yet, it’s also likely worth considering policies and incentive programs that will help municipalities and private companies go beyond simply installing EVSE to adding solar panels, wind turbines, or other clean energy sources that increase EVs’ environmental impact.
Get Charged Up With Big Data
EV charging infrastructure has come a long way in the U.S.—but there’s still a long road ahead to add enough EVSE to support a truly all-electric transportation system. Reliable data is crucial at this stage, regardless of where you’re looking to add more charging equipment. The pressing issue doesn’t simply come down to adding more chargers, but knowing where to put them so that they best serve real-world demand efficiently and equitably.
With purpose-built EV metrics and emissions analytics, StreetLight InSight® can help planners, policymakers, and businesses make smart decisions in this crucial sector. This software unlocks access to relevant transportation data, including O-D, vehicle miles traveled, traveler demographics, travel times, and more. This helps pinpoint where people are traveling and when traffic is highest to measure the potential GHG impact of adding EV charging stations to any specific location, while ensuring the grid can handle rising electric demand.
To start using StreetLight to plan your EV charging infrastructure today, contact us here.
Yale Climate Connections. “Don’t get fooled: Electric vehicles really are better for the climate.”
BloombergnNEF. “ElectricVehicle Outlook 2024.”
International Energy Agency. “Global EV Outlook 2024: Trends in electric cars.”
U.S. Department of Transportation. “Charger Types and Speeds.”
J.D. Power. “What Does an EV Home Charger Cost?”
State of New York. “Exhibit I Cost of charging stations.”
Stanford University. “Charging cars at home at night is not the way to go, Stanford study finds.”
International Energy Agency. “Global Outlook 2024: Trends in electric vehicle charging.”
National Renewable Energy Laboratory. “The 2030 NationalCharging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure.”
Alternative Fuels Data Center. ”U.S. Public Electric Vehicle Charging Infrastructure.”
World Resources Institute. “Many US Communities Face EV ‘Charging Deserts.’ 5 Strategies Can Help.”
Alternative Fuels Data Center. “Emissions from Electric Vehicles.”
Explore the resources listed above and don’t hesitate to reach out if you have any questions. We’re committed to fostering a collaborative community of transportation professionals dedicated to building a better future for our cities and communities.
Public Data from NYCDOT Validates the Reliability of StreetLight’s Speed Metrics
Public Data from NYCDOT Validates the Reliability of StreetLight’s Speed Metrics
Access to accurate vehicle speed data is critical for effective road safety interventions, congestion mitigation, and more. We compared StreetLight’s speed metrics to data from New York City’s OpenData portal to ensure we’re delivering the most reliable insights.
To collect data on vehicle speeds, many agencies rely on permanent roadway sensors, speed cameras, or manual speed studies. But tight budgets and project timelines prevent the installation of sensors on every road, and manual studies only capture a small snapshot of roadway conditions, while also putting workers at risk.
Meanwhile, businesses and firms may not have access to the already limited speed data that is available via these methods, limiting their ability to make informed decisions about real estate, traffic operations, or events management.
For these and other reasons, many agencies, firms, and businesses turn to analytics platforms like StreetLight that leverage a big data approach to deliver vehicle speed metrics for any road, at any time. But when sourcing your data, it’s important to understand how reliable it is compared to more traditional ground truth methods.
Thanks to the City of New York’s OpenData portal, with publicly available vehicle speed data provided by NYCDOT, we were able to perform a validation of StreetLight’s speed data against New York City’s documented speeds. We’ll explain how our speed data is collected, how it compares to NYCDOT’s data, and what that means for your own vehicle speed analyses.
How NYCDOT Collects Speed Data
The speed data available in NYC’s OpenData portal is collected through E-ZPass readers located on approximately 110 road segments throughout the city. Vehicle speeds are calculated based on the travel time and distance between two E-ZPass readers.
This approach captures average vehicle speed in real time, and the portal is updated with the most recent data several times per day.1
How StreetLight Collects Speed Data
StreetLight’s Vehicle Speed metrics are derived from Aggregated GPS Data, which includes data from a blend of device navigation apps, traditional mobile data apps, and in-vehicle navigation apps.
This method has the advantage of strong penetration rates across various road sizes and regions for a highly representative sample, even on rural and lower-volume roads. StreetLight’s sample penetration rate averaged 27% nationally in 2023 and was observed as high as 40%+ in some locations.
To develop segment-level speed metrics, StreetLight maps this data onto the StreetLight InSight® Zone Library, derived from OpenStreetMap (OSM). Based on the length of the segment and how long it takes a vehicle to travel from one end to the other, we estimate the average vehicle speed along that segment.
For more information on how StreetLight collects, aggregates, and validates our vehicle speed metrics, you can download this white paper.
Comparing the Data: How Accurate Are StreetLight’s Speed Metrics?
To ensure an apples-to-apples comparison, StreetLight analysts first cleaned the NYCDOT data, removing certain obviously incorrect datapoints that may have been caused by malfunctioning E-ZPass readers. Next, the cleaned NYCDOT data was aggregated such that the mean speed could be calculated per segment by day of week and hour of day.
Using StreetLight’s Network Performance analysis, analysts obtained the average speeds of groups of OSM segments that aligned with NYCDOT’s segments, looking at data for October 2023. This allowed for a close comparison between StreetLight and NYCDOT average speeds on 11 NYCDOT segments.
In this example comparison for a portion of FDR Drive Northbound, analysts averaged vehicle speeds from 10 StreetLight OSM zones (middle) aligned to the corresponding NYCDOT segment (left). On the right, NYCDOT speeds by day and time are marked with a blue line, while StreetLight speeds are marked in yellow.
Because StreetLight’s OSM-based segments do not have a one-to-one correspondence with NYCDOT’s segments (which are derived based on the distance between E-ZPass readers), special care was taken to align StreetLight segments with those used by NYCDOT, but some discrepancies persist, which we will discuss further in the analysis below.
Speed comparisons by day of week and hour of day for RFK Bridge Southeast Bound (left), Staten Island Expressway Eastbound (middle) and Long Island Expressway Westbound (right) segments. Monthly Average Daily Traffic (MADT) for each segment is marked below its graph.
Speed comparisons for Bronx Whitestone Bridge Southbound (left), Gowanus Expressway Southbound (middle), and FDR Drive Northbound (right).
Speed comparisons for Bruckner Expressway Westbound (left), Brooklyn-Queens Expressway (BQE) Southbound between Atlantic and 9th St (middle), and the Brooklyn Battery Tunnel Eastbound (right).
The above nine segment analyses showed StreetLight speed metrics closely aligned with speed data reported by NYCDOT. Where the data differs, StreetLight speeds tend to be slightly higher than those reported by NYCDOT.
Overall, StreetLight’s daily and hourly speed variations for each segment also track closely with the NYCDOT data, indicating that StreetLight’s speed metrics deliver reliable insights for real-world applications like safety and congestion studies, which can save agencies the considerable cost of installing physical sensors.
The two remaining segments (pictured below) display the greatest divergence between the StreetLight and NYCDOT datasets.
Speed comparisons for 12th Avenue Southbound (left) and Lincoln Tunnel Eastbound (right). These graphs show segments where StreetLight’s OSM zones could not be perfectly aligned to NYCDOT segments.
These discrepancies are likely caused, at least in part, by misaligned segment boundaries. As discussed above, sometimes StreetLight OSM zones could not be perfectly aligned to the NYCDOT segments.
In the case of 12th Avenue (AKA West Side Highway), this segment is part of a signalized corridor with closely spaced intersections, which could exacerbate the impact of the misaligned segments. Because the comparison segments do not have the same signalized-intersection approaches, this could lead to larger differences in average speed.
Despite these localized limitations in segment comparability, the overall results of our comparison show a high degree of alignment between StreetLight’s big data-based speed metrics and NYCDOT’s speed data derived from E-ZPass sensors.
More about StreetLight’s Vehicle Speed Data – Segment Speed and Spot Speeds
Because the vehicle speed metrics provided by StreetLight include average segment speeds, they can provide a helpful perspective, even for agencies that already collect speed data through physical sensors.
Unlike NYCDOT’s average segment speeds used in the above analysis, the speed data available to most agencies are spot speeds. Spot speeds capture vehicle speed at a specific location rather than the average vehicle speed along a whole segment.
Spot speeds and segment speeds each capture a different nuance of vehicle traffic, and comparing the two can help agencies better understand the causes of unsafe speeds or congestion, as well as their most effective solutions.
To ensure clients can take advantage of these nuanced speed insights, spot speeds are now available from StreetLight! To stay updated on all our product releases, consider subscribing to our newsletter.
1. City of New York. NYC OpenData. “DOT Traffic Speeds NBE.” https://data.cityofnewyork.us/Transportation/DOT-Traffic-Speeds-NBE/i4gi-tjb9/about_data
