6 Ways to Drive Sustainability in Transportation

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How Can We Reduce Emissions From Urban Transportation?

Emissions of greenhouse gases (GHGs) and other pollutants are a pressing concern for environmental and human health. Despite recent declines in the U.S., the global level of emissions remains at historic levels, leading to alarm among public health advocates and climate change activists alike. [1]

Although there are many factors behind these historically high emissions levels, none is as significant as transportation. In the U.S., the transportation sector accounts for 29% of all GHG emissions, ahead of even electricity generation and industry. [2] These emissions are highly concentrated in urban areas. According to the United Nations, 60% of GHGs come from cities, where cars and other modes of transportation relying on Internal Combustion Engines (ICE) are especially prominent. [3]

This makes urban transportation a strategic target for reducing emissions and curbing their impact on the environment and public health. The good news is that more than 10,000 cities have already committed to reducing carbon emissions by 2050. [4] Still, if history is any indication, curbing transportation emissions is easier said than done. Ultimately, city planners, transportation agencies, and many other stakeholders must come together with a strategic plan.

What will it take to reduce emissions from transportation, and just how important is this task? In this article, we’ll explore:

The cost of urban emissions
Shifting the urban transportation paradigm
Picturing the future with big data

The Cost of Urban Emission

Emissions are more than a nuisance — they exact a heavy toll on the global economy. In the U.S. alone, pollution accounts for around 5% of the nation’s gross domestic product in damages each year, or $1.3 trillion in 2023. More than mere dollars and cents, however, the costs of pollution are particularly prominent in terms of public and environmental health. [5]

Damaging Public Health

By any estimation, pollution is a serious public health concern. According to one in-depth study, fine particulate matter from numerous toxic pollutants contributes to between 100,000 and 200,000 deaths in the U.S. each year. The transportation sector is responsible for the second-largest number of these deaths, behind only industrial and commercial activity. [6]

No matter who is involved, such a large number of deaths is tragic. Yet, the tragedy is made worse by inequity, as pollution disproportionately impacts already vulnerable Americans. Children, pregnant people, older adults, people of color, and those living in poverty are among those most at risk for adverse outcomes from pollution. [7]

smog over NYC — *Transportation emissions are a primary source of city smog impacting residents’ health.*

Accelerated Climate Change

GHG emissions are the single largest contributor to climate change since the mid-20th century. [8] Research has connected emissions from human activity to a host of environmental events, including temperature extremes, surges in precipitation, more frequent droughts and wildfires, and more devastating weather patterns.

The risk of these events continues to grow, and the Intergovernmental Panel on Climate Change (IPCC) warns of serious peril for major ecosystems if global temperatures aren’t brought under control. If global averages reach temperatures of at least 1.5 degrees Celsius above pre-industrial levels, the effect on human, plant, and animal life may be irreversible, even catastrophic. [9]

Shifting the Urban Transportation Paradigm

In light of such devastating consequences, reducing carbon emissions is becoming a top priority for many involved in public policy and planning. Urban transportation represents an important target for these changes, as small adjustments in this sector could have an outsized impact on reducing pollution.

Realizing these outcomes requires three critical shifts in how we approach transportation in urban areas.

Move People First, Not Cars

The first and most important step in reducing urban transportation emissions is to shift away from a car-centric approach to transportation planning. The purpose of any type of transportation is to move people from one place to another, but many of our cities focus on moving cars.

Instead of merely building more and wider roads designed only for vehicles, planners can focus on building complete streets — ones that make room for all kinds of commuters, including pedestrians, bikers, and users of public transit. Centering multimodal transportation will help incentivize and enable more commuters to use these alternative methods.

Reducing reliance on household vehicles could have a substantial effect on urban emissions. According to the United Nations, each person who switches from cars to public transport could reduce their carbon emissions by up to 2.2 tons per year. [10] Another study shows that while public transit cuts GHG emissions by 58% compared to cars, cycling lowers them by 98% — meaning both offer substantial emissions reduction potential. [11]

The more transportation planners can leverage detailed data to inform their plans for new or updated roads, the more effective these changes can be. For instance, planners in Oregon’s largest special park district, the Tualatin Hills Park & Recreation District, were able to use detailed origin-destination data to confirm the value and potential impact of installing a bike-pedestrian bridge to move more commuters over a busy highway — without adding more car traffic.

See what emissions reduction tactics your city needs most

Emphasize Electric

Although it’s possible to reduce emissions and other urban transportation problems by shifting the focus away from vehicles, it’s not feasible to entirely eliminate the need for cars in our cities. Where they are still needed, then, it’s critical to accelerate the move toward electric vehicles (EVs) and away from gas-powered vehicles.

One recent study showed that adopting EVs would reduce carbon emissions significantly in every state. In states like Washington or Vermont, which already rely on clean electricity sources, EV usage could reduce pollution from carbon emissions by more than 90%. Even in states like Kentucky and West Virginia, where electricity generation relies heavily on fossil fuels, emissions would drop by over 30% with a full transition to EVs. [12]

With more federal support for the EV initiative than ever, now is an ideal time for cities to encourage drivers to increase fuel efficiency and electrify their driving. In addition to the National Electric Vehicle Infrastructure (NEVI) grant program introduced by the Bipartisan Infrastructure Law (BIL), massive federal tax credits are also available for EVs and chargers, and many cities and states are taking a step further by providing credits of their own or encouraging utility companies to create rebate programs and other incentives. Cities themselves can also leverage such programs to expand public charging installations and electrify public transit.

*Public EV chargers in cities help overcome barriers to accelerated EV adoption.*

Rethink City Planning

As essential as investments in electrification and multimodal transportation are to reducing carbon emissions, they aren’t sufficient solutions to the problem. Urban planners must think bigger, considering land use, transportation, operations, policy, and more in a comprehensive approach to emissions-reducing city planning.

With a holistic view, city planners can make progress by focusing on initiatives such as investing in green buildings, expanding renewable energy production, and improving waste management. [13] They can also consider the best ways to invest in tomorrow’s transportation infrastructure.

This requires thoughtfulness and intentionality. The BIL provides historic levels of funding for cities to upgrade their transportation infrastructure, but studies show that these investments could actually lead to increased emissions if not used properly. For instance, the Georgetown Climate Center recommends that planners focus on a “fix it first approach” of maintaining existing roads and investing in public transit, EVs, and other low-carbon options — rather than building more roads or expanding existing ones, which could induce demand and bring more pollution. [14]

Again, choosing the right updates and planning initiatives requires access to extensive data, both in terms of transportation patterns and existing urban emissions levels. Only when properly informed can planners choose initiatives that will result in successful emissions reductions.

Picturing the Future With Big Data

At every turn in the fight against carbon emissions, data is critical for making informed, effective decisions. In transportation, planners must have access to a wide range of emissions-related metrics, such as:

Vehicle Miles Traveled (VMT)
Annual Average Daily Traffic (AADT)
Vehicle Hours of Delay (VHD)
Origin-Destination (O-D) and routing patterns
Average trip speed and duration
Electric vehicle usage
Changes in walking and biking activity
Truck traffic by vehicle class (light-, medium-, and heavy-duty)

As urbanization continues to transform U.S. cities, this data has never been more critical for the decision-making process. Big data providers like StreetLight are helping to fill data gaps that would otherwise prevent planners from understanding their city’s impact on the climate. That’s how the  Twin Cities Metropolitan Council was able to share critical emissions data with local governments, equipping them with crucial insights for local planning, rather than generic national numbers.

The Southern Maine Planning and Development Commission took a similar approach, using big data to power urban planning that reduces emissions. In the video below, see how they measured statewide VMT to develop regional mitigation strategies.

To learn more about how you can use data to cut emissions and improve your city’s climate impact, download our eBook, Measure & Mitigate: Transportation Climate Data Solutions.

Stanford. “Global carbon emissions from fossil fuels reached record high in 2023.” https://sustainability.stanford.edu/news/global-carbon-emissions-fossil-fuels-reached-record-high-2023
United States Environmental Protection Agency. “Fast Facts on Transportation Greenhouse Gas Emissions.” https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions
United Nations. “Generating power.” https://www.un.org/en/climatechange/climate-solutions/cities-pollution
United Nations. “Seven Ways Cities Can Take Climate Action.” https://unfccc.int/news/seven-ways-cities-can-take-climate-action#
Standford. “How much does air pollution cost the U.S.?” https://sustainability.stanford.edu/news/how-much-does-air-pollution-cost-us
Environmental Science and Technology Letters. “Reducing Mortality from Air Pollution in the United States by Targeting Specific Emission Sources.”  https://pubs.acs.org/doi/10.1021/acs.estlett.0c00424
American Lung Association. “Who is Most Affected by Outdoor Air Pollution?” https://www.lung.org/clean-air/outdoors/who-is-at-risk
United States Environmental Protection Agency. “Climate Change Indicators: Greenhouse Gases.” https://www.epa.gov/climate-indicators/greenhouse-gases
Intergovernmental Panel on Climate Change. “Climate Change 2022: Impacts, Adaptation and Vulnerability.” https://www.ipcc.ch/report/ar6/wg2/
United Nations. “Your guide to climate action: Transport.” https://www.un.org/en/actnow/transport
ScienceDirect. “The climate change mitigation effects of daily active travel in cities.” https://www.sciencedirect.com/science/article/pii/S1361920921000687
Yale Climate Connections. “Electric vehicles reduce carbon pollution in all U.S. states.” https://yaleclimateconnections.org/2023/09/electric-vehicles-reduce-carbon-pollution-in-all-u-s-states/
National League of Cities. “The Top 5 Ways Cities Are Addressing Climate Change.” https://www.nlc.org/article/2022/04/22/the-top-5-ways-cities-are-addressing-climate-change/
Georgetown Climate Center. “Issue Brief: Estimating the Greenhouse Gas Impact of Federal Infrastructure Investments in the IIJA.” https://www.georgetownclimate.org/articles/federal-infrastructure-investment-analysis.html

traffic on highway interchange used for aadt calculation

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The Data Behind How Speed Cameras Curbed Crash Rates on One of Philadelphia’s Most Dangerous Roads

To fix deadly speeding on one of America’s most dangerous roads, Philadelphia installed speed cameras along eight sections of Roosevelt Blvd. Now, before-and-after analysis by StreetLight reveals how effective the strategy really was, and whether it could save lives in other cities.

Roosevelt Boulevard (US Route 1) in Philadelphia, PA has been dubbed one of America’s most dangerous roads. This twelve-lane highway is host to both local and commuter traffic, with at-grade express and local lanes traveling along its length.

Dozens of crashes resulting in severe injury or death occurred on the boulevard in 2020 alone, many of them involving pedestrians. [1] And because Northeast Philadelphia is home to a number of densely populated disadvantaged communities, the victims this boulevard claims are disproportionately people of color, whose communities have long been bisected by these twelve lanes of fast-moving vehicles.

In 2020, the city took measures to address the boulevard’s high crash rates, installing speed cameras along eight sections of Roosevelt Blvd. To understand how these cameras impacted safety conditions on the corridor, StreetLight used its transportation data platform to analyze vehicle speeds before and after the camera installation, looking at data from before COVID, during the pandemic, and as recently as 2024.

Then we investigated where high vehicle speeds persist on the boulevard, revealing potential locations for the next set of traffic calming interventions.

In this article, we’ll explore:

Roosevelt Boulevard’s speed problem
What Philadelphia is doing to reduce speeding
Whether speed cameras made Roosevelt Blvd safer (and how much)
How cities can choose the right traffic calming measures and evaluate their impact

Roosevelt Boulevard’s Speed Problem

At the heart of Roosevelt Blvd’s high crash rates is a history of dangerous vehicle speeds. Although the posted speed limit for much of the highway is 45 miles per hour, residents have complained that drivers on Roosevelt Blvd routinely exceed this limit. In one extreme case in 2013, four pedestrians, including three children, were struck and killed by two motorists traveling over 40mph above the posted speed limit. [2]

Data from PennDOT corroborates residents’ testimony, indicating that prior to 2020, 55% of crashes on the boulevard were attributed to speeding and aggressive driving.

A number of factors make the boulevard’s speed problem particularly deadly. Surrounding the 12-lane freeway, a growing population of Northeast Philadelphians generate significant pedestrian traffic as they access goods and services from the businesses that call Roosevelt Blvd home. Because many of these residents are from Disadvantaged Communities (DAC), they are also less likely to have access to a car, making them reliant on more vulnerable modes of transportation like walking and biking.

In the image below, StreetLight’s Justice 40 map layer highlights in purple the many Disadvantaged Community census tracts that surround Roosevelt Blvd.

Roosevelt Boulevard with Justice40 communities highlighted — *Roosevelt Boulevard (in blue) is flanked by clusters of Disadvantaged Communities (in purple), shown by the StreetLight Insight® Justice 40 map layer.*

Meanwhile, the roadway design has limited infrastructure designed to improve pedestrian safety or slow vehicles, such as pedestrian islands, bulb-outs, or signalized crossings, dramatically increasing the risk pedestrians face on the stroad.

Considering that pedestrians are five times more likely to die from crashes when cars are traveling 40 mph vs. 20 mph, according to data from the AAA Foundation, any vehicle exceeding the boulevards’ posted speed limit of 45 is likely to kill any pedestrian it strikes. [3]

Philadelphia’s Plan to Reduce Speeding

Now for the good news: a number of safety improvement projects are already in the works to address high crash rates on Roosevelt Blvd.

The City of Philadelphia has secured $10 million in state grants from PennDOT to be used on curb extensions, realignments to crosswalks, traffic lanes, and turning lanes, upgraded traffic signals, and other projects. Another $2 million will go toward the planning of future road design improvements as part of the city’s Route for Change program. [4]

While some of these improvements will be completed as far out as 2040, speed cameras offered the city a faster way to curb dangerous vehicle speeds in the short term.

In 2020, the City of Philadelphia, along with the Philadelphia Parking Authority, installed speed cameras along eight particularly dangerous stretches of Roosevelt Blvd to automate speed enforcement and ticket offenders.

Did Speed Cameras Make Roosevelt Blvd Safer?

Initial reports from the city have shown positive impacts from the speed cameras, with a 90% reduction in excessive speeding, a 36% drop in car crashes, and 50% fewer traffic deaths in the first seven months. [5]

How did speed cameras achieve such a dramatic effect, and will they continue to positively impact crash rates on the boulevard beyond their initial install? Furthermore, will the tactic be as effective in other cities, or along other roadways in Philadelphia’s high-injury network? Finally, are additional safety improvements needed to achieve the city’s Vision Zero goals for the boulevard?

To investigate these questions, we used StreetLight’s Network Performance tool to look back in time at speed conditions before cameras were installed, track the changes in average speeds (as well as rates of speeding) after cameras were installed, and follow up on where speeds are at now, in 2024, to identify where additional safety improvements may still be critical.

Establishing a Baseline

To understand how speed cameras impacted speeds on Roosevelt Blvd, we need to look back at speed conditions prior to their installation in June 2020. Because StreetLight’s Network Performance tool offers five years of comparable data, we can go all the way back in time to March 2019 to establish our baseline.

This timeframe is particularly useful as a baseline, because it allows us to look at typical speed conditions before the COVID pandemic disrupted traffic patterns across the country (we’ll look at how COVID impacted speeds in the next section).

To establish our baseline, we’ve chosen to analyze a typical Tuesday during the peak morning commute hours (8-9 a.m.). (Notably, this section of Roosevelt Blvd is relatively uncongested so even during peak hours, speeds are not tamped down significantly due to traffic.)

Roosevelt Blvd Speed Distribution map in 2019 — *A map of average traffic speeds along Roosevelt Blvd. Higher speeds appear in red, while lower speeds appear in green.*

In the map above, we can already see that average speeds exceed the 45 mph speed limit along many segments of the boulevard, and we can see where speeding is at its worst, with segments near Pennypack Park, Northeast Philadelphia Airport, and the Woodhaven Rd (PA-63) interchange standing out.

Roosevelt Blvd speed distribution graph 2019 — Speed distribution by hour of day and day of week in March 2019 on the Southbound express lane over Pennypack Creek. A beige line marks the mean speed, while the 85th percentile speed is shown with a golden line.

In the image above, we zoom in on a segment of Roosevelt where multiple fatal crashes have occurred — the Southbound express lane over Pennypack Creek. Looking at speed distribution by hour of day reveals that at 8 a.m. on an average Tuesday, the mean speed on this segment is 51 mph. Meanwhile, the 85th percentile speed (i.e. the speed that 85% of vehicles on the corridor are travelling at or below), which is commonly used to estimate rates of speeding, is 59 mph.

Bearing in mind that the posted speed limit is 45 mph along most of the boulevard, these figures reveal that speeding was indeed a significant issue in 2019. And that was before the COVID road safety crisis.

See how dangerous traffic speeds impact walking and biking in your region

How COVID Worsened Speeding

It has now been well established that as roads emptied out during the March 2020 stay-at-home orders and subsequent rise in remote work and social distancing, vehicle speeds increased. Unsurprisingly, deadly crashes also spiked. This phenomenon is likely due to the relationship between road capacity and vehicle speed — as capacity increases, drivers tend to take advantage of the extra space to speed up. (This is also why road diets, with their reduction of lane capacity, are considered an effective safety intervention.)

Roosevelt Boulevard was no exception. Looking at the same deadly segment of roadway above Pennypack Creek, we can see that rates of speeding increased above their already high levels.

Roosevelt Blvd speed distribution in 2020 — *Speed distribution by hour of day and day of week in March 2020 on the Southbound express lane over Pennypack Creek.*

During the peak a.m. hours, average speeds remain the same compared to 2019 (51 mph), but the 85th percentile speed has increased slightly to 60 mph. We also observe that speeds tend to spike even higher during non-peak hours, especially during the late evening.

To address this crisis, just a few months later, in June 2020, the City of Philadelphia installed speed cameras along eight particularly deadly stretches of Roosevelt.

Slowing Down: How Cameras Curbed Dangerous Speeding

Looking at average and 85th percentile speeds along Roosevelt Blvd in March 2022, our analysis corroborates the city’s initial reports of reduced speeding.

Roosevelt Blvd speed distribution graph in 2022 — *Speed distribution by hour of day and day of week in March 2022 on the Southbound express lane over Pennypack Creek, 21 months after speed cameras were installed.*

After over a year of automated speed enforcement from the new cameras, average and 85th percentile speeds on the Southbound express lane over Pennypack Creek dropped significantly. The mean speed of 46 mph nearly matches the 45 mph posted speed limit. Meanwhile, the 85th percentile speed has been reduced to 52 mph – just 1 mph higher than the mean speed two years prior.

Following Up: Did Reduced Speeds Stick?

A look at recent data from March 2024 can help confirm whether the speed reduction observed in 2022 has continued, and where further safety interventions may still be critical to saving lives.

Roosevelt Blvd speed distribution graph 2020-2024 — Speed cumulative frequency distribution from 2020 to 2024, highlighting the change in speed profile on the Southbound express lane over Pennypack Creek before and after the installation of speed cameras.

In the graph above, we chart speed distributions from each year analyzed (except 2019, which was identical to 2020 above the 30th percentile). The leftward shift highlights that overall speed continued to drop between 2022 and 2024. As of March 2024, a much larger percentage of vehicles are now traveling at or below the posted speed limit of 45 mph.

So has the boulevard’s speed problem been fixed? While rates of dangerous speeding have significantly dropped — and fatal crash rates along with them, according to city reports — some segments of the corridor may still need further intervention.

Roosevelt Speed Distribution map 2024 — *Segments of Roosevelt Blvd. with average traffic speed above 40 mph on a typical Tuesday between 8 a.m. and 9 a.m., March 2024.*

Using the data trimming tool in StreetLight’s Network Performance product, we can zero in on sections of the boulevard where high-speed traffic still poses significant risk to pedestrians. The map above highlights in red the segments with average vehicle speeds above 40 mph during peak morning hours on an average Tuesday.

Although some of these segments have average vehicle speeds that fall below the 45 mph speed limit, we chose to highlight all segments with average speeds above 40 mph because these speeds fall within the range that is particularly deadly for pedestrians, according to the AAA Foundation.

At least nine highway segments of varying lengths emerge as potential candidates for further safety intervention. As we observed in 2019, segments near Pennypack Park, Northeast Philadelphia Airport, and the Woodhaven Rd (PA-63) interchange are among these high-speed areas.

Insights like these could help city officials determine where to prioritize state grant funds slated for additional traffic calming measures along the boulevard.

Spot Speeds on Roosevelt Boulevard

While the bulk of this analysis examines segment speeds, which are derived from a vehicle’s travel time from one end of a roadway segment to another (and the distance between those points), it can also be useful to examine spot speeds at specific locations along a corridor when evaluating potential safety improvements and the success of past projects.

Spot speeds measure a vehicle’s speed at a specific point in time and space, rather than the average speed across a given segment. This means spot speeds are particularly useful when analyzing safety or congestion on smaller roadway segments, such as a single intersection. In our case, they can also help shed light on exactly where drivers slow down and speed up, revealing whether and how quickly drivers speed back up after they’ve passed a speed camera.

In the data viz below, 15 spot speeds taken on a typical Tuesday between 8 and 9 a.m. in March 2024 show vehicles slow down after passing Pennypack Creek as they approach a speed camera located near Strahle Street. Drivers then speed up again as they approach Solly Ave, slowing once more as they approach an intersection with pedestrian crosswalks at Rhawn Street. These granular insights can help cities like Philadelphia determine the most effective safety measures to further the benefits of speed cameras.

spot speed data for Roosevelt Blvd 2024 — *Colored dots show spot speeds along Roosevelt Blvd. near Pennypack Creek on a typical Tuesday between 8 a.m. and 9 a.m., March 2024.*

More About StreetLight’s Network Performance Tool

StreetLight’s Network Performance tool is ideal for before-and-after analyses like this. It offers five years of data comparability so cities can look back in time to understand how roadway conditions have changed over time, including traffic patterns from before COVID, which are often sought out as a baseline to understand “typical” past conditions. They can also analyze the impact of policy interventions to show the public the efficacy of their work.

Since many roadways lack permanent traffic counters (or only recently had counters installed), this ability to access historical traffic data for any road unlocks before-and-after analyses that would otherwise be impossible.

As we’ve demonstrated in our analysis above, agencies can use this Network Performance tool to proactively identify locations with a trend of excessive speeding, particularly where it overlaps with high crash rates, pedestrian/bicycle activity, or Justice40 communities.

With the data trimming option shown in the section above, agencies can easily pinpoint problematic road segments instead of relying on anecdotal observations about excessive speeding, or worse, waiting for the next crash to identify an unsafe traffic pattern. Likewise, this tool offers agencies the ability to monitor the impacts of changes in land use (e.g., new development), infrastructure (e.g. lane additions), traveler behaviors (e.g. work-from-home patterns due to COVID), traffic calming measures (e.g. speed limit reductions or speed cameras), and more.

The ability to analyze both segments speeds and spot speeds also offers added granularity that can be useful in understanding driver behaviors and diagnosing dangerous locations along a roadway.

To learn more about StreetLight’s Network Performance tool, check out our white paper: Network Performance Analysis Methodology and Validation.

And for more ways to implement data-driven safety improvements in your city, download our free eBook: Practitioner’s Guide to Solving Transportation Safety.

Delaware Valley Regional Planning Commission. Crash Statistics for the DVRPC Region. https://www.dvrpc.org/webmaps/crash-data/
CBS News. “Philadelphia’s Roosevelt Blvd. Among most dangerous roads in US” July 10, 2023. https://www.cbsnews.com/philadelphia/video/philadelphias-roosevelt-blvd-among-most-dangerous-roads-in-us/
AAA Foundation for Traffic Safety. “Impact Speed and a Pedestrian’s Risk of Severe Injury or Death.” September 2011.
Michaela Althouse. Philly Voice. “Philadelphia gets $19.3 million for road safety projects from PennDOT, most directed to Roosevelt Boulevard work.” February 3, 2024. https://www.phillyvoice.com/roosevelt-boulevard-traffic-safety-projects-philadelphia-grants-penndot/
Philadelphia Parking Authority. Roosevelt Boulevard Automated Speed Camera Annual Report. April 2023. https://philapark.org/wp-content/uploads/2023-Speed-Camera-Report-Final-32023.pdf

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What is AADT, why does it matter, and how does big data make it more powerful?

As agencies grapple with budget and staff limitations, big data analytics enable officials to overcome gaps in Annual Average Daily Traffic (AADT) data for local streets and low-volume roads. But first, what is AADT, and how does it inform transportation decisions?

In recent years, infrastructure improvement has become a hot topic in the US.

At the center of that interest are our streets, roads, and highways. Road conditions are a key factor in an area’s quality of life, economic dynamism, as well as access to schools, jobs, and healthcare.

Jurisdictions across the US are gearing up to improve their road networks, in part thanks to the federal Bipartisan Infrastructure Law (BIL), also referred to as the Infrastructure Investment and Jobs Act (IIJA), which makes $110 billion available for these improvements.

Behind the scenes, there’s one transport metric that is fundamental to nearly every federal funding request or routine budgeting at the state or local level: Average Annual Daily Traffic (AADT).

So what is AADT and why is it so central to transportation planning and funding? We unpack what AADT is, how it differs from other fundamental roadway metrics, and how measurement is going digital to fill data gaps and add richness to planning and modeling.

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What is AADT?

Annual Average Daily Traffic (AADT) is the transportation industry’s most essential metric for analyzing and forecasting traffic volume. Among other things, it’s used for the planning and design of infrastructure, tracking traffic congestion, estimating road safety, and as an empirical measure to help allocate highway funds.

In its simplest form, AADT takes in all vehicle trips on a segment of road or highway during a yearlong interval, in both directions, and then divides the total by 365 days to arrive at the average number of daily trips.

In addition to infrastructure and transport planning, AADT data is applied in many other contexts as a way to measure traffic flow and allow for “apples-to-apples” comparisons of traffic volume. For example:

Retailers and commercial real-estate professionals consider AADT data when evaluating sites for development and for marketing and pricing properties
Lawyers and law enforcement officials use the metric in accident cases and investigations
Lawmakers use AADT to inform budgeting and prioritization of transport budgets and projects

But AADT is also a simple measure, which flattens away seasonal and weekday variations in traffic patterns. Nonetheless, AADT remains the most widely-referenced benchmark for how busy a road or street is.

AADT vs. ADT

There are two main types of traffic measurement: Annual Average Daily Traffic (AADT) and Average Daily Traffic (ADT).

These measures are starkly different in how they are calculated and applied. They are often confused in casual discussions so it’s important to understand the differences.

As described above, AADT is the total volume of vehicle travel on a road for an entire year, divided by 365.
ADT is the average number of vehicles traveling through a location during a period shorter than a year. For example, it may be applied to a season, or a selected month or week, a short span of days, or a specific day in the year.

AADT is generally used to measure long-term trends or changes in travel demand, while ADT is more useful for short-term planning and operations.

For example, if a city wants to know how many people use a particular bridge to estimate wear-and-tear and traffic loads, it would use AADT. But if that same city needs to know how many cars will cross the bridge during a summer weekday to plan construction, it would use ADT calculated from a sample taken in the target interval.

Both measures have their advantages and disadvantages.

AADT can obscure seasonal variation (e.g., fewer people travel in winter), special events (e.g., increased travel for holidays or road closures within the measurement period), and day-to-day variation in demand (e.g., less travel on weekends). This can sometimes make it difficult to compare year-over-year changes or identify important micro-trends.

ADT can pick up these kinds of fluctuations, but they will not reflect overall demand on a road since by definition they only consider shorter periods. On the other hand, they have a limited lens: they will not reflect in any variations occurring outside of the measurement period.

As discussed below, AADT data is relatively difficult and expensive to collect since it requires real-world data spanning an entire year. Even when using calculation methods that don’t require 365 days of data, the need for accurate and continuous data is still relatively burdensome.

ADT is more forgiving from a measurement standpoint since by definition it requires the counting of traffic during shorter intervals.

How to calculate AADT

To calculate Annual Average Daily Traffic (AADT) in its simplest form as described above, analysts must know total traffic volume on the target road segment for every day in a given year.

This baseline method isn’t simple or cheap to implement since by definition it requires permanent devices known as counters or ATRs (automatic traffic recorders) detecting passing vehicles and collecting complete trips data for 365 continuous days in a year. Even missing a few hours of traffic means the calculation can be thrown off. Not to mention the recorders are not easy or inexpensive to install and maintain. During the COVID-19 lockdown, agencies saw their ability to collect counter data hamstrung by lockdown orders that kept workers off the road.

Even when permanent counters are in place and putting out continuous AADT traffic data, sometimes special events such as roadwork or adverse weather can distort averages with outlier days of abnormally low or high trip counts.

Finally, and most commonly, there are simply too many road segments in any jurisdiction to allow for comprehensive AADT data from permanent counters.

In the video below, Keith Nichols explains why Hampton Roads TPO commonly encounters data gaps with traditional AADT data collection methods, and how the agency supplements traditional methods for long-range transportation planning.

Traditional methods of closing AADT data gaps

Statistical methods, complemented with temporary data collection, are commonly deployed to address common data-collection issues. When officials turn to these methods, they do so knowing they are sacrificing accuracy for savings.

It’s worth understanding how these methods work together, and some of the tradeoffs.

Short-term expansion

One industry-standard method for arriving at Annual Average Daily Traffic (AADT) in the absence of permanent counters is known as “short-term expansion.”

In this method, a road section’s traffic is calculated on the basis of a temporary counter collecting two days or more of data. That incomplete short-term data is then “expanded,” or scaled up, to calculate AADT. To do this, analysts derive scaling “factors” from a nearby permanent traffic counter that has a year’s worth of data.

Ideally, that permanent counter used as a reference has same-year data. But it’s not uncommon for transport analysts to be forced to rely on permanent counter data from past years when calculating AADT using this method.

Obviously, even when the short-term expansion model bakes in some math, this tactic relies on the differences between the target road’s short-term traffic data versus the same-day measurements collected by the nearby permanent counter on a different road.

If these differences are not consistent across the year, i.e. if the short-term data was not taken on representative days, AADT accuracy will be compromised.

In the instances where permanent counters are in place but there are small gaps in the annual data or road closures and other outlier events that may throw off the averages, a separate established method is to limit the number of days for which complete datasets are required.

The AASHTO method

In one industry-recognized approach, officials collect total traffic volume on seven separate days in each month that correspond to the different days of the week. This method leaves planners with 84 days of data to work with that nonetheless will account for variations in traffic across different weekdays and on weekends.

Then, they take an average for each day of the week sampled across the year, giving them seven averages, and then they take the average of those averages for an AADT.

The American Association of State Highway Transportation Officials (AASHTO) has promoted this technique, known as an “average of averages” method.

Other methods

Many other estimation methods can be used depending on what data and counters are available and affordable. In fact, the proliferation of methods adds to the complexity faced by transport planners as they consider approaches for data collection and AADT calculation.

In fact, one in-depth review of the relevant academic literature on AADT estimation identified 30 separate methods just for estimating AADT on low-volume roads.

Adding to the difficulty, these techniques were hardly one-size-fits-all.

“Some AADT estimation techniques are only applicable in specific locations,” write the authors, Edmund Baffoe-Twum and Eric Asa of the West Virginia University Institute of Technology, along with Bright Awuku from North Dakota State University. “Others require significant data to provide accurate estimates. Several processes to adjust models for a location may be needed for other locations.”

Traditional methods vs. traffic analytics

Arguably the biggest change for Annual Average Daily Traffic (AADT) in recent years is the availability of instant up-to-date AADT estimates right from a computer through traffic-analytics providers.

On-demand traffic-analytics platforms reduce the need for expensive and sometimes hazardous fieldwork and are able to fill data gaps whenever they arise.

As a result, jurisdictions have more flexibility in the extent to which they rely on permanent and temporary counters for AADT metrics.

For example, recently StreetLight helped fill gaps in AADT data by the Indian Nations Council of Governments in Tulsa, Oklahoma. Due to budget issues, many jurisdictions in the area had stopped reporting traffic counts to INCOG, a metropolitan planning organization or MPO. In minutes, StreetLight was able to generate traffic counts for all of the untracked road segments.

In the video below, we explore other ways agencies commonly use on-demand traffic analytics platforms to leverage AADT metrics, such as developing crash rates and understanding the traffic impact of road or lane closures.

How do traffic-analytics platforms come up with their AADT measurements?

Typically, traffic analytics rely on connected devices and Internet of Things data, and then layer in parcel data and road-network data for a complete picture. With this data, it’s possible to create analytics that model vehicle trips on a stretch of road in the absence of temporary or permanent counters.

The real technical challenge for deriving AADT from traffic analytics is not just in collecting and organizing the large volume of location data, but in the next steps:

Algorithms are needed to match this raw data to vehicle trips
The dataset, for greater accuracy, is enhanced with additional sources of data such as US Census Data or street-map data to account for changes in demographics and road networks
The resulting AADT model must be tweaked for greater accuracy by testing results against real-world “ground truth” data, which should encompass different road and vehicle types, e.g. heavy trucks

In the case of StreetLight, which provides AADT metrics for 4.5 million miles of roadway in the US and Canada, all these steps were important and detailed in the whitepaper, “AADT 2023: U.S. Methodology and Validation.”

By comparing their own AADT metrics to AADT produced by thousands of permanent counters nationwide, StreetLight was able to determine that their AADT figures fall within a 98% prediction interval for all road types.

Challenges in measuring AADT: local and low-volume roads

AADT measurement is increasingly being shaped by many jurisdictions’ need for more granular and complete coverage of road systems.

What’s driving this demand? The answer is a set of interrelated traffic, demographic, and environmental concerns that are only growing stronger with time.

First, local streets are increasingly seeing overflow traffic from overburdened highways and multi-lane roads.

“Estimating AADT on local streets becomes a necessity as local street traffic continues to grow and the capacity of arterial roads becomes insufficient,” write Peng Chen, Songhua Hu, Qing Shen, Hangfei Lin, and Chie Xie. They are the authors of a 2019 paper on AADT measurements in the Seattle area, published in the Transportation Research Record.

Second, jurisdictions are also newly anxious to track traffic increases on low-volume roads in rural and semi-rural areas. Many of these areas have seen a major influx of short- and long-term visitors and new residents arrive over the last two years.

A 2022 analysis by StreetLight showed how this trend had impacted the resort town of Jackson Hole, Wyoming. The analysis showed that AADT on a section of unpaved road —an access point to Grand Teton National Park — had already significantly surpassed 2019 levels in 2021, even after dipping dramatically during the COVID pandemic.

Annual Average Daily Traffic (AADT) counts for Moose-Wilson Road — *AADT counts for Moose-Wilson Road from StreetLight suggest 2021 traffic volume had already exceeded 2019.*

Thirdly, environmental and road-safety concerns are also behind the need for more comprehensive AADT data. Since a significant proportion, if not a majority, of vehicle miles in a state are driven on these roads, it’s impossible to form a complete picture of emissions or accident trends without it.

Despite the demand, coverage of low-volume and local streets poses a formidable challenge for traditional AADT calculation.

As we’ve seen, it is cost-prohibitive to deploy permanent counters widely on local streets or low-volume roads. Not to mention, rural roads are far-flung and cover many miles of sometimes difficult terrain. Local roads in urban areas are dense and highly varied in traffic patterns, which would mean putting counting stations on virtually every corner.

Many jurisdictions, in the cases where they have the budget to take the measurements at all, turn to temporary counts and estimation methods to measure AADT on these segments.

For these reasons, traffic analytics–based AADT metrics are a cost-effective and simple solution for filling data gaps on local and low-volume roads.

For example, StreetLight’s AADT metrics include urban and rural roads — even unpaved roads, as seen in the case study from Jackson Hole. Analyses covering hundreds of low-volume road segments can be run in minutes.

Annual Average Daily Traffic (AADT) and technology

Traffic-analytics platforms relying on big data approaches are only the latest tech innovations to transform how Annual Average Daily Traffic (AADT) is collected and calculated.

In the 1930s, AADT was based solely on manual counts, which required considerable manpower and intensive fieldwork, according to David Albright in a 1991 article on the history of AADT measurement. Counting devices only began to be used in the 1940s, and became widespread only after a couple of decades, with methods continuing to evolve in the years since.

More recently, computers and algorithms have helped run some of the sophisticated statistical models used by transport engineers and planners for AADT estimation. As discussed, StreetLight itself uses advanced software algorithms to process data, tie it to vehicle trips, and enhance it with U.S. Census and OpenStreetMap data.

While conceptually AADT has remained the same metric over all this time, and surprisingly resilient as a keystone metric in transport planning, technology has completely transformed data collection, calculation, and estimation methods. It’s a good bet that technology, and specifically big data and software-driven algorithms, will continue to drive innovation around AADT in coming years.

Test-drive StreetLight AADT metrics today with a free trial

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How Advanced Traffic Counts are Powering Better Business Decisions

Understanding traffic patterns is critical when choosing a store location, designing automotive products, or analyzing property values. Now, businesses can get the most up-to-date traffic counts on 2.5x more roads to drive more profitable decisions.

For many businesses, transportation intelligence is critical to making the right decisions. The number of vehicles and pedestrians on a given road — and how they move throughout a region — can dramatically impact how many visits a store gets, where drivers need to fuel up, or if EVs need to charge.

But historically, commercial decisionmakers have had to rely on incomplete and outdated traffic counts for information on where and how potential customers drive.

Now, StreetLight offers Advanced Traffic Counts to inform real estate decisions and products for professionals across industries like:

Commercial real estate
Retail and restaurants
Software
Electric vehicles
New mobility

These counts are updated frequently and cover millions of road segments across the U.S. and Canada, so you can make confident decisions based on the most comprehensive, up-to-date information on travel patterns.

StreetLight Advanced Traffic Counts is designed specifically for the commercial sector, to understand how potential customers move so you can choose the right street corner for a new store, select EV charger locations based on nearby traffic patterns, or develop new products and services based on real customer travel behaviors.

Here’s how it works.

StreetLight Advanced Traffic Counts offer pre-processed traffic volumes for over 25 million road segments, totaling over five million miles in the U.S. and Canada including motorways and trunk roads, arterials, and on/off ramps for comprehensive coverage. But it doesn’t end with traffic volumes. This data can be further contextualized with additional details like trip characteristics and historical demographics so you can make much more nuanced business decisions.

StreetLight Advanced Traffic Counts offer pre-processed traffic volumes for over 25 million road segments, totaling over five million miles in the U.S. and Canada including motorways and trunk roads, arterials, on/off ramps, and residential streets for comprehensive coverage.

coverage of advanced traffic counts in Chicago, Illinois

A visualization of traffic counts in the broader Chicago metropolitan area. StreetLight’s extensive coverage provides traffic counts for millions of road segments, enabling more granular location intelligence to power the most profitable business decisions.

This means that as you begin to identify the next corner for your coffee shop, convenience store, or electric vehicle (EV) charger location, you can take into consideration factors like vehicle traffic, trip purpose (e.g. home-to-office vs. non-commute trips), and the overall demographics (e.g. income, education, family status, and more) of travelers passing by your potential location.

Likewise, these same factors can inform portfolio management for existing commercial real estate locations, diagnosing why some locations perform better than others, and where certain locations should be closed, where open hours should be extended or shortened, or where downsizing or expansion would help maximize overall revenue.

Market research firms and consultancies also benefit from these same insights when advising clients on commercial real estate decisions.

visualization of traffic counts in Chicago, zoomed in

A zoomed-in view of Chicago traffic counts highlights roadways with the most trips (seen in dark red) and the least trips (in yellow).

How StreetLight’s Traffic Counts Support Better Site Selection and Operations

Because of the extensive coverage of our traffic counts, and the ability to filter counts by time of day and day of week in the relevant month or year, retailers, real estate professionals, investors, and other customers can now more easily identify promising store locations along nearly any roadway in the U.S. or Canada.

Importantly, businesses that rely on foot traffic (such as retail and restaurants) can also view historical pedestrian traffic counts to understand where high foot traffic will translate into more sales, while businesses that rarely benefit from foot traffic (such as car washes) can narrow their focus to vehicle traffic counts exclusively.

Importantly, businesses that rely on foot traffic (such as retail and restaurants) can also view historical pedestrian traffic counts to understand where high foot traffic will translate into more sales, while businesses that rarely benefit from foot traffic (such as car washes) can narrow their focus to vehicle traffic counts exclusively.

These same insights can also be used to forecast sales at new and existing stores. Portfolio managers can now understand where traffic is most likely to drive store visits and sales. Using traffic counts by time of day and day of week, they can also determine if store hours or staff operations should be shifted to capture demand when it is highest.

Trip and Traveler Attributes Help Determine Where Demand is High

Traffic counts are just the tip of the iceberg when it comes to site selection, research and development, and more. That’s why StreetLight Advanced Traffic Counts also include trip speeds, trip distance, and historical trip purposes and traveler demographics to further inform important decisions impacting your business.

Visualization of traffic volume with trip and traveler characteristics for two locations

For example, a commercial real estate professional evaluating potential locations for a new coffee shop could zero in on road segments that morning commuters take on their way to the office. Similarly, someone looking to open up a new location for a budget-friendly grocery store chain could pinpoint road segments that are frequently used by travelers with lower household incomes.

Likewise, adding the context of trip characteristics can further inform commercial real estate decisions. For example, traffic volume may not tell the full story for brands who rely on drivers traveling slowly enough to read their signage and turn into their location.

Luckily, with StreetLight Advanced Traffic Counts, these customers can take trip characteristics like speed into consideration to ensure the traffic at the site they select isn’t speeding by too quickly to bring in business.

Likewise, trip characteristics like direction of travel can inform which street corner or side of the road is most advantageous for a new store.

Ensuring Reliable Data for Confident Business Decisions

We’ve already discussed how StreetLight Advanced Traffic Counts allow commercial customers to access recent and historical data for a full-picture view of mobility patterns impacting their business. But how reliable are the metrics we provide?

Our data validation processes for StreetLight Advanced Traffic Counts follow the same trusted methodology used for StreetLight InSight®, the most-adopted big data platform for transportation. Metrics are validated against permanent traffic counters and by transportation industry professionals.

Every month, we ingest, index, and process vast amounts of location data from connected devices and the Internet of Things, then add context from numerous other sources like parcel data and digital road network data to develop a view into North America’s vast network of roads, bike lanes, and sidewalks.

This data is then delivered in bulk through either a data file (such as a CSV) or via API, to integrate seamlessly into your existing data analysis platforms.

To understand how your customers move and get more information on the travel patterns impacting your business, click the banner below to get started.

What Is a Smart City?

Smart cities use innovative technologies to improve operations, expand access to goods and services, and get things done more efficiently. At the center of it all, transportation data is changing how cities make decisions that impact how people move.

smart city aerial view - NYC lights at night

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Today, nearly everything has become “smart,” from phones to fridges, doorbells, and cars. And cities are no exception. Across the globe, cities large and small are adopting new technologies to improve quality of life, automate essential services, and integrate with the digital world.

So how exactly are “smart city” technologies changing how we live? And what role does transportation data play in making our cities “smarter”?

In this article, we’ll discuss:

What is a smart city?
Smart city technologies in use today
How smart cities use transportation data
Real-world examples of smart city transportation projects

What Is a Smart City?

A smart city is a city that uses data to optimize operations, planning, and governance. Data collected from residents and visitors, electronic devices and systems, and the broader Internet of Things is analyzed to understand patterns, monitor how things are going, communicate with constituents, and more.

If this all sounds incredibly broad, it is. Most cities today are smart cities, because data collection and analysis have become ingrained in just about everything we do.

Still, the extent and sophistication of data usage varies greatly from city to city. Below, we’ll explore some of the common ways smart cities are using this data.

*Ride hailing apps for taxis, microtransit, and rideshare services are common in today’s smart cities.*

Smart City Technologies

There are many types of smart cities technologies in use today. Some common examples include:

Real-Time Transit Tracking – GPS data from buses, subway cars, etc. are used to calculate wait times for public transit, communicated via mobile apps or station signage.
Energy & Utilities Optimization – usage data for electric grids, water, and more can be monitored and in some cases automatically adjusted to prevent service disruptions, detect leaks, anticipate the need for infrastructure improvements, or aid conservation efforts.
Smart Lighting – light detection technology can be used to automatically turn on overhead lights for roadways, parking lots, and more during poor lighting conditions (not just at nighttime).
Smart Traffic Signals – smart traffic lights use real-time data on the volume and speeds of vehicles approaching an intersection to automatically adjust the timing of signals to reduce idling time, improve traffic flow, or prevent unsafe conditions.
Air Quality Monitoring – sensors may be deployed throughout the city to monitor air quality, detect potential sources of pollution, communicate current conditions with the public, and inform air quality improvement efforts.

These and other technologies are becoming more and more common in cities across the globe. But one of the most frequently used forms of smart city technology is transportation intelligence. Below, we’ll discuss some of the key ways smart cities are using transportation data to improve mobility, air quality, climate impact, and more.

See how data can help you fix traffic jams, speeding, and more

How Smart Cities Use Transportation Data

Data on how people and vehicles move can help cities plan roadway infrastructure improvements, make land-use decisions, plan for special events, reduce traffic fatalities, monitor public health, boost economic activity, reduce commute times, mitigate greenhouse gas emissions, and so much more.

In many cases, cities will install physical sensors on the road that capture data on vehicle volumes, speeds, turning movements, and more. But these sensors are too expensive to install on every roadway, and they may not capture non-vehicle traffic such as bikes and pedestrians.

To get more comprehensive and cost-effective insights on how people move, cities often supplement physical roadway sensors with big data analytics. Big data platforms like StreetLight collect, process, anonymize, and validate massive numbers of datapoints from connected devices and the Internet of Things to make precise inferences about how people move.

Data privacy assurances are crucial when collecting big data on mobility. That’s why StreetLight’s Route Science® algorithm anonymizes and aggregates all datapoints, to ensure individuals can never be identified or tracked.

In addition to smart traffic signals (mentioned above), which typically use data on vehicle volumes and speeds, there are many other ways cities are using transportation data. Common examples include:

Vehicle volumes to fix traffic jams, ensure smooth traffic flow during special events, and prioritize infrastructure investments
Speed data to understand where congestion or unsafe speeds are impacting mobility for residents and visitors
Turning movement counts to help time traffic signals
Origin-Destination and Routing patterns to measure tourism and plan detours, bus routes, or new EV charger locations
Annual Average Daily Traffic (AADT) counts to anticipate the need for repairs and inform safety planning
Bike and pedestrian activity to figure out where to install bike lanes, implement traffic calming measures, or add crosswalks
Truck activity and routing data to move goods efficiently, highlight emissions hotspots, or reduce dangerous cut-through traffic in residential areas

Real-World Examples of Smart City Transportation Projects

Cities across the globe are using transportation data to make smarter decisions. Here are just a few examples of smart city transportation projects in the U.S. and Canada.

Temecula Retimes Traffic Signals

After securing safety funds for new smart signals, Temecula, California needed intersection data to understand which signals were most in need of synchronization.

They used StreetLight’s turning movement counts to diagnose congestion across 40 intersections within a few hours, saving at least three months on data collection and analysis, and deploying funds effectively to optimize signal timing.

Vancouver Makes the Case for Bus Infrastructure

Multimodal transportation options help reduce congestion, improve climate impact, and ensure equitable access to essential goods and services. But with many transit agencies facing exceptionally tight budgets, proving the value of bus infrastructure investments is crucial to getting improvements implemented.

In Vancouver, city analysts used big data to reveal that bus mode share was much higher than previously understood, helping them gain political buy-in for new bus lanes and rapid lines to provide commuters accessible alternatives to personal vehicle use.

Richmond Measures Equitable Access to Multimodal Transportation

Transportation equity is gaining more attention, with grant programs like Reconnecting Communities and Safe Streets and Roads for All incentivizing projects that expand equitable access. But getting reliable measurements of transportation access is often a barrier to diagnosing and addressing existing inequities impacting city residents, especially among communities of color, low-income households, and people with disabilities.

In the video below, Alex Bell of Renaissance Planning explains how data from StreetLight helped city planners for Richmond, Virginia develop measures of multimodal accessibility and understand who was most underserved by existing infrastructure. This analysis revealed where infrastructure investments were most critical to improve transportation equity throughout the city.

To learn more about how big data transportation analytics can make your city smarter, download our free eBook, Any Road, Any Mode: Your Guide to the Transportation Data Revolution.

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How Can a Road Diet Improve Safety for Everyone on the Road?

road diet blog header - new york street with center turn , bike lane, and parking

America’s roads just keep getting wider. According to a Washington Post report, over a third of U.S. states spent more than $19 billion on expanding existing highways and road infrastructure in 2019. [1]

It’s a common way to attempt to curb the traffic congestion plaguing U.S. cities, but many transportation buffs say it’s the wrong approach. In fact, America’s road safety troubles may be directly related to its bloated urban corridors. The data appears to indicate that, in many cases, our roads don’t need to be fed more pavement — they need a diet.

The Federal Highway Administration (FHWA) points out that undivided four-lane roads — so ubiquitous in American cities — play host to a wide range of dangerous collisions, from lane-changing side swipes to bike and pedestrian crashes. These crashes are directly related to the volume and speed of traffic, but they’re also tied to the width and layout of the road. And, while it may seem counterintuitive, the path to increased safety without worsening congestion often lies in reducing lanes, rather than adding them, through a method called road diets. [2]

In this article, we’ll explore how road diets work to increase safety not only for vehicles, but also for pedestrians and cyclists. We’ll tackle the following:

What is a road diet?
Key safety considerations
How effective is a road diet?
Considering the alternatives
Finding the best approach with big data

So, What Is a Road Diet?

A road diet, or lane reduction, is a type of traffic calming measure designed to reduce speed and traffic congestion on an existing undivided roadway. Instead of expanding the existing road width with additional lanes, planners reduce the number of standard traffic lanes to make room for other types of lanes or road features.

Road diets are most common on four-lane highways. Typically, planners exchange the existing four driving lanes for a center left-turn lane flanked by two standard driving lanes and bike or pedestrian lanes. Other road diet examples include replacing two lanes with a tree-lined median or adding parking on the sides and narrowing existing lanes.

VDOT road diet example — *Photo source: Virginia DOT, reprinted in the U.S. DOT’s Road Diet Informational Guide*

Whatever the specific approach, the overarching goal of a road diet is simple: to lower traffic speed and thus reduce the number and severity of crashes. In the process, this measure can also enhance the overall quality of life and appeal of an urban area by making more room for cyclists and pedestrians, expanding common areas, and even adding more green space to popular thoroughfares.

Key Safety Considerations

Understanding the value of road diets first requires an appreciation of just how threatening many of our busy four-lane, undivided roadways are. These popular road layouts are riddled with safety concerns for drivers, passengers, pedestrians, and cyclists alike. Before making any change to an established roadway, it’s essential to understand the dangers of the existing layout.

For Vehicles

In urban and suburban areas, four-lane, undivided highways are hot zones for vehicle-to-vehicle crashes. There are simply too many potential collision points for drivers to track. Such corridors are commonly home to all sorts of collisions:

Rear-end collisions from sudden stops or vehicles waiting to turn
Sideswipe crashes during lane changes
T-bone or angle collisions from cars turning left across oncoming traffic
Multi-car crashes at intersections

These collisions occur due to a range of factors, many of which can be addressed through road diets. High speed differential between lanes or intersecting streets can lead to sudden stops or miscalculations that result in collisions. Left turns across multiple lanes are also inherently dangerous. [2]

The specific traffic patterns and risks vary by location. When assessing the potential benefits of lane reductions, planners need data that allows them to analyze metrics like average speed, speed differentials, average traffic volumes, and typical turn counts at intersections.

For Pedestrians

As problematic as four-lane roads are for vehicle-to-vehicle crashes, they’re even more hazardous for pedestrians. Drivers hit and killed more pedestrians in 2022 than in any year since 1981, and these types of roads are at the center of many such collisions. [3] As one recent study revealed, 97% of the hot spots for pedestrian deaths in the U.S. are multi-lane roads. [4]

pedestrians crossing multilane highway — *Pedestrians cross a multilane roadway.*

Crashes on such roads occur for many reasons, from a lack of sidewalks to pedestrians misjudging oncoming vehicles when crossing multiple lanes. However, the central problem on wide urban streets is vehicle speed. Studies show that pedestrians are five times more likely to die from crashes when cars are traveling at 40 miles an hour than at 20 miles an hour.

Here again, data is crucial for determining the right solution. Understanding vehicle speeds, the volume of pedestrian traffic, and the frequency of pedestrian collisions can help planners assess whether a road diet — and which type — would provide an effective solution.

For Cyclists

According to the National Safety Council, more than 850 cyclists died in collisions with vehicles in 2021. [5] Many of these incidents were linked to unsafe road conditions. [6] The FHWA reports that the simple addition of a bike lane could reduce bike crashes by 49% on four-lane roads. [7]

Beyond being an argument for bike lane additions, this is a strong point in favor of considering road diets on cyclist-heavy city roadways. Road diets are designed to reduce traffic speed, and they can make room for more extensive road modifications such as protected bike lanes, which are physically separated from vehicle traffic.

See how dangerous traffic speeds impact walking and biking in your region

How Effective Is a Road Diet?

Overall, the data shows that road diets are often highly effective at increasing safety. Based on broad studies, the FHWA states that road diets can reduce crashes by between 19% and 47%. [2] In Seattle, a 2008 road diet reduced pedestrian collisions by 80%. The same project saw a 35% increase in cyclist traffic without any increase in bicycle-involved crashes.

To project the impact in a specific area, planners must account for various factors, particularly existing traffic volume. Lane reductions tend to be most effective on roads with between 8,000 and 20,000 average daily vehicles. [8]

Despite these benefits, many people are skeptical of road diets. In Los Angeles, the firefighters union recently launched a campaign against proposed road diets, claiming they would hinder emergency vehicles by increasing congestion. [9] Others argue they would slow down public transportation or simply divert heavy traffic elsewhere. [8]

Broadly speaking, the evidence to support these claims is slim. Although lane-reduction projects occasionally get pulled back, research shows that these road diet failures often occur due to a disconnection between public perception and reality. In other words — the project accomplishes its goal, even if the public thinks it’s not working.

That’s not to say these tactics are the right solution for every safety or traffic congestion problem. However, the evidence does indicate that when planners closely evaluate the many variables of a situation and decide to implement a road diet, the resulting safety enhancements are usually significant. [10]

Finding the Best Approach With Big Data

As noted, road diet success is much more likely with a deep analysis of traffic patterns. Understanding metrics like turning movement counts, average daily traffic volume and speed, speed differentials, and stopping frequency is essential for an accurate assessment of any potential project.

Big data platforms like StreetLight InSight® can expedite data collection when evaluating a potential road diet, providing detailed visualizations of traffic patterns and trends to help planners conduct a thorough road safety audit before making any decisions. This approach also makes the data collection itself safer because agency staff need not put themselves at risk to take manual counts along dangerous roadways. Furthermore, big data can provide a more complete view of roadway conditions than manual counts, which are usually limited to a small snapshot of traffic during peak driving hours on a particular day or a few days.

Many agencies are already using big data to evaluate potential road diets, as well as the success of past road diets. As the video below explains, Maine’s Department of Transportation used Streetlight tools to conduct a detailed analysis of its Bangor Street road diet proposal, filling in critical data gaps to understand turning movement counts, side-road traffic, and complex roundabout traffic flows.

Similarly, a StreetLight analysis of roadway capacity, vehicle speeds, routing patterns, and pedestrian activity suggested that a road diet on Oakland’s Grand Avenue could help address safety concerns without causing major congestion.

potential road diet locaiton on Grand Avenue in Oakland — *Grand Avenue, part of Oakland’s High Injury Network, could be a good candidate for a road diet treatment.*

After implementing a road diet, big data transportation analytics also allow planners to easily measure any changes in traffic volumes, routing, safety, and critical metrics like Vehicle Hours of Delay (VHD). These insights simplify the process of assessing project potential and outcomes so planners can do their work more effectively.

For more information on how big data can help you find the best safety solutions for your streets, check out our Safety Data Handbook.

The Washington Post. “Infrastructure plan calls for fixing the nation’s existing roads. Some states are still focused on expansion.” https://www.washingtonpost.com/transportation/2021/05/23/highway-funding-infrastructure/
U.S. Federal Highway Administration. “Road Diet Informational Guide.” https://safety.fhwa.dot.gov/road_diets/guidance/info_guide/ch1.cfm
Governors Highway Safety Administration. “Pedestrian Traffic Fatalities by State: 2022 Preliminary Data.” https://www.ghsa.org/resources/Pedestrians23
Journal of Transport and Land Use. “United States fatal pedestrian crash hot spot locations and characteristics.” https://jtlu.org/index.php/jtlu/article/view/1825
National Safety Council. “Bicycle Deaths.” https://injuryfacts.nsc.org/home-and-community/safety-topics/bicycle-deaths/#:~:text=In%202021%2C%20most%20deaths%20occurred,for%20Health%20Statistics%20mortality%20data
NPR. “More cyclists are being killed by cars. Advocates say U.S. streets are the problem.” https://www.npr.org/2022/05/25/1099566472/more-cyclists-are-being-killed-by-cars-advocates-say-u-s-streets-are-the-problem
U.S. Federal Highway Administration. “Bicycle Lanes.” https://highways.dot.gov/safety/proven-safety-countermeasures/bicycle-lanes
AARP. “Road Diets: A Livability Fact Sheet.” https://www.aarp.org/content/dam/aarp/livable-communities/livable-documents/documents-2014/Livability%20Fact%20Sheets/Road-Diets-Fact-Sheet.pdf
Los Angeles Times. “Firefighters launch campaign against Measure HLA, saying ‘road diets’ threaten safety.” https://www.latimes.com/california/story/2024-02-14/firefighters-launch-campaign-against-measure-hla
Scientific Research. “A Comprehensive Study of a Road Diet Implementation in the US and Abroad.” https://www.scirp.org/journal/paperinformation?paperid=127799#:~:text=The%20road%20diets%20were%20implemented,were%20implemented%20across%2066%20projects

See where vehicle speeds are putting all road users at risk

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Better Budget Planning with Big Data: 4 Ways Transportation Agencies Use Data to Build Budgets, Secure Funds, and Save Money

blue background with U.S. dollars, credit card, and hand holding stopwatch

With transportation agencies across the U.S. facing a dreaded “fiscal cliff,” budget challenges are top of mind for many StreetLight users. Thoughout the year, but especially as fall approaches, transportation professionals are under pressure to make next year’s budget as efficient as possible, and find last-minute ways to use remaining funds effectively before they’re lost in the budget rollover.

Recent years have introduced added complexity to the budgeting process. While the Bipartisan Infrastructure Law (BIL) passed in November 2021 offered billions in new funding opportunities, the traffic disruptions of the pandemic also introduced added difficulty in anticipating travel demand, planning projects, and following through on existing plans.

But budget concerns didn’t end in 2021. Agencies of all sizes are facing limited funds while working to address a pedestrian safety crisis, returning traffic congestion, urgent emissions reduction efforts, a growing EV revolution, and lots more.

Amid these challenges, big data–powered traffic analytics are helping agencies with a few key budgeting needs:

Planning and prioritizing next year’s projects to build effective, justifiable budgets
Save money over traditional data collection methods with validated, on-demand data
Secure additional funding with data-supported grant applications
Use leftover funds quickly and effectively, before they are lost in the fiscal year turnover

Below, we’ll explore specific examples collected from panels and discussions that illustrate how shrewd practitioners are using Big Data analytics to meet these budgeting challenges.

1. Prioritizing Projects for Next Year’s Budget

At our 2020 StreetLight Summit, when many projects were in limbo, transportation professionals told us they were ramping up on planning. That way, when budgets were settled and projects could begin again, planners would be confident they’d analyzed and prioritized the most important work.

Although this was top-of-mind during the disruptions of the early COVID pandemic, this insight is evergreen. When agencies have the data to effectively prioritize next year’s projects, that data can also be used as evidence to justify next year’s budget and support its approval.

At the 2020 StreetLight Summit, planners shared examples of running multiple analyses for each project, and studying before-and-after results to measure impact of existing projects.

For example, FDOT needed to decide where to install 11 continuous bike and pedestrian counters along thousands of miles of bike trails and lanes in Florida. Planners instinctively felt that the University of Florida in Gainesville would be a prime location, and took the time to verify that instinct.

“If we are going to invest in putting up count stations that cannot be moved, we need to be confident there will actually be activity at that count station,” explained Eric Katz at FDOT.

Figure 1: A Zone Activity analysis marking potential location for a continuous bike counter on University Avenue in Gainesville, Florida.

Katz confirmed a steady stream of bike/ped travel in the area, plus trip duration and trip speed, by running a StreetLight Zone Activity analysis. The study pinpointed additional Florida locations for short-term counters, including a popular bike trail in the Panhandle.

2. Save Money Over Traditional Collection Methods

To operate effectively, agencies rely on data-informed project planning. But answering key questions about existing mobility conditions can often strain budgets.

Access to on-demand traffic data is often far more cost effective than sensors or surveys, allowing agencies to operate with a more comprehensive understanding of mobility in their communities.

For example, understanding routing — where trips start and end, and the path they take along the way — often requires costly surveys that may also face sampling limitations.

But big data can provide O-D analytics at larger sample sizes without the delay or cost of surveys.

At the 2020 StreetLight Summit, Lucile Kellis of Steer shared an origin-destination analysis designed to help a transit client size unmet travel demand. First, Steer used StreetLight to identify where transit users were ultimately going to and coming from, to highlight strategic travel corridors.

Then they went further by gleaning additional insights about the travelers themselves. But rather than incur what would be an additional survey expense, Steer leveraged the StreetLight InSight platform.

“Typically our clients have a good understanding of who their customers are, but not the people who they want to attract,” Kellis explained. “Surveys are a good tool to collect that information, but we all know that good surveys are expensive and they return results with low sample size and biases. StreetLight helps us solve these issues at a much cheaper cost.”

Figure 2: Using demographic information from StreetLight InSight Steer’s analysts built a dashboard capturing transit persona data.

Similarly, when MnDOT wanted to understand sources of traffic on Dodd Road, on-demand traffic analytics helped them avoid prohibitive survey costs.

Figure 3: MnDOT’s analysis confirmed that Dodd Road’s traffic was primarily made up of local trips, not cut-through traffic.

“We normally wouldn’t answer this community question because the survey spend wouldn’t be cost effective,” says Michael Corbett at MnDOT. Corbett’s team was receiving complaints about cut-through traffic in a particular neighborhood. But with unlimited analyses via the agency’s StreetLight InSight subscription, MnDOT was able to identify the source of neighborhood traffic, and share results with constituents.

Budget limitations often demand that some concerns remain unaddressed, even for well-funded state DOTs and large MPOs. Meanwhile, small to medium-sized cities may have an even tougher time addressing resident reports of cut-through traffic, congestion and delays, or unsafe vehicle speeds.

Access to big data can allow these cities to investigate and validate resident complaints more affordably, shifting funds that would have been spent on data collection to actually implementing solutions. Plus, with information on the entire transportation network, and the ability to evaluate the impact of past projects with before-and-after analyses, cities can more confidently choose the most effective solutions and ensure funds are not wasted.

Other traditional data collection methods — such as automatic traffic counters or manual field observation — can also drive up costs. In 2019, one DOT ran a cost-benefit analysis to compare StreetLight’s platform to traditional methods and found a 50:1 benefit-cost ratio (click here to learn more about cost-benefit analyses).

3. Secure Grant Funding

Data-supported grant applications help agencies stretch their budgets and fund projects that might otherwise end up on the chopping block or face considerable delays.

To tell a story grant administrators want to hear, agencies need evidence that their proposed project will significantly benefit their community. But as we’ve explored above, traditional data collection can be costly and time-consuming, leaving agencies with a catch 22.

Big data can help build a compelling grant application with clear visualizations and benefit-cost analyses using recent, validated data on existing conditions.

For example, when ODOT sought funding for Phase 4 of a multi-year, $1.3 billion highway restructuring project, they used StreetLight metrics and visualizations to secure a $25 million Infrastructure for Rebuilding America (INFRA) grant.

Big data has also been pivotal in securing funds for safety projects. Agencies that have leveraged StreetLight for Safe Streets and Roads for All (SS4A) applications have won ~$8 Million in SS4A grant funding.

4. Use Funds Effectively Before It’s Too Late

Because many agencies have use-it-or-lose-it funds, the end of the fiscal year often adds urgency to fund implementation. How can agencies use leftover funding effectively for their communities, with limited time left in the year?

Because big data analytics don’t face the same delays that traditional data collection methods do, they can allow agencies to quickly identify optimizations on existing roadways and allocate funds for quick fixes like signal retiming or optimizing bus schedules.

Big data can also help agencies funnel these leftover funds into planning for next year.

For example, when planners in the Shreveport, Louisiana area had concerns about traffic congestion and safety, they used StreetLight to get a comprehensive look at regional traffic, pinpoint congestion hotspots, and quickly build a data-supported mitigation plan.

This approach also relieves budget stress by allowing managers to use their staff resources more efficiently. Chris Petro, AICP, of the Northwest Louisiana Council of Governments (NLCOG) put it this way:

What Is a Stroad, and Why Is It Dangerous?

Part road, part street, the all-too-common stroad creates dangerous conditions, especially for non-motorists like pedestrians and cyclists. But what makes them so deadly, and how can data on traffic conditions help find and fix them?

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In our increasingly suburbanized North American landscape, stroads are a cornerstone of the car-centric transportation paradigm. These bare-bones high-speed roadways are often devoid of any pedestrian- or bike-friendly infrastructure, subjecting drivers and non-motorists alike to dangerous conditions when commuting, shopping, or accessing essential services.

The drawbacks of the stroad stem from its hybrid nature: it attempts to connect people with key destinations (goods, services, city amenities) while also getting people from point A to B as quickly as possible. Far from being the best of both worlds, stroads often fail to accomplish either of these goals particularly well.

So what exactly is a stroad, and how can we make them less dangerous? In this article, we’ll discuss:

What is a stroad?
The problem(s) with stroads
How to fix stroads
Data to improve safety on stroads

What is a stroad?

The term “stroad” was first coined in 2011 by Charles Marohn to describe a roadway that combines elements of a street and a road.1 So what is the difference between a street and a road?

Streets are destinations — they provide access to goods, services, and recreation. They are often found in dense urban cores, especially downtowns, where shops, restaurants, parks, salons, doctors’ offices, and more dot every corner. These kinds of roadways typically have fewer lanes, slower traffic, and signalized crosswalks to accommodate plenty of foot traffic.

*Streets typically have low speed limits, a low number of narrow lanes, ample sidewalks, and provide access to businesses, housing, and recreation.*

Roads, by contrast, connect destinations to one another, prioritizing efficient traffic flow to get people from point A to point B. Consider a highway between cities, or between a downtown core and a suburb or exurb. These types of roadways typically have more lanes, higher-speed traffic, and limited to no access for non-motorists like walkers and bikers.

*Roads typically have a larger number of wider lanes, higher speed limits, and limited to no access to non-vehicle traffic. They act as efficient connectors between destinations.*

Stroads combine elements of both, acting as both destination and thoroughfare, which results in wide, high-speed roadways with sparse walking and biking infrastructure that are nevertheless crucial access points for goods, services, and recreation. These kinds of roadways are common around malls, strip malls, and other suburban hubs, but also frequently cut through urban cores — many downtown Main Streets are also stroads. Wherever they crop up, they usually feature speed limits between 20 and 50 miles per hour. [2]

When stroads manifest in the suburbs, it’s often because roads that were once used to connect suburban residents to the downtown core become attractive places for new shopping centers, restaurants, movie theaters, and more, boasting plenty of cheap space for sprawling supercenters and parking lots. Thus, roads that become host to these kinds of destinations are often doomed to morph into the dangerous stroad hybrid.

Stroads are also common in urban cores, especially where priority is placed on accommodating the high vehicle throughput that is common in these areas.

example of a stroad — Stroads typically provide access to business and recreational destinations while featuring multiple wide lanes, higher traffic speeds, and minimal infrastructure for non-motorists.

The problem(s) with stroads

In addition to being dangerous, stroads also have other negative impacts on public health and the local economy.

Why are stroads dangerous?

As mentioned above, stroads lack many of the necessary features that make streets safe for walking and biking, such as signalized crosswalks, protected bike lanes, slow vehicle speeds, and short crossing distances.

But it’s not just Vulnerable Road Users who experience dangerous conditions on stroads. These roadways are also difficult for drivers to safely navigate due to high-speed traffic across multiple lanes with many entry and exit points from driveways and parking lots providing access to roadside destinations.

Stroads often subject drivers to unprotected left turns to cross multiple lanes of high-speed traffic, or right turns where they could easily collide with cyclists and pedestrians traveling in the same direction across unmarked driveway crossings.

How do stroads impact emissions?

Stroads incentivize vehicle travel over lower emission forms of transportation like walking and biking. This, compounded by the fact that high-speed travel produces more emissions than slower driving, makes stroads emissions hotspots.

This not only contributes to climate-harming greenhouse gases, but also worsens air quality for those who live near these hybrid roadways.

Are stroads bad for the economy?

Although stroads connect consumers with shopping centers and other businesses, many are also unattractive and costly, compromising their ability to support the local economy.

Vast, undifferentiated expanses of asphalt and concrete are common to stroads, multi-lane roadways and parking lots dominate the landscape. Coupled with poor safety, this creates an uninviting environment, disincentivizing frequent trips and extended visits.

These factors also make stroads costly. While these high-volume, high-speed roadways must undergo expensive maintenance due to considerable wear and tear, they also often generate less economic activity and property tax revenue than true streets do. [3]

See how dangerous traffic speeds impact walking and biking in your region

How to fix stroads

Fixing a stroad isn’t always simple, because they represent a confluence of competing land use and transportation goals: promote efficient vehicle travel while also facilitating economic opportunity and access to goods, services, and recreation.

A stroad usually evolves because land use practices have already allowed for business developments along an existing roadway, at which point it is too late to prevent the evolution of a stroad, but not too late to fix it.

Can stroads be prevented?

But it is possible to prevent the creation of a stroad in the first place through intentional land use practices. This would involve restricting new development along existing roads and allowing for more dense development along more people-friendly streets. Restricting the expansion of existing roadways (through lane widening or added lanes) can also help prevent street-to-stroad evolutions.

Similarly, reducing or eliminating parking minimums can allow for denser, less car-dependent hubs of development while also incentivizing non-vehicle modes of travel.

Preventing stroads could also involve converting roads into streets when new developments are proposed along existing roads. Below, we’ll explore some strategies for turning roads or stroads into streets.

Turning stroads into roads

Because stroads already have businesses, services, and/or city amenities in residence, fixing a stroad usually means turning it into a proper street. Turning a stroad into a road is not always possible, because it might require removing existing destinations and potentially relocating them elsewhere — a costly, unpopular, and time-consuming prospect for businesses and residents alike.

In cases where stroads are converted into roads, this means prioritizing vehicle throughput, minimizing entrance and exit points along the road (e.g. reducing the number of driveways leading to shopping centers and other roadside destinations) and eliminating pedestrian and bike access to the road (potentially creating separated bikeways and pedestrians paths).

Turning stroads into streets

To make stroads safer, more sustainable, and better for the economy, cities can explore redesigning roadways to implement more street-like elements.

One powerful tool for turning stroads into streets is the road diet. Road diets remove or repurpose existing lanes of traffic in order to create safer, lower speed corridors. While this tactic is sometimes controversial due to concerns that removing lanes will increase congestion, careful traffic analysis often reveals that road diets do not create significant delays in travel time, especially if they make roadways safer for non-vehicle modes of travel or reroute a portion of traffic to other high-capacity roads.

*A road diet may repurpose existing vehicle lanes to create space for buffered bike lanes (highlighted in green) or other multimodal infrastructure.*

Similarly, adding multimodal infrastructure, especially infrastructure focused on bike and pedestrian safety such as protected bike lanes, signalized crossings, and pedestrian islands can help eliminate the common dangers of stroads. A Complete Streets policy can establish helpful guidelines for turning stroads into streets that accommodate all road users.

In addition to road diets, other traffic calming tactics such as reducing speed limits, narrowing lanes, or adding street art and landscaping can protect vulnerable road users from one of the top factors contributing to high traffic fatality rates: dangerous vehicle speeds. While street art and landscaping have been shown to reduce average driving speeds, they also help beautify stroads, making them more inviting and better for business. Adding lighting and benches can also further these goals.

Reducing parking, especially in locations with vast parking lots such as shopping centers, also helps disincentivize vehicle use and encourage other modes of travel like public transit, walking, and biking. Furthermore, eliminating parking can also free up space for housing, parks, dining courts, or other developments that give people reasons to visit more frequently and for longer periods of time, boosting economic activity.

In any case, converting a stroad into a street often takes time and may involve multiple strategies, so cities should focus on incremental improvements. [3]

parking lot with hundreds of spots empty

Data to improve safety on stroads

To find and fix stroads effectively, analyzing current traffic conditions is crucial. The following metrics can help planners and engineers understand where stroads are putting people at risk and determine the best strategies to address them:

Vehicle speeds – to pinpoint which roadways may qualify as stroads and monitor risk for Vulnerable Road Users like pedestrians and cyclists
Biking and Walking activity – to understand where Vulnerable Road Users contend with dangerous conditions and where to prioritize infrastructure to improve their safety and connectivity
Vehicle volumes – to understand roadway capacity and help assess whether a stroad should be converted into a road or a street
Routing and Origin-Destination patterns – to evaluate the potential for stroad-to-street conversions and where traffic could be rerouted or shifted to other modes like public transport
Turning Movement Counts – to determine where traffic lights, signalized crosswalks, or signal retiming can keep traffic flowing while improving safety

For a comprehensive guide to transportation safety analytics, download our Practitioner’s Guide to Transportation Safety.

Ben Abramson. Strong Towns. “5 Telltale Signs You’re on the Stroad From Hell.” January 19, 2023. https://www.strongtowns.org/journal/2023/1/19/5-telltale-signs-youre-on-the-stroad-from-hell
Strong Towns. “What’s a STROAD and Why Does It Matter?” March 2, 2018. https://www.strongtowns.org/journal/2018/3/1/whats-a-stroad-and-why-does-it-matter
Reliance Foundry. “Bad Urban Design: How do you fix a stroad?” https://www.reliance-foundry.com/blog/how-to-fix-a-stroad

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What Is Congestion Pricing, and Is It Always the Right Choice?

congestion pricing scenario in Manhattan, NYC

In the nation’s busiest urban areas, transportation planners are faced with the urgent task of finding the best way to solve traffic congestion. According to data from Texas A&M, commuters in America’s largest cities lose 84 hours per year, on average, to congestion. In sprawling cities like Los Angeles and Washington, D.C., the problem is particularly egregious, with drivers losing more than two hours per day to slow-moving traffic. [1]

In the past, planners would have turned to the obvious but unreliable solution of expanding highways and adding lanes to address this problem. However, as that approach has often been shown to make congestion worse, planners are now expanding their array of potential solutions. Chief among them, in many cases, is congestion pricing.

This method of addressing traffic congestion pushes more of the cost of maintaining the busiest roadways onto those who use them. Ultimately, it aims to reduce traffic and speed up the commute for those who lack other options. Congestion charges can be an effective way to reduce congestion and support other transportation and public health goals, but it’s worth analyzing other solutions to determine the best route for your city and to make sure you can make a compelling case for any pricing strategy.

In this article, we’ll explore the following:

The critical issue of traffic congestion
What is congestion pricing?
Perks and drawbacks of a price-based approach
Other options to consider
How to know if congestion pricing is right for your city

The Critical Issue of Traffic Congestion

For many U.S. drivers, unmitigated traffic congestion may just seem like an irritating fact of life. However, its economic implications are far more serious, and its environmental consequences much more dire than a mere daily nuisance.

In cities with some of the worst congestion, the daily lurch of traffic amounts to billions of dollars in annual losses. From the expense of wasted fuel to the cost of increased accidents, congestion takes a severe economic toll. In high-traffic cities like Chicago, Los Angeles, and New York, these costs range from $7.6 billion to $11 billion every year. [2]

The price may be even greater when it comes to pollution and its adverse health effects. For instance, research has shown a direct correlation between traffic volume and the concentration of nitrogen oxides and other pollutants. Regular exposure to these high concentrations during daily rush hour traffic has been associated with increased risks of health problems and even death. [3]

The rise in pollution in conjunction with congestion isn’t merely a function of a higher number of cars. Studies have shown that slower-moving, stop-and-go vehicles produce higher levels of pollutants, exacerbating poor air quality for those who sit in traffic every day or who live and work near congested highways — people who are often members of already disadvantaged communities. [4]

Such immediate costs and public health concerns make traffic congestion a top concern for transport planners in America’s most populated areas.

What Is Congestion Pricing?

Congestion pricing is an increasingly popular way to address traffic problems in busy areas. This approach can take various forms, but they all work in basically the same way — they charge drivers to use busy roadways. This is designed to encourage drivers to carpool or consider alternate routes or methods of transportation.

These tollways may operate at all times or only during certain high-traffic periods. They may apply to all drivers on a specific route or, for instance, only to those using high-occupancy vehicle (HOV) lanes without meeting the occupancy requirements. Others may apply road pricing to those who use express lanes to bypass busy corridors. Whatever the specifics, the idea is to apply a “congestion tax” to those who contribute to the clogging, rather than levying a road or fuel tax against all drivers in a given region. [5]

Perks and Drawbacks of a Price-Based Approach

Are congestion fees the best way to address the problem? That depends on several factors, which we’ll detail below.

Proven Benefits

In the right context, congestion charges bring several important benefits, from reducing traffic to supporting broader transportation initiatives.

Congestion Fees Are Effective at Reducing Traffic

In many cities that have implemented congestion pricing, the effect on traffic volume has been dramatic. In Orange County, California, the tactic doubled vehicle throughput while increasing speed in free lanes three- to fourfold. [5] When London instituted road pricing two decades ago, it reduced congestion by 30%. [6] Stockholm, which introduced its congestion tax a few years after London, saw a net drop in traffic of 20%. [7]

But achieving these outcomes requires a detailed analysis to ensure it will be implemented effectively.

Road Pricing Has Health Benefits

As expected, these drops in traffic are accompanied by reductions in pollution. During the first year of London’s congestion pricing program, the city saw nitrogen oxide emissions drop by 13.5% and particulate matter in the air diminish by 15.5%. Research estimates this has added nearly 1,900 years to the lives of London residents over the course of the program. In Stockholm, meanwhile, hospital visits for childhood asthma have dropped by nearly 50%. [8]

It Supports Other Transportation Goals

Congestion pricing can also be introduced in conjunction with other forms of traffic engineering to support a comprehensive strategy for reducing traffic in overloaded areas. London’s system shifted 10% of commuters toward walking, cycling, or relying on public transportation. In the city center, bus travel increased by a third. [6]

Cities can use the revenue generated by congestion fees to directly support other transportation initiatives, whether that involves renovating bus stops, adding bike lanes, or supporting forms of micromobility. While congestion pricing programs do bring their own expenses, even a small boost in revenue from these programs can be beneficial for cash-strapped planning departments. Again, it’s essential to delve into traffic data and project the impact of any changes before implementing any specific program.

Criticisms and Limitations

Despite these benefits, congestion pricing garners its fair share of criticism, most of which falls into one of three categories.

Get the data-driven congestion solutions used by other cities

The Model Is Unfair

Many critics have raised legitimate concerns over where congestion pricing can be implemented equitably. After all, drivers with lower incomes will find the tolls more difficult to manage than those with more resources.

However, it’s important to note that many models are structured to address these potential inequities. San Francisco’s proposed pricing model, for example, would offer a sliding scale for congestion charges based on income level or disabilities. Likewise, the proposal offers to put some of the revenue from fees toward boosting transit service to underserved locations and even offering free transit to low-income riders. [9]

Congestion Pricing Is Unconstitutional

In some cases, opponents of this type of traffic engineering have gone so far as to question its legality. New York City’s Metropolitan Transportation Authority is now facing a lawsuit from New Jersey claiming that its current plan for congestion pricing violates the U.S. Constitution, which doesn’t allow states to enact laws that may place undue burdens on interstate commerce. [10] It’s not clear whether the suit will stand up in court, but it’s safe to say there is precedent for states enacting tollways, even where it affects out-of-state residents.

Other Options to Consider

Where there are significant barriers to successful implementation, it’s worth exploring alternatives that can be deployed in conjunction with (or as an alternative to) congestion pricing.

Road Diets

Road diets take a counterintuitive approach to solving congestion issues, but they can be quite effective. Instead of adding lanes, this method seeks to remove or repurpose existing lanes to slow down traffic, reduce demand, and increase safety.

For instance, Santa Monica, California, was able to reduce crashes by 65% in one corridor simply by converting a four-lane road into three lanes with a shared left-turn lane and outer bike lanes. [11]

Adjusting Traffic Flow

Planners can also look for ways to modify the existing flow of traffic in busy corridors. For instance, this might involve rerouting trucks to free up space or making more proactive plans for when and where roadwork is done. Signal timing adjustments can be particularly helpful in this regard.

However, as city planners found in Temecula, California, this often requires extensive data on turning movement counts (TMC) for various intersections. Fortunately, that’s no problem with the right tools on hand.

Multimodal Transportation

Finally, planners should also consider how they can facilitate more diverse forms of transportation. This is especially necessary if they implement congestion pricing, as it’s critical to have alternative modes of transport for people to choose if the tolls are cost prohibitive.

A multimodal transportation approach expands these options by adding more public transit, bike lanes, pedestrian routes, micromobility, and more. In the same vein, Complete Streets policies can help ensure accessibility for non-motorists, and counteract the equity concerns that are often raised by congestion pricing.

How to Know if Congestion Pricing Is Right for Your City

As with any approach to solving congestion problems in your city or region, there is no definitive solution. Before implementing congestion pricing, it’s essential to conduct a deep analysis of traffic data in congested areas so you can project how any proposed adjustments might affect the flow of traffic.

StreetLight InSight® makes such a deep analysis possible by providing traffic data from virtually every angle. In Napa Valley, data on commute origins helped planners realize that focusing on public transportation could significantly reduce congestion. In Temecula, TMC metrics facilitated a revamp of signal timing. In Atlanta, data on driver demographics and rush hour volume is shedding light on whether congestion fees will improve traffic flow.

To learn how you can use data to determine the right congestion solutions for your city, download Everything But Highway Expansion: Better Data for Faster Congestion Mitigation.

Or, watch how quickly you can analyze local congestion with StreetLight’s Congestion Management solutions in the 3-minute demo below.

Texas A&M Transportation Institute. “2021 Urban Mobility Report.” https://static.tti.tamu.edu/tti.tamu.edu/documents/mobility-report-2021.pdf
Forbes. “Traffic Congestion Costs U.S. Cities Billions Of Dollars Every Year [Infographic].” https://www.forbes.com/sites/niallmccarthy/2020/03/10/traffic-congestion-costs-us-cities-billions-of-dollars-every-year-infographic/?sh=3af966ea4ff8
National Library of Medicine. “Air pollution and health risks due to vehicle traffic.” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243514/
Research Journal of Chemistry and Environment. “Urban traffic congestion: its causes-consequences-mitigation.” https://www.researchgate.net/publication/367208770_Urban_traffic_congestion_its_causes-consequences-mitigation
U.S. Federal Highway Administration. “What Is Congestion Pricing?” https://ops.fhwa.dot.gov/congestionpricing/cp_what_is.htm
Transport for London. “Congestion Charge marks 20 years of keeping London moving sustainably.” https://tfl.gov.uk/info-for/media/press-releases/2023/february/congestion-charge-marks-20-years-of-keeping-london-moving-sustainably
University of Illinois at Urbana-Champaign. “Downtown congestion pricing in practice.” https://www.researchgate.net/publication/331152719_Downtown_congestion_pricing_in_practice
Natural Resources Defense Council. “What is congestion pricing?” https://www.nrdc.org/stories/what-is-congestion-pricing
San Francisco County Transportation Authority. “Downtown Congestion Pricing.” https://www.sfcta.org/downtown
Bloomberg. “NY Congestion Pricing Plan Violates US Constitution, NJ Governor Says.” https://www.bloomberg.com/news/articles/2024-01-16/ny-congestion-pricing-plan-violates-us-constitution-murphy-says
Federal Highway Administration, Road Diet Case Studies. “Santa Monica, California – Ocean Park Boulevard: Road Diet Improves Safety Near School.” https://highways.dot.gov/safety/other/road-diets/road-diet-case-studies/santa-monica-california-ocean-park-boulevard

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