Category: Freight Planning

Investigating how trucks impact social equity with new freight data

Investigating how trucks impact social equity with new freight data

How does truck activity impact disadvantaged communities compared to their non-disadvantaged neighbors? StreetLight uses new truck data to show how you can investigate inequitable truck impacts in your community.

Truck activity can have a huge impact on local emissions and traffic congestion, but not all communities are equally affected. Now, new truck data from StreetLight helps analysts investigate how truck traffic impacts disadvantaged communities (DACs) across the U.S., and how factors like urban density, vehicle weight class, and industry type contribute – and help diagnose – inequitable impact.

Key Takeaways

In New York state, StreetLight’s data indicates disadvantaged communities are impacted by worse truck travel delays than non-disadvantaged communities, potentially exacerbating negative outcomes, such as air pollution and emissions.

Communities in the urban core are the most impacted by truck travel delays and the added emissions and pollution they often generate.

Overall, medium-duty trucks show the most unequal impact on disadvantaged communities. Trucks in this category from the public administration and transportation and warehousing industries may warrant special attention for DACs.

Trucks are some of the worst offenders when it comes to vehicle emissions. Although they are indispensable to today’s freight logistics, they also emit more CO2 and other air pollutants than cars because they typically emit more GHGs per mile in addition to traveling much longer average distances.

Compounding trucks’ oversized climate impact is a dramatic spike in freight activity. A 2021 analysis by USDOT’s Bureau of Transportation Statistics predicted total U.S. freight activity would grow 50% by 2050, with trucks accounting for 65% of that total.¹ This proliferation of truck traffic also challenges existing road capacity in many communities, exacerbating rising congestion and safety issues.

And these ramifications often come down hardest on disadvantaged communities — people who live in low-income neighborhoods where high congestion, noise pollution, and poor air quality are common. For this reason, analyzing how truck activity impacts disadvantaged communities is critical to reducing harms.

Now, freight planners, fleet operators, and businesses can use new truck data from StreetLight to understand how freight activity impacts different communities, as well as investigate related questions about who truck activity serves, which industries are most represented, and how travel delays impact logistics, emissions, and equity.

Below, StreetLight analyzes commercial vehicle activity in New York, comparing its impact on DACs vs. non-disadvantaged communities.

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How Trucks Impact Disadvantaged Communities in New York

Since 2021, disadvantaged communities (DACs) have been a key focus of efforts to improve transportation equity. These communities are “marginalized by underinvestment and overburdened by pollution,” as defined by Executive Order 14096, which established the Justice40 Initiative, a policy promising to funnel at least 40% of federal investments in clean energy, clean transit, sustainable housing, and similar programs into low-income census tracts that qualify as disadvantaged communities.²

In the state of New York, about 36% of all census tracts — accounting for about 35% of the state population — are considered to be DACs.

While all truck traffic can negatively impact a community’s air quality, traffic congestion, and local emissions levels, analyzing how different weight classes impact DACs vs. non-DACs can add helpful nuance to planning efforts aimed at mitigating this impact and targeting policy interventions.

To unpack the impact of trucks on DACs. vs. non-DACs, StreetLight’s analyzed truck activity in New York state by weight class and how it differed in these community types. Streetlight finds that light-duty and medium-duty truck activity have a disproportionate impact on disadvantaged communities per mile. Meanwhile, heavy-duty activity per roadway mile is slightly higher within non-DACs.

The impact of truck activity on New York communities

Truck Activity Per Roadway Mile is segmented by commercial vehicle weight class to compare trucks’ impact on DACs vs. non-DACs. See our Methodology section at the end for more details.

Going further and factoring in travel delays among trucks, the picture changes substantially.

The chart below illustrates the inequitable impact of congestion, with DACs encountering significantly more traffic delays per roadway mile, even among truck classes that have a lesser presence in DACs compared to non-DACs. In fact, DACs are faced with more traffic delays across every class of truck, suggesting that DACs may also be exposed to disproportionately high emissions from both gas-powered and diesel-powered trucks as these vehicles linger through delays, contributing to poor health outcomes.

how truck travel delays impact new york communities

Measuring the average delay from free-flow speed per roadway mile reveals that traffic delays impact disadvantaged communities significantly more, regardless of which classes of truck are driving in their community.

While trucks are not necessarily the cause of these traffic delays, congestion mitigation efforts can still help reduce the impact that trucks — and indeed all vehicles — have on the communities they travel through.

Because these delays are averaged across the entire available roadway network, the per-mile delay impacts appear small. Nevertheless, the fact that average delays are higher for DACs than non-DACs suggests that analyzing congestion is a worthy step when evaluating the impact of truck traffic within disadvantaged communities.

While traffic delays are a nuisance for communities in and of themselves, lengthening commute times and making it harder to access essential goods and services, delays also intensify other negative impacts of vehicle activity like emissions and air pollution.

Where Truck Delays Have the Biggest Impact by Density

For agencies to manage truck activity in a way that improves transportation equity, it’s also important to understand that DACs are diverse in makeup, as are non-DACs – no two communities look exactly the same. For example, urban density can have a significant impact on how communities are impacted by trucks.

To understand these nuances, let’s first establish how DACs are distributed across different urban densities – from rural locations to the urban core.

The map above highlights important nuances to consider when comparing census tracts—firstly, rural census tracts cover far more area, and therefore far more roadway network miles, than urban tracts. For this reason, StreetLight normalizes Truck Activity by roadway mile for this analysis (see Methodology section for more details).

Furthermore, the map shows that DACs are concentrated in urban areas. Zooming in on New York City reveals just how many DACs call the city home compared to rural areas throughout the state. The chart below explores this distribution by population.

Since DACs are concentrated in urban areas, you might expect to find that truck delays are only an issue for DACs in urban and urban core locations.

However, in the chart below, we can see that while delays are worse in urban DACs, even in rural and suburban areas, DACs are impacted by more travel time delays than their non-DAC counterparts, highlighting the importance of targeting congestion mitigation efforts and improving freight planning within disadvantaged communities across all urban densities.

How Different Truck Weight Classes Impact Communities

To further contextualize StreetLight’s truck findings by density, let’s zero in on the roles different truck weight classes play in each type of tract.

Truck Weight Class Distribution by Urban Density

How Different Industries Contribute to Truck Impact, by Weight Class

Given the disproportionate impact medium-duty vehicles have on DACs by overall activity, and especially when factoring in travel delays, it’s helpful to understand what roles these vehicles play in communities, and which industries drive medium-duty truck activity.

As the image above depicts, commercial vehicles range from class 1 to class 8, with classes 1 and 2 considered “light-duty” vehicles, ranging from commercial vans to pickup trucks. Classes 7 and 8 are “heavy-duty” vehicles, including garbage trucks, city transit buses, and traditional semi-trailer trucks. Everything in between (classes 3-6) is considered “medium-duty,” ranging from local delivery trucks to school buses.³

Based on mileage, medium-duty truck activity in New York’s DACs is predominantly comprised of Public Administration and Transportation and Warehousing vehicles, with Real Estate and Rental and Leasing (the front-runner for medium-duty activity in non-DACs) trailing just behind.

Meanwhile, Real Estate and Rental and Leasing and Transportation and Warehousing also make a strong showing within the heavy-duty vehicle activity breakdown. Although heavy-duty vehicles show more truck activity per roadway mile within non-DACs compared to their DAC neighbors, these trucks still have a significant impact on DACs, especially as they contribute more emissions and noise pollution per mile traveled compared to smaller trucks. For this reason, understanding the industry breakdown among heavy-duty trucks could also generate valuable lessons for equity-focused freight planning.

Based on these findings, medium- and heavy-duty trucks serving the transportation and warehousing industry could warrant special attention from freight planners. These trucks often travel to and from larger hubs of freight activity, such as distribution warehouses and ports, making communities impacted by these high-traffic freight routes that much more likely to experience air and noise pollution due to nearby truck activity. Moreover, much of this truck traffic may not be ending in these communities, merely passing through. For this reason, the Bipartisan Infrastructure Law (BIL) provides special funding opportunities for decarbonization efforts that target ports.⁴

Key Takeaways for Managing Truck Traffic Impacts

To improve outcomes for disadvantaged communities, planners must consider the inequitable impacts of truck traffic as they work to mitigate congestion, reduce emissions, and route trucks efficiently.

StreetLight’s analysis highlights that some classes of truck impact DACs more than their non-DAC counterparts, suggesting that analyzing this truck activity and targeting electrification efforts toward these weight classes and industries in particular may help address inequitable impact and target the most impactful improvements.

Additionally, the analysis shows traffic delays are a problem disproportionately impacting DACs across all urban densities, but especially in the urban core. Though these delays are not necessarily caused by trucks themselves, they can exacerbate the impacts trucks have on local emissions and air quality, making efforts to mitigate congestion and route trucks more efficiently in disadvantaged communities especially critical.

Finally, the Transportation and Warehousing industry emerges as a significant contributor to trucks’ impact on disadvantaged communities. This industry could warrant special attention from planners or businesses looking to address freight’s equity impact. And for businesses with large logistics operations, placing emphasis on improved routing and electrification could mitigate congestion and emissions impacts on disadvantaged communities.

For more information on how you can use transportation data to address congestion in your area, download StreetLight’s Congestion Solutions Guide: Everything But Highway Expansion.

And to learn how you can analyze truck activity and prioritize the most impactful improvements, watch our webinar, Better Freight Planning with New Truck Data: Improve Economics & Emissions.

Methodology

This analysis includes truck data for March 2024 within the state of New York, including residential roadways.

To measure Truck Activity, StreetLight analyzes sample Vehicle Miles Traveled (VMT) for commercial vehicles per mile of available roadway network in each census tract.

To analyze traffic delay, StreetLight uses a weighted average of travel time delay per mile of available roadway network in each of the urban density and DAC/non-DAC categories. The weight is the segment truck sample count, so that segments with higher numbers of trips have their delay represented proportionately. In other words, the travel time delay value for each of these categories represents how much delay a driver can expect when travelling one mile within that category.

StreetLight analyzes the urban density of census tracts based on the density of their roadway networks.

Census tracts are labeled “disadvantaged” vs. “non-disadvantaged” based on how they are classified by the Justice40 Initiative. In general, census tracts labeled “disadvantaged” meet a threshold for “environmental, climate, or other burdens” and “an associated socio-economic burden.”⁵

1. USDOT Bureau of Transportation Statistics. “Freight Activity in the U.S. Expected to Grow Fifty Percent by 2050.” November 22, 2021. https://www.bts.gov/newsroom/freight-activity-us-expected-grow-fifty-percent-2050

2. The White House. “Justice40, a whole-of-government initiative.” https://www.whitehouse.gov/environmentaljustice/justice40/

3. U. S. Department of Energy, Alternative Fuels Data Center. “Maps and Data – Vehicle Weight Classes & Categories.” https://afdc.energy.gov/data/10380

4. Office of Energy Efficiency & Renewable Energy. “Federal Funding Opportunities for Port Low- to Zero-Emissions Technologies.” https://www.energy.gov/eere/federal-funding-opportunities-port-low-zero-emission-technologies

5. Office of Energy Justice and Equity. “Justice40 Initiative.” https://www.energy.gov/justice/justice40-initiative

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As Freight Activity Rises, Trucking Analytics Help Planners Keep Up

interstate truck movement measured with trucking analytics

A StreetLight analysis shows overall truck activity has risen annually at six major retail fulfillment centers across the U.S. To keep up with increased truck activity, access to comprehensive, validated truck input is critical.

Trucks have a significant impact on roadway conditions, and that impact is becoming even more pronounced as warehouses open near residential areas. This puts the onus on transportation planners to closely monitor trucking activity to understand road stress, air quality conditions, safety concerns, and more.

But actually being able to quantify truck movement is challenging and typically relies on rudimentary estimates based on old truck data, as well as difficulty assessing truck cut-through routes, and poorly maintained truck route maps. There’s also a high cost associated with manual truck data collection.

StreetLight’s newly introduced Truck Volume Metrics allow planners and commercial freight operators to overcome this challenge by offering access to truck counts for any road, any season, segmented by vehicle class (light-, medium-, heavy-duty).

Managing trucking activity is critical for state DOTs setting and reporting on freight performance targets, as well as MPOs that must manage freight plans. Cities also need to make sure local roads are safe for residents and manage freight emissions, especially around heavy truck utilization corridors like warehouses and ports. Even commercial operators need to be able to identify more efficient routes for trucks to optimize freight operations and forecast revenue.

Using StreetLight’s Truck Analytics To Study Warehouse Traffic Trends

To understand how truck activity has trended over the last few years, the StreetLight team used Truck Volume Metrics to analyze 6 different retail distribution centers around the country. We chose popular distribution center locations like Walmart (Arizona), Best Buy (NJ), Amazon (Mt Juliet, TN), among others.

map of distribution centers assessed with truck analytics

Locations of distribution entrances where StreetLight ran analyses to study truck movements for years 2019, 2020, and 2021.

The analysis looked specifically at average all day weekday trips.

Average daily Truck Volume by year across 6 different major U.S. retail warehouses.

As the above chart shows, average truck activity has gone up annually in each of the past 3 years. We see a 7% increase in retail distribution truck activity in 2020, and a 3% increase in 2021.¹ While retail sales increased dramatically in 2021, the slower growth in truck activity may reflect driver shortages² during the year as well as new warehouse openings.

To better understand truck activity at a specific warehouse location, we ran a month over month analysis for 2019-2021 to understand heavy-duty trucking activity near a separate Amazon fulfillment center in San Bernardino, CA.

amazon distribution center where truck analytics were assessed

Amazon fulfillment center in San Bernardino, California

The Inland Empire in California has seen a rise in warehouse operations in the past few years, which has caused some tension, as the job growth that comes with expanded operations is balanced against potentially worsening air quality and traffic congestion.³

line graph showing average weekday heavy-duty truck volumes at amazon fulfillment center

Average daily heavy-duty truck volumes by month at San Bernardino fulfillment center entrance/exit.

Looking at average weekday volumes for heavy-duty trucks (14,000 lbs or more) at the entrance/exit of the San Bernardino center, Amazon’s e-commerce sales spike in 2020 is reflected in the trendline. Truck activity shows major upticks in February, likely as pandemic stockpiling began in earnest, and then again in July and September.

Overall, 2020 was an extremely spiky year in terms of truck activity at the San Bernardino warehouse. By comparison, 2019 and 2021 saw less dramatic fluctuation in trucking activity.

Using Truck Volume to measure congestion and multimodal safety planning

As new warehouses open near residential communities, planners will need to pay close attention to overall trucking activity, especially among heavy-duty vehicles, which can impact air quality, roadway conditions, and safety. They will also want to monitor routing to help ensure trucks are using corridors with the least impact on residents.

To quickly identify congestion bottlenecks on major freight corridors, StreetLight’s Truck Volume can be combined with other congestion Metrics available within the platform like speed, travel time, vehicle hours of delay (VHD), and vehicle miles traveled (VMT).

Further, the platform can be used to measure the share of personal vehicles and commercial truck activity around heavy traffic areas like ports, highways, and industrial zones, as well as to estimate roadway vehicular traffic by weight class to create safe corridors for personal and commercial vehicles.

How StreetLight validates its U.S. Truck Volume Metrics

StreetLight’s Truck Volume Metrics have undergone robust data validation, including validation against temporary and permanent counters and other industry standards. The platform’s vehicle classifications by weight comply with FHWA and HCM (Highway Capacity Manual) vehicle classification categories.

To learn more about how StreetLight develops and validates its Truck Volume Metrics, access the white paper here.

1. Statista, Total retail sales in the United States from 1992 to 2021. Aug 24, 2022.

2. Transport Topics, Driver Shortage Defines Trucking for 2021. December 17, 2021.

3. Lee, Kurtis. The New York Times, As Warehouses Multiply, Some Cities Say: Enough. Oct 10, 2022.

How Analytics Secured a $25 Million Infrastructure Grant

With an increased federal focus on rebuilding critical infrastructure, cities and states are gearing up to prioritize projects to qualify for funding. Federal grants such as the Infrastructure for Rebuilding America (INFRA) currently hold billions of dollars in possible grants, and states that want to qualify need compelling evidence that funds are well spent.

Planners in Ohio targeted an ongoing $1.3 billion project as a potential focus for an INFRA application in part because of the project’s impact on freight moving through I-70 and I-71, and also because of the project’s ability to reconnect neighborhood connections across interstate highways and improve the surrounding urban avenues.

Although planners knew there was significant freight movement through the project corridor, they hadn’t quantified the geographic scope until they leveraged StreetLight’s Origin-Destination Metric for truck traffic..

Analytics Identify Grant Potential

The Ohio Department of Transportation and the City of Columbus are in the middle of a $1.3 billion, multi-phase project to transform the crossroads of Interstates 70 and 71. The project improves key corridors of the National Primary Highway Freight System and helps restore and reconnect communities that were adversely impacted first by redlining and second by constructing I-70 and I-71.

Limited funding on an annual basis forced ODOT to subdivide multiple project phases, including Phase 4. However, subdividing the project phases is not the most efficient way to complete the project work, and ODOT and the City of Columbus sought ways to bring in additional funding for the remaining project work. Phase 4 had significant impact on freight and vehicles moving through the I-70/71 overlap, as well as reconnecting Downtown Columbus to the neighborhoods south of the overlap.

Although planners knew that a high volume of truck traffic traveled the project corridors, they needed an easy way to identify and show the regional and national impact on freight movement. StreetLight InSight® analytics helped evaluate truck travel and prove that their project would qualify for funding.

Clear Data Visualizations Show Funding Impact

Planners used ODOT’s subscription to StreetLight InSight® to run Origin-Destination (O-D) analyses isolating truck traffic.

Figure 1: Visualization of the starting and ending census blocks of truck movements travelling through the I-70/71 overlap demonstrated the intersection’s far-reaching impact on freight travel.

The results were significant. Analysts produced compelling analytical and visual proof that the project had not only a positive local traffic impact but also far-reaching improvements in statewide and national freight movement.

The images showed at a glance the regional and national extent of freight movements, confirming that the I-70/I-71 corridor was a significant nexus for the national supply chain.

The visualization was key for funding officials to see the data in context. With freight traffic spread evenly between local, state, and national trips, easing congestion with Phase 4 construction offered benefits beyond the city of Columbus. The pictures worth a thousand words put national impact at the forefront of the grant application.

The proposal proved to be so compelling that INFRA officials not only said yes to funding, but with no additional petitioning required.

Win Funding to Keep Projects Rolling

Project officials knew they had the data, but the visualizations instantly shared that knowledge in an easy-to-digest format, solidifying widespread stakeholder support for the project.

Transportation analytics that identify how people move through cities, down to single intersections, can help secure competitive funding and provide confident predictions for achieving project goals.

Get more detail on how Columbus captured the kind of data that won federal infrastructure grant funding: Download the case study to see how Columbus mined the truck data for key insights, then produced visualizations with million-dollar impact.

Calculating Population’s Effect

We wanted to assess if population was the real driver of a county’s higher-than-average HE per VMT, so we also analyzed harsh events per capita (HE per capita). We looked at each county’s HE per capita relative to the U.S. average (0.3), illustrated in Figure 2. Counties in green have lower than average HE per capita, while counties in blue have higher than average HE per capita.Summarized from “Private Versus Shared, Automated Electric Vehicles for U.S. Personal Mobility: Energy Use, Greenhouse Gas Emissions, Grid Integration, and Cost Impact” by Colin J. R. Sheppard, Alan T. Jenn, Jeffery B. Greenblatt, Gordon S. Bauer, and Brian F. Gerke, Environmental Science & Technology 55, no. 5 (March 2, 2021): 3229–39.

New York Infrastructure: To BQE or Not to BQE

For decades, the Brooklyn-Queens Expressway (BQE) has existed as the Robert Moses-era eyesore of New York City. Moses, a mid-20th century “master builder,” planned the 16-mile stretch of roadway through Northern Brooklyn and Queens in the 1960s as an extension of Interstate 278, but years of congestion and deteriorating conditions have transformed the BQE into what is arguably America’s “least-loved highway.”

Reimagining the BQE and Nearby Community

As part of the Institute for Public Architecture’s 2020 Residency program, a group of Stantec engineers were faced with a daunting task: to research and envision a transportation system that could replace this anachronistic piece of highway infrastructure with community-focused mobility modes and other community amenities.

A quick Google search of the BQE will surface countless headlines pointing out failing infrastructure, including crumbling concrete, chipped paint, and overwhelming rust.

Further, inequities in the surrounding boroughs have been attributed to the BQE: adjacent communities are subject to air and noise pollution from the continuous traffic, while structural design has cut off access to the waterfront and other areas of public space.

Data-Driven Redesign

While many proposals to reimagine the BQE have been put forward, most of them include burying the highway in tunnels. Instead, the Stantec team imagined a stepwise approach that could gradually allow freight and passenger travel modes to shift while reclaiming space for the community.

Some alternate plans shifted freight movement from truck to barge to reduce congestion and pollution in the city, and Stantec wanted to specifically link that opportunity with proposals to rethink the BQE corridor altogether.

With the help of StreetLight Data, the team decided to look for opportunities within the existing geography of freight movement to begin enabling shifts to marine and rail-based movement. Stantec knew that to fix the problem, they’d have to understand it first—and the best way to do that was to dig into the data.

Redistributing Demand

While NYC’s commercial freight network is distributed across three main modes—truck, rail, and marine freight—that demand is disproportionately placed on high-polluting truck travel. Nearly 90% of the goods brought into New York City each year are moved in trucks, 20,000 of which travel along the BQE on a daily basis.

How can this demand be redistributed? Using StreetLight’s Origin-Destination Metric, Stantec was able to identify “hot spots” where truck trips along the BQE were coming from or going to, and determine their correlation to major industrial areas. This network was then analyzed alongside the local rail and marine freight networks to find significant route overlap—and opportunity for a gradual mode shift away from trucks.

The result? A data-driven, “future future” vision of the BQE that virtually eliminates freight truck travel, allowing for the introduction of new mobility modes and the creation of more than 90 acres of public space to serve as a community amenity. It’s a 21st-century overhaul of a 20th-century problem.

To learn more about Stantec’s plan to revitalize the BQE, read our case study here.

Figure 1: National heatmap of U.S. counties as measured by a ratio of harsh events (HE) per million VMT.

When we filter for the counties with higher-than-average HE per VMT, we see that certain regional patterns emerge. In Figure 1, areas around the Gulf Coast, the Appalachian region, and coastal/border counties light up. This pattern suggests further analysis into local factors such as topography, road curvature, percentage truck and pedestrian activity, and more to help us understand why these counties have such high HE per VMT ratios.

We also see that the top counties are confined to certain states. In fact, 13 states do not have a single county in the top 10%. For states with counties in the top 10%, some show up far more often than others. For example, 58% of Michigan’s population lives in counties with high rates of harsh braking events, while only 10% of Ohio’s population lives in similar high-risk counties.

Table 1: Top Counties by HE per VMT and Population Distributions

Calculating Population’s Effect

Exploring Sample Size with a Daily Trip Sample Ratio for Commercial Vehicles Across the U.S.

Sample size is not a simple concept when it comes to Massive Mobile Data Analytics. In this post, we’re analyzing our commercial vehicle data’s sample size across the U.S., updating a 2016 study we performed, based on a Daily Trip Sample Ratio. In short, our archival data captures ~18% of commercial vehicle trips that took place across the 881 permanent counters to which we compared our data.

Daily Trip Sample Size

For this analysis, we chose the Daily Trip Sample Ratio as the unit of sample analysis because “trips” are the active unit for most of our clients’ projects. Therefore, we consider it a “useful” measure of sample (compared to pings, for example).

First, we collected truck data from the Federal Highway Administration (FHWA). We used a high-quality sample of 881 permanent counters across the United States with robust counts, representing over 25,000 hourly observations per month across 2019 (see Figures 1 and 2 below). This data set gave us the estimated Heavy Duty and Medium Duty truck trips that pass each location each day. For each day available, we counted the trips captured by StreetLight Data, and divided them by the permanent counter’s observation, for both Heavy Duty and Medium Duty vehicles.

Daily Trip Sample Size

Figure 1: Permanent count locations from FHWA’s Weigh-in-Motion (WIM) stations data set.

Figure 2: FHWA comparison counter sample size by state.

On average, StreetLight’s sample size represents 18% of the trips made across these stations. This was higher for Medium Duty trucks (31% of trips captured), and lower for Heavy Duty trucks (11%). StreetLight’s trip count also correlated extremely well with the permanent counters with an ^R² of .91, with similar values for both Heavy and Medium Duty trucks. We consider this a very strong result.

Rural vs. Urban Regions

It’s important to know whether the Daily Trip Sample Ratio was consistent across rural and urban regions, or between state jurisdictions. We classified regions as low, medium, and high density depending on the population per square kilometer.

The results held up quite consistently, with Daily Trip Sample Ratios from 15% (low-density regions) to 22% (high-density regions). Figure 3 (below) is a map of the commercial truck penetration rate across the U.S. – it is good to see a consistent rate across geographies. Looking at this by state, results ranged from a penetration rate of 10% to 30%.

Of course, this is just one approach, and there are several other approaches that we could look at in the future. For example, we could also explore how sample size holds up hourly – this could be important when looking to measure rush hour, congestion, speed reductions during heavy traffic hours, and more.

Figure 3: StreetLight Data commercial truck penetration rate across the U.S.

Check back on the blog for upcoming posts about personal vehicle sample, other techniques to measure sample size, and much more! Let us know in the comments if there’s a specific topic you’re interested in.

StreetLight InSight Analysis of Income of Bridge Travelers

I was one of those voters when I realized I have access to all the data I need. I set up a Project in StreetLight InSight to get an income breakdown for all of 2017 for travelers across the eight Bay Area bridges that will be affected by the toll increases. I included the Golden Gate Bridge too (although it is not included in the measure) because I was curious.

transportation-equity-regional-measure-3

Map of the 9 bridges analyzed for this comparison.

First, we compared the average income of bridge users during weekdays, weekends, and also compared that to average county incomes. The chart below shows the results. As you can see, there is a significant difference in the income of different bridge users. For examples, users of the Dumbarton Bridge on average make over 20% more than users of the Carquinez Bridge. Weekends are much more equitable in terms of bridge income than weekdays.

Because averages can sometimes be misleading, we also created charts showing the breakdown of income classes for bridge users, and the counties. All this just took a few clicks in StreetLight InSight.

Regional-Ballot-Measure-3-Income-Travelers

This type of data should be considered when distributing resources that will help mitigate the impact of the toll increases on the users of the bridge. For example, the measure could allow for moving up the schedule to relieve lower income bridges with new transit and carpool lane options sooner.

What is Jason’s Law? Truck Parking Analysis Made Simpler

Jason’s Law requires states to evaluate truck parking options by creating a system of metrics, but truck traffic can be difficult to measure. Fehr & Peers and StreetLight Data teamed up for a presentation at TRB Innovations in Freight Data Workshop in April 2019. We presented our proof of concept for identifying authorized and unauthorized truck parking along the I-5 corridor in the Central Valley region of California.

We will share results from our study here, to illustrate how Big Data can help DOTs prepare for Jason’s Law-inspired initiatives to increase highway safety for all drivers.

How Jason’s Law Helped Drive Truck Parking Changes

Driving an 80,000 pound, 50-foot truck comes with its own set of complications and challenges that the typical auto driver doesn’t face. One striking difference involves where and when to park. While an automobile may park at any parking location at any time, a 50-foot truck cannot. Trucks can only legally park in designated truck parking locations, which are not widely available.

Trucks also must follow regulations regarding when to stop and for how long. While these rules always existed, they are stricter now with the new Electronic Logging Device (ELD) mandate passed in December 2017. The current FHWA regulations state that truck drivers may not drive more than 11 consecutive hours, after which 10 consecutive off-duty hours are required. A truck driver must also have a 30-minute break every 8 hours. This creates two types of necessary truck parking: short term and long term.

Unfortunately, road conditions and traffic are never predictable enough to plan out the most efficient route with stops ahead of time. Therefore, trucks can reach their 11th consecutive hour with no truck parking in sight, forcing them to park in unauthorized areas.

Unauthorized parking is dangerous to truck drivers and to others. Other vehicles are in danger of collision from trucks parked on shoulders, while the truck drivers are in danger of robbery and violent assault – a risk brought to light by the tragic death of driver Jason Rivenburg in 2009, which led to Jason’s Law, passed in 2012.

Improving Traditional Truck Parking Research

The obvious solution from the implementation of Jason’s Law was to start driving more truck parking facilities. But where do you strategically add them? What policies can we implement regarding existing, underutilized parking? And are there options to get buy-in from local businesses that may be able to help?

Figure 1: Truck parking analysis methodology flow.

These are questions that states, MPOs, and the Federal government are starting to ask. Currently, the questions are being explored by one or a combination of the following approaches:

Surveys/Outreach: This approach may provide detailed insight, but it is only a very small sample and time-consuming to collect. It is also not scalable for statewide study.
FHWA Demand Equation: This option gives a very rough estimate and requires many inputs regarding the system it is to be tailored to. These inputs are largely variant depending on the corridor, and they are difficult to accumulate.
Raw GPS: Working with raw GPS data requires high-level skill sets to interpret terabytes of data, making it a time-consuming and intensive process.

Fehr & Peers and StreetLight Data collaborated to produce a new take on analyzing navigation-GPS data. By incorporating StreetLight’s interface and dynamic data approach with the freight expertise of Fehr & Peers, we were able to present informative data in a format rarely represented in truck parking analysis. The process included running a complete analysis, post-processing the multipliers.

The heavy data-lifting was completed by StreetLight using an already-established validation, analysis, and visualization process.

We summarize the general approach to the truck parking analysis in Figure 1. The graphics also include future work to expand the analysis.

overhead view of state road for Jason's Law study

Figure 2: SR 140 unauthorized truck parking.

Through the process above, we identified areas with occurrences of unauthorized truck parking. These were further confirmed through a simple Google Earth search. One of these locations includes the on- and off-ramps of SR-140 and a dirt patch near them. Most of the stops are short-term stops, potentially to accommodate hours of service regulations.

Turning Jason’s Law Analysis Into Reality

Other than modeling the truck parking demand to comply with Jason’s Law, and for future analysis and planning, there are other potential efforts that could be added to the work for creating a more informative picture. These include the following:

Adding O-D information to the work would be interesting and insightful, so that we know if these are long haul or short haul trips and may interpret their stopping behavior.
Adding the time of day or season to the data.
Noting capacity of truck parking facilities in order to understand utilization of the parking facilities.
Creating very focused and strategic outreach based on data analysis to gain some insights the data is not able to provide.

Big Data supports Jason’s Law truck parking research by quickly and efficiently analyzing existing conditions and demand. The quicker the problem areas are identified, the quicker infrastructure can be constructed to resolve the shortage. Ultimately, states can create a safer and more comfortable working environment for truck drivers, and a safer environment for auto passengers.

New Ways to Accurately Measure Truck Traffic Data

Freight-loaded trucks create the most wear and tear on highways and local roads, but what’s the best way to accurately measure truck traffic? Vehicle miles traveled (VMT) is a helpful indicator, but not all regions have the ability to quickly and affordably separate heavy trucks from personal vehicle traffic counts.

That’s where Big Data comes in. Today’s truck-focused data sets using GPS can pinpoint truck metrics including trip origin, destination, route, duration, and stops along the way. Traditional truck measurement methods simply can’t keep up.

Challenges for Truck Traffic Counts

Trucks have long been a priority for transportation analysts to measure. In fact, today many Departments of Transportation (DOTs) have permanent highway sensors in place that can separate heavy truck counts from cars, light freight, motorcycles, and other vehicles.

Unfortunately, these sensors can only capture volume in pre-set time increments, which means the raw volume number doesn’t account for traffic entering and exiting via highway ramps. Additional detail like trip origin, destination, duration, and route can inform infrastructure investments at, for example, rest stops.

To capture more detailed truck metrics, agencies have traditionally relied on driver surveys. But surveys can return unreliably small sample sizes when drivers fail to complete them. Even the surveys that are completed risk a margin of error on accurate driver recall.

These traditional methods can be expensive to mount, slow to complete, and, once finished, only one-time sources of data. In other words, analysts can’t easily go back and ask new questions to dig deeper into report findings.

How Big Data Tames Truck Traffic Metrics

Big Data is changing the face of truck traffic measurements. At StreetLight Data, for example, we can track percentages of truck trips from a specified zone, or between a specified origin and destination pair. We can also track truck “tours,” or trips that pass through several specified zones.

What this means for transportation experts is that truck movements can be analyzed for more accurate guidance on infrastructure requirements. For example, we have been able to support DOTs in accomplishing these key goals:

Identify key corridors for freight traffic mitigation
Guide effective infrastructure investments
Pinpoint freight movements around distribution centers
Validate existing infrastructure projects
Scope potential truck stop locations
Revisit the data for quick follow-up analyses

Reliable data is critical, and a larger sample size creates more accuracy. Our commercial vehicle GPS sources provide us with 12% of heavy and medium-truck activity, giving our data set a robust sample size. Our counts have been validated by several DOT permanent sensor counts.

If truck travel is a priority metric for your agency, join the Big Data revolution. Click below to read one of our case studies about working closely with DOT officials to secure fast and accurate analyses to better inform their transportation mission.

Scanning California for Truck Stops

A major strength of Massive Mobile Data Analytics is that it’s, well, massive, and we can easily “scan” across large geographies to identify specific patterns of actual travel behavior. To leverage this, we ran a sample analysis by scanning every kilometer in California for truck stops for a given year, and found some fascinating patterns about the movement of these big (and medium) rigs throughout the state.

This simple truck analysis is a great example of a high level exploration that will identify “hot spots” of high or unusual activity, which can then be the focus for drilling down with more granular analyses. Modelers can use this type of analysis to help identify and define key origin and destination areas, as well as top routes.

To calculate this metric, we first chopped up California in a grid of 1km x 1km blocks. Next, we analyzed the relative volume of trucks stopping, or ending a trip, in each 1km grid cell. We split out the results by weight class – Medium Duty (between 14,000 and 26,000 lbs) and Heavy Duty (over 26,000 lbs). Powering this analysis was over 10 million truck trips spanning all of 2015. The trips were derived from GPS data from commercial fleet management systems that we license from our partner INRIX. Figure 1 below is a dot map representing the truck trip ends points throughout the state.

Figure 1: Dot map of truck end points within California.

The two heat maps below visualize the Truck Stop Index values for the grids across the state. Red indicates highest values, then orange, yellow and green. We eliminated grid cells with the lowest values to help simplify the visualizations. Heavy Duty trucks, unsurprisingly, tend to stop for rest, refueling, and work near to major highways and ports. Medium Duty trucks, which often are used for activities such as parcel delivery, maintenance, etc., collect in urban areas.

Figure 2: Medium duty truck heat map.

Figure 3: Heavy duty truck heat map.

We then drilled down further into some of the hot spots. Starting in an area we know well, we reviewed Heavy Duty stops in the East Bay to see if they matched our local knowledge. All but one were obvious industrial/heavy commercial areas. But not the one seemingly random spot indicated in Figure 4 below.

Figure 4: Heavy duty truck stops in the East Bay region.

Google Maps to the rescue. We first identified the area as a commercial portion of a neighborhood with big box retail and another nearby property that clearly contained parking lots housing trucks. Google Street View clarified that this was a KAG West Tanker facility, hence the big lot and numerous trucks.

Figure 5: Deep dive into one East Bay neighborhood.

Next, we looked for an area at the other extreme and landed in Quartz, California. In this rural, desert community in Southern California, two grid cells adjacent to each other popped up as red for Heavy Duty vehicles, meaning that a lot of trucks are stopping here. Our initial thought was — is that correct? But, as shown in Figure 6 below, a review of the area confirmed there are two big warehouse/distribution facilities on that block. This does make sense, given Quart’s proximity to the Greater LA market and it’s position between Southern California and markets in the north of the state.

Figure 6: Deep dive into rural distribution centers.

In the end, we were able to create these metrics for truck travel throughout the entire state in a few hours, and have only begun to scratch the surface on where else to drill down for further insights. We’d love to hear if you there are any regions you’d like to explore.

Communities Using Big Data to “Improve” Goods Movement

Communities Using Big Data to "Improve" Goods Movement

Today’s commercial travel analysis is becoming more complicated, and traditional data methods are struggling to keep up. For example, small-package delivery is done more and more by people in their own personal cars – how can we accurately capture that information? This increasing complexity necessitates better and more granular data about commercial traffic, and Big Data can help.

Here we’ll review six popular examples we see over and over again when planners and agencies are trying to understand commercial vehicles in their communities.

The first three examples focus on communities trying explicitly to improve goods movement and commercial vehicle flow. The next three, which are in fact more common in our experience, focus on trying to understand and manage the impact of commercial traffic on some other aspect of the community.

A few quick clarification points – we currently only measure road movement, so will not include trains, planes, or shipping in this discussion. In addition, a “community” could be a city, neighborhood, MPO, or State DOTs, as well as the consultants who work with them. Finally, in these examples we combine all commercial vehicle movement but in practice communities often like to break out heavy-duty freight from medium- and light-duty goods movement. Thus, we refer to this group of vehicles as “commercial vehicles” throughout the examples.

How StreetLight InSight Can improve Commercial Vehicle and Goods Movement Analyses

Example 1: O-D Matrices for Transportation Demand Modeling

This example will be familiar to most people in the planning world. Many of our customers use the StreetLight InSight web app to run an origin-destination analysis to calibrate or seed freight-demand models, as part of their ongoing planning activities. The image below shows a simple O-D visualization for American Canyon, California.

Users often compare morning to midday to evening traffic, and compare commercial to personal O-D patterns all with a few clicks. Users also like to “zoom in” and do special O-Ds for particular areas of interest, like ports or warehouse clusters.

Figure 1: Simple O/D matrix visualization for commercial vehicles in the Bay area.

Example 2: Commercial Vehicle Routing

Because we use GPS data, which has five-meter spatial precision, we can determine which routes trucks took between origins and destinations. Many customers use this for transportation demand modeling for freight, as in our example 1. In addition, customers look at which on- and off-ramps trucks take, and differences in travel times to improve way finding, understand road wear, and more.

Example 3: Planning for New Environmental Regulation

Some of our customers use StreetLight InSight to help plan new regulation, both modeling the potential impact and figuring out how to support local commercial vehicle owners in complying.

For example, as shown in figure 2, customers can look at the distribution of total trip length for trucks in a particular county. This highlights what share of trucks could reasonably use an alternative fuel (like natural gas or electricity) that has a shorter range than conventional diesel trucks.

Hisogram of Daily Miles for Commercial Vechicles in X County

Example 4: How Does Commercial Traffic Compare to Personal In this Traffic Jam?

Commercial vehicles often take a lot of blame for traffic jams, and policy makers often find it easier to work with commercial traffic to provide incentives to shift driving time. Many of our customers like to compare the behavior of commercial and personal vehicles in the same traffic jam.

For example, as shown below, we see the “load curve” of personal vehicles throughout the day on a particular segment that has terrible morning rush hour congestion (purple line). Local stakeholders complain that big trucks exacerbate the problem. We can also look at the load curve for commercial vehicles (yellow line). Finally, we look at the share of commercial vehicles on the road segment going to two nearby warehouses (yellow area).

We see in fact that the commercial vehicles peak after the morning rush with the exception of the trucks going to the two warehouses. Now policy makers can target their outreach and incentives to this particular set of drivers. This is a simplified example, but demonstrates the value of analyzing both types of driving together.

Traffic Peaks by Hour of Weekday on Segment X

Example 5: Freight and Economic Development

Many, many customers want to answer a simple question – what share of the vehicles using my roads are actually doing something for my community (either stopping for lunch or picking up goods)? This is also called an Internal-External analysis. StreetLight InSight can answer this question in a few clicks.

For example, the image below shows an analysis of I-95 southbound going into Richmond, Virginia. The chart shows the share of vehicles that stop in the city versus those that pass through and leave by a particular roadway. We see that more personal vehicles than commercial vehicles are actually going to the greater Richmond area ( 42% vs. 29%). This alone is useful information to a transportation planner.

We also see that passing-through trucks are twice as likely to take I-295 SB out than personal vehicles. This offers intriguing possibilities – why do the trucks prefer it? Are there toll or road stop implications worth considering?

Figure 4: Example of a combined personal/commercial internal-external analysis for Richmond, VA.

Example 6: Freight and Tolling

Many clients are interested in the tolling implications of our metrics. For example, a client in San Diego wanted to know if recent changes in tolling prices had in fact resulted in drivers moving to a less-congested roadway. The metrics revealed that, in fact, personal drivers reacted as expected in the morning rush hour. But commercial drivers appeared not to react at all. Thus, the policy makers not only had confirmation that their policy worked, but guidance in how to spend time and outreach efforts to make it work even better.

The Next Five Years

In conclusion, here are the main opportunities, challenges, and trends we see for Big Data for commercial and freight analytics in the next five years.

Opportunities

Empirical measurement radically speeding up and enriching what behaviors can be measured and modeled.
Measuring changes over time for deeper understanding of impacts of policies and infrastructure investments.
An emphasis on measuring goods movement and non-goods movement transportation interacting in the same system.
Integration of mobile data with IoT sensors data.

Challenges

Ensuring Industrial and personal data privacy (hint: use professional processors like StreetLight!)
Figuring out how to define “goods movement.”
Helping communities go beyond using Big Data to simply replace surveys, but implement transformational measurement practices.

In the future, we see a broader, more complex definition of goods movement in more constrained city environments with more complex regulatory and infrastructure planning demands. The best way to begin to measure, manage, and model this future is to use Big Data-driven analytics.