research report

Transport Pricing Policies and Emerging Mobility Innovations

Abstract

Transportation pricing policies aim to manage vehicular demand for parking, dense urban areas, roadways, and highway lanes. Although pricing policies take various forms, most were designed in a world before the sharing economy and ride-sourcing companies. Hence, the efficacy of existing pricing policies in a world with shared mobility services requires further consideration. Moreover, future pricing policies designed to handle private vehicles and shared ride-sourcing vehicles must consider the behavior of both sets of travelers and vehicle fleets. This study develops a conceptual framework to support systems-level analysis of pricing policies for a world with private and shared vehicle usage. It qualitatively analyzes the impact of shared vehicles on the effectiveness of various pricing policies, while also considering the role of vehicle-to-infrastructure technology. This conceptual framework will support future research that uses activity-based travel demand and dynamic network assignment models to evaluate congestion pricing policies in an era of shared mobility. Additionally, the study presents a detailed review of the literature related to transportation pricing together with a trend analysis on congestion pricing policies in Transportation Research Board annual meeting titles and abstracts.

research report

New Data and Methods for Estimating Regional Truck Movements

Publication Date

September 22, 2023

Author(s)

Abstract

This report describes how current methods of estimating truck traffic volumes from existing fixed roadway sensors could be improved by using tracking data collected from commercial truck fleets and other connected technology sources (e.g., onboard GPS-enabled navigation systems and smartphones supplied by third-party vendors). Using Caltrans District 1 in Northern California as an example, the study first reviews existing fixed-location data collection capabilities and highlights gaps in the ability to monitor truck movements. It then reviews emerging data sources and analyzes the analytical capabilities of StreetLight 2021, a commercial software package. The study then looks at the Sample Trip Count and uncalibrated Index values obtained from three weigh-in-motion (WIM) and twelve Traffic Census stations operated by Caltrans in District 1. The study suggests improvements to StreetLight’s “single-factor” calibration process which limits its ability to convert raw truck count data into accurate traffic volume estimates across an area, and suggests how improved truck-related calibration data can be extracted from the truck classification counts obtained from Caltrans’ weigh-in-motion and Traffic Census stations. The report compares uncalibrated StreetLight Index values to observed truck counts to assess data quality and evaluates the impacts of considering alternate calibration data sets and analysis periods. Two test cases are presented to highlight issues with the single-factor calibration process. The report concludes that probe data analytical platforms such as StreetLight can be used to obtain rough estimates of truck volumes on roadway segments or to analyze routing patterns. The results further indicate that the accuracy of volume estimates depends heavily on the availability of sufficiently large samples of tracking data and stable and representative month-by-month calibration data across multiple reference locations.

policy brief

How Are Transit Agencies in California Addressing the Travel Needs of People Experiencing Homelessness?

Abstract

Increasing numbers of people experiencing homelessness in California cities have prompted some transit agencies to address the needs of unhoused people and riders more comprehensively in their service plans. Some of these efforts respond to the presence of transit riders who are visibly homeless and seek shelter on transit vehicles, at transit stops, and on other agency property. Many people experiencing homelessness, however, are also active users of public transit, relying on buses and trains to access services, get to work, visit family, and more. Public transit is especially critical for those working to exit homelessness who do not have access to a personal vehicle.

policy brief

Low-Income Suburban Residents in the San Francisco Bay Area Face Significant Housing and Transportation Issues

Abstract

Growing poverty in America’s suburbs challenges their image as single-family residential communities for middle class, predominantly white families. Research shows that suburban areas now have the largest share of households under the poverty line. Since these areas have lower density development and lower levels of public transit service compared to urban areas, living in the suburbs may pose accessibility challenges for low-income households, particularly those without a personal vehicle. To explore housing and transportation issues associated with the suburbanization of poverty, we combined U.S. Census data from Contra Costa County, which has the highest rates of suburban poverty in the San Francisco Bay Area, and online and in-person surveys with individuals who earn less than 80% of the Area Median Income (AMI), around $75,000. This research identifies demographic and external factors that lead low- and moderate-income households to move to suburban areas, accessibility barriers faced by low- and moderate-income suburban households, and how transportation use and transportation and housing costs differ between urban and suburban low-income residents in the Bay Area.

research report

How Regional Transit Agencies Can Serve the Daily Mobility Needs of the Unhoused Population

Abstract

With more people experiencing homelessness in California cities, some transit agencies have begun to comprehensively address the needs of people experiencing homelessness, a population that historically may not have been included in their planning. Research suggests that people experiencing homelessness rely on public transit for the same variety of reasons that all riders do, and that, like other riders, they find it difficult to reach those destinations due to prohibitive costs and transit schedules that do not meet their needs. California transit agencies vary in terms of whether, and how well, they engage with the issue of homelessness. Interviews and a review of policy and programming documents show that most major transit agencies in California made some reference to people experiencing homelessness, but just ten of fifteen addressed their transit needs, and only three addressed those needs through dedicated programs. The research team uses this research synthesis to draw greater attention to the ways that transit agencies can serve the mobility needs of people experiencing homelessness. The team presents findings from a case study on transit accessibility in San Diego County to supplement the statewide review. This includes a geospatial analysis of transit accessibility from locations where people experiencing homelessness have been known to congregate in San Diego County, as well as interviews with three people who have experienced homelessness in the region and three advocates for the unhoused population. The team identifies the ways that transit accessibility is a complex issue, requiring consideration of proximity, ease of physical access, and programmatic support. Based on the research, the researchers recommend that transit and service organizations consider the following: (1) establish coordinated outreach in transit environments, (2) offer shuttles to services and employment to help one resolve their homelessness, (3) improve the reliability and connectivity of public transit, (4) support fare assistance programs, and (5) incorporate expertise from people with lived experience of homelessness.

research report

Struggling to Connect: Housing and Transportation Challenges of Low-Income Suburban Residents in the San Francisco Bay Area

Abstract

Suburban areas have lower density development than urban areas, which may make them less accessible for the growing population of low- and moderate-income suburban residents, particularly those without a personal vehicle. This research examines factors that lead these households to move to suburban areas and identifies accessibility barriers they face. We use a mixed-methods approach with Public Use Microdata Sample data from the U.S. Census, online/in-person surveys (n=208), and interviews conducted in English and Spanish (n=25) with households in Contra Costa County with an income of less than $75,000. To understand key differences in housing and transportation choices between urban and suburban residents, these data were compared to survey and interview data from low-income Oakland residents from 2020-2021. We found that low- and moderate-income households choose to live in the suburbs due to rising rents and other requirements (e.g., credit score, rental history) in urban areas, and a desire for home ownership and a safer environment for children. Yet, the lack of tenant protections is leaving them vulnerable to rising rents in suburban areas. Transportation costs are higher in suburbs due to longer commutes and higher reliance on personal vehicles. Despite higher levels of car-ownership in the suburbs, households often go without a car due to maintenance issues or inability to make car payments. When faced with the lack of an automobile, suburban households have few quality transportation alternatives.

research report

Improved California Truck Traffic Census Reporting and Spatial Activity Measurement

Abstract

The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation operational and planning needs. Many transportation agencies rely on Weigh-In-Motion and automatic vehicle classification sites to collect vehicle classification count data. However, these systems are not widely deployed due to high installation and operations costs. One cost-effective approach investigated by researchers has been the use of single inductive loop sensors as an alternative to obtain FHWA vehicle classification data. However, most models do not accurately classify under-represented classes, even though many of these minority classes pose disproportionally adverse impacts on pavement infrastructure and the environment. As a consequence, previous models have not been able to adequately classify under-represented classes, and the overall performance of the models is often masked by excellent classification accuracy of the majority classes, such as passenger vehicles and five-axle tractor-trailers. This project developed a bootstrap aggregating (bagging) deep neural network (DNN) model on a truck-focused dataset obtained from Truck Activity Monitoring System (TAMS) sites, which leverage existing inductive loop sensor infrastructure coupled with deployed inductive loop signature technology and already deployed statewide at over ninety locations across all Caltrans Districts. The proposed method significantly improved the model performance on truck-related classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research studies. Remarkably, the proposed model is also capable of distinguishing classes with overlapping axle configurations, which is generally a challenge for axle-based sensor systems.

policy brief

Connected Eco-Driving Technology Can Help Improve Traffic Flow While Reducing Truck Emissions

Abstract

California has experienced faster growth in freight volume than freight-related infrastructure, leading to travel delays as well as traffic congestion and air pollution. Onestrategy to improve the efficiency of freight movement while also reducing environmental impacts is to encourage “connected eco-driving.” This could be accomplished by utilizing innovative connected vehicle technology to provide truck drivers real time traffic signal phase and timing information that could be used to determine the best driving speed for passing smoothly through multiple intersections without stopping. The technology has been in research and development for over a decade. While initially developed for passenger cars the connected eco-driving technology has also been applied to other types of vehicles, including Class 8 diesel trucks.

research report

Evaluation of Benefits and Costs of Truck Connected Eco-Driving Program on Urban Freight Corridors

Abstract

This research estimates the costs and benefits of implementing connected eco-driving technology for freight trucks on signalized freight corridors as a strategy to mitigate the impacts of truck traffic. The costs associated with enabling the technology include capital investment for infrastructure upgrades such as upgrading traffic controllers and installing communication modems. The costs also include operating costs for wireless data plans and computing servers. Over a period of 20 years, the total cost for one intersection is estimated to be $18,200. The benefits of the technology include reductions in energy consumption and emissions from a connected truck traveling on connected corridors. Under cold start conditions, the technology could help reduce overall fuel consumption by 20%, and emissions of carbon dioxide, nitrogen oxides, and particulate matter by 22%, 20%, and 15%, respectively. Under hot running conditions, the technology could help reduce overall fuel consumption by 10% and emissions of carbon dioxide, nitrogen oxides, and particulate matter by 10%, 0%, and 41%, respectively. Based on these estimates, connected eco-driving technology can play an important role in addressing greenhouse gas emissions from freight trucks, as well as mitigating the air quality and health impacts associated with truck emissions in communities that are heavily impacted by truck traffic.

policy brief

Congestion Pricing Can Be Equitable If a Portion of the Revenue is Returned to Drivers

Abstract

Economists have long argued in favor of congestion pricing, under which drivers pay a fee or toll to enter roadways during peak times. An increasing number of global cities have adopted or are considering pricing programs. Even so, these regimes remain relatively rare and controversial. One key concern with congestion pricing is fairness. Road pricing can pose a substantial burden for low-income drivers, many of whom have little option to avoid travel during peak times and limited opportunity to choose other modes of travel. Prior research has shown that congestion pricing regimes tend to be regressive in terms of their initial burden, that is, in terms of who ends up paying more to use the roads.1 But, the ultimate effect of a road pricing program depends also on how its revenue is used. Some or all of the revenue from a congestion pricing program can be returned to households, and this can fundamentally change the program’s fairness.