Institute of Transportation Studies at UC Berkeley

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’ WIM 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.

This report highlights risk and prioritization factors for housing developments with expiring affordability protections, focused on preserving transit-accessible affordable housing. It presents a regional framework for identifying and preserving subsidized affordable housing in the Southern California Association of Governments (SCAG) region (Los Angeles, Imperial, Orange, Riverside, San Bernardino, and Ventura counties). First, we analyze data from the California Housing Partnership (CHPC) and the National Housing Preservation Database (NHPD) to understand risk factors for expiring housing units, and design a prioritization tool for entities in the region to use when prioritizing developments to focus preservation and anti-displacement efforts. Second, we highlight affordable housing preservation policy solutions and characteristics unique to the Southern California context. Third, we draw on the strategies and experiences of other jurisdictions in the United States with experience strategizing around affordable housing preservation efforts to present key lessons and takeaways.

Transit bus operations in California are experiencing new challenges due to economic conditions and the ongoing global pandemic. A confluence of factors has created a focus on this critical public-needs serving industry, due to state and local efforts to reduce emissions of pollutants and climate-changing gases. Transit bus operations in California provide essential and additional useful services that offer critical mobility to needy populations (elderly and handicapped) as well as many other groups for whom transit buses provide the most economical, convenient, and low-emission options. To address the role of transit bus operations in meeting California’s aggressive greenhouse gas (GHG) and emissions, the California Air Resources Board (ARB) has implemented an ambitious Innovative Clean Transit (ICT) regulation that requires all public transit agencies to gradually transition to a 100 percent zero-emission bus (ZEB) fleet.1 Beginning in 2029, 100% of new purchases by transit agencies must be ZEBs, with a goal for a full transition by 2040. Prior to that 25% of purchases of new buses must be ZEBs in 2023-2025 for large transit agencies, rising to 50% in 2026-2028. For smaller transit agencies, defined as those with less than 100 buses in annual maximum service, there is no requirement for 2023-2025 and the requirement for 2026-2028 is 25%, but the 100% ZEB purchase requirement starting in 2029 applies to all agencies.

The Freeway Service Patrol (FSP) is an incident management program implemented by Caltrans, the California Highway Patrol and local partner agencies to quickly detect and assist disabled vehicles and reduce non-recurring congestion along the freeway during peak commute hours.  The first FSP program was piloted in Los Angeles and was later expanded to other regions by state legislation in 1991.  As of June 2020, there were sixteen participating FSP Programs operating in California, deploying 305 tow trucks and covering over 1,900 (centerline) miles of congested California freeways.

The purpose of this research project was to evaluate the effectiveness of the Caltrans FSP program in reducing incident durations and removal of other obstructions that directly contribute to freeway congestion for Caltrans fiscal year 2020-2021.  The project provides valuable information to agencies managing the FSP program so that resources are distributed within the various statewide FSP operations in the most efficient and cost-effective manner possible.  The tools used and the operational performance measures provided by this research effort will significantly contribute on the ongoing agencies’ efforts to improve the efficiency and effectiveness of the FSP program.

Forty-three households in the Sacramento region representing diverse demographics, modal preferences, mobility barriers, and weekly vehicle miles traveled (VMT) were provided personal chauffeurs for one or two weeks to simulate travel behavior with a personally-owned, fully autonomous vehicle (AV). During the chauffeur week(s), the total number of trips increased on average by 25 percent, 85 percent of which were “zero-occupancy” (ZOV) trips (when the chauffeur is the only occupant). Average VMT for all households increased by 60 percent, over half of which came from ZOV trips. VMT increased most in households with mobility barriers and those with less auto-dependency but least in higher VMT households and families with children. Transit, ridehailing, biking, and walking trips dropped by 70 percent, 55 percent, 38 percent, and 10 percent, respectively. The results highlight how AVs can enhance mobility, but also adversely affect the transportation system.

Congestion pricing improves economic efficiency, but it may lead to inequitable outcomes. A key policy priority in California is identifying ways to avoid the hardship of congestion pricing on low income or other vulnerable populations. This study uses data from a congestion pricing experiment in the Seattle metro area to examine the feasibility of using revenue from congestion pricing to compensate those harmed by the policy. Results indicate that the initial burden of congestion pricing is highly inequitable, with the lowest income drivers paying an average of 7 percent of their weekly income in congestion charges. There are also considerable differences in burdens within income groups. We show that policymakers face a tradeoff in ameliorating these two types of unequal burdens. Returning an equal fraction of the toll revenue to all drivers can make a policy progressive on average, but doing so leaves many drivers either overcompensated or under-compensated. We then show that while compensation packages based on basic demographic information could improve targeting, many low-income drivers would be left with large proportional burdens because of the fundamental difficulty in predicting individual-level tax burdens. Survey data on travel behavior from Seattle and California metro areas show that the difficulty of designing equitable transfers would be similar in the California metro areas most likely to consider adopting some form of congestion pricing.

California law (SB 743) requires school districts to measure the impact of school construction on the production of greenhouse gas emissions (GHG) and identify feasible mitigation measures that eliminate or substantially reduce the number of vehicle miles traveled (VMT) generated. This study analyzes 301 new schools constructed between 2008-2018 with respect to four VMT mitigation measures identified by the Governor’s Office of Planning and Research (OPR) known to minimize VMT (proximity to high quality transit areas, proximity to roads with bicycle facilities, walkability scores, and proximity to electric vehicle charging stations). The analysis reveals mixed findings. Only about 16% of the new schools sited are located within ½ mile from high quality transit. About 65% of new school sites either connected or are close to (.06 miles or less) a bicycle network. Walkability scores varied greatly by location; approximately 60% of new school sites in “city” locales are considered walkable while sites in “rural” areas have low walkability scores. Nearly 60% (179) of new school sites are located within one mile of an EV charger, but only 19% are within one quarter mile.

Passenger and heavy-duty vehicles make up 36% of California’s greenhouse gas (GHG) emissions. Reducing emissions from vehicular travel is therefore paramount for any path towards carbon neutrality. Efforts to reduce GHGs by encouraging mode shift or increasing vehicle efficiency are, and will continue to be, a critical part of decarbonizing the transportation sector. Emerging technologies are creating an opportunity to reduce GHGs. Human driving behaviors in congested traffic have been shown to create stop-and-go waves. When waves form, cars periodically slow down (sometimes to a stop) and then speed back up again; this repeated braking and accelerating leads to higher fuel consumption, and correspondingly increasingly GHG emissions. Flow smoothing, or the use of a specially designed adaptive cruise controllers to dissipate these waves, can reduce fuel consumption of all the cars on the road. By keeping all vehicles at a constant speed, flow smoothing can minimize system-wide GHG emissions. This report presents the results of flow-smoothing when used in simulation, discusses current work on implementing flow-smoothing in real world-highways, and presents policy discussions on how to support flow smoothing.

During the COVID-19 outbreak, serious concerns were raised over the risk of spreading the infection on public transportation systems. As the pandemic recedes it will be important to determine optimal timetable design to minimize the risk of new infections as systems resume full service. In this study, we developed an integrated optimization model for service line reopening plans and timetable design. Our model combines a space-time passenger network flow problem and compartmental epidemiological models for each vehicle and platform in the transit system. The algorithm can help policy makers to design schedules under COVID-19 more efficiently. The report develops an optimized timetable for the Bay Area Rapid Transit system. We found that if passengers choose other mode of transportation when closing part of the system or decreasing the frequency of service can prevent the spread of infections, otherwise, if passengers choose to use the closest open station, closings will lead to longer waiting times, higher passenger density and greater infection risk. We found that the goal of stopping the spread of infection could be achieved by minimizing the total delay when infections were similar in different districts across the service area. Where infection rates are different in different districts, minimizing the risk of exposure can be achieved by minimizing weighted travel time where higher weights are applied to areas where the infection rate is highest.

Quality data is critically important for research and policy-making. The availability of device location data carrying rich, detailed information on travel patterns has increased significantly in recent years with the proliferation of personal GPSenabled mobile devices and fleet transponders. However, in its raw form, location data can be inaccurate and contain embedded biases that can skew analyses. This report describes the development of a method to process, clean, and enrich location data. Researchers developed a computational framework for processing large scale location datasets. Using this framework several hundred days of location data from the San Francisco Bay Area was (a) cleaned, to identify and discard inaccurate or problematic data, (b) enriched, by filtering and annotating the data, and (c) matched to links on the road network. This framework provides researchers with the capability to build link-level metrics across large scale geographic regions. Various applications for this enriched data are also discussed in this report (including applications related to corridor planning, freight planning, and disaster and emergency management) along with suggestions for further work.