Institute of Transportation Studies at UC Berkeley

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.

In recent years, “smart city” technologies have emerged that allow cities, counties, and other agencies to manage their infrastructure assets more effectively, make their services more accessible to the public, and allow citizens to interface with new web-and mobile-based alternative service providers. This project developed an innovative user-friendly web interface for local and state policymakers that tracks and displays information on the adoption of such technologies in California across the policing, transportation, and water and wastewater sectors for a comprehensive set of local service providers: connectedgov.berkeley.edu. Contrary to conventional smart city indices, our platform allows users to view rates of adoption in maps that attribute adoption to the local public agencies or service providers actually procuring or regulating the technologies in question. Users can construct indices or view technologies one by one. Users can also explore the relationship between technology adoption and local service area conditions and demographics, or download the raw data and scripts used to collect it. This report illustrates the utility of the data we have collected, and the analytics one can perform using our web interface through an analysis of the rollout of three technologies in the transportation sector: electric vehicle (EV) chargers, transportation network company (TNC) service areas, and micromobility services across California.

From March 2020 through March 2021, researchers monitored three San Francisco Bay Area transit agencies: two large – Alameda-Contra Costa Transit District (AC Transit), Valley Transportation Authority (VTA); and one small – Tri Delta Transit. As the lockdown was imposed in response to the COVID-19 pandemic, white-collar commuters, students, and the elderly stopped using public transit. Initially, ridership fell 90 percent, and then over the year slowly climbed to less than 50 percent for AC Transit and VTA, and to around 60 percent for Tri Delta Transit. The pace of recovery was not steady as ridership declined during protests in June 2020, during fare reinstatements in autumn 2020 and during the second COVID-19 wave in winter 2020-21. Agencies’ responses to the pandemic consisted of three parts: 1) maintaining health and safety of their employees; 2) minimizing COVID risk for their riders by keeping buses clean and enabling social distancing through capping the number of passengers on buses; and 3) changing their service. There was a direct relationship between the socioeconomic status of the population and transit ridership during the year studied. Higher ridership was observed in low-income areas with a high percentage of Latino, Black and Asian populations. These are generally renters, who do not have a car, but have to go to work either because they are essential workers and/ or are undocumented immigrants who cannot afford staying jobless. On the other hand, in wealthy areas of the Bay Area transit activity all but disappeared.

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 2019, there were fourteen participating FSP Programs operating in California, deploying 338 tow trucks and covering over 1,806 (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 2019-2020. 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. 

An agent-based, multichannel simulation of a downtown area reveals the impacts of both redistributing traffic demand with time-dependent congestion pricing, and supplying extra capacity by banning left turns. The downtown street network was idealized, and loosely resembles central Los Angeles. On the demand-side, prices were set based on time-ofday and distance traveled. On the supply side, left-turn maneuvers were prohibited at all intersections on the network. Although both traffic management measures reduced travel costs when used alone, the left-turn ban was much less effective than pricing. When combined with pricing under congested conditions, however, the left-turn ban’s effectiveness increased considerably—it more than doubled in some cases. Furthermore, the two measures combined reduced travel costs in synergistic fashion. In some cases, this synergistic effect was responsible for 30% of the cost reduction. This strong synergy suggests that turning bans should be considered as an added option when contemplating congestion pricing.

As demonstrated by the Camp Fire evacuation, communications (city-to-city, city-to-residents) play important roles in coordinating traffic operations and safeguarding region-wide evacuation processes in wildfire events. This collaborative report across multiple domains (fire, communication and traffic), documents a series of simulations and findings of the wildfire evacuation process for resource-strapped towns in Northern California. It consists of: (1) meteorological and vegetation-status dependent fire spread simulation (cellular automata model); (2) agency-level and agency-to-residents communication simulation (system dynamics model); and (3) dynamic traffic assignment (spatial-queue model). Two case studies are conducted: one for the town of Paradise (and the surrounding areas) and another for the community of Bolinas. The data and models are based on site visits and interviews with local agencies and residents. The integrated simulation framework is used to assess the interdependencies among the natural environment, the evacuation traffic and the communication networks from an interdisciplinary point of view, to determine the performance requirements to ensure viable evacuation strategies under urgent, dynamic wildfire conditions. The case study simulations identify both potential traffic and communication bottlenecks. This research supports integrating fire, communication and traffic simulation into evacuation performance assessments.

In recent years, “smart city” technologies have emerged that allow cities, counties, and other agencies to manage their infrastructure assets more effectively, make their services more accessible to the public, and allow citizens to interface with new web- and mobile-based operators of alternative service providers. This project reviews the academic literature and other sources on potential strengths, weaknesses, and risks associated with smart city technologies. No dataset was found that measures the adoption of such technologies by government agencies. To address this gap, a methodology was developed to guide data collection on the adoption of smart city technologies by urban transportation agencies and other service providers in California. The strategy used involved webscraping; interviews with experts, public agency, and senior level staff; and consultations with technology vendors. The approach was tested by assembling data on the adoption of smart city technologies in California by municipalities and other local public agencies.