Skip to main content
eScholarship
Open Access Publications from the University of California

The Institute of Transportation Studies at UC Berkeley has supported transportation research at the University of California since 1948. About 50 faculty members, 50 staff researchers and more than 100 graduate students take part in this multidisciplinary program, which receives roughly $40 million in research funding on average each year. Alexandre Bayen, Professor of Civil and Environmental Engineering and Professor of Electrical Engineering and Computer Science, is its director.

Cover page of Automated Vehicles Industry Survey of Transportation Infrastructure Needs

Automated Vehicles Industry Survey of Transportation Infrastructure Needs

(2022)

Automated vehicle (AV) deployment can bring about transformational changes to transportation and society as a whole. The infrastructure owner-operators (IOOs), who own, maintain, and operate the infrastructure, have the opportunity to work jointly with the AV industry to provide safe and efficient operations. A key question for the IOOs is, “What transportation infrastructure improvements do AV manufacturers believe will facilitate and improve AV performance?” This study was designed to address this question through a comprehensive survey approach, including an online survey and follow-up interviews. A list of ten questions was discussed, covering the physical and digital infrastructure, infrastructure maintenance, standards and specifications, policy support, data sharing, and so forth. The researchers reached out to more than 60 entities who hold the AV testing permit in California. In total, 20 companies responded. They were from different sectors and well represented the AV industry. From the results of this study, it is concluded that the most important roadway characteristics that have the potential to benefit the automated driving system (ADS) are: (1) digital mapping and signage; (2) lane markings; (3) work zone and incident information; (4) vehicle-to-everything (V2X) communications; (5) actual traffic signals; (6) general signage; and (7) lighting. The digital features considered most critical to help accelerate ADS deployment include work zone and road closure information, traffic signal phase and timing, and traffic congestion. This study provides diverse voices and in-depth insights into topics that the AV industry and IOOs should engage in to advance AVs’ deployment.

Cover page of Trust and Compassion in Willingness to Share Mobility and Sheltering Resources in Evacuations: A case Study of the 2017 and 2018 California Wildfires

Trust and Compassion in Willingness to Share Mobility and Sheltering Resources in Evacuations: A case Study of the 2017 and 2018 California Wildfires

(2020)

Advances in the sharing economy – such as transportation network companies (e.g., Lyft, Uber) and home sharing (e.g., Airbnb) – have coincided with the increasing need for evacuation resources. While peer-to-peer sharing under normal circumstances often suffers from trust barriers, disaster literature indicates that trust and compassion often increase following disasters, improving recovery efforts. We hypothesize that trust and compassion could trigger willingness to share transportation and sheltering resources during an evacuation.

To test this hypothesis, we distributed a survey to individuals impacted by the 2017 Southern California Wildfires (n=226) and the 2018 Carr Wildfire (n=284). We estimate binary logit choice models, finding that high trust in neighbors and strangers and high compassion levels significantly increase willingness to share across four sharing scenarios. Assuming a high trust/compassion population versus a low trust/compassion population results in a change of likelihood to share between 30% and 55%, depending on scenario. Variables related to departure timing and routing – which capture evacuation urgency – increase transportation sharing willingness. Volunteers in past disasters and members of community organizations are usually more likely to share, while families and previous evacuees are typically less likely. Significance of other demographic variables is highly dependent on the scenario. Spare seatbelts and bed capacity, while increasing willingness, are largely insignificant. These results suggest that future sharing economy strategies should cultivate trust and compassion before disasters via preparedness within neighborhoods, community-based organizations, and volunteer networks, during disasters through communication from officials, and after disasters using resilience-oriented and community-building information campaigns.

Cover page of A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study

A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study

(2020)

Regret is often experienced for difficult, important, and accountable choices. Consequently, we hypothesize that random regret minimization (RRM) may better describe evacuation behavior than traditional random utility maximization (RUM). However, in many travel related contexts, such as evacuation departure timing, specifying choice sets can be challenging due to unknown attribute levels and near-endless alternatives, for example. This has implications especially for estimating RRM models, which calculates attribute-level regret via pairwise comparison of attributes across all alternatives in the set. While stated preference (SP) surveys solve such choice set problems, revealed preference (RP) surveys collect actual behavior and incorporate situational and personal constraints, which impact rare choice contexts (e.g., evacuations). Consequently, we designed an RP survey for RRM (and RUM) in an evacuation context, which we distributed from March to July 2018 to individuals impacted by the 2017 December Southern California Wildfires (n=226). While we hypothesized that RRM would outperform RUM for evacuation choices, this hypothesis was not supported by our data. We explain how this is partly the result of insufficient attribute-level variation across alternatives, which leads to difficulties in distinguishing non-linear regret from linear utility. We found weak regret aversion for some attributes, and we identified weak class-specific regret for route and mode choice through a mixed-decision rule latent class choice model, suggesting that RRM for evacuations may yet prove fruitful. We derive methodological implications beyond the present context toward other RP studies involving challenging choice sets and/or limited attribute variability.

Cover page of A Report on the Future of Electric Aviation

A Report on the Future of Electric Aviation

(2020)

UC Berkeley has long been known as the home of important societal movements. In early October 2019, the electric aircraft movement came to UC Berkeley (UCB) courtesy of UCB’s Institute for Transportation Studies (ITS) and the College of Engineering. At what some have called the “Woodstock of Aviation”—the Sustainable Aviation Symposium (SAS) convened leaders of that movement from across the globe for two full days in UC’s Pauley Ballroom to explore how to solve important societal-enviro-economic issues in transportation with breakthroughs and innovations in high-tech physics, chemistry and electrical engineering. Beyond Science, Technology, Engineering, Art and Mathematics (STEAM) topics, the faculty presentations spanned a broad spectrum of UC’s graduate and undergraduate curriculum and included those by prominent UC faculty members, professors from other universities, leaders from NASA as well as several by experts in private industry. SAS 2019 was unique among conferences in focusing on how the future driverless, emission-free sky taxis of urban air mobility (UAM) could affordably transform transportation and neighborhoods at scale in metro regions and beyond. The socio-enviro-economic prospects for that transformation’s potential for regional mass transit by air that could ease surface gridlock, untenable infrastructure costs and climate change, showed why SAS 2019 engaged for the first time the disciplines of urban and environmental planning and civil engineering. SAS 2019 resulted in a growing awareness of the pan-topic relevance of UAM and justified both the continuation of SAS at UC Berkeley as well as further activities of the Aviation Futures Lab at UCB.

Cover page of Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

(2020)

Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations.

Cover page of Advancing the Science of Travel Demand Forecasting

Advancing the Science of Travel Demand Forecasting

(2019)

Travel demand forecasting models play an important role in guiding policy, planning, and design of transportation systems. There is no shortage of literature critiquing the accuracy of model forecasts (see, for example, Pickrell, 1989; Wachs, 1990; Pickrell, 1992; Flyvbjerg, Skamris Holm, and Buhl 2005; Richmond, 2005; Flyvbjerg, 2007; Bain, 2009; Parthasarathi and Levinson, 2010; Welde and Odeck, 2011; Hartgen, 2013; Nicolaisen and Driscoll, 2014; Schmitt, 2016; Odeck and Welde, 2017, and Voulgaris, 2019), not to mention several high-profile lawsuits (Saulwick 2014, Stacey 2015, Rubin 2018). Many researchers and practitioners feel more can be done to advance rigorous travel analysis methods for the public good (see, e.g., zephyrtransport.org). Motivated by these critiques, a two-day, NSF-funded workshop was held at UC Berkeley in the Spring of 2017 to engage in a fundamental review of the state of the art in travel demand modeling, to discuss the future of the field, and to propose new directions and processes for advancing the science.

Travel demand forecasting is an inherently practical enterprise. While academics drive the fundamental research, the users of travel demand models and forecasts are typically government agencies and transport operators that use the models to inform long-range investment, funding, and planning decisions. Private firms play a key role in assisting the agencies in both development and application of the models, and, more recently, high-tech firms have entered the development fray. While all of these actors have important roles in advancing the science of the field, in this report we focus our attention primarily on the academic side of the enterprise, consistent with the orientation of the funding agency (NSF), and in order to make the task manageable. That said, other sectors are represented in various parts of this report as they interface with academics or play particularly central roles in our proposals for advancing the science.