Institute of Transportation Studies at UC Berkeley

California’s Senate Bill (SB) 743, enacted in 2013, marks a historic shift in how the traffic impacts of development projects are to be evaluated and mitigated statewide. To help achieve state climate policy and sustainability goals, SB 743 eliminates traffic delay as an environmental impact under the California Environmental Quality Act. State implementing guidelines for SB 743 instead require an assessment of vehicle miles traveled (VMT). The adoption of the guidelines sparked debate and raised far-reaching questions about development planning. Our research consisted of four parts. First, we considered how the state guidelines might be applied by analyzing travel patterns across and within California cities in relation to the guidelines. We also interviewed fortythree professional transportation consultants and regional and local planners to provide insights on SB 743 implementation. In addition, we carried out extensive case studies of San Francisco and Pasadena, where policies had already been adopted to align with SB 743. Finally, to help assess the technical challenges involved in SB 743 implementation, we tested two VMT estimation tools in common use and considered the practical challenges facing tool users. We find that SB 743 implementation is likely to present some transitional challenges for city planners, but the long-term prospects for improving transportation planning as a result of the law are promising.

Transportation is arguably experiencing its most transformative revolution since the introduction of the automobile. Concerns over climate change and equity are converging with dramatic technological advances. Although these changes – including shared mobility and automation – are rapidly altering the mobility landscape, predictions about the future of transportation are complex, nuanced, and widely debated. California is required by law to renew the California Transportation Plan (CTP), updating its models and policy considerations to reflect industry changes every five years. This document is envisioned as a reference for modelers and decision makers. We aggregate current information and research on the state of key trends and emerging technologies/services, documented impacts on California’s transportation ecosystem, and future growth projections (as appropriate). During 2017, we reviewed an expanded list of 20 topics by referencing state agency publications, peer-reviewed journal articles, and forecast reports from consulting firms and think tanks. We followed transportation newsletters and media sources to track industry developments, and interviewed six experts to explore their opinions on the future of transportation. We consulted an advisory committee of over 50 representatives from local and state transportation agencies, who provided input throughout the project’s evolution. We also obtained feedback on our draft report from a panel of U.S. experts.

In recent years, technological and social forces have pushed smartphone applications (apps) from the fringe to the mainstream. Understanding the role of transportation apps in urban mobility is important for policy development and transportation planners. This study evaluates the role and impact of multimodal aggregators from a variety of perspectives, including a literature review; a review of the most innovative, disruptive, and highest-rated transportation apps; interviews with experts in the industry, and a user survey of former multimodal aggregator RideScout users. Between February and April 2016, researchers conducted interviews with experts to gain a stronger understanding about challenges and benefits of data sharing between private companies and public agencies. Key findings from the expert interviews include the critical need to protect user privacy; the potential to use data sharing to address integrated corridor and congestion management as well as various pricing strategies during peak hours; along with the potential benefits for improving coordination between the public and private sectors. In March 2016, researchers surveyed 130 people who had downloaded the RideScout app to evaluate attitudes and perceptions toward mobile apps, travel behavior, and modal shift. The goal was to enhance understanding of how the multimodal apps were impacting the transportation behavior. The survey did found that respondents used multimodal apps in ways that yielded travel that was less energy intensive and more supportive of public transit. Looking to the future, smartphone applications and more specifically multimodal aggregators, may offer the potential for transportation planners and policymakers to enhance their understanding of multimodal travel behavior, share data, enhance collaboration, and identify opportunities for public-private partnerships.

Integrated management of travel corridors comprising of freeways and adjacent arterial streets can potentially improve the performance of the highway facilities. However, several research gaps exist in data collection and performance measurement, analysis tools and control strategies. In this project first we analyzed high resolution data consisting of time-stamped records of every event involving vehicles, together with the signal phase at real-world signalized intersections and developed procedures for estimating performance measures. Next, we assessed the performance of a new microscopic simulator for signalized arterials. The model predictions were in close agreement with the predictions from widely used models in practice. We also developed and applied control strategies for freeway-arterial coordinated control to avoid queue override and developed a methodology to provide estimates of the amount and impacts of freeway diverted traffic in case of no-recurrent (incident related) congestion.

Transfers are a major source of travel time variability for transit passengers. Coordinating transfers between transit routes in real time can reduce passenger waiting times and travel time variability, but these benefits need to be contrasted with the delays to on-board and downstream passengers, as well as the potential for bus bunching created by holding buses for transfers. We developed a dynamic holding strategy for transfer coordination based on control theory. We then obtained the optimal control strategy, where maximum holding time is a function of real-time estimates of bus arrivals and passengers and the uncertainty in these estimates. Total travel time (waiting plus in-vehicle) with the optimal control is found to be globally less than or equal to total travel time without control when uncertainty is bounded. The time savings from transfer coordination increase with the ratio of transferring to through passengers but diminish as uncertainty in the real-time estimates of bus arrivals increases. Field observations at a multimodal transfer point in Oakland show that the proposed control strategy could reduce net transfer delay by 30-39% in a real-world scenario. The data collected also confirm that the upper bound on uncertainty in bus arrivals can be satisfied with existing bus location technology. We conclude with a discussion of complementary measures, such as the provision of real-time information at transfer points and conditional signal priority, which could allow coordination to be applied in more cases.

Public transit systems with efficient designs and operating plans can reduce greenhouse gas (GHG) emissions relative to low-occupancy transportation modes, but many current transit systems have not been designed to reduce environmental impacts. This motivates the study of the benefits of design and operational approaches for reducing the environmental impacts of transit systems. For example, transit agencies may replace level-of-service (LOS) by vehicle miles traveled (VMT) as a criterion in evaluating design and operational changes. Previous studies have demonstrated in an idealized singletechnology transit system the potential of reducing GHG emissions by lowering the transit level-of-service (LOS) provided to the users. In this research, we extend the analysis to account for a more realistic case: a transit system with a hierarchical structure (trunk and feeder lines) providing service to a city where demand is elastic. By considering the interactions between the trunk and the feeder systems, the study provides a quantitative basis for designing and operating integrated urban transit systems that can reduce GHG emissions and costs to both transit users and agencies. The study shows that highly elastic transit demand may cancel emission reduction potentials resulting from lowering LOS, due to demand shifts to lower occupancy vehicles, causing unintended consequences. However, for mass transit modes, these potentials are still significant. Transit networks with buses, bus rapid transit or light rail as trunk modes should be designed and operated near the cost-optimal point when the demand is highly elastic, while this is not required for metro. We also find that the potential for unintended consequences increases with the size of the city. The results are robust to uncertainties in the costs and emissions parameters. The study also includes a discussion of a current transit system. Since many current transit systems have not yet been optimally designed, it should be possible to reduce their GHG emissions without sacrificing the LOS. A case study of the MUNI bus system in San Francisco is used to validate this conjecture. The analysis shows that reductions in GHG emissions can be achieved when societal costs are reduced simultaneously. The cost-optimal MUNI bus system has a societal cost of 0.15 billion $/year and emits 1680 metric tons of greenhouse gases. These figures only amount to about half of the cost and a third of the emissions in the current MUNI bus system. The optimal system has a lower spatial availability but a higher temporal availability of bus service than the current system, which highlights the potential benefits of providing more frequent express bus services.

Electric vehicles (EV) are promoted as a foreseeable future vehicle technology to reduce dependence on fossil fuels and greenhouse gas emissions associated with conventional vehicles. This paper proposes a data-driven approach to improving the electrification rate of the vehicle miles traveled (VMT) by a taxi fleet in Beijing. Specifically, based on the gathered real-time vehicle trajectory data of 46,765 taxis in Beijing, we conduct time-series simulations to derive insight for the public charging station deployment plan, including the locations of public charging stations, the number of chargers at each station, and their types. The proposed simulation model defines the electric vehicle charging opportunity from the aspects of charge time window, charging demand and charger availability, and further incorporates the heterogeneous travel patterns of individual vehicles. Although this study only examines one type of fleet in a specific city, the methodological framework is readily applicable to other cities and types of fleets with similar dataset available, and the analysis results contribute to our understanding on electric vehicles’ charging behavior. Simulation results indicate that: i) locating public charging stations to the clustered charging time windows is a superior strategy to increase the electrification rate of VMT; ii) deploying 500 public stations (each includes 30 slow chargers) can electrify 170 million VMT in Beijing in two months, if EV’s battery range is 80 km and home charging is available; iii) appropriately combining slow and fast chargers in public charging stations contributes to the electrification rate; iv) breaking the charging stations into smaller ones and spatially distributing them will increase the electrification rate of VMT; v) feeding the information of the availability of chargers at stations to drivers can increase the electrification rate of VMT; and vi) the impact of stochasticity embedded in the trajectory data can be significantly mitigated by adopting the dataset covering a longer period.

Electric vehicles (EV) are promoted as a foreseeable future vehicle technology to reduce dependence on fossil fuels and greenhouse gas emissions associated with conventional vehicles. This paper proposes a data-driven approach to improving the electrification rate of the vehicle miles traveled (VMT) by taxi fleet in Beijing. Specifically, based on the gathered real-time vehicle trajectory data of 46,765 taxis in Beijing, we conduct timeseries simulations to derive insight for the public charging station deployment plan, including the locations of public charging stations, the number of chargers at each station and their types. The proposed simulation model defines the electric vehicle charging opportunity from the aspects of time window, charging demand and charger availability, and further incorporates the heterogeneous travel patterns of individual vehicles. Although this study only examines one type of fleet in a specific city, the methodological framework is readily applicable to other cities and types of fleet with similar dataset available, and the analysis results contribute to our understanding on electric vehicle’s charging behavior. Simulation results indicate that: i) locating public charging stations to the clustered charging time windows is a superior strategy to increase the electrification rate of VMT; ii) deploying 500 public stations (each includes 30 slow chargers) can electrify 170 million VMT in Beijing in two months, if EV’s battery range is 80 km and home charging is available; iii) appropriately combining slow and fast chargers in public charging stations contributes to the electrification rate; iv) breaking the charging stations into smaller ones and spatially distribute them will increase the electrification rate of VMT; v) feeding the information of availability of chargers in charging stations to drivers can increase the electrification rate of VMT; vi) the impact of stochasticity embedded in the trajectory data can be significantly mitigated by adopting the dataset covering a longer period.