Institute of Transportation Studies (UCB)

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.

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.

The San Francisco Bay Area regularly experiences some of the most severe traffic congestion in the U.S. This past year both Inrix and the Texas Transportation Institute (TTI) ranked the Bay Area third only to Washington D.C. and Los Angeles in the time drivers spend stuck in traffic. The TTI estimated that traffic congestion cost the Bay Area economy a staggering $3.1 billion in 2014 (Lomax et al., 2015). Such estimates are based on the premise that moving more slowly than free-flow speeds wastes time and fuel, and that these time and fuel costs multiplied over millions of travelers in large urban areas add up to billions of dollars in congestion costs. But while few among us like driving in heavy traffic, do such measures really capture how congestion and the conditions that give rise to it affect regional economies? This study explores this question for the San Francisco Bay Area by examining how traffic congestion is (i) related to a broader and more conceptually powerful concept of access and (ii) how it affects key industries, which are critical to the performance of the region’s economy.

This project documents and analyzes the recent change in California transportation revenue collection programs that end discontinued the state sales tax on motor fuels and increased the state per gallon excise taxes on motor fuels.

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.

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.

Amid growing recognition of the costs of downtown congestion and scarcity of revenues for new roads, congestion pricing for downtown areas -- a practice we call “zone pricing” -- has begun to receive wide attention. From 1975-2003, zone pricing failed to spread beyond Singapore, but by the 2000’s technological advances had made the practice more widely practical. Now London, Stockholm, Milan and Gothenburg have schemes of their own, and zone pricing is on the agenda in many world cities. The research summarized in this report has sought to advance practical knowledge of zone pricing in several ways. First, we have created a very detailed, scholarly history of zone pricing, covering the circumstances under which cities have implemented zone pricing, what technologies have been used and what results these cities have obtained. Second, we investigated the theory of “usage tolls.” A drawback of all tradition zone pricing systems is that, for practical reasons, they fail to charge different tolls to drivers who use the network to different degrees: someone who enters the downtown and immediately parks pays the same toll as someone who circles for an hour. But with new technology it will be possible to charge drivers for some index of road use, such as how far or how long they travel inside the network. Our research highlights two major advantages of usage tolling: (i) it can reschedule trips in optimal ways; (ii) it can discourage long trips -- such as those traveling across the downtown between points outside -- from happening by car in the first place. In both cases, an interesting result is the added precision of usage tolls means congestion reduction can be accomplished while charging drivers relatively little. We cite this as a political advantage that will help make zone pricing more palatable.