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.

This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficiently solves a quantile loss optimization problem using the Alternating Direction Method of Multipliers (ADMM). The resulting parameter vectors allow determining a probability distribution of upcoming traffic flow. These predictions establish an efficient, delay-minimizing control policy for the intersection. The approach is demonstrated on a case study with two years of high resolution flow measurements. It is emphasized that the results are applicable to any traffic intersection equipped with sensors that provide sufficiently high resolution of data acquisition. In particular, the data must have sufficient spatial resolution, e.g., measuring turning counts, and sufficient temporal resolution, e.g., measurements each 15 minutes. For example, numerous sites in California, including a large number of intersections in LA County, possess sensors that provide the required data to a central server.

The objective of this report is to first review what we know from the literature about long distance travelers, analyze the contents of the long distance travel log of the California Household Travel Survey (CHTS), demonstrate the augmentation of the trip/tour records with destination attractiveness indicators, derive prototypical traveler profiles, and provide amore detailed analysis of long distance tours. The data are from a simplified travel log that asked respondents from households to report all the trips 50 miles or longer they made in the 8-weeks preceding the day they were assigned a full travel diary. The survey instrument used for this reporting is shown in Figure 1. In this report we identify a few issues with the data collected using this travel log, and these issues motivate us to also investigate the long distance travel reported in the daily diary. The range of variables that we can analyze depends heavily on the accuracy with which respondents reported their trips, and we found they were generally more accurate in the daily diary. However, the long distance travel log contains data that span longer periods than 24 hours and therefore a better record of trips with overnight stays away from home. Past studies of long distance travel have found that commuting by people who sought out lower cost housing is a major contributor to long distance travel, and that higher income and employed persons travel more, but there are multiple shortcomings in the literature that we seek to address here. The literature contains a variety of definitions for “long distance” travel, including ones based on distance (e.g., longer than 50 miles, 100 miles, or longer than 100 kilometers) and travel time (e.g., 40 minutes). Long distance travel researchers have considered a variety of indicators including number of long distance trips, activity before and/or after commute, mode used, time of day of trip, and destination (Georggi and Pendyala, 2000, Axhausen, 2001, Beckman and Goulias, 2008, LaMondia and Bhat, 2011, Caltrans, 2015, Shahrin et al., 2014, Holz-Rau et al., 2014). Most studies did not address trip chaining (e.g., people going to a work place, then to a leisure destination, and then back home). Very little analysis is also found in differentiating trips with an overnight stay, despite the important differences between these trips and daily commuting. The choice of analysis in past studies was presumably driven by: a) an emphasis in the literature on trips to and from work; and b) a focus on a single trip by an individual person as the unit of analysis instead of multiple trips from household members. This focus on commute trips is also reflected in the multitude of person factors used to explain variation in travel behavior in past research (Table 1.1). Table 1.1 also shows household and location characteristics that have been considered as determinants of long distance travel behavior. It is also important to note that a few researchers (de Abreu et al., 2006, 2012) consider long distance travel, car ownership, and residential and job location (and the distance between the two) as a system of joint decisions that are best explained using methods that can disentangle the complex relationships among all these behavioral facets. From this viewpoint, long distance travel (particularly for commuters) cannot be separated from the choice of work and home location and should be modeled jointly. The review in Mitra (2016) is particularly useful in mapping recent literature on long distance travel and its determinants. His findings are exactly what one would expect: age, gender, education, employment and occupation, car ownership, household structure, place of residence and workplace as well as housing cost and accessibility influence long distance travel in a variety of ways. His analysis also shows that developing traveler profiles at the level of a household (rather than the individual) is a better choice to understand how and why long distance travel happens, and our analysis follows this lead. In another analysis of CHTS, Bierce and Kurth (2014) identified an issue of underreporting of repetitive trips in the 8-week long distance data. In essence, long distance commuters did not report all their commuting trips. We find that this underreporting may also exist for longer trips, though less severely than it does for shorter ones. Identifying the correct mix of distances and overall volume of travel is particularly important when one seeks to estimate the contribution of VMT from long distance travel to California estimates of VMT (see also Chapman, 2007). 

The Transportation Sustainability Research Center at UC Berkeley conducted a series of tasks to assist the California Department of Transportation (Caltrans) with an understanding of prosperity, accessibility, and livability metrics. Research findings were collected through a combination of literature reviews and expert interviews. Researchers found that prosperity, accessibility, and livability metrics all involve a component of cooperation with partner jurisdictions. A flexible approach that accounts for local and corridor considerations and evolves over time is emphasized. The white paper highlights the importance of equity considerations, data availability, and the scale of measurement. Prosperity emphasizes long-term or short-term strategies to improve quality of life, focusing on economic indicators, such as income, business, and property values. Prosperity metrics can be used to prioritize transportation projects based on social, environmental, or equity concerns. Accessibility metrics reflect the ability for transportation systems to provide people with access to opportunities. Metrics are centered on travel time and length, land use, mobility, and the availability of public transit. Livability focuses on quality of life improvements with community outcomes and impacts at the local level. Metrics—such as affordability, public health, quality of accessibility, environment, aesthetics, and public participation—all pertain to livability.

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.