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.

This papers presents the design of the travel mode detection component within a generic architecture of processing individual mobility data. It approaches mode detection in two steps, each aiming at a particular objective. The first step develops a discriminative classifier that detects the mode of the observed trips or a sequence of modes in a multiple leg journey. It requires a considerable amount of ground truth data with known modes to be available for training. It also relies on a k-shortest path algorithm that generates plausible alternatives routes for the journey. The second step utilizes the discriminative recognition step of the observed mode in order to build a behaviorally grounded model that predicts the chosen mode within a set of available alternatives as a function of user characteristics and transportation system variables. It is based on the discrete choice modelling paradigm and results in a set of parameters calibrated for distinct neighborhoods and/or segments of population. The overall framework therefore enables travel mode choice modeling and a consequent policy analysis and transportation planning scenario evaluation by leveraging privacy-sensitive individual mobility data possibly held in a secure private repository. It provides a set of algorithms to drastically reduce the latency and costs of obtaining a crucial information for models used in transportation planning practices. The performance and accuracy of the algorithms is evaluated experimentally within a large metropolitan region of the San Francisco Bay Area.

We address the problem of shockwave formation in uncoordinated highway traffic. The problem is caused by the combination of heavy traffic and small traffic perturbations or unexpected drivers actions. We propose a novel distributed communication protocol that helps mitigate upstream shockwave formation even with extremely low system penetration rates. Based on traffic information ahead, the Cooperative Advanced Driver Assistance System (CADAS) recommends non-intuitive velocity reductions in order to redistribute traffic more uniformly and eliminate traffic peaks. Simulation results show that CADAS significantly increases the average velocity and therewith reduces the overall travel time and avoids unnecessary slowdowns. As a next step, for realism, we propose to apply CADAS to real traffic traces. Also, we extend the shockwave model from single to multiple lanes (to reduce accidents caused by lane switching).

As central cities in California continue their renaissance, commercial gentrification is often identified by residents as a concern. For many, commercial gentrification means the intrusion of new businesses that force out a favorite food shop or a longstanding retail store because of higher rents. For others, it means an influx of hip cafés, trendy retail boutiques, and gourmet fast food restaurants - places that change the fabric of their familiar neighborhood, for better or for worse. For many merchants, commercial gentrification can have implications for economic survival, as increased rents may lead to displacement and business closures. This report was born out of these concerns, which we uncovered when interviewing community stakeholders as part of our earlier research on residential gentrification in Los Angeles and the Bay Area (See Chapple, Loukaitou-Sideris, Waddell, Chatman, & Ong, 2017). Over the course of this past work, interviews with community members and planners revealed rapidly-changing storefronts to be a recurring concern. As we looked deeper into this phenomenon, we found that potential relationships between commercial gentrification and transit-oriented development (TOD), transit ridership, and traffic safety were relatively unexplored. This report focuses on the San Francisco Bay and Los Angeles regions and addresses gaps in our understanding of the relationship between commercial gentrification and TOD, rail transit ridership, and traffic safety. The primary elements of this report are: ● A literature review of research on commercial gentrification. ● The development of a quantitative metric that defines commercial gentrification based on four objective parameters. ● Statistical analyses that explore associations between commercial gentrification and rail transit stations, changes in transit ridership, and traffic safety. ● Qualitative examinations of four case study neighborhoods: two in Los Angeles and two in the Bay Area. Using these methods, we produced the following research findings: ● Commercially gentrified stations are generally characterized by an influx of eateries, cafés, and bars. ● Proximity to a transit station is likely not associated with commercial gentrification. More important factors that may relate to commercial gentrification are the demographic characteristics of a neighborhood, particularly the percent of non-Hispanic black, foreign born, and renter residents, as well as overall population density. In some contexts, residential gentrification may lead to commercial gentrification. Commercial gentrification may contribute to increases in total, cyclist-involved, and pedestrian-involved average annual crashes around rail transit stations. It is unclear if this is directly due to the phenomenon of commercial gentrification or if it is related to an increase in traffic that occurs in commercially gentrified areas. ● Commercial gentrification does not appear to have a significant effect on rail transit ridership. Residential gentrification in Los Angeles, on the other hand, may lead to reduced rates of transit ridership in the decade after the residential gentrification occurs. 5 ● Merchants generally indicated that rising rent costs were the most prominent aspect of neighborhood change putting pressure on their businesses’ bottom line. Following these conclusions, we recommend the following as prudent municipal, state, and regional policies to mitigate traffic crash impacts and empower transit-oriented development: ● While our quantitative research does not find a significant relationship between a neighborhood’s proximity to transit and commercial gentrification, this may not represent a universal truth, and this issue certainly requires further probing. Policymakers should not simply assume that transit neighborhoods are not susceptible to commercial gentrification. ● The relationship between residential and commercial gentrification also needs further exploration. The results of this study are rather mixed, and it is not clear when and where one type of gentrification follows the other, or which comes first. We suspect that there may not be a universal pattern, and this relationship may change from one neighborhood to the other. ● Our findings indicate that commercial gentrification is context-specific. Policymakers, therefore, should not only rely on aggregate data but also seek to identify what is happening on the ground in specific commercial transit neighborhoods. Commercial neighborhood stakeholders, such as merchants, property owners, and realtors can provide good information about gentrification trends, business closures, relocations, rent increases, etc. ● Commercial gentrification in a transit neighborhood is often accompanied by an increased incidence of crashes involving pedestrians and cyclists. This may well be because more pedestrians and cyclists are present in the neighborhood, increasing rates of exposure. Regardless of cause, the increased occurrence of crashes tells us that policymakers should focus resources towards traffic calming, safe streets infrastructure provision, and other proven traffic safety improvements.

Highway pavement rehabilitation (HPR) is a service provided by departments of transportation (DOTs) worldwide. The process of delivering HPR projects involves not only a transportation department but also many other project participants and stakeholders; furthermore, it is subject to numerous technical- as well as socio-political considerations. Interestingly—though not surprisingly—the processes DOTs use to deliver this service vary widely, not only between countries or between states in the US, but also regionally within a given state such as California. While some variation is to be expected, it is not necessarily of value to some or all concerned. Management practices such as Lean and Six Sigma can be key to driving out unwanted variation and thereby lead to performance improvements locally and overall. Addressing “Goal 5 Operational Excellence” in Caltrans’ (2015a) Strategic Management Plan, this research set out to view HPR projects through the lenses of Lean and Six Sigma, in combination referred to as Lean Six Sigma. These management philosophies—herein broadly referred to by the broad term “Lean Thinking”—overlap in concepts and methods, but they all aim to promote continuous improvement and value delivery. Caltrans started to launch Lean Six Sigma initiatives in 2015 (e.g., Dunning 2016, Tusup 2017) and its employees have to date already achieved significant process improvements in their day-today operations. However, it appears that Caltrans has not yet pursued such initiatives in the delivery of its projects. The literature overview provided in this report describes applications of Lean and Six Sigma in transportation departments in the US and abroad, and the cases referenced demonstrate the applicability of Lean and Six Sigma to project delivery. Lean applied to HPR project delivery and, more generally, applied to project-based production, in the literature gets referred to using the term “Lean Construction” (Koskela et al. 2002, Ballard et al. 2002). The exploratory research with findings presented in this report, set out to investigate if and how a state DOT might standardize the delivery of HPR projects. The researchers investigated this by collecting data on three projects that Caltrans completed recently. Using this data and building on the Caltrans (2016) work breakdown structure, they were able to map the processes used to deliver two of them. The researchers then obtained further data and gauged the performance of these projects’ delivery processes. Comparison of the resulting process maps, and their combination into a single process map that may function as a draft “standard,” serve as the basis for formulating recommendations to Caltrans. The researchers recommend that Caltrans personnel with a Lean mindset review the maps provided and fine-tune them for further use in collaborative efforts within their organization (e.g., engaging multiple functional units within districts and engaging multiple districts) as well as with supply chain partners (e.g., contractors) while using Lean Thinking to identify and pursue opportunities for continuous improvement of its project delivery practices.

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 report describes operational data analysis and modeling of arterial networks with signalized intersections as follows: The setup for data collection, analysis and simulation is presented in Section 2.1. Detailed analysis of collected signal phasing and traffic data is provided in section 2.2. Arterial traffic and platoon modeling is described in Section 2.3. Simulation results of the Rollins Park network is discussed in Section 2.4. Research conducted under this task is an important stepping stone for building a three-level information and control system for urban networks with high-density traffic. In this task researchers focused on elements of link-level information (signal phasing and timing (SPaT) estimation and prediction) and vehicle-level control (Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC)). In SPaT analysis researchers presented several novel algorithms to estimate the residual duration of a signal phase for a semi-actuated intersection. These algorithms predict the times for all future phase transitions, based on previous phase measurements and on the real time information that locates the current time within the current phase. With respect to the vehicle-level control, researchers analyzed sensitivity of intersection throughput to car following models and related parameters. The Improved Intelligent Driver Model (IIDM) was chosen for traffic simulation. Finally, researchers implemented the platoon model in Simulation of Urban MObility (SUMO) and tested it in simulation of scenarios on Rollins Park network.

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.

This report examines equity among local option sales tax (LOST) measures for transportation in California between 1976 and 2016. Since the first was enacted in 1976 in Santa Clara County (Silicon Valley), 76 LOST measures have appeared on county ballots, 48 of which (63%) were approved by voters. These measures have proven to be popular methods to finance transportation system construction, operations, and maintenance over the past four decades, increasing in number even after a 1995 ruling in Santa Clara County Local Transportation Authority v. Guardino required that LOSTs secure two-thirds support to pass. LOSTs are currently in place in 24 of California’s 58 counties that are home to 88 percent of the state’s population. Sales tax revenues dedicated to transportation today produce over $4 billion per year for transportation construction and maintenance in these “self-help counties.” Sixteen counties have enacted more than one sales tax measure: Alameda, Los Angeles, and Santa Clara counties have three, four, and five passed measures, respectively.

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).