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Open Access Publications from the University of California

Research Reports

Cover page of Travel Demand Modeling Methodology Recommendations for the Link21 Program

Travel Demand Modeling Methodology Recommendations for the Link21 Program


This project aims to provide recommendations on the methodology and design specifications for the travel demand model to be built for the Link21 program in the Northern California megaregion. The Link21 program is a major rail investment program that will considerably improve and upgrade the passenger rail services in the Northern California megaregion, centered around the Transbay Corridor between Oakland and San Francisco in the San Francisco Bay Area. To support this effort, we reviewed the current and potential travel markets for the Link21 program, assessed the available travel demand models that could be used to support the modeling efforts for the Link21 program, and conducted interviews with experts from academic institutions, metropolitan planning organizations, state and federal agencies, and US DOE national labs. Considering the goals and objectives of the Link21 program, a list of 20 critical, important, and optional modeling features were identified, which should be considered for the Link21 program. We reviewed 11 existing travel demand models based on the evaluation of their modeling features, and present four proposed modeling approaches which could be considered to support the Link21 program. For each modeling approach, we summarize pros and cons in terms of fulfilling the requirements of the Link21 program. The four modeling approaches include: 1) building on the Metropolitan Transportation Commission (MTC) TM 2.1 regional travel demand model without a dedicated long-distance travel model component; 2) building on the MTC TM 2.1 regional travel demand model with a dedicated long-distance travel model component; 3) building on the San Francisco County Transportation Authority (SFCTA) regional travel demand model with or without a dedicated long-distance travel model component; and 4) building on the California High Speed Rail (CHSR) or the new statewide rail model that is currently under development. The study also discusses some sources of uncertainties that might affect future travel demand and the modeling practice in the Link21 regions. These include the impacts of the COVID-19 pandemic on work patterns and activity/travel choices, the introduction of shared mobility services, micromobility, the potential deployment of Mobility as a Service (MaaS) solutions, and the forthcoming deployment of connected and automated vehicles (CAVs). Given the complexity of the Link21 program and the requested 18-month timeline for developing a new travel demand model to support the program, we recommend that the model development for the Link21 program build on an existing modeling framework and adopt a modular system, which can be updated over time. An initial model release would become available in the proposed timeline of 18 months, while future updates and improvements in the model components could be added in future model updates. This process also would be well-suited to address eventual modeling issues that could arise with the initial model release, and it would benefit from the development and updates of other models in the Northern California megaregion that are being carried out in parallel.

Cover page of Panel Study of Emerging Transportation Technologies and Trends in California: Phase 2 Findings

Panel Study of Emerging Transportation Technologies and Trends in California: Phase 2 Findings


Emerging transportation services, whose development and adoption have been enabled by information and communication technology, are largely transforming people’s travel and activity patterns. This study investigates the emerging transportation trends and how they transform travel-related decision-making in the population at large through the application of a unique longitudinal approach. As part of this project, a second wave of data collection in 2018 was built with a rotating panel structure as a continuation of the research efforts that started with the collection of the 2015 California Millennials Dataset. This report focuses on the analyses of the data collected in this project, in particular on the differences in attitudes towards transportation and the environment among different generational groups, the adoption and use of shared mobility services, and their relationship with vehicle ownership, the interest in the adoption of alternative fuel vehicles, and the interest in the future adoption of connected and automated vehicles. Due to the small number of respondents who participated in both surveys, for the purposes of the analyses contained in this report, we treated the data as repeated cross-sectional and analyzed the data from each survey separately. The study helps researchers evaluate the complex relationship between observed/latent characteristics and individual travel-related choices and decision-making. The study highlights attitudinal and mode-choice differences across generations. It explores the factors impacting current adoption of and future interest in new transportation technology including alternative fuel vehicles, automated vehicles and shared mobility. Divergent consumer segments are witnessed within each of these markets, with distinctive socio-demographics, latent attitudes, built environment, and level of familiarity with new technologies, which shape the uniqueness of their vehicle ownership, residential location, travel behavior, activity patterns, and lifestyle.

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Cover page of Emissions Impact of Connected and Automated Vehicle Deployment in California

Emissions Impact of Connected and Automated Vehicle Deployment in California


This study helps understand how the anticipated emergence of autonomous vehicles will affect various aspects of society and transportation, including travel demand, vehicle miles traveled, energy consumption, and emissions of greenhouse gases and other pollutants. The study begins with a literature review on connected and automated vehicle (CAV) technology for light-duty vehicles, the factors likely to affect CAV adoption, expected impacts of CAVs, and approaches to modeling these impacts. The study then uses a set of modifications in the California Statewide Travel Demand Model (CSTDM) to simulate the following scenarios for the deployment of passenger light-duty CAVs in California by 2050: (0) Baseline (no automation); (1) Private CAV; (2) Private CAV + Pricing; (3) Private CAV + Zero emission vehicles (ZEV); (4) Shared CAV; (5) Shared CAV + Pricing; (6) Shared CAV + ZEV. The modified CSTDM is used to forecast travel demand and mode share for each scenario, and this output is used in combination with the emission factors from the EMission FACtor model (EMFAC) and Vision model to calculate energy consumption and criteria pollutant emissions. The modeling results indicate that the mode shares of public transit and in-state air travel will likely sharply decrease, while total vehicle miles traveled and emissions will likely increase, due to the relative convenience of CAVs. The study also reveals limitations in models like the CSTDM that primarily use sociodemographic factors and job/residence location as inputs for the simulation of activity participation and tour patterns, without accounting for some of the disruptive effects of CAVs. The study results also show that total vehicle miles traveled and vehicle hours traveled could be substantially impacted by a modification in future auto travel costs. This means that the eventual implementation of pricing strategies and congestion pricing policies, together with policies that support the deployment of shared and electric CAVs, could help curb tailpipe pollutant emissions in future scenarios, though they may not be able to completely offset the increases in travel demand and road congestion that might result from CAV deployment. Such policies should be considered to counteract and mitigate some of the undesirable impacts of CAVs on society and on the environment.

Cover page of Designing Robo-Taxis to Promote Ride-Pooling

Designing Robo-Taxis to Promote Ride-Pooling


Robo-taxis (automated vehicles operating in a ride-hailing model) have the potential to improve mobility while reducing traffic, emissions, and energy use. However, such outcomes depend largely on increasing riders per vehicle. Public policy that incentivizes industry to design robo-taxis to support ride-pooling may be critical to achieving positive outcomes. This research reviews current shared automated vehicle designs and literature related to potential consumer risks and benefits of ride-pooling in robo-taxis in order to articulate potential design solutions to promote pooling.

Cover page of The Adoption of Shared Mobility in California and Its Relationship with Other Components of Travel Behavior

The Adoption of Shared Mobility in California and Its Relationship with Other Components of Travel Behavior


Emerging technologies and shared mobility services are quickly changing transportation. The popularity of these services is particularly high among millennials and those living in the dense central parts of cities. Still, the reasons behind the adoption of these services and their impacts on the use of other transportation modes and on total travel demand are largely unclear. How are shared mobility services changing transportation demand and supply? This report provides useful insights to answer this question. The research explores the use of various types of shared mobility services in California, focusing in particular on the factors affecting the adoption and frequency of use of ridehailing services (such as those provided by Uber and Lyft), and the impacts that the use of these services has on other components of travel behavior. The authors analyze a dataset that they collected with a detailed online survey in fall 2015 as the first round of data collection in a panel study of emerging transportation trends and adoption of technology in California. More than 2,000 respondents, including millennials (i.e., young adults born between 1981 and 1997) and members of Generation X (i.e., middle-aged adults born between 1965 and 1980), completed the survey.

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Cover page of What Affects Millennials’ Mobility? PART II: The Impact of Residential Location, Individual Preferences and Lifestyles on Young Adults’ Travel Behavior in California

What Affects Millennials’ Mobility? PART II: The Impact of Residential Location, Individual Preferences and Lifestyles on Young Adults’ Travel Behavior in California


Young adults (“millennials”, or members of “Generation Y”) are increasingly reported to have different lifestyles and travel behavior from previous generations at the same stage in life. They postpone the time at which they obtain a driver’s license, often choose not to own a car, drive less if they own one, and use alternative non-motorized means of transportation more often. Several explanations have been proposed to explain the behaviors of millennials, including their preference for urban locations closer to the vibrant parts of a city, changes in household composition, and the substitution of travel for work and socializing with telecommuting and social media. However, research in this area has been limited by a lack of comprehensive data on the factors affecting millennials’ residential location and travel choices (e.g. information about individual attitudes, lifestyles and adoption of shared mobility is not available in the U.S. National Household Travel Survey and most regional household travel surveys).

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