In August 2024, ITS awarded 7 projects through the Statewide Transportation Research Program (STRP), funded by the Road Repair and Accountability Act (Senate Bill 1) for a total amount of $595,000.
Michael Cassidy: Congestion Pricing to Support Transit
We will explore cordon placements that, for any toll amount, maximize desirable impacts of tolling, including the revenues generated, to subsidize transit. We will determine how these optimal placements are affected by local conditions and quantify benefits.
Our preliminary research shows that a cordon’s favorable impacts are maximized by centering it either tightly around, or somewhere inside of what would be the fully expanded footprint of congestion, when left uncontrolled (1). The rule that we have uncovered entails choosing the placement that intercepts (tolls) the largest number of inbound car drivers who must cross the cordon to reach their destinations inside the protected area. Drivers in this category are henceforth called “toll captives.” We have formulated simple means to mimic how cordon-based tolls affect commuters’ choices to travel by car; and a rational model of driver-navigation behavior to emulate, among other things, how some non-toll-captive car commuters will detour to avoid tolls. We have embedded these features in the Aimsun traffic simulation platform (2). Simulations to date provide limited but promising support for our cordon-placement rule.
Additional simulations are expected to confirm that cordons placed as per our rule: (i) are most impactful at favorably shifting demand for car travel and raising revenue; (ii) minimize, or nearly minimize, vehicle-miles travelled (VMT) networkwide, by shifting demand and limiting opportunities for non-toll-captive drivers to avoid tolls by detouring around the cordon; and (iii) maximize commuter welfare, defined as the difference between a travel budget and the cost of the trip itself, including any toll. We further expect to show that cordons poorly placed cause harm by unintentionally encouraging detours. Initial experiments will be conducted for generalized (i.e., idealized) regions of varying geographic configuration. Final simulations will be of San Francisco, to optimize the cordon placement for its planned tolling system.
Ken Alex: An Open-Access Tool for Equity-Focused Local Electric Mobility Investment Planning
This project will develop an equity-centered, open-access tool for local EV and mobility infrastructure site prioritization in California jurisdictions. With federal and state programs offering hundreds of millions of dollars for EV charging and associated investments–and private developers looking to invest in high-traffic areas–local governments need to proactively identify locations that are responsive to community needs, build connectivity, meet grant criteria, and more. However, they largely lack the technical and staff capacity to conduct this exercise iteratively, publicly, and in a manner that centers equity and local mobility priorities.
This project, building on a demonstration in development by CLEE and ERG, will develop a tool that enables users–local agencies, community stakeholders, developers–to identify areas that will best serve the needs of local communities, on a basis of shared data tailored to local demographics and geography.
The project will center on the following elements:
● Building and refining an innovative approach to integrate spatial data across different scales and geographies (e.g., block, census tract, ZIP code) to enable analysis of a) priority and b) feasibility for infrastructure siting, tailored to local, application-specific criteria.
● Completing the tool’s dataset to incorporate federal environmental justice and equity data, granular demographic and environmental indicator data, additional electrical grid elements, and additional co-location points and transportation network components.
● Expanding the tool’s geographical coverage to include all California cities and counties.
● Developing automation strategies to a) integrate new geographies and b) update data sources as applicable.
● Creating a user interface viable for agency staff and stakeholder use.
● Building a user guide and manual to inform decision-making processes using the tool.
Julia Griswold: Autonomous Vehicle Safety Performance in Mixed Traffic: Insights from NHTSA Crash Data
As Autonomous Vehicles of Level 3 or higher gain prominence, numerous manufacturers are testing these vehicles on U.S. public roads, particularly in California. Consequently, the safety performance of these AVs has emerged as a critical issue within the transportation industry. When AVs are involved in a crash, the National Highway Traffic Safety Administration (NHTSA) (1) and the California Department of Motor Vehicles (DMV) (2) have mandated that AV companies report these crashes under a General Order. These AV crash reports provide detailed descriptions. However, a clean and preprocessed dataset for AV crashes is not available to provide insights into safety performance and real-world crashes. This proposed project will enhance accessibility to AV safety-related data by extracting useful information from AV crash narratives from the NHTSA dataset, using Natural Language Processing techniques and a manual review of the narratives, as well as preprocessing the data to create a unique real-world AV crash dataset. This dataset can help safety practitioners, researchers, and stakeholders to actively contribute to building a collective understanding of AV safety. Furthermore, by analyzing the dataset, we will cluster real-world AV crash scenarios using an unsupervised machine learning technique to identify rare (strong edge case), complex (weak edge case), and usual scenarios in real-world AV crashes. Furthermore, we will identify the contributing factors of each class and focus particularly on the human behavior of those involved in AV crashes (i.e., crash partners). This can aid developers in enhancing AV technologies and tackling safety concerns in mixed traffic, which will help better manage public expectations and alleviate potential apprehensions.
Daniel Chatman: Understanding the Impacts of Working at Home and Online Shopping on Post-Pandemic Travel and Transportation Policy in California
importance during the pandemic, specifically working from home and urban freight delivery, on household travel behavior in the post-pandemic period. In stage 1, we will review empirical literature that has described trends in working from home and home freight delivery (and the relationship between them) before and during the pandemic, along with the most recent studies that attempt to do so for post-pandemic travel behavior. In stage 2, we will carry out data description and analysis of four national secondary data sources available through 2022, the last year considered to be completely during the Covid-19 pandemic, along with 2023 data as they become available. This stage of the project will explore the relationship between working at home, home freight delivery, online shopping, and patterns in trip frequency and distance by mode. We will also focus on spatial and socioeconomic variance to explore the distribution of these new patterns among different population groups. In stage 3 of the project we will conduct an online survey using a convenience sample in one or more large metropolitan areas in California to explicitly investigate current self-reported and pre-pandemic household behavior, focusing on working at home, online shopping, home freight delivery, and travel behavior as mediated by occupation, income, neighborhood characteristics, and race/ethnicity. In the final stage of the project, we will explore how our findings help us better understand potential policy and planning responses by regional and local agencies.
Marta Gonzalez: The Interplay of Remote Work, Economic Complexity, and City Structure in Reshaping Mobility Dynamics at the Individual Level and a Metropolitan Scale
The research project dissects the integrated impacts of remote work, economic complexity, and city structure in reshaping mobility dynamics of California's core-based-statistical-areas (CBSAs). The team will analyze location-based data from Replica made available to UC Berkeley, which cover individual trip records of one typical weekday and weekend for the entire U.S. from 2019 to 2023, and contain detailed sociodemographic, labor, and travel information, including remote work status and worker industry, trip mode, and origin-destination coordinates finest to the census block group level.
It advances the existing body of knowledge by:
1. Synthesizing well-established mobility metrics to conduct a comprehensive assessment of telecommuting behavior. Most existing studies incorporate a few parameters, while we will deploy 10 quantitative metrics that delineate activity patterns, trip patterns, spatial habits, and transportation characteristics.
2. Examining regional mobility dynamics by connecting telecommuting behavior with economic context and city structure. Existing literature focuses on individual behavioral shifts under WFH and does not reflect regional WFH potential, which can inform "new normal" scenarios, trajectories, and policy planning.
3. Introducing knowledge of economic complexity to transportation research. To our knowledge, there have not been explicit applications of economic complexity in studying travel behavior and transportation planning, despite its relevance and novelty to quantify telecommuting potential and explain regional diversification patterns.
Our study deciphers metropolitan dynamics through the lens of post-pandemic mobility and provides actionable insights for equitable, localized transportation and development planning. To address the proposed questions, the project is organized into the following tasks:
• Task 1: Literature Review and Data Collection
• Task 2: Individual Telecommuting Behavior Analysis
• Task 3: Regional Telecommuting Dynamics Assessment
• Task 4: Stakeholder Engagement
Tasks 5 and 6 are allocated to final deliverables and research dissemination, respectively.
Joshua Meng: Bridging Transit Service Assessment and Community Needs for Equitable Mobility: A Case Study in San Francisco Chinatown
To address the problem of aligning transit assessment with community needs, we will build on the findings from a multi-approach community transportation equity assessment conducted by the Chinatown Community Development Center (CCDC) and the Chinatown Transportation Research Improvement Project (TRIP). This assessment will leverage the outcome of the Berkeley-CCDC Community Partnering project which collected over 2,000 surveys from Chinatown residents, focusing on key questions about travel patterns, transit service satisfaction, and specific mobility challenges faced by transit-dependent groups such as immigrants, working-class families, and seniors.
The project will first identify research gaps by investigating historical transit AVL and APC data and the aforementioned community survey data. By adopting previous research approaches [2,3,4], the data from the transit agency can generate metrics on service coverage, frequency, ridership, productivity, and reliability [1], while the survey data can provide demands and the current ridership status for residents with different equity characteristics [4]. The satisfactions and barriers will then be summarized in the forms of qualitative descriptions and quantitative statistics, which will be illustrated through Tableau Stories or ArcGIS StoryMaps. The gap identification will involve determining which aspects of community needs are not currently reflected in public transit data and their planning/redesign processes.
The project will then develop a methodological framework for community equity-oriented transit assessment. This framework will have two successive components.
The first component is constructing transit equity indexes based on the integration of community survey data and transit operational data. For example, the Relative Travel Time (also referred to as the transit-to-drive time ratio) [8] will illustrate transit modal competitiveness, and the On-board Time Ratio (Time in-vehicle to Time for total trip ratio) will impose the walking and transfer segment portion that influences transit preference for disadvantaged residents. These indexes will reflect equity effects by incorporating community demographic variables including income, age, ethnicity, disability, etc.
Following this, the second component is developing protocols for a community-based data solicitation method that can be implemented in the future to enable the designed data integration. The protocols will guide the community to conduct more efficient and informative surveys and recommend new approaches for service assessments and decision-making processes for transit agencies.
By developing and following the work plan, we aim to bridge the gap between community needs and public transit planning and redesign, promoting a more equitable and efficient transportation system. This approach ensures that the voices of the most transit-dependent populations are heard and addressed in future transit decisions.
Julia Griswold: A Time and Space Exploration of Traffic Crash Trends During the Covid Recovery
The proposed research aims to leverage post-pandemic congestion patterns to understand how reduced congestion impacts traffic crashes. Utilizing freely available data from Caltrans' PeMS platform and crash records from SafeTREC's TIMS platform (SWITRS data), this study will incorporate congestion metrics into the predictive method used for network monitoring and infrastructure design, prescribed in the Highway Safety Manual (HSM) (2). Currently, the predictive method employs generalized linear regression models
(e.g., Poisson or Negative Binomial) to model crash occurrences as a function of Average Daily Traffic (ADT), without accounting for whether traffic occurs in free flow or congested conditions.
The project will retrieve data from the aforementioned platforms to construct a dataset of highway segments with traffic count-derived data, Vehicle Miles Travelled (VMT), Vehicle Hours Travelled (VHT), and traffic crash counts for various severity levels recorded in the SWITRS data. This research will ultimately enhance our understanding of how congestion affects safety performance, complementing current methodologies.
The compiled data will be used to calibrate and test predictive models similar to those prescribed by the HSM, incorporating congestion metrics derived from VHT data. The calibration process will identify when the new variables are significant, while the testing phase will ensure models do not suffer from overfitting. Additionally, the project will conduct a series of tests to identify locations (from those compiled in the
dataset) that would be detected as High Crash Concentration Locations using both the traditional and proposed methods. The results will highlight the merits of using congestion as an explanatory variable for predicting traffic crashes.