UC Berkeley SafeTREC is excited to be a part of the Center for Pedestrian and Bicyclist Safety (CPBS), a Tier-1 University Transportation Center (UTC) supported by the United States Department of Transportation (USDOT) and led by the University of New Mexico (UNM).
US DOT welcomes members from new UTC’s at the University of New Mexico, City College of New York, and University of Texas Rio Grande Valley (Photo: US DOT)
CPBS is one of the twenty Tier One UTCs announced in March 2023, and will receive $2 million a year for the next five years to advance research on pedestrian and bicyclist safety. CPBS’s goal is to eliminate pedestrian and bicyclist fatalities and serious injuries.
The UC Berkeley team, led by SafeTREC Director Julia Griswold, is one of five partnering institutions, along with the University of New Mexico, San Diego State University, University of Tennessee, Knoxville, and University of Wisconsin-Milwaukee.
CPBS Focus Areas
- Research
- Education
- Technology Transfer
- Workforce Development
CPBS Research in Progress at SafeTREC
Pedestrian Fatalities & Injuries in Hit-and-Run Crashes in California, Tennessee & the US: Recent Trends and Risk Factors
Principal Investigator: Julia Griswold, University of California, Berkeley
Both pedestrian fatalities and overall hit-and-run (HAR) fatalities in the US are at a 40-year high, but no post-COVID trends in fatal HAR pedestrian crashes have been examined despite increased reports of reckless driving, increasing vehicle weight and height, and increases in distracted driving. Further, few studies have examined trends in non-fatal pedestrian HAR crashes. Using 2009-2022 national crash fatality data and data on crashes at all severity levels in California and Tennessee, we will examine time trends in all HAR crashes, all pedestrian victim crashes, and how these are related. We will also examine the risk of serious injury or death among HAR vs. non-HAR crashes to try to elucidate the relationship between HAR and outcome severity. Using regression techniques, we will then examine risk factors for single vehicle-pedestrian crashes, including comparing risk factors for HAR vs non-HAR crashes and predictors of whether drivers are eventually identified in HAR crashes. Factors to be examined include crash characteristics, victim characteristics, and driver/vehicle characteristics, where available. We also plan to examine the joint characteristics of driver-pedestrian pairs, such as by age, race or sex, to understand whether this pairing affects the likelihood of fleeing. Finally, we will examine the effect of several inflection points on HAR crash rates and outcomes for pedestrians, including the effects of the COVID pandemic and, potentially, the effects of specific state-level policy changes around licensing laws, given past research linking HAR to unlicensed drivers. Learn more about this research project.
Fire Safety and Safe Streets: Understanding Conflicts between Safe Streets Improvements and Emergency Response
Principal Investigator: Zachary Lamb, University of California, Berkeley
In this project we propose to address the following questions related to conflicts between fire departments and safe streets efforts: When and why do fire safety and street safety goals come into conflict? What institutional arrangements, design processes, and other practices are emerging to reconcile these conflicts to improve overall community safety? How might best practices for avoiding conflict and finding synergies be replicated from city to city?
The project includes four main components: 1) assembling a national Community Advisory Committee of 8-12 members from across a range of relevant areas of expertise; 2) a review of existing scholarly and gray literatures on conflicts between bike and pedestrian safety and emergency response; 3) construction of a national database of conflicts between street safety upgrades and emergency response demands from 2010 to 2024; 4) development of 3 to 5 in-depth case studies of street safety / emergency response conflicts drawing on stakeholder interviews, local media, project documentation, and public meeting records. Deliverables include: the conflict database, a research paper, and a public report. Learn more about this research project.
Leveraging Retrieval Augmented Generation (RAG) to Analyze Crash Reports Narratives
Principal Investigator: Julia Griswold, University of California, Berkeley
Crash reports serve as a vital source of information for understanding road crashes, devising strategies for prevention, and informing policies. However, the coding on these reports often lacks detailed characteristics crucial for comprehensive analysis of pedestrian and bicyclist crashes. Crash reports typically contain structured data, which may lack the nuanced details often found in the narrative section regarding the circumstances surrounding a crash. Information such as unhoused status of a pedestrian, detailed explanation of the vehicle movement before hitting a pedestrian, witness description of a speeding vehicle’s behavior pre-crash, and description of a hit-and-run crash conditions may be embedded within the narrative descriptions but remain unrecorded in the structured fields of the report form. Extracting this implicit data poses a significant challenge for traditional analysis methods. Retrieval Augmented Generation (RAG), employs an embedding model to scan extensive text, seeking similarities between the query—here, the presence of a vulnerability factor or demographic context—and segments of the text. Once relevant portions are pinpointed, both the query and context undergo analysis by a Large Language Model (LLM). In this instance, the LLM validates the presence of and extracts pertinent information. This study will explore the ability of RAG to identify crash characteristics found only in the crash report narratives using crash reports from California. Learn more about this research project.
CPBS Completed Research at SafeTREC
A Context-Sensitive Street Classification Framework for Speed Limit Setting
Principal Investigators: Julia Griswold, University of California, Berkeley; Robert J. Schneider, University of Wisconsin-Milwaukee
Historically, speed limit setting (SLS) procedures in many states have relied on driver-behavior-based approaches such as the 85th percentile speed. Researchers, however, have identified several shortcomings, including that drivers underestimate their speeds, issues with speed creep, and the lack of consideration for vulnerable road users. States can move towards a context-sensitive approach to SLS by developing a street classification framework that includes context. New Zealand provides a leading example with its SLS procedure that uses a street category framework based on the Movement and Place principle. We will develop a US street category framework for SLS using objective, publicly available datasets that capture functional classification (movement) and variables associated with vulnerable road user activity (place), such as land use mix, population density, job density, urban/rural designation, and transit access. We will perform a pilot study of 5 geographically diverse states to categorize the streets on their road networks based on the Movement and Place principle. The research will include a best practice literature review, GIS data preparation and linking, application of classification methods, validation of classification, and reporting. Learn more about this research project.
Creating a Data Resource of California Police Stops for Use in Traffic Safety Applications
Principal Investigator: Julia Griswold, University of California, Berkeley
Traffic stops are one the most common ways in which the American public interacts with police. Although one of the leading reasons given for police traffic stops is a violation of the vehicle code, there is limited and mixed research on the impact of traditional police traffic enforcement on traffic safety outcomes. At present, few large data resources with an appropriate level of detail exist to facilitate investigations of this type. The 2015 Racial and Identity Profiling Act (RIPA) requires all law enforcement agencies in California to collect and submit vehicle (including bicycle) and pedestrian stop data to the State Department of Justice annually, starting no later than 2022. This project will use 2018-2022 confidential RIPA stop data to categorize all police traffic stops using known risk factors for fatal and severe collisions and to create new variables relevant to traffic safety, yielding a standardized statewide data set useful for examining and controlling for police traffic stops as they relate to traffic safety outcomes. Further, we will both establish clear guidance for how to process RIPA data efficiently for future data releases and will also geospatially join the processed RIPA data files with traditional transportation and land use data sources using stop location so that this data resource can be made available to others for future research. Learn more about this research project.
Understanding Pedestrian and Bicyclist Safety Trends in the Post-Pandemic Era
Principal Investigators: SangHyouk Oum, Julia Griswold, Iman Mahdinia, University of California, Berkeley
This project aims to analyze trends in pedestrian and bicyclist crashes and deaths in the post-pandemic era and investigate the underlying factors that contribute to these trends. The study will examine the changes in crash rates during different phases of the pandemic, considering variations in vehicle-miles traveled and commuting patterns. Factors such as infrastructure, road usage patterns, and transportation mode preferences will be studied to understand their influence on pedestrian and bicyclist safety. Additionally, the project will integrate census tract-level transportation disadvantage indicators with crash data to explore the relationship between disadvantaged communities and crash trends, addressing equity concerns. The insights gained from this research will inform evidence-based interventions and strategies to enhance pedestrian and bicyclist safety. By identifying the key factors contributing to the observed trends, policymakers and transportation agencies can develop targeted measures to mitigate risks and improve safety for pedestrians and bicyclists in the post-pandemic era. Learn more about this research project.
CPBS Education at SafeTREC
As part of education efforts at the CPBS, SafeTREC provides student led research and fellowship opportunities.
Summer 2024 CPBS Fellowship
Masuma Mollika Miti, Department of Civil and Environmental Engineering
Bicycle Crash Incidents in San Francisco before, during, and after COVID-19
This study examines how bicycle crash patterns in San Francisco changed before, during, and after the COVID-19 pandemic. It found a drop in crashes during the pandemic that aligned with reduced bicycle counts, with most reductions occurring in high-density areas and on weekdays. Weekend crash rates and lower-density areas saw little change, and the lower crash levels have continued post-pandemic. The findings emphasize the need to incorporate population density into urban safety planning, especially during large-scale disruptions like the COVID-19 pandemic.
Learn more about the CPBS and current research, education, technology transfer, and workforce development activities at the CPBS website.
