Research Synthesis for the California Zero Traffic Fatalities Task Force

This research synthesis consists of a set of white papers that jointly provide a review of research on the current practicefor setting speed limits and future opportunities to improve roadway safety. This synthesis was developed to inform thework of the Zero Traffic Fatalities Task Force, which was formed in 2019 by the California State Transportation Agencyin response to California Assembly Bill 2363 (Friedman). The statutory goal of the Task Force is to develop a structured,coordinated process for early engagement of all parties to develop policies to reduce traffic fatalities to zero. Thisreport addresses the following critical issues related to the work of the Task Force: (i) the relationship between trafficspeed and safety; (ii) lack of empirical justification for continuing to use the 85th percentile rule; (iii) why we need toreconsider current speed limit setting practices; (iv) promising alternatives to current methods of setting speed limits;and (v) improving road designs to increase road user safety

Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction

Surrogate safety measures can provide fast and pro-active safety analysis and give insights on the pre-crash process and crash failure mechanism by studying near misses. However, validating surrogate safety measures by connecting them to crashes is still an open question. This paper proposed a method to connect surrogate safety measures to crash probability using probabilistic time series prediction. The method used sequences of speed, acceleration and time-to-collision to estimate the probability density functions of those variables with transformer masked autoregressive flow (transformer-MAF). The autoregressive structure mimicked the causal relationship between condition, action and crash outcome and the probability density functions are used to calculate the conditional action probability, crash probability and conditional crash probability. The predicted sequence is accurate and the estimated probability is reasonable under both traffic conflict context and normal interaction context and the conditional crash probability shows the effectiveness of evasive action to avoid crashes in a counterfactual experiment

The Goal of Road Safety in the Safe Systems Context

Safe Systems is an approach to road safety that envisions the elimination of fatal and serious injuries and seeks to provide both a theoretical framework and practical roadmap for accomplishing such an ambitious goal. The Safe Systems approach involves a paradigm shift from traditional approaches to road safety planning and responsibility. This fact sheet provides an overview of the Safe Systems Approach and how it relates to both current road safety practice and initiatives such as Vision Zero

Shared Responsibility for Road Safety in Safe Systems Context

Safe Systems is an approach to road safety that envisions the elimination of fatal and serious injuries and seeks to provide both a theoretical framework and practical roadmap for accomplishing such an ambitious goal. The Safe Systems approach involves a paradigm shift from traditional approaches to road safety planning and responsibility. This fact sheet discusses a principle integral to a Safe Systems Approach, shared responsibility for road safety

On the legal deterrence of pedestrian hit-and-run collisions

Hit-and-run collisions—those in which a driver involved in the collision leaves the scene before the arrival of law enforcement officials—are a unique type of traffic violation because the driver's decision is a question of damage control rather than damage prevention. To reduce hitand-run violations, individual state laws impose legal sanctions to deter drivers from leaving the collision scene prematurely. Deterrence Theory dictates that compliance with laws is associated with the certainty, severity, and swiftness of punishment. The purpose of this study is to explore the deterrent effect of legal sanctions on the rate of hit-and-run collisions. Legal sanctions for hitand-run violations across the United States were compared with the prevalence of pedestrian hitand-run collisions in those states. Specifically, the severity of punishment, the certainty of punishment, and the excess legal sanctions of hit-and-run were compared with the rates of hitand-run. The results of these analyses suggest that legal sanctions do not have a significant deterrent effect on hit-and-run collisions. Uncertainty regarding the likelihood of being caught or the severity of punishment may impair the effects of legal sanctions to reduce hit-and-run incidents. However, the data indicate that social sanctions may deter drivers from hit-and-run violations. Further study is necessary to determine which deterrence mechanism would be the most effective for reducing hit-and-run violations

Evaluate the Safety Effects of Adopting a Stop-as-Yield Law for Cyclists in California

The escalating number of injuries and fatalities among cyclists is a pressing safety concern. In the United States, communities are actively seeking strategies to boost cyclist safety, with some states implementing bike-specific policies, such as stop-as-yield laws, to support cyclists. Stop-as-yield laws allow cyclists to treat stop signs as yield signs. The laws are not yet widely implemented, and their potential safety impact is a subject of debate among transportation experts and advocates. This study investigates how stop-as-yield laws can positively or negatively affect safety and provides insights and guidelines for California policymakers and safety practitioners if the law passes in California. We collected cyclist data from five states that have enacted stop-as-yield laws—Idaho, Arkansas, Oregon, Washington and Delaware—and data from some of their contiguous states without such legislation. Using an observational before-after study with comparison groups at the state level, the research examined changes in cyclist crash frequencies after the laws were implemented. Additionally, a random-effects negative binomial regression model with panel data was employed to estimate a law’s overall impact. The results did not indicate a significant change in cyclist crashes among the states with stop-as-yield laws

Developing a Framework to Combine the Different Protective Features of a Safe System

69A3551747113While the overarching objective of the transportation system is to provide mobility, transportation professionals dedicate significant resources to build a safe system. The aspirational objective is to establish a system on which no road user can suffer catastrophic outcomes. To accomplish such a safe system, it is necessary to effectively harness all the core protective opportunities provided by the system. Despite the increasing consensus that this needs to be thought of as a systems problem, the considerations for each of these layers of protection are siloed, and many of the protective features are evaluated in terms of potential lives saved due to a specific improvement. To address this, the research identifies kinetic energy as the appropriate common denominator that captures the overall protective characteristics of the system. The researchers concluded that it is not practical to aggregate the additive capability of the system\u2019s elements to control or contain kinetic energy. However, it is valuable to evaluate the cumulative kinetic energy of the entire system and the researchers propose a framework that can support researchers and practitioners in better understanding the safety mechanism and identifying strategies that may have been overlooked

Assessing the Variation of Curbside Safety at the City Block Level

UC-ITS-2019-15Investigating the dynamics behind the likelihood of vehicle crashes has been a focal research point in the transportation safety field for many years. However, the abundance of data in today's world generates opportunities for deeper comprehension of the various parameters affecting crash frequency. This study incorporates data from many different sources including geocoded police-reported crash data, curbside infrastructure data and socio-demographic data for the city of San Francisco, CA. Findings revealed that the GFMNB model provides a better statistical fit than the FMNB and NB model in terms of AIC and log likelihood, while the NB model outperformed both mixture models in terms of BIC due to model complexity of the latter. Among the significant variables, TNC pick-ups/drop offs and duration of parked vehicles were positively associated with segment-level crashes