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The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers. Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload. However, whether moderation and mediation effects exist among warning information, driver’s cognition, behavior and risky avoidance performance is unclear.

This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior. A driving simulator experiment was conducted with waring and command information.

Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario. The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk. At the same time, command information can weaken this negative effect. Moreover, improved collision avoidance behavior can be achieved through increasing drivers’ mental workload.

The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior, thus contributing to improved in-vehicle information system design.

The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’ collision avoidance ability. Through further calibration with larger sample size, the proposed structural model can be used to predict the effect of in-vehicle warnings in different risky driving scenarios.

This study aims to investigate the safety effects of work zone advisory systems. The traditional system includes a dynamic message sign (DMS), whereas the advanced system includes an in-vehicle work zone warning application under the connected vehicle (CV) environment.

A comparative analysis was conducted based on the microsimulation experiments.

The results indicate that the CV-based warning system outperforms the DMS. From this study, the optimal distances of placing a DMS varies according to different traffic conditions. Nevertheless, negative influence of excessive distance DMS placed from the work zone would be more obvious when there is heavier traffic volume. Thus, it is recommended that the optimal distance DMS placed from the work zone should be shortened if there is a traffic congestion. It was also revealed that higher market penetration rate of CVs will lead to safer network under good traffic conditions.

Because this study used only microsimulation, the results do not reflect the real-world drivers’ reactions to DMS and CV warning messages. A series of driving simulator experiments need to be conducted to capture the real driving behaviors so as to investigate the unresolved-related issues. Human machine interface needs be used to simulate the process of in-vehicle warning information delivery. The validation of the simulation model was not conducted because of the data limitation.

It suggests for the optimal DMS placement for improving the overall efficiency and safety under the CV environment.

A traffic network evaluation method considering both efficiency and safety is proposed by applying traffic simulation.

Within the last 15 years, law enforcement agencies have increased their collection of data on vehicle stops. A variety of resource guides, research reports, and peer‐reviewed articles have outlined the methods used to collect these data and conduct analyses. This literature is spread across numerous publications and can be cumbersome to summarize for practical use by practitioners and academics. This article seeks to fill this gap by detailing the current best practices in vehicle stop data collection and analysis in state police agencies.

The article summarizes the data collection techniques used to assist in identifying racial/ethnic disparities in vehicle stops. Specifically, questions concerning why, when, how, and what data should be collected are addressed. The most common data analysis techniques for vehicle stops are offered, including an evaluation of common benchmarking techniques and their ability to measure at‐risk drivers. Vehicle stop outcome analyses are also discussed, including multivariate analyses and the outcome test. Within this summary, strengths and weaknesses of these techniques are explored.

In summarizing these approaches, a body of best practices in vehicle stop data collection and analysis is developed.

Racial profiling continues to be a contentious issue for law enforcement and the community. A considerable body of research has developed to assess the prevalence of racial profiling. This article offers social scientists and practitioners a comprehensive, succinct, peer‐reviewed summary of the best practices in vehicle stop data collection and analysis.