Regional Disparities in Work Zone Crashes: Understanding Factors and Predictive Modeling for Targeted Safety Measures

Roadway work zones play a vital role in maintaining and improving infrastructure, yet they often expose workers and drivers to dangerous situations, leading to concerning frequencies of occupational and traffic accidents in the United States. With over 700 fatalities and thousands of injuries annually attributed to work zone crashes, efforts to enhance safety have been hindered by the complexity and variability of contributing factors. The escalating fatalities, coupled with growing infrastructure demands in USDOT Region 7—encompassing Missouri, Iowa, Nebraska, and Kansas—underscore the imperative to address underlying causes and improve work zone safety. This study aims to address this persistent issue by analyzing work zone crash data in Region 7 and comparing it with other regions to identify influential factors. By leveraging recurrent neural networks (RNNs) to develop region-specific predictive models, the research seeks to forecast crash occurrences and provide targeted insights for policymakers and transportation authorities. Ultimately, the research aims to deepen understanding of regional disparities in work zone crash dynamics, enabling effective resource prioritization and implementation of tailored safety measures. The development of predictive models using RNNs holds promise for enhancing proactive safety planning and resource allocation, ultimately contributing to a nationwide reduction in work zone crashes and advancing the overarching goal of improving road safety for workers and motorists.

Regional Disparities in Work Zone Crashes: Understanding Factors and Predictive Modeling for Targeted Safety Measures

The research directly aligns with key USDOT strategic goals, notably enhancing safety and promoting infrastructure resilience. It addresses the critical issue of work zone crashes in USDOT Region 7 states, contributing to the USDOT's objective of reducing fatalities and injuries from such incidents. The study also aims to enhance infrastructure resilience by developing predictive models for each region, improving safety planning and resource allocation to lessen the impact of work zone crashes. Additionally, by examining regional disparities in work zone crash dynamics and providing tailored insights, the research supports the USDOT’s goal of advancing equity in transportation systems. The project will engage local residents, advocacy groups, and policymakers in developing final recommendations, fostering ownership and accountability through community engagement that ensures diverse perspectives are represented and reflected in the research outcomes. Overall, this research furthers the USDOT's mission to ensure the safety, reliability, and efficiency of the nation’s transportation systems by addressing safety concerns, enhancing infrastructure resilience, and mitigating regional disparities

This project aims to develop region-specific predictive models using recurrent neural networks (RNNs) to enable accurate forecasting of future work zone crash occurrences. The research findings will be synthesized into actionable insights and recommendations through a policy brief or report. This document will provide policymakers and transportation authorities with guidance on prioritized interventions and targeted safety measures to reduce work zone crash risks and enhance safety outcomes across USDOT Region 7 states. Although tailored for the USDOT Region 7, work zone practices and policies are largely standardized across the US, making the findings potentially applicable and transferable to most, if not all, other states. This broad applicability offers significant insights into work zone safety within the wider US context. Overall, this research supports the USDOT's mission of ensuring the safety, reliability, and efficiency of the nation’s transportation systems by addressing critical safety concerns, promoting infrastructure resilience, and mitigating regional disparities in transportation outcomes.