SafeSpeed: Enhancing Work Zone Safety through Speed Enforcement

The large number of work zone crashes has been a significant concern of transportation agencies and researchers. In the US, a work zone crash occurred every five minutes during 2015-2019. One approach for transportation agencies to reduce work zone crashes is to lower the speed within work zones, for example, posting speeding limits and installing speeding cameras. This approach is supported by studies that highlighted that average traffic speed is associated with crash risk. However, the findings of the relationship between traffic speed and crashes are inconsistent, which could lead to conflicting or even misleading interventions with the speed enforcement in work zones. Work zone presence could lead to the reduction of actual traffic speed that influences crash risk and, at the same time, directly impose effects on crash risks. It is challenging to rigorously separate these direct and indirect impacts. Furthermore, the actual impact of speed enforcement countermeasures on work zone crash risk has been rarely studied among the literature, providing limited knowledge on whether these countermeasures are effective in reducing crash risk near work zones in practice. In this research project, the research team will apply a comprehensive causal analysis and Web-Geographic Information Systems (GIS) approach to enhance work zone safety through speed enforcement in Pennsylvania and Maryland. It contains three core initiatives. First, it develops a causal inference model to analyze the impact of work zones on crash risk controlling for traffic speed with the equational g-estimation and regression discontinuity design (RDD), using multiple large-scale and high-granular data sets. Second, it examines the work zone impact on crash risk under different speed enforcement countermeasures. Lastly, the research team creates an interactive Web-GIS platform for comprehensive traffic safety analysis in work zones, enabling stakeholders to access and analyze crashes related to work zones, speed enforcement measures, and other important crash contributors, with continuous data updates planned until 2025. This platform aims to identify high-risk areas and provide insights for safety improvements in work zones. First, the team will establish a rigorous causal inference model to infer the causal impact of work zones on crash risk when the traffic speed is controlled with high-granular and multi-source data sets. The team proposes to use an innovative approach, i.e., the combination of the sequential g-estimation and RDD, to examine the causal effect of the presence of work zones on crash occurrences when the traffic speed is controlled. The sequential g-estimation removes the effect of traffic speed on crash risk. RDD mitigates the potential confounding bias caused by roadway characteristics. The proposed method will be implemented using high-granular and multi-source data of thousands of work zones in Pennsylvania (PA) and Maryland (MD) between 2018 and 2023 to control for the complex built and natural environments and reduce the associated bias of the estimation. The results can provide insights for most desired and actual traffic speeds to reduce work zone crash risk. Second, the team will examine the impact of work zones on crash risk under different speed enforcement countermeasures. The team will apply the same framework in the first step to examine the heterogenous causal impact of work zones on crash risk under different speed enforcement countermeasures, including no speed enforcement, posting speed limit, and posting speed limit along with enforcement (e.g., automated speed enforcement and high-visibility enforcement), and compare the impacts for the work zones in PA and MD. In addition, the team will further estimate these heterogenous impacts (by speed enforcement countermeasure) under various work zone characteristics, time of day, and traffic volumes. The results can offer information on how different speed enforcement countermeasures modify the causal impact of work zones on crash risk and, accordingly, provide implications for better deploying these countermeasures. Third, the team will build an interactive Web-GIS platform for work zone traffic safety analysis using the safety data in PA and MD. The digital platform provides users with an online interactive interface to explore all work zones in PA and MD by multiple aspects, including speed enforcement countermeasures, average speed, traffic volumes, roadway characteristics. In addition, the platform can help users identify high-risk locations, highlight potential crash contributors, and offer suggestions on how to improve work zone safety for each work zone based on their characteristics and locations. In addition, the team will continue to collect and archive up-to-date data from various data providers in both PA and MD from 2024 to 2025 and enhance the web platform. The safety data providers include Pennsylvania Department of Transportation (PennDOT), Maryland Department of Transportation (MDOT SHA), Waze, NOAA, and private data sources, including INRIX, TomTom, and Replica. The team will integrate and analyze large-scale crash data and develop an additional function to the platform to visualize and forecast crash types, frequencies, and severity for each road segment in the two states, especially those with work zones and different speed enforcement countermeasures. With that said, the platform allows transportation agencies and other related stakeholders, such as urban planning departments, local communities, consulting firms, and academic institutions, to access historical, real-time, and forecasted traffic safety metrics for all work zones. The team will continue to interview various data providers to enhance the quality and quantity of massive data in both states.

Language

  • English

Project

  • Status: Active
  • Funding: $200002
  • Contract Numbers:

    69A3552344811

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Carnegie Mellon University

    Pittsburgh, PA  United States 

    Safety21 University Transportation Center

    Carnegie Mellon University
    Pittsburgh, PA  United States  15213
  • Project Managers:

    Stearns, Amy

  • Performing Organizations:

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Principal Investigators:

    Qian, Zhen (Sean)

  • Start Date: 20240701
  • Expected Completion Date: 20250630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01933389
  • Record Type: Research project
  • Source Agency: Safety21 University Transportation Center
  • Contract Numbers: 69A3552344811
  • Files: UTC, RIP
  • Created Date: Oct 12 2024 12:18PM