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Enhanced Crash Risk Estimation in Urban Environments: Integrating Multi-Source Data and Advanced Modeling Approaches for the City of Pittsburgh

Crash risk estimation is a critical aspect of transportation safety study, aimed at identifying high-risk locations and mitigating potential hazards/risks for road users. Limited resources can be allocated to those high-risk areas for effectively improving safety for the entire network. As travel demand grow, new mobility options emerges and traffic volumes increase, the complexity of factors influencing crash risk has also grown. Historically, crash risk estimation has been approached using statistical models, such as Safety Performance Functions (SPFs), which predict the expected crash frequency or risk on road segments based on exposure and other factors. However, the dynamic nature of urban environments and the growing availability of diverse data streams, such as video analytics, near-miss reports, real-time traffic data, and social media, present opportunities to enhance traditional methods. In particular, data directly measuring near-misses in addition to observed and rare accidents enable a more holistic and accurate measure of crash risks. These sensing and methodological advancements can improve model accuracy, reduce biases, and provide more granular insights into high-risk locations for both motorized and non-motorized traffic. By leveraging long-term multi-source data related to crash risks, diverse sources such as police reports, traffic stops, and near-miss incidents from the city of Pittsburgh, and applying both statistical and machine learning approaches, this project aims to enhance the precision of crash risk estimations. It addresses key challenges related to data aggregation, modeling performance, and bias mitigation to provide more reliable safety insights that can inform future interventions. Methodology and steps The approach is to estimate SPFs for both motorized and non-motorized crash risks in the city of Pittsburgh with multiple data sources, including historical crash data, UPMC urgent care data, police traffic stop reports, 311 near miss report, 911 Dispatch data, Waze reports (social media), Velo.ai video analytics data, and so on. Specifically, the research team processes the video, medical, police and Waze data among all locations and in the past few years to either improve or validate the SPFs (namely current risk maps). The team applies both statistical and machine learning models and select the best model based on model performance. The team develops a new crash map and compare it with the the current risk map network. The team explores the potential biases and how to improve the accuracy of crash risk estimation in general. This work will be conduct particularly for the City of Pittsburgh, but can be replicated in any other communities in general. The detail for each step is listed below. Step 1: Conduct a comprehensive literature review on risk factors and video analytics algorithm for near-miss detection The team plans to carry out a comprehensive literature review on types of risk factors that make significant contributions to the crash risk (particularly near-misses related to vulnerable road users). This literature review helps collecting necessary datasets for building robust SPFs to estimate crash risk for streets in the following task. In addition, the team will review the approaches of detecting incidents from the video data, which could help improve the algorithm in detecting near-miss cases from the camera videos. Step 2: Data collection and video processing for near-miss incidents In this step, the team will collect the related datasets which could benefit the safety performance function estimation from multiple data sources. Table 1 lists potential datasets that could be applied in this research project. Furthermore, the team plans to carry out video processing with advanced deep learning techniques to detect the near-miss incidents from the camera videos from selected intersections. The result of the number of near-miss incidents and driving factors could be either used to improve the SPFs or validate the estimated SPFs. Table 1. Source and year of the datasets Dataset Data source Year High injury network City of Pittsburgh - Crash data PennDOT 2017-2023 UPMC urgent care UPMC In request Police traffic stop report City of Pittsburgh 2018-2022 311 near miss report City of Pittsburgh 2019-2023 911 Dispatch data WPRDC 2015-2022 Network of bike facilities City of Pittsburgh - Built environment data EPA - Socio-demographic data NHGIS - Waze data Waze for Cities 2018-2024 Cyclists’ waypoints and distances to vehicles Velo.ai 2022-2024 Camera video from selected intersections City of Pittsburgh In request Strava pedestrian and bicyclist count data Strava Metro 2023-2024 Step 3: Estimate SPFs with both statistical and machine learning modeling approaches. The team will estimate two sets of SPFs for motorized crash risk and non-motorized crash risk, respectively, with the data collected and processed in Step 2. The team plans to apply both statistical (e.g., linear regression, Poisson regression, and Negative Binomial regression) models and machine learning models (e.g., random forest, gradient boosting decision tree, and neural network). The team will compare the model performance and select the best one as the final model. Step 4: Develop a crash map for all street segments in Pittsburgh, compare it with the original high injury network, and prioritize high-risk locations. The team plans to use the estimated SPFs to predict the expected crash risk (both motorized and non-motorized) for all streets in the city of Pittsburgh and develop a crash map dashboard. The team will compare the crash risk with those estimated in the high injury network and find the differences. The team will explore the sources of bias and study how to improve crash risk estimation. Finally, the team will rank the locations and prioritize high-risk ones for further examination.

Language

  • English

Project

  • Status: Completed
  • Funding: $200,000.00
  • Contract Numbers:

    69A3552344811

    69A3552348316

  • 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:

    Safety21 University Transportation Center

    Carnegie Mellon University
    Pittsburgh, PA  United States  15213

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Performing Organizations:

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Principal Investigators:

    Qian, Sean

    Tao, Tao

    Weiss, Leonard

  • Start Date: 20250701
  • Expected Completion Date: 20260831
  • Actual Completion Date: 20260831
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 02002853
  • Record Type: Research project
  • Source Agency: Safety21 University Transportation Center
  • Contract Numbers: 69A3552344811, 69A3552348316
  • Files: UTC, RIP
  • Created Date: Sep 17 2026 4:28PM