Develop a Real-Time Decision Support Tool for Urban Roadway Safety Improvements

Details

• Creators:
  Das, Subasish ; Liu, Jinli ; Mills, David ; Mimi, Mahmuda Sultana ; Wu, Lingtao ; Geedipally, Srinivas ; Dzinyela, Richard
• Corporate Creators:
  Texas State University--San Marcos ; Texas A&M Transportation Institute
• Corporate Contributors:
  Texas. Dept. of Transportation. Research and Technology Implementation Office ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Crash Records Cyclists Data Analysis Decision Support Systems Design Of Specific Facilities Diagnostic Tests Highway Safety Pedestrians Performance Real Time Control Safety Performance Functions Short Duration Model Speed CMF Transportation Safety Two Lane Highways Urban Facilities Urban Highways

  More +
• Publication/ Report Number:
  FHWA/TX-26/0-7144-R1
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Texas
• Edition:
  January 2023–August 2025
• Corporate Publisher:
  Texas. Department of Transportation. Research and Technology Implementation Office
• Abstract:
  Speed-related crashes remain a critical concern for Texas, identified as one of the seven key emphasis areas in the Texas Strategic Highway Safety Plan (SHSP) for 2018–2022. Traditional crash risk models often overlook essential real-time factors such as operating speed, traffic variability, and weather, limiting their effectiveness. To address this, TxDOT Project 0-7144 integrated data from diverse sources, including NPMRDS and INRIX XD (real-time speeds), TMAS (traffic volumes), CRIS (crashes), RHiNO (roadway inventory), and NOAA (weather), to develop both annual and short-duration Safety Performance Functions (SPFs) for a wide range of urban facility types. Annual SPFs were developed for urban facilities, including freeways, multi-lane divided and undivided highways, continuous left-turn lane roads, and two-lane highways, incorporating geometric and operational features such as lane and shoulder width, truck proportion, driveway density, average speed, and precipitation. This project also employed a Negative Binomial-Lindley (NBL) modeling framework to produce monthly SPFs from 2019 to 2022. These shortduration models captured the dynamic influence of seasonal factors, monthly average daily traffic (MADT), speed and travel time variability, and weather patterns on crash frequency across facility types. A key outcome of the project is the development of an interactive, GIS-based decision support tool using the Shiny platform. This tool enables users to visualize, filter, and download crash prediction outputs at both annual and short-duration levels. It offers dynamic data uploading capabilities, allowing local engineers to input updated roadway and traffic data for customized analyses. The project concludes with recommendations to update Texas SPFs for urban roadways by integrating operating speed and weather data, emphasizing speed consistency in guidelines, and expanding the tool's capacity for daily-level modeling and facility-specific diagnostics.
• Format:
  PDF
• Funding:
  0-7144
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:e4a0efbfa5431e6f15936ac9755ce77147c44f574e4989a67e291cd3bad0c537d2515c278f34247811ba5d8aaf51a1ea632f70910f5f8fddb5805d311f74ccc7
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88899/dot_88899_DS1.pdf
• File Type:
  [PDF - 8.37 MB ]