Development of Automated Roadway Lighting Diagnosis Tools for Nighttime Traffic Safety Improvement

Roadway lighting is a conventional roadway infrastructure to ensure nighttime safety and security for multimodal road users (motorists, pedestrians, cyclists, and transit passengers). To cost-effectively maintain a roadway lighting system, key tasks in infrastructure management include periodically measuring roadway lighting levels, diagnosing lighting performance based on collected data, and providing decision-making support for maintenance and improvement. The Advanced Lighting Measurement System (ALMS) developed by the Center for Urban Transportation Research (CUTR) provides a low-cost and time-effective solution to collect high resolution lighting data for a big-scale roadway network. This project aimed to develop innovative methods and tools to effectively analyze the “big” lighting data. A computer tool developed on the ArcGIS web-geographic information system (GIS) platform in this study integrates three core modules and provides core functions for lighting pattern diagnosis, nighttime crash risk prediction, and data visualization. The hierarchical clustering algorithm is used to recognize lighting patterns based on the similarity of a photometric measure (average illuminance or uniformity). Safety performance function and Empirical Bayesian model were adopted to predict nighttime crash frequency with given lighting conditions. The study developed Crash Modification Factors to assess crash reductions due to lighting pattern improvement. The developed methods and tools were applied in CUTR’s new lighting data collection and analysis tasks from the Florida Department of Transportation (FDOT) and Johnson, Mirmiran & Thompson (JMT), Inc. Two case studies demonstrated the performance of the computer tool in various application scenarios, such as lighting pattern recognition, lighting system upgrading validation, and nighttime crash risk analysis. To implement the developed tools in an operational environment, the research team will upgrade the tools, including developing Standard Operation Procedures, improving the lighting pattern diagnosis model, and providing planning-level lighting management for decision-makers.

• Record URL:
  https://rc.library.uta.edu/uta-ir/bitstream/handle/10106/29593/ZhenyuWang_Report_CTEDD.pdf?sequence=1&isAllowed=y
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/55963
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Center for Transportation Equity, Decisions & Dollars (CTEDD)

  University of Texas at Arlington
  Arlington, TX  United States  76019

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Wang, Zhenyu
  • Lin, Pei-Sung
  • Katkoori, Srinivas
  • Li, Mingchen
  • Vasili, Abhijit
  • Yang, Runan
• Publication Date: 2020-8-31

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Maps; Photos; References; Tables;
• Pagination: 59p

Subject/Index Terms

• TRT Terms: Algorithms; Case studies; Crash modification factors; Crash risk forecasting; Data analysis; Geographic information systems; Nighttime crashes; Street lighting; Traffic safety
• Identifier Terms: ArcGIS
• Subject Areas: Design; Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01765215
• Record Type: Publication
• Report/Paper Numbers: CTEDD 019-13
• Contract Numbers: 69A3551747134
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Feb 22 2021 10:16AM