Automated Lighting Diagnosis Tools Applied to a 2.76-Mile Corridor in Tampa, Florida Contributed to 12.5 Fewer Crashes per Year.

University Study Evaluated Use of Automated Lighting Diagnosis Tools.

Date Posted
01/31/2023
Identifier
2023-B01711
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Development of Automated Roadway Lighting Diagnosis Tools for Nighttime Traffic Safety Improvement

Summary Information

Roadway lighting is a conventional roadway infrastructure that ensures nighttime safety and security for motorists, pedestrians, cyclists, and transit passengers. This study aimed to develop innovative methods and tools that can automatically and intelligently diagnose roadway lighting performance based on big lighting data collected using the Advanced Lighting Measurement System (ALMS) . The developed methods and tools were applied to data collection efforts as part of a case study in 2020, for Florida Department of Transportation (FDOT) and a private company. The case study, focusing on a 2.76-mile principal arterial in Tampa, FL, 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.

METHODOLOGY

The hierarchical clustering algorithm was 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. This project developed crash modification factors to assess crash reductions due to lighting pattern improvement. A total of 440 roadway corridors (split into 2,440 segments) in urban and/or suburban areas with street lighting data were identified based on a wide range of criteria such as the roadway sections being between two successive signalized intersections, being 600 ft. or longer, being equipped with High Pressure Sodium (HPS) light bulbs, and having had no street lighting upgrades in the past several years. Nighttime crash data for 2011–2014 were matched to each segment. A case was defined as a segment in which at least one nighttime crash occurred, and a control was defined as a segment in which no nighttime crashes occurred. When applied to the FDOT case study, the developed diagnosis module recognized the diversity of lighting patterns and split the whole segment of 2.76 miles into four sections (zones) with lengths varying between 0.21-1.282 miles.

FINDINGS

  • Assuming a lighting upgrading project is proposed to increase the lighting patterns to FDOT standards, the estimated nighttime crash reduction in this study for the arterial analyzed was 12.5 crashes per year considering all four zones collectively.
  • On a per-zone basis, the study results indicated estimated crash reductions varying between zero to 4.9 crashes per year.

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

Development of Automated Roadway Lighting Diagnosis Tools for Nighttime Traffic Safety Improvement
Source Publication Date
08/31/2020
Author
Wang, Zhenyu; Pei-Sung Lin; Srinivas Katkoori; Mingchen Li; Abhijit Vasili; and Runan Yang
Publisher
Prepared by the University of Texas at Arlington for USDOT RITA
Other Reference Number
Report No. CTEDD 019-13
Goal Areas
Results Type
Deployment Locations