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

Wang, Zhenyu; Lin, Pei Sung; Katkoori, Srinivas; Li, Mingchen; Kolla, Rama; Yang, Runan · 2022 · ROSA P / University of South Florida. Center for Urban Transportation Research

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Summary

This Phase II research project, conducted by the University of South Florida’s Center for Urban Transportation Research, addresses the need for improved automated tools to diagnose roadway lighting patterns and enhance nighttime traffic safety. Building upon a Phase I prototype, the study focuses on three primary gaps: the lack of safety analysis for vulnerable users (pedestrians), the absence of specific metrics for Light Emitting Diode (LED) technology effectiveness, and limitations in uniformity diagnosis algorithms. The research aims to develop implementable Crash Modification Factors (CMFs) and Safety Performance Functions (SPFs) to support decision-making for lighting maintenance and upgrades, specifically within the Florida Department of Transportation (FDOT) District 7. The methodology employed a matched case-control study to investigate the impacts of lighting patterns on nighttime pedestrian crashes, addressing the statistical confounding between illuminance mean and standard deviation. Researchers used the Advanced Lighting Measurement System (ALMS) to collect high-resolution horizontal illuminance data from 440 roadway corridors in Tampa. Segments were categorized as cases (experiencing at least one nighttime pedestrian crash) or controls (no crashes) and matched based on lighting metrics. Conditional logistic regression was used to derive odds ratios equivalent to CMFs. Additionally, the study developed an SPF for LED technologies using FDOT District 7 inventory data and created a sliding window algorithm to diagnose lighting uniformity by scanning patterns within areas corresponding to driver vision fields, rather than analyzing whole segments. Key findings include the development of reliable CMFs for both average lighting levels and uniformity, quantifying their specific impacts on pedestrian crash risks. The analysis of LED technology revealed that upgrading from High-Pressure Sodium (HPS) to LED lighting in Florida tends to decrease nighttime crash frequency by 17%. The new sliding window algorithm provided more detailed and reasonable uniformity diagnoses compared to traditional ratio-based measures for entire segments. Furthermore, the analysis engine was recoded to integrate these new models, resulting in a 90% reduction in processing time while adding functions for data integration, lighting diagnosis, and crash prediction. The significance of this work lies in the provision of validated, automated tools for nighttime safety management. By addressing the specific safety effects of lighting on pedestrians and quantifying the benefits of LED upgrades, the study offers actionable data for infrastructure investment. The developed tools are currently being applied in FDOT District 7’s district-wide lighting collection and analysis tasks, providing direct support for roadway lighting maintenance and safety management decisions. This research advances the field by overcoming previous statistical limitations in lighting safety studies and delivering a technology-proven system for operational use.

Key finding

Lighting upgrades from High-Pressure Sodium to LED technology in Florida are associated with a 17% decrease in nighttime crash frequency, and the developed sliding window algorithm provides a more detailed diagnosis of lighting uniformity than whole-segment analysis.

Methodology

field_study

Sample size: 1234

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.

Topics

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• roadway lighting effects
• visibility analysis litigation
• dark adaptation mesopic

Information type

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• Empirical Findings: crash risk outcomes