Application of Emerging Data Sources for Pedestrian Safety Analysis in Charlotte, NC. Issue 11 (November 2022)

• Record Type: Journal Article
• Authors: Hamilton, Ian; Kersavage, Kristin; Porter, R.J.; Gayah, Vikash; Sanchez, Josie; Smith, Keith; Tan, Carol; Eigen, Ana Maria
• Is Part Of: Transportation research record. Volume 2676:Issue 11(2022)
• Page Start: 396
• Page End: 407
• Publication Date: 2022-11
• Subjects: data and data science -- general -- urban planning -- pedestrians -- bicycles -- human factors -- safety -- safety -- crash analysis -- crash prediction models -- crash severity; Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981221093330
• Languages: English
• ISSNs: 0361-1981

Abstract

Pedestrian safety is a growing concern for transportation planners and safety engineers at both local and state levels. Continued advancements in data availability, data integration abilities, and analysis methodologies offer new opportunities to identify factors influencing pedestrian safety and to quantify their effects to inform data-driven road safety management. The main objective of this study was to spatially integrate Highway Safety Information System data with multijurisdictional and emerging datasets to analyze two measures of pedestrian safety performance in Charlotte, NC: (1) the severity of a pedestrian crash that has occurred, and (2) the probability that a pedestrian crash will occur on a given roadway segment. To accomplish the objectives, the study explored several high-priority research topics in safety data and analysis, including pedestrian exposure analysis and probe data integration. The research team developed a pedestrian count model to predict pedestrian volumes at locations without pedestrian counts and integrated speed information from probe data to supplement other roadway and contextual transportation data available from several agencies. Pedestrian exposure at a given intersection was found to be significantly influenced by demographic and socioeconomic characteristics, employment, land use, sidewalk presence, transit access, and roadway and intersection characteristics. The project team identified numerous significant factors that influenced pedestrian crash severity and probability, including outputs from the pedestrian exposure model, observed vehicle speeds, traffic volumes, intersection proximity, and other crash-related factors. The results could be used to identify locations that are more susceptible to pedestrian safety issues.