title: Multi-level hot zone identification for pedestrian safety.
abstract: According to the National Highway Traffic Safety Administration (NHTSA), while fatalities from traffic crashes have decreased, the proportion of pedestrian fatalities has steadily increased from 11% to 14% over the past decade. This study aims at identifying two zonal levels factors. The first is to identify hot zones at which pedestrian crashes occurs, while the second are zones where crash-involved pedestrians came from. Bayesian Poisson lognormal simultaneous equation spatial error model (BPLSESEM) was estimated and revealed significant factors for the two target variables. Then, PSIs (potential for safety improvements) were computed using the model. Subsequently, a novel hot zone identification method was suggested to combine both hot zones from where vulnerable pedestrians originated with hot zones where many pedestrian crashes occur. For the former zones, targeted safety education and awareness campaigns can be provided as countermeasures whereas area-wide engineering treatments and enforcement may be effective safety treatments for the latter ones. Thus, it is expected that practitioners are able to suggest appropriate safety treatments for pedestrian crashes using the method and results from this study.
authors.affiliation: Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida 32816-2450, United States. Electronic address: jaeyoung@knights.ucf.edu.
authors.forename: Jaeyoung
authors.initials: J
authors.lastname: Lee
authors.affiliation: Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, Florida 32816-2450, United States.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abdel-Aty
authors.affiliation: Department of Transportation Systems Engineering, Ajou University, Suwon 443-749, Republic of Korea.
authors.forename: Keechoo
authors.initials: K
authors.lastname: Choi
authors.affiliation: Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China.
authors.forename: Helai
authors.initials: H
authors.lastname: Huang
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality* / Q000517:prevention & control*; D001499:Bayes Theorem; D004779:Environment Design / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D000069636:Pedestrians / Q000193:education; D016012:Poisson Distribution; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012449:Safety / Q000706:statistics & numerical data*; D014481:United States
keywords: Bayesian approach; CAR; Gaussian conditional autoregressive; Macroscopic analysis; Pedestrian safety; Poisson lognormal model; Screening; Simultaneous equations modeling; Spatial error modeling