Macroscopic hotspots identification: A Bayesian spatio-temporal interaction approach.
This study proposes a Bayesian spatio-temporal interaction approach for hotspot identification by applying the full Bayesian (FB) technique in the context of macroscopic safety analysis. Compared with the emerging Bayesian spatial and temporal approach, the Bayesian spatio-temporal interaction model contributes to a detailed understanding of differential trends through analyzing and mapping probabilities of area-specific crash trends as differing from the mean trend and highlights specific locations where crash occurrence is deteriorating or improving over time. With traffic analysis zones (TAZs) crash data collected in Florida, an empirical analysis was conducted to evaluate the following three approaches for hotspot identification: FB ranking using a Poisson-lognormal (PLN) model, FB ranking using a Bayesian spatial and temporal (B-ST) model and FB ranking using a Bayesian spatio-temporal interaction (B-ST-I) model. The results show that (a) the models accounting for space-time effects perform better in safety ranking than does the PLN model, and (b) the FB approach using the B-ST-I model significantly outperforms the B-ST approach in correctly identifying hotspots by explicitly accounting for the space-time variation in addition to the stable spatial/temporal patterns of crash occurrence. In practice, the B-ST-I approach plays key roles in addressing two issues: (a) how the identified hotspots have evolved over time and (b) the identification of areas that, whilst not yet hotspots, show a tendency to become hotspots. Finally, it can provide guidance to policy decision makers to efficiently improve zonal-level safety.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 610031, China; Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, 410075, China. Electronic address: dongni@home.swjtu.edu.cn.
Ni
N
Dong
Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, 410075, China. Electronic address: huanghelai@csu.edu.cn.
Helai
H
Huang
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, 32816-2450, United States. Electronic address: jaeyoung@knight.ucf.edu.
Jaeyoung
J
Lee
School of Science, Wuhan University of Technology, Wuhan, Hubei, 430063, China. Electronic address: wh14_gao@126.com.
Mingyun
M
Gao
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, 32816-2450, United States. Electronic address: mabdel@mail.ucf.edu.
Mohamed
M
Abdel-Aty
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D004779:Environment Design; D005431:Florida; D006801:Humans; D008962:Models, Theoretical; D012449:Safety; D062211:Spatio-Temporal Analysis
Bayesian spatio-temporal interaction model; Hotspot identification; Ranking criteria