About 1100 vehicle-pedestrian crashes occur in Melbourne metropolitan area every year. Identifying the temporal and spatial patterns of pedestrian injuries is essential to enhance the safety of these vulnerable road users. In this paper, Decision Tree (DT) and interactive DT are applied to identify the influence of temporal, spatial and personal characteristics on vehicle-pedestrian crash severity. DT is a simple but powerful form of data analyses using machine learning technique. Result of DT indicates that time of crash is the most significant variable in classifying and predicting the severity of vehicle-pedestrian crashes in Melbourne metropolitan area. According to this model, accidents occurring between 7:00 p.m. and 6:00 a.m. are more severe than other times. Moreover, spatial correlation shows that there are positive correlation between time and location of crashes. Kernel Density Estimation (KDE) is applied to explore the spatial distribution of vehiclepedestrian crashes. KDE results show that most vehicle-pedestrian crashes between 7:00 p.m. and 6:00 a.m. occur around hotels, clubs and bars. Safety measures should be applied around these areas to assist in preventing and reducing the severity of vehicle-pedestrian crashes.
Spatial and temporal distribution of pedestrian crashes in Melbourne metropolitan area
Road & transport research ; 26 , 1
2017
Aufsatz (Zeitschrift)
Englisch
Lokalklassifikation TIB: | 135/7005/1710 | |
BKL: | 55.84 Straßenverkehr |
DOAJ | 2016
|Temporal Analysis of Predictors of Pedestrian Crashes
Transportation Research Record | 2020
|A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile
Online Contents | 2013
|Urban Air Transportation for Melbourne Metropolitan Area
AIAA | 2019
|