Pedestrian crash severity modelling at midblocks

Toran Pour, A 2017, Pedestrian crash severity modelling at midblocks, Doctor of Philosophy (PhD), Engineering, RMIT University.

Document type: Thesis
Collection: Theses

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Title Pedestrian crash severity modelling at midblocks
Author(s) Toran Pour, A
Year 2017
Abstract In the Melbourne metropolitan area in Australia, an average of 34 pedestrians were killed in traffic accidents every year between 2004 and 2013, and vehicle-pedestrian crashes accounted for 24% of all fatal crashes. Mid-block crashes accounted for 46% of the total pedestrian crashes in the Melbourne metropolitan area and 49% of the pedestrian fatalities occurred at mid-blocks. Many studies have examined factors contributing to the frequency and severity of vehicle-pedestrian crashes. While many of the studies have chosen to focus on crashes at intersections, few studies have focussed on vehicle-pedestrian crashes at mid-blocks. Since the factors contributing to vehicle crashes at intersections and mid-blocks are significantly different, more research needs to be done to develop a model for vehicle-pedestrian crashes at mid-blocks.

Furthermore, socioeconomic factors are known to be contributing factors to vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the locations of crashes, few studies have considered the socioeconomic factors of the neighbourhoods where road users live in vehicle-pedestrian crash modelling. In vehicle-pedestrian crashes in the Melbourne metropolitan area 20% of pedestrians, 11% of drivers and only 6% of both drivers and pedestrians had the same postcode for the crash and residency locations. Therefore, an examination of the influence of socioeconomic factors of their neighbourhoods, and their relative importance will contribute to advancing knowledge in the field, as very limited research has been conducted on the influence of socioeconomic factors of both the neighbourhoods where crashes occur and where pedestrians live.

In order to identify factors contributing to the severity of vehicle-pedestrian crashes, three models using different decision trees (DTs) were developed. To improve the accuracy, stability and robustness of the DTs, bagging and boosting 2 techniques were used in this study. The results of this study show that the boosting technique improves the accuracy of individual DT models by 46%. Moreover, the results of boosting DTs (BDTs) show that neighbourhood social characteristics are as important as traffic and infrastructure variables in influencing the severity of pedestrian crashes.

In this research, neighbourhood factors associated with road users’ residents and location of crash are investigated using BDT model. Furthermore, partial dependence plots are applied to illustrate the interactions between these factors. We have found that socioeconomic factors account for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighbourhoods where road users live and where crashes occur are important in determining the severity of crashes, with the former having a greater influence. Hence, road safety counter-measures, especially those focussing on road users, should be targeted at these high-risk neighbourhoods.

Furthermore, in order to develop effective and targeted safety programs, the location and time-specific influences on vehicle-pedestrian crashes must be assessed. Therefore, spatial autocorrelation was applied to the examination of vehicle-pedestrian crashes in geographic information systems (GISs) to identify any dependency between time and location of these crashes. Spider plotting and kernel density estimation (KDE) were then used to determine the temporal and spatial patterns of vehicle-pedestrian crashes for different age groups and gender types.

Temporal analysis shows that pedestrian age has a significant influence on the temporal distribution of vehicle-pedestrian crashes. Furthermore, men and women have different crash patterns. In addition, the results of the spatial analysis show that areas with high risk of vehicle-pedestrian crashes can vary during different times of the day for different age groups and gender types. For example, for the age group between 18 and 65, most vehicle-pedestrian crashes occur in the central business district (CBD) during the day, but between 7:00pm and 6:00am, crashes.3 for this age group occur mostly around hotels, clubs and bars. Therefore, specific safety measures should be implemented during times of high crash risk at different locations for different age groups and gender types, in order to increase the effectiveness of the countermeasures in preventing and reducing the vehicle-pedestrian crashes.
Degree Doctor of Philosophy (PhD)
Institution RMIT University
School, Department or Centre Engineering
Subjects Transport Engineering
Keyword(s) Machine Learning
Crash Severity Model
Crash Black Spot
Boosted Decision Tree
Neighbourhood Socioeconomic Influences
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Created: Tue, 13 Feb 2018, 10:33:53 EST by Denise Paciocco
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