Spatial and Temporal Distribution of Pedestrian Crashes

Spatial and Temporal Distribution of Pedestrian Crashes

DOI: 10.4018/978-1-5225-7943-4.ch006


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 7:00 pm and 6:00 am, crashes 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.
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6.1. Introduction

Pedestrians are known as vulnerable road users in road safety literature because they are more likely to be harmed or injured in traffic crashes. Pedestrians are about four times more likely to be injured in traffic crashes than other road users (Elvik, 2009). In addition, because their body is exposed and unprotected in traffic crashes, they are 23 times more likely to be killed than vehicle occupants (Miranda-Moreno et al, 2011). According to the World Health Organisation’s report, every year about 1.24 million people are killed in traffic crashes in the world and more than 22% of these deaths are pedestrians (WHO 2013).

In Australia, vehicle-pedestrian crashes account for more than 13% of total fatal crashes. Every year, pedestrians are involved in about 1,100 traffic crashes in Melbourne and about 38 pedestrians are killed in these traffic crashes, which comprise about 18% of total pedestrian fatalities in Australia (Pink, 2010). Therefore, pedestrians and other vulnerable road users are specifically targeted in the recent Road Safety Agenda of the Victorian government (VicRoads, 2015). Design and implementation of effective countermeasures to improve the safety of these vulnerable road users will require not only a better understanding of the major crash contributing factors but the temporal-spatial patterns of vehicle-pedestrian crashes as well.

Spatial and temporal characteristics of traffic crashes are known to be important factors in traffic crash in many countries. For instance, different studies show that spatial and temporal parameters have an influence on traffic crash, including vehicle-pedestrian crashes in different states of U.S. (Levine et al, 1995, Aguero-Valverde & Jovanis, 2006, Li et al, 2007). In addition, a report from the National Highway Traffic Safety Administration (NHTSA) shows that location and time of crashes are main influencing factors on vehicle-pedestrian crashes in U.S. (Nhtsa, 2015).

Several studies have shown that these variables are also significant in traffic crashes in other countries. For instance, Al-Shammari et al. (2009) show that time and location of crashes are two important variables in vehicle-pedestrian crashes in the Kingdom of Saudi Arabia. Fox et al. (2015), Hosseinpour et al. (2013), and Loo et al. (2005) show the importance of location and time of crash in vehicle-pedestrian crashes in Colombia, Malaysia and Hong Kong, respectively.

Pedestrian age and gender type could influence on walking behaviour. For instance, females spend more time than men walking in their local environments and walking increased with age (2010). Therefore, many studies tried to identify the influence of pedestrian age and gender types on vehicle-pedestrian crashes. These studies identified age and gender types as two contributing factors in vehicle-pedestrian crashes (Al-Ghamdi, 2002, Henary et al, 2006, Holland & Hill, 2007). These studies revealed that age and gender types could influence on frequency and severity of vehicle-pedestrian crashes. Therefore, these two factors could influence on spatial and temporal distribution of vehicle-pedestrian crashes.

This chapter aims to identify the temporal and spatial distribution of vehicle-pedestrian crashes for different pedestrians’ age groups and gender types. Specifically, it aims to answer the following research questions:

  • RQ1: Is there any spatial dependency between pedestrian age and gender and the crash location?

  • RQ2: What time-of-day are hot times for each age group and gender type?

  • RQ3: Do crash hot spots vary with time-of-day?

  • RQ4: Where are the crash hot spots for each age and gender group?

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