Identification of Suburban Motorcycle Traffic Accident Hotspots Using GIS-Based Spatial Analysis

Abstract

There has been a growing concern in traffic road accident rates in recent years. Thailand's average death rate of 24,326 people per year is the second highest globally. It is now down to 32.7 per 100,000 inhabitants, with 22,491 deaths per year and an average of 60 deaths per day. While Thailand has the third-highest proportion of motorcycle-related deaths globally at 74.4%, it has a population of over 66 million with a fatality rate of 24.3 motorcyclists per 100,000 people. In addition, when considering the report of the leading presumptive causes of road accidents, it was found that the leading causes of the crash can be classified by general characteristics of collisions and their contributing factors collisions (human factors, vehicle factors and road and environment factors). This paper focuses on the case of the suburban area by applying the spatial-statistical analysis to identify the risk locations of motorcycle traffic accidents (known as “hot spots”). Kernel Density Estimation (KDE), data plots and space and time pattern mining tools are used to describe spatial hotspots. The finding can help in the measurement of motorcycle traffic accidents reduction and effectively improve road safety situation, which is crucial to investigate the root cause of road traffic accidents with an in-depth understanding of spatial patterns of this problematic.