Examining traffic crash scene using density estimation and its relevance to determine intersection zone road network blackspot location

Abstract

Identifying road traffic crash blackspot locations and the proper method of assessment is a step towards reducing traffic crashes. The objective of this study was assessing the pertinence of the point density estimation and kernel density estimation in identifying blackspot locations using the ArcGIS tool. Based on the availability, consistency, and nature of the data this study considers five-year traffic crashes of Budapest city intersection zone of the road network to envision the aptness of the stated method. To address the goal, this study considers the minimum spacing between intersection to define the radius of output cell size (0.001093 degree) and its neighborhood radius (0.002 degree). Based on the above parameter this study identifies five common intersection zones of the road network as blackspot locations. The overall inference of this study; point density estimation is more desirable than kernel density estimation for identifying highly extreme dense blackspot locations. Furthermore, this study discovered that intersection zones with an enormous number of legs are more likely to experience high traffic crashes. This study recommends that point density estimation is capable of investigating blackspot location at macroscopic level of road network to analyze highly extreme dense traffic crash location. In addition to that, it is advisable to assess the performance of the stated blackspot location and propose remedial action to minimize the rate of road traffic crashes and its outcome.

Keywords

• Blackspot
• Density Estimation
• Neighborhood Radius
• Traffic Crashes