UTFacultiesETDepartmentsCEMNewsA comprehensive analysis of the relationships between the built environment and traffic safety in the Dutch urban areas

A comprehensive analysis of the relationships between the built environment and traffic safety in the Dutch urban areas

A comprehensive analysis of the relationships between the built environment and traffic safety in the Dutch urban areas

Accident Analysis and Prevention 172 (2022) 106683

Mehrnaz Asadi a,*, Mehmet Baran Ulak a, Karst T. Geurs a, Wendy Weijermars b, Paul Schepers c

a University of Twente, Department of Civil Engineering, Faculty of Engineering Technology, P.O. Box 217, 7500 AE Enschede, the Netherlands
b SWOV Institute for Road Safety Research, P.O. Box 93113, 2509 AC The Hague, the Netherlands
c Ministry of Infrastructure and the Environment, Rijkswaterstaat, P.O. Box 2232, 3500 GE Utrecht, the Netherlands

A B S T R A C T

Built-environment factors potentially alleviate or aggravate traffic safety problems in urban areas. This paper aims to investigate the relationships of these factors with vehicle-bicycle and vehicle-vehicle property damage only (PDO) and killed and severe injury (KSI) crashes in urban areas. For this purpose, an area-level analysis using 100x100m2 cells, along with a Spatial Hurdle Negative Binomial regression model were employed. The study area is composed of a selection of municipalities in the Netherlands-Randstad Area where major land-use developments have occurred since the 1970s. The study was conducted by developing a rich dataset composed of various national and local databases. The findings reveal that built-environment factors and land-use policies have substantial impacts on safety, which cannot be neglected. The factors explaining the land-use density and diversity in the area (e.g., urbanity and function mixing levels), as well as the land-use design characteristics (indicated by average age of the neighborhoods), traffic and road network characteristics, and proximity to different destinations influence the probability, frequency, and severity of crashes in urban areas. Furthermore, low socioeconomic levels are associated with a higher frequency of traffic crashes.