Investigating Safety Impacts of Roadway Network Features of Suburban Arterials in Shanghai, China

TL;DR

This study develops a macro-level Bayesian model to analyze how roadway network features like signal spacing and access density affect safety on suburban arterials in Shanghai, revealing that certain network patterns reduce crashes while higher densities increase them.

Contribution

It introduces a new macro-level safety modeling approach using Bayesian Conditional Autoregressive models to analyze multiple network features simultaneously.

Findings

Parallel collector roads reduce crash frequency.

Higher signal density increases crash risk.

Higher access density correlates with more crashes.

Abstract

With the rapid changes in land use development along suburban arterials in Shanghai, there is also a corresponding increase in traffic demand along these arterials. With a preference toward increased accessibility and efficiency, these arterials have been installed with an increased number of signalized intersections and accesses to serve local traffic needs. The absence of a defined functional hierarchy along the road network, together with the non-uniform installation of signals and accesses tends to deteriorate arterial safety. Previous studies on arterial safety have generally been based on a single type of road entity (either intersection or roadway segment). These studies only analyzed partial safety impacts of signal spacing and access density, as these factors would significantly influence the safety performance of both intersections and roadway segments. Macro level safety modeling was usually applied to investigate the relationship between the zonal crash frequencies and demographics, road network features and traffic characteristics. In this study, a new modeling strategy was proposed to analyze the safety impacts of roadway network features (i.e., road network patterns, signal spacing and access density) of arterials by applying a macro level safety modeling technique. Bayesian Conditional Autoregressive models were developed for arterials covering 173 Traffic Analysis Zones in the suburban area in Shanghai. The results identified that the road network pattern with collector roads parallel to the arterials was shown to be associated with fewer crashes than those without parallel collectors. Higher signal density and access density also tended to increase crash frequencies on arterials.