Street-based Topological Representations and Analyses for Predicting Traffic Flow in GIS

TL;DR

This paper demonstrates that street-based topological representations outperform axial maps in predicting traffic flow, offering a new GIS analytical approach for geographic knowledge discovery.

Contribution

It introduces street-based topological representations as a superior alternative to axial maps for traffic flow prediction in GIS.

Findings

Street-based topologies have higher correlation with traffic flow than axial maps.

Street topologies provide better morphological insights for traffic analysis.

Proposes a new GIS analytical method based on street topologies.

Abstract

It is well received in the space syntax community that traffic flow is significantly correlated to a morphological property of streets, which are represented by axial lines, forming a so called axial map. The correlation co-efficient (R square value) approaches 0.8 and even a higher value according to the space syntax literature. In this paper, we study the same issue using the Hong Kong street network and the Hong Kong Annual Average Daily Traffic (AADT) datasets, and find surprisingly that street-based topological representations (or street-street topologies) tend to be better representations than the axial map. In other words, vehicle flow is correlated to a morphological property of streets better than that of axial lines. Based on the finding, we suggest the street-based topological representations as an alternative GIS representation, and the topological analyses as a new analytical means for geographic knowledge discovery.