Using Hybrid Clustering to Approximate Fastest Paths on Urban Networks

Abstract

Estimating fastest paths on large networks is a crucial problem for dynamic route guidance systems. The present paper proposes a statistical approach for approximating fastest paths on urban networks. The traffic data used for conducting the statistical analysis is generated using a macroscopic traffic simulation software developed by us. The traffic data consists of the input flows, the arc states or the number of cars in the arcs and the paths joining the various origins and the destinations of the network. To find out the relationship between the input flows, arc states and the fastest paths of the network, we subject the traffic data to hybrid clustering. The hybrid clustering uses two methods namely k-means and Ward’s hierarchical agglomerative clustering. The strength of the relationship among the traffic variables was measured using canonical correlation analysis. The results of hybrid clustering are decision rules that provide fastest paths as a function of arc states and input flows. These decision rules are stored in a database for performing predictive route guidance. Whenever a driver arrives at the entry point of the network, the current arc states and input flows are matched against the database parameters. If agreement is found, then the database provides the fastest path to the driver using the corresponding decision rule. In case of disagreement, the database recommends the driver to choose the shortest path as the fastest path in order to reach the destination.