Augmented Betweenness Centrality

Rami Puzis, Yaniv Altshuler, Yuval Elovici, Shlomo Bekhor, Yoram Shiftan, Alex S, y Pentl,

Network planning and traffic flow optimization requires the acquirement and analysis of large quantities of data such as the network topology, its traffic flow data, vehicle fleet composition, emission measurements etc. Data acquirement is an expensive process that involves household surveys and automatic as well as semi-automatic measurements performed all over the network. For example, in order to accurately estimate the effect of a certain network change on the total emissions produced by vehicles in the network, assessment of the vehicle fleet composition for each origin-destination pair is required. As a result, problems that optimize non-local merit functions becomes highly difficult to solve. One such problem is finding the optimal deployment of traffic monitoring units. In this paper we suggest a new traffic assignment model that is based on the concept of Shortest Path Betweenness Centrality measure borrowed from the domain of complex network analysis. We show how Betweenness can be augmented in order to solve the traffic assignment problem given an arbitrary travel cost definition. The proposed traffic assignment model is evaluated using a high resolution Israeli transportation dataset derived from the analysis of cellular phones data. The group variant of the augmented Betweenness Centrality is then used to optimize the locations of traffic monitoring units, hence reducing the cost and increasing the effectiveness of traffic monitoring.