A Systematic Method to Analyze Transport Networks: Considering Traffic Shifts

Abstract

Current network modeling practices usually assess the network performance at specified time interval, i.e. every 5 or 10 years time horizon. Furthermore, they are usually based on partially predictable data, which are being generated through various stochastic procedures. In this research, a new quantitative based methodology which combines combinatorial optimization modeling and transportation network engineering has been implemented to identify the network performance over time horizon. This method incorporates both uniform traffic demand growth and demand shifts towards more attractive zones (demand uncertainty) in the network. The proposed combinatorial programming approach defines a quantitative measure of growth and shift in the traffic load that a network can sustain. This method can assess the various potential growth topology of a transport network and investigate if a specified topology can sustain more traffic demand or if any specific topology can handle traffic shifts without significant need to amend the network infrastructure. It is believed that this quantitative measure is useful both in transport network design and in the performance analysis of the existing networks. In this paper, the application of this method is demonstrated by applying the method to a part of the Melbourne's transportation network.