Transport Network Design Problem under Uncertainty: A Review and New Developments

Abstract

This paper aims to provide a state-of-the-art review of the transport network design problem (NDP) under uncertainty and to present some new developments on a bi-objective-reliable NDP (BORNDP) model that explicitly optimizes the capacity reliability and travel time reliability under demand uncertainty. Both are useful performance measures that can describe the supply-side reliability and demand-side reliability of a road network. A simulation-based multi-objective genetic algorithm solution procedure, which consists of a traffic assignment algorithm, a genetic algorithm, a Pareto filter, and a Monte-Carlo simulation, is developed to solve the proposed BORNDP model. A numerical example based on the capacity enhancement problem is presented to demonstrate the tradeoff between capacity reliability and travel time reliability in the NDP.

Acknowledgements

The authors are grateful to Prof. Moshe Givoni (Associate Editor of Transport Reviews) and three referees for providing useful comments and suggestions for improving the quality and clarity of the paper. The work of the first author was supported by a CAREER grant from the National Science Foundation of the United States (CMS-0134161), a William Mong Visiting Fellowship to the University of Hong Kong, and a Oriental Scholar Fellowship to Tongji University, the work of the fifth author was supported by two grants from the China National High-Tech Research and Development Project (863 Project) (2007AA11Z206) and the New Century Excellent Talents Program in University (NCET-08-0406), and the work of the sixth author was supported by a grant from the University Research Committee of the University of Hong Kong (10400582/00002771).