A hyperpath-based network generalized extreme-value model for route choice under uncertainties. (October 2015)

• Record Type: Journal Article
• Authors: Ma, Jiangshan; Fukuda, Daisuke
• Is Part Of: Transportation research. Volume 59(2015)
• Page Start: 19
• Page End: 31
• Publication Date: 2015-10
• Subjects: Hyperpath -- Network GEV model -- Route choice -- Uncertainties; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2015.05.015
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A hyperpath-based N-GEV model is developed to analyze route choice under general uncertainties. By originally integrating the hyperpath and N-GEV methodologies, this paper presents a general initial framework based on previous studies for modeling route choice behavior under uncertainties. We demonstrate the advantages of the proposed approach over the inverse proportionality hyperpath in producing more reasonable link choice possibilities and capturing link correlations as well. In addition to providing insights into dealing with route choices with uncertainties with hyperpath approaches, the added benefit of using the N-GEV framework is that the model can deal with randomness by way of utility maximization. The modeling framework is expected to be helpful in various research contexts dealing with both randomness and other non-probabilistic uncertainties that cannot be exactly perceived. Abstract: Previous route choice studies treated uncertainties as randomness; however, it is argued that other uncertainties exist beyond random effects. As a general modeling framework for route choice under uncertainties, this paper presents a model of route choice that incorporates hyperpath and network generalized extreme-value-based link choice models. Accounting for the travel time uncertainty, numerical studies of specified models within the proposed framework are conducted. The modeling framework may be helpful in various research contexts dealing with both randomness and other non-probabilistic uncertainties that cannot be exactly perceived.