Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75036
Title: A select link analysis method based on logit–weibit hybrid model
Authors: Liu, P
Xu, X
Chen, A 
Yang, C
Xiao, L
Keywords: Bell loading
Hybrid model
Logit model
Select link analysis
Weibit model
Issue Date: 2017
Publisher: Springer Berlin Heidelberg
Source: Journal of modern transportation, 2017, v. 25, no. 4, p. 205-217 How to cite?
Journal: Journal of modern transportation
Abstract: Select link analysis provides information of where traffic comes from and goes to at selected links. This disaggregate information has wide applications in practice. The state-of-the-art planning software packages often adopt the user equilibrium (UE) model for select link analysis. However, empirical studies have repeatedly revealed that the stochastic user equilibrium model more accurately predicts observed mean and variance of choices than the UE model. This paper proposes an alternative select link analysis method by making use of the recently developed logit–weibit hybrid model, to alleviate the drawbacks of both logit and weibit models while keeping a closed-form route choice probability expression. To enhance the applicability in large-scale networks, Bell’s stochastic loading method originally developed for logit model is adapted to the hybrid model. The features of the proposed method are twofold: (1) unique O–D-specific link flow pattern and more plausible behavioral realism attributed to the hybrid route choice model and (2) applicability in large-scale networks due to the link-based stochastic loading method. An illustrative network example and a case study in a large-scale network are conducted to demonstrate the efficiency and effectiveness of the proposed select link analysis method as well as applications of O–D-specific link flow information. A visualization method is also proposed to enhance the understanding of O–D-specific link flow originally in the form of a matrix.
URI: http://hdl.handle.net/10397/75036
ISSN: 2095-087X
EISSN: 2196-0577
DOI: 10.1007/s40534-017-0139-5
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