A Subpath-based Logit Model to Capture the Correlation of Routes

  • Xinjun Lai Guangdong university of technology
  • Jun Li Sun Yat-sen University
  • Zhi Li Guangdong university of technology
Keywords: cross-nested Logit, stochastic route choice, subpath, correlation,

Abstract

A subpath-based methodology is proposed to capture the travellers’ route choice behaviours and their perceptual correlation of routes, because the original link-based style may not be suitable in application: (1) travellers do not process road network information and construct the chosen route by a link-by-link style; (2) observations from questionnaires and GPS data, however, are not always link-specific. Subpaths are defined as important portions of the route, such as major roads and landmarks. The cross-nested Logit (CNL) structure is used for its tractable closed-form and its capability to explicitly capture the routes correlation. Nests represent subpaths other than links so that the number of nests is significantly reduced. Moreover, the proposed method simplifies the original link-based CNL model; therefore, it alleviates the estimation and computation difficulties. The estimation and forecast validation with real data are presented, and the results suggest that the new method is practical.

Author Biographiesaaa replica rolex repwatches replica rolex watches for men replica iwc watch

Xinjun Lai, Guangdong university of technology
Xinjun Lai, PhD, school of electro-mechanical engineering, Guangdong university of technology, Guangzhou 510006, China. xinjun.lai@gdut.edu.cn.
Jun Li, Sun Yat-sen University
Jun Li, PhD, associate professor, A303 School of engineering, Sun Yat-sen University, Guangzhou 510006, China. stslijun@mail.sysu.edu.cn
Zhi Li, Guangdong university of technology
Zhi Li, PhD, Lecturer, school of electro-mechanical engineering, Guangdong university of technology, Guangzhou 510006, China. lizhi_piers@gdut.edu.cn.

References

Ben-Akiva ME, Bierlaire M. Discrete Choice Methods and Their Applications to Short Term Travel Decisions. In: Hall R, editor. Handbook of Transportation Science. Dordrecht, Netherlands: Kluwer; 1999. p. 5-33.

Vovsha P, Bekhor S. Link-Nested Logit Model of Route Choice: Overcoming Route Overlapping Problem. Transportation Research Reord. 1998;1645:133-42.

McFadden D, Train K. Mixed MNL Models for Discrete Response. Journal of Applied Econometrics. 2000;15(5):447-70.

Prato CG. Route choice modelling: Past, present and future research directions. Journal of Choice Modelling. 2009;2(1):65-100.

Fosgerau M, Frejinger E, Karlstrom A. A link based network route choice model with unrestricted choice set. Transportation Research Part B. 2013;56:70-80.

Papola A, Marzano V. A Network Generalized Extreme Value Model for Route Choice Allowing Implicit Route Enumeration. Computer-Aided Civil and Infrastructure Engineering. 2013;00:1-21.

Frejinger E, Bierlaire M, Ben-Akiva M. Sampling of alternatives for route choice modelling. Transportation Research Part B. 2009;43:984-94.

Lai X, Bierlaire M. Specification of the cross-nested logit model with sampling of alternatives for route choice models. Transportation Research Part B: Methodological. 2015;80:220-34.

Ramming MS. Network Knowledge and Route Choice [PhD thesis]. Cambridge, USA: Massachusetts Institute of Technology; 2002.

Axhausen K, Schönfelder S, Wolf J, Oliveira M, Samaga U. 80 weeks of GPS-traces: Approaches to enriching the trip information. IVT, ETH Zurich; 2003.

She X, He Z, Nie P, Zeng W, Cen X, Dai X. Online Map-Matching Framework for Floating-Car Data with Low Sampling Rate in Urban Road Network. Transportation Research Board 91st Annual Meeting; Washington DC; 2011.

Rahmani M, Koutsopoulos HN. Path inference from sparse floating car data for urban networks. Transportation Research Part C: Emerging Technologies. 2013;30:41-54.

Li J, Xie L, Lai X. Route Reconstruction from Floating Car Data with Low Sampling Rate Based on Feature Matching. Research Journal of Applied Sciences, Engineering and Technology. 2013;6(12):2153-8.

Bierlaire M, Frejinger E. Route choice modelling with network-free data. Transportation Research Part C: Emerging Technologies. 2008;16:187-98.

Barton RR, Hearn DW. Network Aggregation in Transportation Planning Models. United States Department of Transportation; 1979.

Fosgerau M, McFadden D, Bierlaire M. Choice probability generating functions. Journal of Choice Modelling. 2013;8:1-18.

Boyles SD. Bushed-based sensitivity analysis for approximating subnetwork diversion. transportation Research B. 2012;46(1):139-55.

Frejinger E, Bierlaire M. Capturing correlation with subnetworks in route choice models. Transportation Research Part B. 2007;41:363-78.

Kazagli E, Bierlaire M. Revisiting Route Choice Modelling: A Multi-Level Modelling Framework for Route Choice Behaviour. 14th Swiss transportation research conference; Ascona, Switzerland; 2014.

Li J, Lai X, Yu Z. A Paired Combinatorial Logit Route Choice Model with Probit-Based Equivalent Impedance. Journal of Transportation Systems Engineering and Information Technology. 2013;13(4):100-4.

Abbe E, Bierlaire M, Toledo T. Normalization and correlation of cross-nested logit models. Transportation Research Part B. 2007;41:795-808.

McFadden D. Modelling the choice of residential location. In: Karlquist, Lundqvist, Snickers, Weibull, editors. Spatial Interaction Theory and Residential Location. Amsterdam: North Holland; 1978. p. 75-96.

Published
2016-06-23
How to Cite
1.
Lai X, Li J, Li Z. A Subpath-based Logit Model to Capture the Correlation of Routes. Promet [Internet]. 2016Jun.23 [cited 2024Apr.24];28(3):225-34. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/1808
Section
Articles