TY - JOUR
T1 - Complementarity Formulation and Solution Algorithm for Auto-Transit Assignment Problem
AU - Zarrinmehr, Amirali
AU - Aashtiani, Hedayat Z.
AU - Nie, Yu (Marco)
AU - Azizian, Hossein
N1 - Funding Information:
The authors are grateful to the three anonymous reviewers for their valuable insights and comments on this paper. The third author’s work was partially funded by U.S. National Science Foundation under the award number CMMI-1402911.
Funding Information:
The authors are grateful to the three anonymous reviewers for their valuable insights and comments on this paper. The third author?s work was partially funded by U.S. National Science Foundation under the award number CMMI-1402911.
Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2019.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - In this paper, a combined model of auto-transit assignment is introduced based on two complementarity formulations in the literature. The model accounts for interactions between the auto and transit modes through non-separable asymmetric demand and cost functions. A path-based solution algorithm is presented based on the three ideas of decomposition, column generation, and linearization, which have proved to be effective in tackling large-size networks. Numerical results over the Chicago sketch network suggest that the algorithm converges quickly within the first iterations, but is less effective as the solution gets closer to the neighborhood of the equilibrium solution. The sluggish convergence behavior is attributed to the difficulty of searching the space of strategy-based transit assignment model.
AB - In this paper, a combined model of auto-transit assignment is introduced based on two complementarity formulations in the literature. The model accounts for interactions between the auto and transit modes through non-separable asymmetric demand and cost functions. A path-based solution algorithm is presented based on the three ideas of decomposition, column generation, and linearization, which have proved to be effective in tackling large-size networks. Numerical results over the Chicago sketch network suggest that the algorithm converges quickly within the first iterations, but is less effective as the solution gets closer to the neighborhood of the equilibrium solution. The sluggish convergence behavior is attributed to the difficulty of searching the space of strategy-based transit assignment model.
UR - http://www.scopus.com/inward/record.url?scp=85063891185&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063891185&partnerID=8YFLogxK
U2 - 10.1177/0361198119837956
DO - 10.1177/0361198119837956
M3 - Article
AN - SCOPUS:85063891185
VL - 2673
SP - 384
EP - 397
JO - Transportation Research Record
JF - Transportation Research Record
SN - 0361-1981
IS - 4
ER -