Latest revision as of 10:00, 15 February 2021

Abstract

Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers’ multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers’ different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions.

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The different versions of the original document can be found in:

• http://dx.doi.org/10.1155/2020/2626084 under the license http://creativecommons.org/licenses/by/4.0/

• https://doi.org/10.1155/2020/2626084

• http://downloads.hindawi.com/journals/jat/2020/2626084.pdf,

http://downloads.hindawi.com/journals/jat/2020/2626084.xml,

http://dx.doi.org/10.1155/2020/2626084 under the license cc-by

• http://dx.doi.org/10.1155/2020/2626084,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2020/2626084,

http://downloads.hindawi.com/journals/jat/2020/2626084.pdf,

https://academic.microsoft.com/#/detail/3036888153