Traffic assignment paradox incorporating congestion and stochastic perceived error simultaneously

ABSTRACT

This paper analyses the effects of congestion and stochastic perceived error in stochastic traffic assignment paradox, by the measure of both actual and perceived travel cost. Two different circumstances are studied: improving an existing link and adding a new link. It is found that different congestion cost functions and perceived error levels will significantly affect the road condition and the demand level under which paradox happens. Moreover, how the interaction between traffic demands of different O-D pairs affects the occurrence of traffic paradox is illustrated by a two O-D pairs’ network. Besides, a counter-intuitive phenomenon when less stochastic perceived error yet increases the average travel cost (information paradox) is also discussed. The results of this paper help to understand the interactional impact of congestion and stochastic perceived error, and give some new insights to traffic paradox.

KEYWORDS:

• Traffic paradox
• stochastic user equilibrium
• congestion effect
• stochastic perceived error

Acknowledgements

The authors are grateful to four anonymous referees and Professor S.C. Wong for their constructive comments and suggestions to improve the quality and clarity of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Jia Yao http://orcid.org/0000-0002-4978-3611

Anthony Chen http://orcid.org/0000-0003-4363-5041

Notes

1 Because of the special structure of the cost functions in the network, route 1 and route 2 always have the same cost and therefore have the same flow. Changing $\theta$ can adjust the flow proportion between routes 1 and 2 and route 3 and can get the same solution with SO. However, in some conditions of SO, inferior routes (routes 1 and 2) have more flow, these conditions can never be reached in SUE by adjusting $\theta$, because the SUE model always assigns more flow to the superior route, such condition can be seen in Figure 13(b).

Additional information

Funding

This research was partially supported by the National Natural Science Foundation of China (71501053, 71390522), China Postdoctoral Science Foundation (2015M570297), the International Postdoctoral Exchange Fellowship (20160076) of China Postdoctoral Council, the Fundamental Research Funds for the Central Universities (HIT.NSRIF.201661), the Research Grants Council of the Hong Kong Special Administrative Region (15212217), and CCF-DiDi Big Data Joint Lab.