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      The Effect of Regret-Based Risky Route Choice on the Traffic Equilibrium for Emergency Evacuation

      1 , 2 , 3 , 4
      Journal of Advanced Transportation
      Hindawi Limited

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          Abstract

          Following the research on human decision-making under risk and uncertainty, the purpose of this paper is to analyze evacuees’ risky route decision behavior and its effect on traffic equilibrium. It examines the possibility of applying regret theory to model travellers’ regret-taking behavior and network equilibrium in emergency context. By means of modifying the utility function in expected utility theory, a regret-based evacuation traffic equilibrium model is established, accounting for the evacuee’s psychological behavior of regret aversion and risk aversion. Facing two parallel evacuation routes choice situation, the effect of evacuees’ risk aversion and regret aversion on traffic equilibrium is numerically investigated as well as the road capacity reduction from natural disaster. The findings reveal that evacuees prefer the riskless route with the lower travel time as the increase of the regret aversion degree. The equilibrium tends to be achieved when more evacuees choose the safer route jointly affected by risk aversion and regret aversion. Moreover, an optimization model for disaster occurring possibility is formulated to assess the traffic system performance for evacuation management. These findings are helpful for understanding how the regret aversion and risk aversion influence traffic equilibrium.

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          A New Model of Random Regret Minimization

          A new choice model is derived, rooted in the framework of Random Regret Minimization (RRM). The proposed model postulates that when choosing, people anticipate and aim to minimize regret. Whereas previous regret-based discrete choice-models assume that regret is experienced with respect to only the best of foregone alternatives, the proposed model assumes that regret is potentially experienced with respect to each foregone alternative that performs well. In contrast with earlier regret-based discrete-choice approaches, this model can be estimated using readily available discrete-choice software packages. The proposed model is contrasted theoretically and empirically with its natural counterpart, Random Utility Maximization’s linearadditive MNL-model. Empirical comparisons on four revealed and stated travel choice datasets show a promising performance of the RRM-model.
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            A Socio-physical and Mobility-Aware Coalition Formation Mechanism in Public Safety Networks

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              Author and article information

              Contributors
              Journal
              Journal of Advanced Transportation
              Journal of Advanced Transportation
              Hindawi Limited
              2042-3195
              0197-6729
              October 10 2020
              October 10 2020
              : 2020
              : 1-8
              Affiliations
              [1 ]Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China
              [2 ]Department of Traffic Management Engineering, Zhejiang Police College, Hangzhou 310053, China
              [3 ]Institute for Future (IFF), Qingdao University, Qingdao 266071, China
              [4 ]Department of Business Management, Zhejiang University of Finance and Economics Dongfang College, Hangzhou 310012, China
              Article
              10.1155/2020/8858302
              26792c96-0986-4976-bd40-1e5030dbc1ba
              © 2020

              https://creativecommons.org/licenses/by/4.0/

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