Risk-averse toll pricing in a stochastic transportation network

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Official URL: http://dx.doi.org/10.1504/EJIE.2017.083248

We consider the toll pricing problem under uncertain network conditions resulting in stochastic travel times. Using the conditional value-at-risk (CVaR) as a risk measure on the alternate functions of the random travel times we introduce several travel time reliability-related network performance measures. CVaR is used to control the possible large realizations of random outcomes based on travel times, and consequently, improve the reliability of the transportation system. We characterize the random network parameters by a finite set of scenarios and propose alternate risk-averse toll pricing models. These optimization models involve decisions of transportation managers that aim to improve the system-wide network reliability and decisions of network users that are assumed to choose their routes to minimize their expected total travel costs. We describe a solution method integrating mathematical programming approaches with a genetic algorithm. We also conduct a computational study to illustrate the effectiveness of the proposed modeling approaches.