Measurement of uncertainty costs with dynamic traffic simulations
AbstractNon-recurrent congestion in transportation networks occurs as a consequence of stochastic factors affecting demand and supply. Intelligent Transportation Systems such as Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) are designed in order to reduce the impacts of non-recurrent congestion by providing information to a fraction of users or by controlling the variability of traffic flows. For these reasons, the design of ATIS and ATMS requires reliable forecast of non-recurrent congestion. This paper proposes a new method to measure the impacts of non-recurrent congestion on travel costs by taking risk aversion into account. The traffic model is based on the dynamic traffic simulations model METROPOLIS. Incidents are generated randomly by reducing the capacity of the network. Users can instantaneously adapt to the unexpected travel conditions or can also change their behavior via a day-to-day adjustment process. Comparisons with incident-free simulations provide a benchmark for potential travel time savings that can be brought in by a state-of-the-art information system. We measure the impact of variable travel conditions by describing the willingness to pay to avoid risky or unreliable journeys. Indeed, for risk averse drivers, any uncertainty corresponds to a utility loss. This utility loss is computed for several levels of network disruption. The main results of the paper is that the utility loss due to uncertainty is of the same order of magnitude as the total travel costs.
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Bibliographic InfoPaper provided by THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise in its series THEMA Working Papers with number 2007-18.
Date of creation: 2007
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-09-09 (All new papers)
- NEP-CMP-2007-09-09 (Computational Economics)
- NEP-UPT-2007-09-09 (Utility Models & Prospect Theory)
- NEP-URE-2007-09-09 (Urban & Real Estate Economics)
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