Measurement of uncertainty costs with dynamic traffic simulations

Non-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.

Risk aversion, the value of information and traffic equilibrium

Information about traffic conditions has traditionally been conveyed to drivers by radio and variable message signs, and more recently via the Internet and Advanced Traveler Information Systems. This has spurred research on how travelers respond to information, how much they are willing to pay for it and how much they are likely to benefit from it collectively. In this paper we analyze the decisions of drivers whether to acquire information and which route to take on a simple congested road network. Drivers vary in their degree of risk aversion with respect to travel time. Four information regimes are considered: No information, Free information which is publicly available at no cost, Costly information which is publicly available for a fee, and Private information which is available free to a single individual. Private information is shown to be individually more valuable than either Free or Costly information, while the benefits from Free and Costly information cannot be ranked in general. Free or Costly information can decrease the expected utility of drivers who are very risk-averse, and with sufficient risk aversion in the population the aggregate compensating variation for information can be negative.Transportation, route choice, information provision, expected utility, congestion

Risk aversion, the value of information and traffic equilibrium

Information about traffic conditions has traditionally been conveyed to drivers by radio and variable message signs, and more recently via the Internet and Advanced Traveler Information Systems. This has spurred research on how travelers respond to information, how much they are willing to pay for it and how much they are likely to benefit from it collectively. In this paper we analyze the decisions of drivers whether to acquire information and which route to take on a simple congested road network. Drivers vary in their degree of risk aversion with respect to travel time. Four information regimes are considered: No information, Free information which is publicly available at no cost, Costly information which is publicly available for a fee, and Private information which is available free to a single individual. Private information is shown to be individually more valuable than either Free or Costly information, while the benefits from Free and Costly information cannot be ranked in general. Free or Costly information can decrease the expected utility of drivers who are very risk-averse, and with sufficient risk aversion in the population the aggregate compensating variation for information can be negative

Modeling the Dynamics of Opinion Formation and Propagation: An Application to Market Adoption of Transportation Services

The objective of this research is to present a model that utilizes social and learning mechanisms to first explore the underlying dynamics of opinion formation and propagation, and then applies those mechanisms to an application of freight mode choice to investigate the effect that opinions have on choice set considerations, attribute perceptions, and the market adoption of a new rail freight service. Primary contributions of this research include the explicit modeling of social and learning mechanisms and their effects on opinion formation and propagation, the evolution of these opinions over time, and an exploration of the role that opinion dynamics have in choice processes. Research findings will offer insight to the process of evolving attitudes, perceptions, and opinions and the effects on individuals' judgment and decision making. It will also offer insight to the effects of attribute distortion on decision making