Travelers’ Risk-Taking Behaviors in Day-to-Day Dynamic Evolution Model

Abstract

Uncertainties often exist in traffic systems because of interday route flow swapping and intraday road capacity degradation. These uncertainties obviously affect the traveler’s individual route adjustment behavior and the corresponding network traffic dynamic evolution at an aggregate level. Seeking to give a more realistic description of traffic dynamics, this study extends the existing day-to-day (DTD) model by considering both travelers’ study of uncertain travel time and travelers’ choice of risky routes. In the proposed model, an exponential-smoothing filter is introduced to describe travelers’ study of uncertain travel time. Meanwhile, on-time arrival probability is used to model travelers’ dynamic adjustment to their reference travel time (RTT). Under the framework of cumulative prospect theory, the perceived stochastic route travel time and the RTT are both integrated into a unified prospect value function to reflect travelers’ perception of route attractiveness. On this basis, a logit model is applied to describe travelers’ stochastic route choice behaviors. The proposed model’s several mathematical properties, including fixed-point existence, uniqueness, and stability, are also investigated through theoretical analyses. Numerical examples are used to illustrate the applications of the DTD model and verify some important dynamic system properties.

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