Abstract
To study the effect of en-route information on driver’s route choice behavior, a dynamic route choice modeling approach is proposed, which takes the driver’s knowledge updating process into consideration. A Bayesian network is developed to describe the en-route travel time updating process. Within the framework of the cumulative prospect theory, a choice model is conducted to analyze the driver’s route choice behavior at each decision node. A numerical example is carried out to illustrate the application of the dynamic route choice modeling approach. The result demonstrates that the route choice behavior considering en-route information is a dynamic process. The traditional route choice model based on cumulative prospect theory without considering en-route information is also employed as a reference in the experiment. From the comparison, the dynamic route choice modeling approach in which a driver’s knowledge of making en-route decisions is taken into account has a better goodness of fit. A stated preference survey is carried out to investigate drivers’ route choice behaviors under different traffic scenario. The result indicates that the proposed approach could provide a more accurate description to driver’s route choice behavior under the conditions of uncertainty.
Highlights
Driver’s travel decision could be influenced by information about the traffic conditions
Traffic information is disseminated via advanced traveler information system (ATIS) which utilizes display devices such as variable message sign (VMS), radio, navigation, or website
The results indicated that the Bayesian network (BN) model performed reasonably well in travelers’ preference classifications for toll road utilization and knowledge extraction
Summary
Driver’s travel decision could be influenced by information about the traffic conditions. A BN, which is known as belief network or Bayesian belief network, is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a DAG. The nodes in a BN represent a set of random variables, X = X1, ..., Xi, ..., Xn, which may be observable quantities, latent variables, unknown parameters, or hypotheses. The arcs between nodes stand for causal relationship, and the direction is always from a parent node to a child node. Each node is associated with a set of conditional probability distribution (CPD) P = {p(x1|p1), ..., p(xn|pn)}, which takes a particular set of values of the node’s parent variables and gives the probability of the variable represented by the node
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