UC Davis Institute of Transportation Studies

Computer sumulation is often-used methodology to study travel behavior as a cost effective alternative to field studies. In this study, we utilize PC-based computer simulation to study the effects of information on route choice and learning. Building on the efforts of prior stage of simulation, further experiments that utilize an expanded traffic network and provide various levels of information to subjects, have been designed. This framework allows us to investigate both pretrip and en route route-choice behavior, and capture the effect of different levels of information of drivers' learning and adaptive processes that are being undertaken in these experiments.

The experiments were designed in two stages. In the first stage, a simple, two route-alternative traffic network was developed. Experiments conducted with this network provided extensive comments from participants, which were modeled using object-oriented programming techniques to produce a better subsequent design. The data from the first stage was analyzed using neural network techniques and the network was trained using back-propagation. The second stage of experiments utilized a multiple-route, expanded network with pretrip and/or en route information, and varying levels of information. The data obtained in this stage is being analyzed using recurrent neural networks. This paper describes the design and analysis of the first stage of experiments, and the redesign of the network siulation using experience gained in the first stage.

The design of the network simulation involved the following steps: requirements analysis, database design, specifications of user-computer interface, design of shortest path module, software development, and prototype testing and refinement. The simulator was developed using and object-oriented programming language, C++. The object-oriented features included inheritance, class hierarchy, message passing and concurence. A recurrent neural network has been built for future modeling of the data obtained in the second stage of experiments. This neural network will be used to predict subjects' choices of whether or not to follow the system-provided advice, depending on past experience. An important feature of the neural network is that the decisions at previous nodes, will be used as an input at subsequent nodes. This allows us to model participants route choice behavior at every node, that is at every decision point.