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71248-1 Report Abstract

In-Vehicle Information Systems for Network Traffic Control: A Simulation Framework to Study Alternative Guidance Strategies

R. Jayakrishnan and H.S. Mahmassani, University of Texas at Austin, June 1992, 282 pp.

Efforts are underway around the world to use advanced telecommunication and information technologies for improving the traffic quality in congested urban areas through new approaches to effect better traffic patterns. However, these efforts to-date have proceeded without much insight on several key elements with profound effect on the resulting system performance. This research develops a simulation framework to study certain aspects that influence the performance of traffic networks under information.

A framework is developed that integrates the modeling of three key elements of traffic systems under information, namely, the traffic flow simulation, the path-processing aspects and the driver response to information. The simulation moves the vehicles using macroscopic traffic flow relations in discretized network segments, while tracking their positions. The boundedly rational behavioral model assumed for the driver response captures the driver decisions to stay on a suboptimal but sufficing paths despite the provided route information. The framework is applied to candidate networks under information, to study the system performance under different levels of usage of technology and different driver behavior parameters.

Two different programs were developed: one for networks with parallel highways towards a single destination, and one for networks of general shapes and multiple destinations. The former model with faster path processing is also used for studying an idealized corridor for its stochastic-dynamic equilibration behavior under information using iterative simulations with available utility functions. The latter model is used for a realistic city network similar to the core network of Austin, Texas.

The path processing component if developed carefully, and is flexible enough to model the driver behavior of selecting from a few paths under non mandatory guidance. Efficient data structures are used to the efficient enumeration and updating the k-shortest paths. If these paths are not updated every simulation time step, the trip times of the existing k-paths are updated by efficient routines using two possible algorithms: one intended for sequential processors and another for a processor with vectorization capabilities.

The results provide important insights on the effectiveness of in-vehicle information. Only a relatively small fraction (less than 30%) of the drivers may need to be equipped to obtain almost all of the advantages of guidance, and the system could get worse for higher percentages depending on the network context.

Keywords: In-Vehicle Information, Congestion Management, Traffic Patterns, Traffic Flow, Driver Response

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Reference Report #71248-1