ESTIMATING TIME DEPENDENT TRIP TABLES

Increasing congestion in metropolitan areas is a problem of concern for transportation planners and engineers. Because of capital investment and time constraints, developing intelligent and smart techniques has become extremely important to alleviate this problem. The primary objective of these techniques is demand management, which focuses on the effective utilization of available capacity and resources. This paper proposes an intelligent framework for enhancing system performance of a large transportation network. This intelligent transportation system framework consists of three subsystems with the following objectives: collect data, analyze data, and communicate information to system users. The data needed for analysis include existing network capacity conditions, travel times, and expected number of users (demand) in the immediate future. Collecting descriptive information about travel times and demand, in real time, for an entire transportation network is difficult. Hence, a model to estimate temporal variations in travel demand is presented in this paper. The morning peak period is considered for the analysis. The model is based on the arrival pattern of work trips during the morning peak period and the distribution of travel times during this period. The 1995 Las Vegas Valley data are considered for demonstrating application of the model. Issues involved with the simulation and modeling process are discussed.

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