Estimating time dependent O‐D trip tables during peak periods

Srinivas S. Pulugurtha · Shashi Nambisan

6 citations0/0 files storedJournal of Advanced Transportation

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Transportation Planning and Optimization Traffic control and management Traffic Prediction and Management Techniques

Abstract

Abstract Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real‐time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin‐destination (O‐D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O‐D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O‐D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding‐off, and, 2) low ratio of total number of trips to total number of O‐D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O‐D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed.

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References

1991Dynamic network models and driver information systems 1991System performance and user response under real-time information in a congested traffic corridor 1991Dynamic traffic assignment for urban road networks 1984Dynamic User Equilibrium Departure Time and Route Choice on Idealized Traffic Arterials 1986Dynamic Model of Peak Period Traffic Congestion with Elastic Arrival Rates 1984Dynamic Model of Peak Period Traffic Congestion with Elastic Arrival Rates 1996Estimation of time-varying origin-destination distributions with dynamic screenline flows 1992Estimating departure times from traffic counts using dynamic assignment

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