Spatial and Temporal Analyses for Estimation of Origin-Destination Demands by Time of Day Over Year

Shen, Liang; Shao, Hu; Wu, Ting; Lam, William H. K. · 2019 · DOAJ

DOI: 10.1109/ACCESS.2019.2909524

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Summary

This paper addresses the challenge of estimating time-dependent origin-destination (OD) traffic demands, a critical input for dynamic traffic assignment (DTA) systems. Conventional methods often rely on survey data, which is costly and difficult to scale, or on surveillance data combined with DTA processes. However, DTA is computationally intensive, requires extensive calibration, and is time-consuming. To overcome these limitations, the authors propose a two-stage model that estimates OD demands using offline traffic data from real-time travel information systems, specifically avoiding the need for a full DTA process. The methodology utilizes data from Hong Kong’s Journey Time Indication System (JTIS), which provides mean traffic speeds every two minutes on major road links. The first stage calculates time-dependent link choice proportions using a novel travel time recursive function. This function derives the spatial and temporal relationships between OD demands and traffic counts by calculating completed travel times from observed speed data, rather than simulating network equilibrium. The second stage formulates the OD estimation problem as a quadratic programming model using a least-squares method. This model minimizes the discrepancy between estimated and observed traffic counts, incorporating historical OD data as a reference. The approach assumes uniform departure flow rates within short time intervals and fixed path sets for each OD pair. The study demonstrates that the proposed model effectively estimates time-dependent OD demands by solving a system of linear equations derived from the recursive travel time function. Numerical examples illustrate the application of the model, showing that it can accurately capture the complex travel behaviors, such as departure time choices, under uncertainty conditions like traffic accidents. The method is shown to be applicable to general networks and large-scale problems, as it avoids the computational bottlenecks associated with traditional DTA-based estimation techniques. The significance of this work lies in its practical implementation and efficiency. By leveraging readily available real-time speed data and eliminating the need for dynamic traffic assignment, the proposed model offers a robust and computationally efficient alternative for traffic monitoring and management. It enables the estimation of OD demands over the entire year, providing valuable insights into travel behavior and network performance. This approach is particularly useful for understanding traffic dynamics during disruptive events and supports more responsive traffic management strategies without the high costs and computational demands of conventional methods.

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