Estimating time-dependent origin–destination demand from traffic counts: extended gradient method

Sajjad Shafiei · Mohsen Nazemi · Seyedehsan Seyedabrishami

17 citations0/0 files storedTransportation Letters

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

Abstract

Time-dependent origin–destination (TDOD) demand is a key input of dynamic traffic assignment (DTA) in advanced traffic management systems. Model reliability is highly dependent on the accuracy of this information. One method to achieve TDOD demand matrices is to use a primary demand matrix and traffic volume counts in some links of a network. This paper proposes a bi-level model to correct the TDOD demand matrix. The extended gradient method (EGM) – an iterative method that minimizes the discrepancy between the counted and estimated traffic volumes – is a suggested means to solve this problem. The methodology is first tested on a small synthetic network to verify its performance. Then, it is applied to a real network to demonstrate its scalability. The results illustrate the effectiveness of this algorithm for the correction of TDOD demand matrices.

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References

1990Econometric Models and Economic Forecasts 2009Transportation Systems Analysis 1998An Overview of the Simultaneous Perturbation Method for Efficient Optimization 2009Transportation Systems Analysis: Models and Applications 1993Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts 2010A gradient approximation approach for adjusting temporal origin–destination matrices 2013Dynamic origin–destination demand flow estimation under congested traffic conditions 2001Estimation of dynamic origin–destination trip tables for a general network

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2017Sensitivity-Based Linear Approximation Method to Estimate Time-Dependent Origin–Destination Demand in Congested Networks 2021Single-level approach to estimate origin-destination matrix: exploiting turning proportions and partial OD flows 2016Application of an exact gradient method to estimate dynamic origin-destination demand for melbourne network 2019Origin-Destination Matrix Estimation Using Big Traffic Data: A Structural Perspective

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