Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications

TL;DR

This paper presents a bi-level optimization approach to estimate and predict dynamic origin-destination trip tables, improving traffic assignment accuracy and supporting short-term traffic forecasting in urban networks.

Contribution

It introduces a novel bi-level optimization framework for OD trip table estimation and integrates a time series prediction model for dynamic traffic demand forecasting.

Findings

High accuracy in OD demand estimation reduces DTA model errors.

Effective short-term traffic prediction demonstrated in Sydney corridor.

Iterative solution algorithm enhances estimation reliability.

Abstract

The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model. A bi-level optimisation problem is formulated and solved to estimate OD flows from pre-existent demand matrix and historical traffic flow counts. The estimated demand is then considered as an input for a time series OD demand prediction model to support the DTA model for short-term traffic condition forecasting. Results show a high capability of the proposed OD demand estimation method to reduce the DTA model error through an iterative solution algorithm. Moreover, the applicability of the OD demand prediction approach is investigated for an incident analysis application for a major corridor in Sydney, Australia.