Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications

Sajjad Shafiei; Adriana-Simona Mihaita; Chen Cai

Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications

Signal Processing 2019-06-13 v1 Artificial Intelligence Machine Learning Machine Learning

Authors: Sajjad Shafiei , Adriana-Simona Mihaita , Chen Cai

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.

Keywords

transportation systems

Cite

@article{arxiv.1906.04739,
  title  = {Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications},
  author = {Sajjad Shafiei and Adriana-Simona Mihaita and Chen Cai},
  journal= {arXiv preprint arXiv:1906.04739},
  year   = {2019}
}

Comments

6 pages, 6 figures, preprint at the 26th ITS World Congress 21-25 Oct 2019

Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications

Abstract

Keywords

Cite

Comments

Related papers