A Data-Driven Approach to Estimate Dynamic and Multimodal Urban Origin-Destination Matrices

Origin-Destination (OD) matrices have an essential role in urban transport planning and design. Transport researchers and practitioners have traditionally relied on household travel surveys to estimate necessary OD matrices. In general, travel surveys suffer from small sample sizes and high implementation costs. However, during the last two decades, there have been some efforts to estimate OD matrices as side-products of emerging large mobility datasets. In this research, data from Automated Vehicle Location (AVL), Automated Fare Collection (AFC), loop detectors and traffic count cameras have been used to enrich OD matrices of Household Travel Surveys (HTS) for public and private modes. The related data from the network of Tehran, Iran (176,993 arcs) is generated from more than 2 million daily AVL records, more than 3 million daily AFC transactions, 1,957 loop detectors and 641 traffic count cameras. The latest developments in the related literature have been implemented to estimate the OD matrices. Therefore, the main contribution of our work is to design and implement a data-driven approach to estimate dynamic and multimodal OD matrices on a real-world large scale case study that is a city of more than 9 million night-time residents and 739 traffic zones. The outcomes of the proposed approach prove the capability of the emerging mobility datasets in estimating dynamic and multimodal OD matrices in urban areas.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 15p

Subject/Index Terms

Filing Info

  • Accession Number: 01857270
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-22-02553
  • Files: TRIS, TRB, ATRI
  • Created Date: Sep 7 2022 9:59AM