Improving the approaches of traffic demand forecasting in the big data era

Since the 2000s, an era of big data has emerged. Since then, urban planners have increasingly applied the theory and methods of big data in planning practice. Recent decades illustrate a rapid increase of the application of big data approaches in transportation, bringing new opportunities for innovation in transport modeling. This article analyzes the theories and methods of big data in traffic demand forecasting. In view of theory, the new models and algorithms are proposed in order to adapt to new big data and response to the limitations of traditional disaggregated approaches. In such approaches, three traffic demand-forecasting methods, the full sample-demand distribution model, the traffic integration model, the model organism protein expression database model, are discussed. Undoubtedly, the development of big data also presents new challenges to travel-demand forecasting methods regarding data acquisition, data processing, data analysis, and application of results. In particular, identifying how to improve approaches to traffic-demand forecasting in the big data era in the Third World will be a challenge to the researchers in the field.

• Record URL:
  • https://doi.org/10.1016/j.cities.2018.04.015
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0264275117315081
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/02642751
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhao, Yongmei
  • Zhang, Hongmei
  • An, Li
  • Liu, Quan
• Publication Date: 2018-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References; Tables;
• Pagination: pp 19-26
• Serial:
  • Cities
  • Volume: 82
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0264-2751
  • Serial URL: http://www.sciencedirect.com/science/journal/02642751

Subject/Index Terms

• TRT Terms: Automatic data collection systems; City planning; Data analysis; Developing countries; Technological forecasting; Traffic models; Travel demand; Travel demand management
• Uncontrolled Terms: Big data
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01689389
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 20 2018 3:33PM