Big Data and the Calibration and Validation of Traffic Simulation Models

Real-world data are the essential elements in the validation and calibration of microscopic traffic simulation models. Availability, accuracy and relevance of real-world data can seriously affect the reliability of the models’ predictions. Traditional sources of traffic data are either limited to a limited number of typical conditions or may not be reliable enough. With the advent of new technologies, information is on the fingertips of users by means of smartphones, GPS-equipped devices, and radio frequency identification (RFID) readers. The rapid rise in information technology has also resulted in innovative ways to obtain space- and time-sensitive information in real time. This, in turn, has led to massive amount of passively collected location and event data for various time periods, also called “Big Data.” With the availability of Big Data there is an opportunity to validate and calibrate traffic simulation models in a way that has never been possible in the past. In this paper, the authors examine the current practice of calibration of traffic simulation models with an emphasis on data needs. They also describe the various sources of Big Data that might be available to the traffic simulation community now being collected through in-vehicle and infrastructure-based technologies. Various real-world case studies are presented to illustrate the importance and future of Big Data in the calibration of traffic simulation models. Future applications of Big Data are also discussed in detail.


  • English

Media Info

  • Media Type: Web
  • Features: Figures; References; Tables;
  • Pagination: pp 92-122
  • Monograph Title: Traffic and Transportation Simulation. Looking Back and Looking Ahead: Celebrating 50 Years of Traffic Flow Theory, a Workshop
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01566119
  • Record Type: Publication
  • Files: TRIS, TRB, ATRI
  • Created Date: Jun 10 2015 11:51AM