Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and global positioning system (GPS) devices.

• Record URL:
  https://doi.org/10.1016/j.trc.2015.07.005
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X15002454
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Nantes, Alfredo
  • Ngoduy, Dong
  • Bhaskar, Ashish
  • Miska, Marc
  • Chung, Edward
• Publication Date: 2016-5

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 99-118
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 66
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Data fusion; Estimation theory; Global Positioning System; Highway corridors; Intersections; Kalman filtering; Loop detectors; Real time information; Traffic models; Urban highways
• Uncontrolled Terms: Traffic state estimation
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01599756
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 21 2016 4:57PM