Predicting the future location of cars on urban street network by chaining spatial web services

The use of web services for analysing and visualising maps has received great attention recently, because the complicated analysis of spatial data requires different processes to be run consecutively. Predicting the future location of a vehicle on a street network is one of the most challenging analyses used for improving context-aware location-based services, intelligent transportation systems and criminology. In this research, the authors present a new short-term prediction algorithm and explore the required analyses and web services. They present an appropriate method for chaining these web services to predict location(s). To assess their methodology, they developed a prototype system and tested for trajectories in Beijing. This system calculates the prediction time for a specified car to show the predicted future location in the street network. Their results showed that the average transferred data volume increases as the prediction period increases. The results also showed that the prediction algorithm has 75% accuracy at 1 min and 87.5% accuracy at 2 and 3 min. The implemented chaining method reduces the complexity of the location prediction algorithm for users because they do not need to know the processes. The outputs from this system can be used as input parameters for other web-based applications.

• Record URL:
  https://doi.org/10.1049/iet-its.2017.0085
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Supplemental Notes:
  • Abstract reprinted with permission of the Institution of Engineering and Technology.
• Authors:
  • Aghdam, Arash Hazeghi
  • Alesheikh, Ali Asghar
• Publication Date: 2018-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 793-800
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 12
  • Issue Number: 8
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Automatic vehicle location; Cartography; Data analysis; Intelligent transportation systems; Mathematical prediction; Urban highways; Visualization
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01684066
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 24 2018 11:18AM