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

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

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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.


    1. 1)
      • 1. Abrahama, S., Lal, P.S.: ‘Spatio-temporal similarity of network-constrained moving object trajectories using sequence alignment of travel locations’, Transp. Res. C, 2012, 23, pp. 109112.
    2. 2)
      • 2. Tseng, V.S., Lu, E.H.: ‘Energy-efficient real-time object tracking in multi-level sensor networks by mining and predicting movement patterns’, J. Syst. Soft., 2009, 82, (4), pp. 697706.
    3. 3)
      • 3. Liu, X., Karimi, H.A.: ‘Location awareness through trajectory prediction’, Comput. Environ. Urban Syst., 2006, 30, (6), pp. 741756.
    4. 4)
      • 4. OGC - Open Geospatial Consortium. Available at, accessed August 2014.
    5. 5)
      • 5. Stollberg, B., Zipf, A.: ‘Development of a WPS process chaining tool and application in a disaster management use case for urban areas’. Urban Regional Data Management, Ljubljana, Slovenia, 2009, pp. 269285.
    6. 6)
      • 6. ISO19119. ‘Geographic information – Services’, 2002.
    7. 7)
      • 7. Alameh, N.: ‘Service chaining of interoperable geographic information web services’, Int. Comput., 2002, 7, (1), pp. 2229.
    8. 8)
      • 8. Xie, X., Bian, Y., Meng, F.: ‘Distributed geospatial analysis through web processing service: a case study of earthquake disaster assessment’, J. Soft., 2010, 5, (6), pp. 671679.
    9. 9)
      • 9. Zhao, P., Di, L., Yu, G.: ‘Building asynchronous geospatial processing workflows with web services’, Comput. Geosci., 2012, 39, pp. 3441.
    10. 10)
      • 10. Rautenbach, V., Coetzee, S., Iwaniak, A.: ‘Orchestrating OGC web services to produce thematic maps in a spatial information infrastructure’, Comput. Environ. Urban Syst., 2013, 37, pp. 107120.
    11. 11)
      • 11. Wang, Z., Li, H., Shen, X., et al: ‘Tracking and predicting moving targets in hierarchical sensor networks’. IEEE Int. Conf. Networking, Sensing and Control, Sanya, China, 2008, pp. 11691173.
    12. 12)
      • 12. Engelbrecht, J., Booysen, M.J., van Rooyen, G.J., et al: ‘Survey of smartphone-based sensing in vehicles for intelligent transportation system applications’, IET Intell. Transp. Syst., 2015, 9, (10), pp. 924935.
    13. 13)
      • 13. Chen, L., Lv, M., Chen, G.: ‘A system for destination and future route prediction based on trajectory mining’, Pervasive Mob. Comput., 2010, 6, (6), pp. 657676.
    14. 14)
      • 14. Chen, L., Lv, M., Ye, Q., et al: ‘A personal route prediction system based on trajectory data mining’, Inf. Sci., 2011, 181, (7), pp. 12641284.
    15. 15)
      • 15. Kim, S.W., Won, J.I., Kim, J.D., et al: ‘Path prediction of moving objects on road networks through analyzing past trajectories’, Knowl. Based Intell. Inf. Eng. Syst., 2007, 4692, pp. 379389.
    16. 16)
      • 16. Schweizer, J., Bernardi, S., Rupi, F: ‘Map-matching algorithm applied to bicycle global positioning system traces in Bologna’. IET Intell. Transp. Syst., 2016, 10, (4), pp. 244250.
    17. 17)
      • 17. Hashemi, S.M., Almasi, M., Ebrazi, R., et al: ‘Predicting the next state of traffic by data mining classification techniques’, Int. J. Smart Electr. Eng., 2012, 1, (3), pp. 181193.
    18. 18)
      • 18. Abduhai, B., Porwal, H., Recker, W.: ‘Short-term traffic flow prediction using neuro-genetic algorithms’, J. Intell. Transp. Syst., 2002, 7, (1), pp. 341.
    19. 19)
      • 19. Lv, Y., Duan, Y., Kang, W., et al: ‘Traffic flow prediction with big data: a deep learning approach’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (2), pp. 865873.
    20. 20)
      • 20. Tan, M., Wong, S.C., Xu, J., et al: ‘An aggregation approach to short-term traffic flow prediction’, IEEE Trans. Intell. Transp. Syst., 2009, 10, (1), pp. 6069.
    21. 21)
      • 21. Kamkar, S., Safabakhsh, R.: ‘Vehicle detection, counting and classification in various conditions’, IET Intell. Transp. Syst., 2016, 10, (6), pp. 406413.
    22. 22)
      • 22. Noulas, A., Scellato, S., Lathia, N., et al: ‘Mining user mobility features for next place prediction in location-based services’. 2012 IEEE 12th Int. Conf. Data Mining (ICDM), Brussels, Belgium, 2012.
    23. 23)
      • 23. Yue, P., Di, L., Yang, W., et al: ‘Semantics-based automatic composition of geospatial Web service chains’, Comput. Geosci., 2007, 33, (5), pp. 649665.
    24. 24)
      • 24. Sheshagiri, M., DesJardins, M., Finin, T.: ‘A planner for composing services described in DAML-S’. The Int. Conf. Automated Planning & Scheduling (ICAPS'03) Workshop on Planning for Web Services, Trento, Italy, 2003.
    25. 25)
      • 25. Sirin, E., Parsia, B., Wu, D., et al: ‘HTN planning for web service composition using SHOP2’, Web Semant. Sci. Services Agents on the World Wide Web, 2004, 1, (4), pp. 377396.
    26. 26)
      • 26. Tiakas, E., Papadopoulos, A.N., Nanopoulos, A., et al: ‘Searching for similar trajectories in spatial networks’, J. Syst. Soft., 2009, 82, (5), pp. 117.
    27. 27)
      • 27. Lee, J., Hanand, J., Whang, K.: ‘Trajectory clustering: a partition-and-group framework’. 2007 ACM SIGMOD Int. Conf. Management of Data, New York, USA, 2007, pp. 593604.
    28. 28)
      • 28. Liu, H., Schneider, M.: ‘Similarity measurement of moving object trajectories’. Third ACM SIGSPATIAL Int. Workshop on GeoStreaming, New York, USA, 2012, pp. 1922.
    29. 29)
      • 29. Shaeri, M., Abbaspour, R.A.: ‘Comparison of distance functions for similarity measurement in spatial trajectories’, J. Geomatics Sci. Technol., 2014, 4, (3), pp. 201211.
    30. 30)
      • 30. Sharif, M., Alesheikh, A.A.: ‘Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method. ‘, GISci. Remote Sens., 2017, 54, (3), pp. 127.
    31. 31)
      • 31. Douglas, D.H., Peucker, Th.K.: ‘Algorithms for the reduction of the number of points required to represent a digitized line or its caricature’, Canadian Cartographer, 1973, 10, (2), pp. 112122.
    32. 32)
      • 32. Zheng, Y., Li, Q., Chen, Y., et al: ‘Understanding mobility based on GPS data’. ACM Conf. Ubiquitous Computing, Seoul, Korea, 2008, pp. 312321.
    33. 33)
      • 33. Zheng, Y., Xie, X., Ma, W.: ‘Geolife: a collaborative social networking service among user, location and trajectory’. Data Engineering Bulletin, 2010, 33, (2), pp. 3239.
    34. 34)
      • 34. Zheng, Y., Zhang, L., Xie, X., et al: ‘Mining interesting locations and travel sequences from GPS trajectories’. Int. Conf. World Wild Web, Madrid, Spain, 2009, pp. 791800.
    35. 35)
      • 35. Yavaş, G., Katsaros, D., Ulusoy, Ö., et al: ‘A data mining approach for location prediction in mobile environments’, Data Knowl. Eng., 2005, 54, (2), pp. 121146.
    36. 36)
      • 36. Ying, J.J., Lee, W., Weng, T.: ‘Semantic trajectory mining for location prediction’. Int. Conf. Advances in Geographic Information Systems, Chicago, Illinois, 2011, pp. 3443.

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