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Mining taxi trajectories for most suitable stations of sharing bikes to ease traffic congestion

Mining taxi trajectories for most suitable stations of sharing bikes to ease traffic congestion

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Sharing bike service is a new emerging public transportation, which has been the hottest topic for months. Sharing bike service provides flexible demand-oriented transit services for city commuters. However, as large amount of sharing bikes flood into big cities, problems caused by chaotic order of sharing bikes are emerging slowly. The authors aim to draw support from taxi trajectories to analyse current traffic condition and improve it with sharing bikes. In this study, the authors propose a traffic congestion finding framework, called CF. In CF, derived from the points density-based clustering method of inspiration, the authors propose a new clustering method (CF-Dbscan) and successfully applied it to the clustering of trajectories. A road network matching algorithm (CF-Matching) helps to match GPS points to road net even if points are in low-sampling-rate. They also employ a ranking feedback mathematical model to adjust the number of sharing bikes of different stations to meet people's demand and reduce redundancy. The first experiment proves that the proposed clustering algorithm performs better than traditional DBSCAN. Another experiment is conducted to verify the effectiveness of the proposed framework in reducing traffic congestion. The experimental results prove that with the proposed framework the authors can achieve the purpose of easing traffic congestion.

References

    1. 1)
      • 1. Lambas, M.E.L., Caąceres, A.M.D.: ‘Shared bike systems: intelligent vehicles for sustainable cities’, Carreteras, 2014, 4, (194), pp. 8088.
    2. 2)
      • 2. Garcia-Palomares, J.C., Gutierrez, J., Latorre, M.: ‘Optimizing the location of stations in bike-sharing programs: a GIS approach’, Appl. Geography, 2012, 35, (1-2), pp. 235246.
    3. 3)
      • 3. Zhang, D., Li, N., Zhou, Z.H., et al: ‘iBAT: detecting anomalous taxi trajectories from GPS traces’. Int. Conf. on Ubiquitous Computing, 2011, pp. 99108.
    4. 4)
      • 4. Liu, Y., Weng, X., Wan, J., et al: ‘Exploring data validity in transportation systems for smart cities’, IEEE Commun. Mag., 2017, 55, (5), pp. 2633.
    5. 5)
      • 5. Zheng, Y., Liu, Y., Yuan, J., et al: ‘Urban computing with taxicabs’. Int. Conf. on Ubiquitous Computing, 2011, pp. 8998.
    6. 6)
      • 6. Lin, J., Zhang, Y., Zhang, H.: ‘Research on the characteristics of resident travel based on the taxi GPS trajectory data mining’, Computer Era, 2017.
    7. 7)
      • 7. Yan, L., Chow, C.Y., Lee, V.C.S., et al: ‘T2CBS: mining taxi trajectories for customized bus systems’. Computer Communications Workshops, 2016, pp. 441446.
    8. 8)
      • 8. Zheng, Y., Yuan, N.J., Zheng, K., et al: ‘On discovery of gathering patterns from trajectories’. IEEE Int. Conf. on Data Engineering, 2013, pp. 242253.
    9. 9)
      • 9. Li, Y., Luo, J., Chow, C.Y., et al: ‘Growing the charging station network for electric vehicles with trajectory data analytics’. IEEE Int. Conf. on Data Engineering, 2015, pp. 13761387.
    10. 10)
      • 10. Zhu, B., Xu, X.: ‘Urban principal traffic flow analysis based on taxi trajectories mining’ (Springer Int. Publishing, Zurich, 2015).
    11. 11)
      • 11. Ferreira, F.A.B.S., Fonseca, C.S., Vital, D., et al: ‘Assignment of shared bike stations based on network sciences’, IEEE Latin Am. Trans., 2016, 14, (9), pp. 39573961.
    12. 12)
      • 12. Liu, F., Zhang, Z.: ‘Adaptive density trajectory cluster based on time and space distance’, Phys. A Stat. Mech. Appl., 2017, 484, pp. 4156.
    13. 13)
      • 13. Bi, F.M., Wang, W.K., Chen, L.: ‘DBSCAN: density-based spatial clustering of applications with noise’, J. Nanjing Univ., 2012, 48, (4), pp. 491498.
    14. 14)
      • 14. Ceder, A.: ‘Public transit planning and operation: modeling, practice and behavior’, Transport, 2015, 30, (4), pp. 448450.
    15. 15)
      • 15. Yao, D., Zhang, C., Zhu, Z., et al: ‘Trajectory clustering via deep representation learning’. Int. Joint Conf. on Neural Networks, 2017, pp. 38803887.
    16. 16)
      • 16. Yuan, H., Qian, Y., Ma, B., et al: ‘From trajectories to path network: an endpoints-based GPS trajectory partition and clustering framework’. Int. Conf. on Web-Age Information Management, 2014, pp. 740743.
    17. 17)
      • 17. Wang, W., Tao, L., Gao, C., et al: ‘A C-DBSCAN algorithm for determining bus-stop locations based on taxi GPS data’. Int. Conf. on Advanced Data Mining and Applications, 2014, pp. 293304.
    18. 18)
      • 18. Lee, J.G., Han, J., Whang, K.Y.: ‘Trajectory clustering: a partition-and-group framework’. ACM SIGMOD Int. Conf. on Management of Data, 2007, pp. 593604.
    19. 19)
      • 19. Sun, Y., Yu, X., Bie, R., et al: ‘Discovering time-dependent shortest path on traffic graph for drivers towards green driving’, J. Netw. Comput. Appl., 2017, 83, pp. 204212.
    20. 20)
      • 20. Raymond, R., Imamichi, T.: ‘Bus trajectory identification by map-matching’. Int. Conf. on Pattern Recognition, 2017, pp. 16181623.
    21. 21)
      • 21. Lou, Y., Zhang, C., Zheng, Y., et al: ‘Map-matching for low-sampling-rate GPS trajectories’. ACM Sigspatial Int. Symp. on Advances in Geographic Information Systems, ACM-GIS 2009, Seattle, Washington, USA, 4–6 November 2009, pp. 352361.
    22. 22)
      • 22. Oghina, A., Breuss, M., Tsagkias, M., et al: ‘Predicting IMDB movie ratings using social media’ (Springer Berlin Heidelberg, Berlin, 2012).
    23. 23)
      • 23. Raviv, T., Tzur, M., Forma, I.A.: ‘Static repositioning in a bike-sharing system: models and solution approaches’, Euro J. Transp. Log., 2013, 2, (3), pp. 187229.
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