Abstract
Transportation systems are called the lifeline of any urban area. Major transportation systems includes cars, taxis, buses, trams etc., which carries most of the local transport in a city. This work encourages the more use of public transport over private vehicles so as to save environment, energy and resources by suggesting improvement in infrastructure of bus services. Experimental work on data collected from city of New York is presented in this work. This data collected from taxis is used to analyze the presence of traffic in the area. Temporal data segmentation with respect to different time zones is performed considering the dynamic patterns of urban traffic. Next, popular data mining techniques of clustering are applied on this segmented data to form clusters for each time zone so as to identify areas of high traffic. Further, another data set of bus stops is used to identify places with no bus stops and high traffic congestions. Henceforth new bus stops are suggested on places with high traffic density and no bus stops. Thus, a comparative study over baseline is done to recommend places that require bus stops.
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References
Aggarwal, A., Toshniwal, D.: Data mining techniques for smart mobility - a survey. In: Proceedings of the 5th International Conference on Advanced Computing, Networking, and Informatics. Springer, Goa (2017)
Business Insider. http://www.businessinsider.in/tech/10-cities-that-are-starting-to-go-car-free/slidelist/53726274.cms
Thiagarajan, A., Ravindranath, L., LaCurts, K., Madden, S., Balakrishnan, H., Toledo, S., Eriksson, J.: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pp. 85–98. ACM (2009)
Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-Drive: driving directions based on taxi trajectories. In: Proceedings of ACM SIGSPATIAL Conference on Advances in Geographical Information Systems, pp. 99–108. ACM (2014)
Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 25–34. ACM (2014)
Zhou, P., Nadeem, T., Kang, P., Borcea, C., Iftode, L.: EZCab: a cab booking application using short-range wireless communication. In: Pervasive Computing and Communications, pp. 27–38. IEEE (2005)
Ge, Y., Xiong, H., Tuzhilin, A., Xiao, K., Gruteser, M., Pazzani, M.: An energy-efficient mobile recommender system. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 899–908. ACM (2010)
Yuan, N.J., Zheng, Y., Zhang, L., Xie, X.: T-finder: a recommender system for finding passengers and vacant taxis. Trans. Knowl. Data Eng. 25, 2390–2403 (2013)
Watkins, K.E., Ferris, B., Borning, A., Rutherford, G.S., Layton, D.: Where is my bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders. Transp. Res. Part A Policy Pract. 45, 839–848 (2011)
Zimmerman, J., Tomasic, A., Garrod, C., Yoo, D., Hiruncharoenvate, C., Aziz, R., Steinfeld, A.: Field trial of Tiramisu: crowd-sourcing bus arrival times to spur co-design. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1677–1686. ACM (2011)
Bastani, F., Huang, Y., Xie, X., Powell, J.W.: A greener transportation mode: flexible routes discovery from GPS trajectory data. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 405–408. ACM (2011)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques, 3rd edn. Elsevier, Waltham (2011)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Walker, J.: Purpose-driven public transport: creating a clear conversation about public transport goals. J. Transp. Geogr. 16, 436–442 (2008)
NYC Taxi and Limousine Commission-Trip Record Data. http://www.nyc.gov//html/tlc/about/trip_record_data.html
Metropolitan Transportation Authority. http://bustime.mta.info/routes
Vincenty, T.: TDirect and inverse solutions of geodesics on the ellipsoid with application of nested equations. Surv. Rev. 23, 88–93 (1975)
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Rajput, P., Toshniwal, D., Agggarwal, A. (2017). Improving Infrastructure for Transportation Systems Using Clustering. In: Reddy, P., Sureka, A., Chakravarthy, S., Bhalla, S. (eds) Big Data Analytics. BDA 2017. Lecture Notes in Computer Science(), vol 10721. Springer, Cham. https://doi.org/10.1007/978-3-319-72413-3_9
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DOI: https://doi.org/10.1007/978-3-319-72413-3_9
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