Analysis of Urban Traffic Based on Taxi GPS Data

  • Li Meng
  • Li Ru-tong
  • Xia Yong
  • Qin Zhi-guang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 279)


Recently, the problem of traffic jam in major cities is getting worse. By leveraging the taxi GPS data of Shenzhen, this paper analyzes the urban traffic status and proposes rational suggestions for urban traffic management. In particular, this paper firstly presents the get-on and get-off points on GIS map based on taxi GPS data. Secondly, by using K-Means algorithm to allocate urban traffic cells, the hot areas where passenger flow is huge are pointed out. Finally, based on the taxi speed, we locate the crowded area and find the crowded period, then analyze the reasons that cause the traffic jam and propose rational suggestions for urban traffic management.


GPS data clustering algorithm map matching traffic analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yu, Z., Xing, X.: Learning travel recommendations from user-generated GPS traces. ACM Transaction on Intelligent Systems and Technology, 2–19 (2011)Google Scholar
  2. 2.
    Yu, Z., Liu, Y., Jing, Y., Xie, X.: Urban Computing with Taxicabs. In: Proceeding of the 13th ACM International Conference on Ubiquitous Computing, pp. 89–98 (2011)Google Scholar
  3. 3.
    Lee, Y.-J., Vuchic, V.R.: Transit Network Design with Variable Demand. Journal of Transportation Engineering 131(1), l–10 (2005)Google Scholar
  4. 4.
    Tom, V.M., Mohan, S.: Transit route network design using frequency code dgenetic algorithm. Journal of Transportation Engineering 129(2), 186–195 (2003)CrossRefGoogle Scholar
  5. 5.
    Fan, W., Machemehl, R.B.: A Tabu Search Based Heuristic Method for the Transit Route Network Design Problem. In: The 9th International Conference on Computer-Aided Scheduling of Public Transport (2004)Google Scholar
  6. 6.
    Fan, W., Machemehl, R.B.: Using a Simulated Annealing Algorithm to Solve the Transit Route Network Design Problem. Journal of Transportation Engineering 132(2), 122–132 (2006)CrossRefGoogle Scholar
  7. 7.
    Fan, L., Mumford, C.: A Metaheuristic Approach to the Urban Transit Routing Problem. Journal of Heuristic (2008)Google Scholar
  8. 8.
    Fan, L., Mumford, C., Evans, D.: A simple multi-objective optimization algorithm for the urban transit routing problem. In: The Eleventh Conference on Congress on Evolutionary Computation, pp. 1–7 (2009)Google Scholar
  9. 9.
    Qian, Z., Xu, E., Wang, Z., Yafei, D.: DNA Algorithm on Optimal Path Selection for Bus Travel Network, pp. 245–248 (2009)Google Scholar
  10. 10.
    Liu, L.-Q., Zhang, Y.: Research of Urban Bus Stop Planning based on Optimization Theory, pp. 551–554 (2009)Google Scholar
  11. 11.
    Tang, M., Ren, E., Zhao, C.: Route Optimization for Bus Dispatching Based on Genetic Algorithm-Ant Colony Algorithm, pp. 18–21 (2009)Google Scholar
  12. 12.
    Xu, C., Ji, M., Chen, W., Zhang, Z.: Identifying travel mode from GPS trajectory through fuzzy reasoning. In: Proceeding of the 7th International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, pp. 889–893 (2010)Google Scholar
  13. 13.
    Chen, W., Ji, M., Shi, B., Xu, C., Zhang, B., Deng, Z.: A prompted recall interview platform for GPS-based household travel surveys: design and development. In: Proceeding of International Transport GIS Conference, CDROM, Wuhan (2009)Google Scholar
  14. 14.
    Schaller Consulting. The New York City Taxicab Fact Book, Schaller Consulting, Brooklyn, NY (EB/OL)Google Scholar
  15. 15.
    Yang, H., Wong, S.C., Wong, K.I.: Demand supply equilibrium of taxi services in a network under competition and regulation. Transportation Research: Part B 36(9), 799–819 (2002)CrossRefGoogle Scholar
  16. 16.
    Yang, H., Ye, M., Wilson, H.T., et al.: Regulating taxi services in the presence of congestion externality. Transportation Research: Part A 39(1), 17–40 (2005)MATHGoogle Scholar
  17. 17.
    Jing, Y., Yu, Z., Zhang, L., Xie, X., Sun, G.: Where to Find My Next Passenger? In: Proceeding of the 13th ACM International Conference on Ubiquitous Computing (2011)Google Scholar
  18. 18.
    Jing, Y., Yu, Z., Xing, X.: Discovering regions of different functions in a city using human mobility and POIs. In: Proceeding of the 18th SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 20–29 (2012)Google Scholar
  19. 19.
    Yang, D., Cai, B., Yuan, Y.: An improved map-matching algorithm used in taxis navigation system. In: Proceedings of Intelligent Transportation Systems, Beijing, pp. 1246–1250 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Li Meng
    • 1
    • 2
  • Li Ru-tong
    • 1
    • 2
  • Xia Yong
    • 1
  • Qin Zhi-guang
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Key Laboratory of Calculation and Application Service of ShenzhenPopular High Performance Computers of Guangdong ProvinceShenzhenChina

Personalised recommendations