Advertisement

Knowledge and Information Systems

, Volume 50, Issue 2, pp 383–415 | Cite as

Intelligent bus routing with heterogeneous human mobility patterns

  • Yanchi Liu
  • Chuanren Liu
  • Nicholas Jing Yuan
  • Lian Duan
  • Yanjie Fu
  • Hui XiongEmail author
  • Songhua Xu
  • Junjie Wu
Regular Paper

Abstract

Optimal planning for public transportation is one of the keys helping to bring a sustainable development and a better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. However, in many cities people prefer to take private transportation other than public transportation due to the inconvenience of public transportation services. In this paper, we focus on the identification and optimization of flawed region pairs with problematic bus routing to improve utilization efficiency of public transportation services, according to people’s real demand for public transportation. To this end, we first provide an integrated mobility pattern analysis between the location traces of taxicabs and the mobility records in bus transactions. Based on the mobility patterns, we propose a localized transportation mode choice model, with which we can dynamically predict the bus travel demand for different bus routing by taking into account both bus and taxi travel demands. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. We also leverage the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real-world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips.

Keywords

Public transportation Bus routing Human mobility 

Notes

Acknowledgments

We thank anonymous reviewers for their very useful comments and suggestions. This research was supported in part by Natural Science Foundation of China (71329201, 71322104, 71531001), the Rutgers 2015 Chancellor’s Seed Grant Program, National High Technology Research and Development Program of China (SS2014AA012303), and the Fundamental Research Funds for the Central Universities. A preliminary version of this work has been accepted for publication as a regular paper in ICDM 2014 [29].

References

  1. 1.
    Ahuja RK (1993) Network flows. PhD Thesis, Technische Hochschule DarmstadtGoogle Scholar
  2. 2.
    Bagloee SA, Ceder AA (2011) Transit-network design methodology for actual-size road networks. Trans Res Part B Methodol 45(10):1787–1804CrossRefGoogle Scholar
  3. 3.
    Agarwal A (2004) A comparison of weekend and weekday travel behavior characteristics in urban areas. PhD Thesis, USFGoogle Scholar
  4. 4.
    Aslam J, Lim S, Pan X, Rus D (2012) City-scale traffic estimation from a roving sensor network. In: Proceedings of the 10th ACM conference on embedded network sensor systems. ACM, pp 141–154Google Scholar
  5. 5.
    Aurenhammer F (1991) Voronoi diagrams: a survey of a fundamental geometric data structure. ACM Comput Surv 23(3):345–405CrossRefGoogle Scholar
  6. 6.
    Bastani F, Huang Y, Xie X, Powell JW (2011) A greener transportation mode: flexible routes discovery from GPS trajectory data. In: GIS. ACM, pp 405–408Google Scholar
  7. 7.
    Beirão G, Cabral JAS (2007) Understanding attitudes towards public transport and private car: a qualitative study. Transp Policy 14(6):478–489CrossRefGoogle Scholar
  8. 8.
    Borzsony S, Kossmann D, Stocker K (2001) The skyline operator. In: 17th international conference on data engineering, 2001. Proceedings. IEEE, pp 421–430Google Scholar
  9. 9.
    Ceder A (2007) Public transit planning and operation: theory, modeling and practice. Elsevier, Butterworth-Heinemann, OxfordGoogle Scholar
  10. 10.
    Ceder A, Wilson NHM (1986) Bus network design. Transp Res Part B Methodol 20(4):331–344CrossRefGoogle Scholar
  11. 11.
    Chakroborty P (2003) Genetic algorithms for optimal urban transit network design. Comput Aided Civ Infrastruct Eng 18(3):184–200CrossRefGoogle Scholar
  12. 12.
    Chakroborty P, Wivedi T (2002) Optimal route network design for transit systems using genetic algorithms. Eng Optim 34(1):83–100CrossRefGoogle Scholar
  13. 13.
    Chen C, Zhang D, Zhou Z-H, Li N, Atmaca T, Li S (2013) B-planner: night bus route planning using large-scale taxi GPS traces. In: PerCom. IEEE, pp 225–233Google Scholar
  14. 14.
    de Dios Ortuzar J, Willumsen LG (2011) Modelling transport, Wiley pressGoogle Scholar
  15. 15.
    de Montjoye Y-A, Hidalgo CA, Verleysen M, Blondel VD (2013) Unique in the crowd: the privacy bounds of human mobility. Sci Rep 3(1376):1–5Google Scholar
  16. 16.
    Fan W, Machemehl RB (2006) Optimal transit route network design problem with variable transit demand: genetic algorithm approach. J Transp Eng 132(1):40–51CrossRefGoogle Scholar
  17. 17.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874MathSciNetCrossRefGoogle Scholar
  18. 18.
    Ge Y, Xiong H, Tuzhilin A, Xiao K, Gruteser M, Pazzani M (2010) An energy-efficient mobile recommender system. In: KDD, pp 899–908Google Scholar
  19. 19.
    Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 330–339Google Scholar
  20. 20.
    Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782CrossRefGoogle Scholar
  21. 21.
    Guihaire V, Hao J-K (2008) Transit network design and scheduling: a global review. Transp Res Part A Policy Pract 42(10):1251–1273CrossRefGoogle Scholar
  22. 22.
    Kepaptsoglou K, Karlaftis M (2009) Transit route network design problem: review. J Transp Eng 135(8):491–505CrossRefGoogle Scholar
  23. 23.
    Kim S, Shekhar S, Min M (2008) Contraflow transportation network reconfiguration for evacuation route planning. IEEE Trans Knowl Data Eng 20(8):1115–1129CrossRefGoogle Scholar
  24. 24.
    Land AH, Doig AG (1960) An automatic method of solving discrete programming problems. Econometrica 28(3):497–520MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Lathia N, Capra L (2011) Mining mobility data to minimise travellers’ spending on public transport. In: KDD. ACM, pp 1181–1189Google Scholar
  26. 26.
    Lathia N, Froehlich J, Capra L (2010) Mining public transport usage for personalised intelligent transport systems. In: ICDM, pp 887–892Google Scholar
  27. 27.
    Liu C-L, Pai T-W, Chang C-T, Hsieh C-M (2001) Path-planning algorithms for public transportation systems. In: Proceedings. 2001 IEEE intelligent transportation systems. IEEE, pp 1061–1066Google Scholar
  28. 28.
    Liu L, Hou A, Biderman A, Ratti C, Chen J (2009) Understanding individual and collective mobility patterns from smart card records: a case study in shenzhen. In: ITSC. IEEE, pp 1–6Google Scholar
  29. 29.
    Liu Y, Liu C, Yuan NJ, Duan L, Fu Y, Xiong H, Xu S, Wu J (2014) Exploiting heterogeneous human mobility patterns for intelligent bus routing. In: ICDM. IEEE, pp 360–369Google Scholar
  30. 30.
    Manning CD, Schütze H (1999) Foundations of statistical natural language processing. MIT Press, CambridgezbMATHGoogle Scholar
  31. 31.
    Monreale A, Pinelli F, Trasarti R, Giannotti F (2009) Wherenext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 637–646Google Scholar
  32. 32.
    Pratt RH, Evans IV et al (2004) Traveler response to transportation system changes. Chapter 10, Bus routing and coverageGoogle Scholar
  33. 33.
    Redman L, Friman M, Gärling T, Hartig T (2013) Quality attributes of public transport that attract car users: a research review. Transp Policy 25:119–127CrossRefGoogle Scholar
  34. 34.
    Song C, Zehui Q, Blumm N, Barabási A-L (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Tilo S (2010) Data fitting and uncertainty: a practical introduction to weighted least squares and beyond. Vieweg, Teubner, Wiesbaden. ISBN:3834810223Google Scholar
  36. 36.
    Sun JB, Yuan J, Wang Y, Si HB, Shan XM (2011) Exploring space–time structure of human mobility in urban space. Phys A 390(5):929–942CrossRefGoogle Scholar
  37. 37.
    Utsunomiya M, Attanucci J, Wilson N (2006) Potential uses of transit smart card registration and transaction data to improve transit planning. Trans Res Rec 1971(1):119–126CrossRefGoogle Scholar
  38. 38.
    Watkins KE, Ferris B, Borning A, Rutherford GS, Layton D (2011) Where is my bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders. Trans Res Part A Policy Pract 45(8):839–848CrossRefGoogle Scholar
  39. 39.
    Yen JY (1971) Finding the k shortest loopless paths in a network. Manag Sci 17(11):712–716MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and pois. In: KDD. ACM, pp 186–194Google Scholar
  41. 41.
    Yuan J, Zheng Y, Xie X, Sun G (2013) T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans Knowl Data Eng 25(1):220–232CrossRefGoogle Scholar
  42. 42.
    Yuan NJ, Wang Y, Zhang F, Xie X, Sun G (2013) Reconstructing individual mobility from smart card transactions: a space alignment approach. In: ICDM, pp 877–886Google Scholar
  43. 43.
    Zheng Y, Liu Y, Yuan J, Xie X (2011) Urban computing with taxicabs. In: Proceedings of the 13th international conference on Ubiquitous computing. ACM, pp 89–98Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Yanchi Liu
    • 1
  • Chuanren Liu
    • 2
  • Nicholas Jing Yuan
    • 3
  • Lian Duan
    • 4
  • Yanjie Fu
    • 1
  • Hui Xiong
    • 1
    Email author
  • Songhua Xu
    • 5
  • Junjie Wu
    • 6
  1. 1.Management Science and Information Systems DepartmentRutgers UniversityNewarkUSA
  2. 2.Drexel UniversityPhiladelphiaUSA
  3. 3.Microsoft ResearchBeijingChina
  4. 4.Hofstra UniversityLong IslandUSA
  5. 5.New Jersey Institute of TechnologyNewarkUSA
  6. 6.Beihang UniversityBeijingChina

Personalised recommendations