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


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.


Public transportation Bus routing Human mobility 



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].


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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

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