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Generalized multipath planning model for ride-sharing systems

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Abstract

Ride-sharing systems should combine environmental protection (through a reduction of fossil fuel usage), socialization, and security. Encouraging people to use ride-sharing systems by satisfying their demands for safety, privacy and convenience is challenging. Most previous works on this topic have focused on finding a fixed path between the driver and the riders either based solely on their locations or using social information. The drivers’ and riders’ lack of options to change or compute the path according to their own preferences and requirements is problematic. With the advancement of mobile social networking technologies, it is necessary to reconsider the principles and desired characteristics of ride-sharing systems. In this paper, we formalized the ride-sharing problem as a multi source-destination path planning problem. An objective function that models different objectives in a unified framework was developed. Moreover, we provide a similarity model, which can reflect the personal preferences of the rides and utilize social media to obtain the current interests of the riders and drivers. The model also allows each driver to generate sub-optimal paths according to his own requirements by suitably adjusting the weights. Two case studies have shown that our system has the potential to find the best possible match and computes the multiple optimal paths against different user-defined objective functions.

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References

  1. http://environment.about.com/od/globalwarming/tp/globalwarmtips.html

  2. Kwon J, Varaiya P. Effectiveness of California’s High Occupancy Vehicle (HOV) system. Transportation Research Part C: Emerging Technologies, 2010, 16(1): 98–115

    Article  Google Scholar 

  3. Jeanes P. Can total car emissions be reduced enough by 2020? Traffic Engineering & Control, 2010, 51(1): 6–8

    Google Scholar 

  4. http://www.ridenow.org/ridenow_summary.html

  5. Agatz N, Erera A, Savelsbergh M, Wang X. Sustainable passenger transportation: dynamic ride-sharing. Technical report, Erasmus Research Institute of Management (ERIM), 2010

    Google Scholar 

  6. Yousaf J, Li J, Chen L, Tang J, Dai X, Du J. Ride-sharing: a multi source-destination path planning approach. Lecture Notes in Computer Science, 2012, 7691: 815–826

    Article  Google Scholar 

  7. Gilbert E, Karahalios K. Predicting tie strength with social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2009, 211–220

    Google Scholar 

  8. Trasarti R, Pinelli F, Nanni M, Giannotti F. Mining mobility user profiles for car pooling. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. 2011, 1190–1198

    Google Scholar 

  9. Kleiner A, Nebel B, Ziparo V A. A mechanism for dynamic ride shar ing based on parallel auctions. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 2011, 1: 266–272

    Google Scholar 

  10. Li X, Pan G, Wu Z, Qi G, Li S, Zhang D, Zhang W, Wang Z. Prediction of urban human mobility using large-scale taxi traces and its applications. Frontiers of Computer Science, 2012, 6(1): 111–121

    MathSciNet  Google Scholar 

  11. Chen C, Zhang D, Castro P S, Li N, Sun L, Li S. Real-time detection of anomalous taxi trajectories from GPS traces. Lecture Notes in Computer Science, 2012, 104: 63–74

    Article  Google Scholar 

  12. Resnick P. Sociotechnical support for ride sharing. Technical report, School of information, University of Michigan, 2006

    Google Scholar 

  13. Kelley K L. Casual carpooling-enhanced. Journal of Public Transportation, 2007, 10(4): 119–130

    Google Scholar 

  14. Murphy P. The smart jitney: rapid, realistic transport. New Solutions Journal, 2007, (12): 1–12

    Google Scholar 

  15. Gidófalvi G, Herenyi G, Bach Pedersen T. Instant social ride-sharing. In: Proceedings of the 15th World Congress on Intelligent Transport Systems. 2008, 1–8

    Google Scholar 

  16. Morris J. Saferide: Reducing single occupancy vehicles. Technical report, Technical report, 2008

    Google Scholar 

  17. Kirshner D. Ride now. http://www.ridenow.org, 2009

    Google Scholar 

  18. Vanoutrive T, Van De Vijver E, Van Malderen L, Jourquin B, Thomas I, Verhetsel A, Witlox F. What determines carpooling to workplaces in Belgium: location, organisation, or promotion? Journal of Transport Geography, 2012, 22: 77–86

    Article  Google Scholar 

  19. Chan N D, Shaheen S A. Ridesharing in North America: past, present, and future. Transport Reviews, 2012, 32(1): 93–112

    Article  Google Scholar 

  20. Herbawi W, Weber M. Evolutionary multiobjective route planning in dynamic multi-hop ridesharing. Lecture Notes in Computer Science, 2011, 6622: 84–95

    Article  Google Scholar 

  21. Martins E d Q V, Santos J. The labeling algorithm for the multiobjective shortest path problem. Technical report, Departamento de Matematica, Universidade de Coimbra, Portugal, TR-99/005, 1999

    Google Scholar 

  22. Müller-Hannemann M, Weihe K. Pareto shortest paths is often feasible in practice. Lecture Notes in Computer Science, 2001, 2141: 185–197

    Article  Google Scholar 

  23. Shad R, Ebadi H, Ghods M. Evaluation of route finding methods in GIS application. In: Proceedings of Map Asia 2003. 2003, 12–20

    Google Scholar 

  24. Mooney P, Winstanley A. An evolutionary algorithm for multicriteria path optimization problems. International Journal of Geographical Information Science, 2006, 20(4): 401–423

    Article  Google Scholar 

  25. Pangilinan J M A, Janssens G. Evolutionary algorithms for the multiobjective shortest path problem. International Journal of Applied Science, Engineering and Technology, 2007, 4(1): 206–210

    Google Scholar 

  26. Sankaranarayanan J, Samet H, Teitler B E, Lieberman M D, Jon S. Twitterstand: news in tweets. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2009, 42–51

    Google Scholar 

  27. Shamma D A, Kennedy L, Churchill E F. Tweet the debates: understanding community annotation of uncollected sources. In: Proceedings of the 1st SIGMM workshop on Social media. 2009, 3–10

    Google Scholar 

  28. Leskovec J, Kleinberg J, Faloutsos C. Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data, 2007, 1(1)

    Google Scholar 

  29. Kristina L, Rumi G. Information contagion: an empirical study of spread of news on digg and twitter social networks. In: Proceedings of the 4th International Conference on Weblogs and Social Media. 2010, 90–97

    Google Scholar 

  30. Lerman K, Hogg T. Using a model of social dynamics to predict popularity of news. In: Proceedings of the 19th International Conference on World Wide Web. 2010, 621–630

    Chapter  Google Scholar 

  31. Borau K, Ullrich C, Feng J, Shen R. Microblogging for language learning: Using twitter to train communicative and cultural competence. Lecture Notes in Computer Science, 2007, 5686: 78–87

    Article  Google Scholar 

  32. Sakaki T, Okazaki M, Matsuo Y. Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web. 2010, 851–860

    Chapter  Google Scholar 

  33. Chaube V. Understanding and Designing for Perceptions of Trust in Rideshare Programs. PhD thesis, Virginia Polytechnic Institute and State University, 2010

    Google Scholar 

  34. Chaube V, Kavanaugh A L, Perez-Quinones M A. Leveraging social networks to embed trust in rideshare programs. In: Proceedings of the 43rd Hawaii International Conference on System Sciences. 2010, 1–8

    Google Scholar 

  35. Wessels R. Combining ridesharing & social networks. Technical report, 2009

    Google Scholar 

  36. North A C, Hargreaves D J, Hargreaves J J. Uses of music in everyday life. Music Perception, 2004, 22(1): 41–77

    Article  Google Scholar 

  37. Rentfrow P J, Gosling S D. Message in a ballad the role of music preferences in interpersonal perception. Psychological Science, 2006, 17(3): 236–242

    Article  Google Scholar 

  38. Knobloch S, Vorderer P, Zillmann D. The impact of music preferences on the perception of potential friends in adolescence. Zeitschrift Fur Sozialpsychologie, 2000, 31(1): 18–30

    Article  Google Scholar 

  39. Lonsdale A J, North A C. Musical taste and ingroup favouritism. Group Processes & Intergroup Relations, 2009, 12(3): 319–327

    Article  Google Scholar 

  40. Selfhout M H, Branje S J, Bogt t T F, Meeus W H. The role of music preferences in early adolescents’ friendship formation and stability. Journal of Adolescence, 2009, 32(1): 95–107

    Article  Google Scholar 

  41. Boer D, Fischer R, Strack M, Bond M H, Lo E, Lam J. How shared preferences in music create bonds between people values as the missing link. Personality and Social Psychology Bulletin, 2011, 37(9): 1159–1171

    Article  Google Scholar 

  42. Boyd D, Golder S, Gilad L. Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In: Proceedings of the 43rd Hawaii International Conference on System Sciences. 2010, 1–10

    Google Scholar 

  43. Leiserson C E, Rivest R L, Stein C, Cormen T H. Introduction to algorithms. The MIT Press, 2001

    MATH  Google Scholar 

  44. Vetter C. Fast and exact mobile navigation with openstreetmap data. Master’s Thesis, University of Karlsruhe, 2010

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, 100084, China

    Jamal Yousaf, Juanzi Li & Jie Tang

  2. General Motors, China Science Lab, Shanghai, 201303, China

    Lu Chen & Xiaowen Dai

Authors
  1. Jamal Yousaf
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  2. Juanzi Li
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  3. Lu Chen
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  4. Jie Tang
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  5. Xiaowen Dai
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Corresponding author

Correspondence to Jamal Yousaf.

Additional information

Jamal Yousaf is a PhD in Computer Science at the Department of Computer Science and Technology, Tsinghua University, China. He received his MS in Mathematics in 2000 from the Quaid-i-Azam University and Masters in Computer Engineering in 2005 from UET Taxila, Pakistan. He has 12 years of software development experience in research based public sector organization. His research interests are social network analysis & mining, knowledge engineering, and path planning.

Juanzi Li is a professor at Tsinghua University, and is the vice director of Chinese Information Processing Society of Chinese Computer Federation in China. Her research interests contain semantic Web, text and social network mining.

Lu Chen was a researcher at General Motors R&D. He received the BS and PhD in Computer Science from Zhejiang University in 2005 and 2011 respectively. His current research interests focus on the intelligent system used in vehicle.

Jie Tang is an associate professor at the Department of Computer Science and Technology, Tsinghua University. His research interests include social network analysis, data mining, and machine learning. He was honored with the CCF Young Scientist Award, NSFC Excellent Young Scholar, IBM Innovation Faculty Award, and the New Star of Beijing Science & Technology. He has published more than 100 journal/conference papers in major international journals and conferences. He is now leading the project Arnetminer.org for academic social network analysis and mining.

Xiaowen Dai is the Lab Group Manager of Connected Driving User Experience Group from GM China Science Lab. Before she takes on this responsibility, Dr. Dai is the Senior Manager of Advance Technology Management in GM China T&E dept, and Staff Researcher in Electric Control Integration Lab from GM R&D organization in Warren MI. Dr. Dai receivecl her PhD from Penn State University, and has published papers and patents.

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Yousaf, J., Li, J., Chen, L. et al. Generalized multipath planning model for ride-sharing systems. Front. Comput. Sci. 8, 100–118 (2014). https://doi.org/10.1007/s11704-013-3021-6

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