SHAREK*: A Scalable Matching Method for Dynamic Ride Sharing | GeoInformatica
Skip to main content
Springer Nature Link
Log in

SHAREK*: A Scalable Matching Method for Dynamic Ride Sharing

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

Due to its significant economic and environmental impact, sharing the ride among a number of drivers (i.e., car pooling) has recently gained significant interest from industry and academia. Hence, a number of ride sharing services have appeared along with various algorithms on how to match a rider request to a driver who can provide the ride sharing service. However, existing techniques have several limitations that affect the quality of the ride sharing service, and hence hinder its wide applicability. This paper proposes SHAREK*; a scalable and efficient ride sharing service that overcomes the limitations of existing approaches. SHAREK* allows riders requesting the ride sharing service to indicate the maximum price they are willing to pay for the service, the maximum waiting time before being picked up and the maximum arrival time for arriving the destination. In the mean time, SHAREK* computes the price of the service based on the distance of the rider trip and the detour that the driver will make to offer the service. Then, SHAREK* returns a set of drivers that can make it to the rider within its price and temporal constraints. Since there could be many of such drivers, SHAREK* internally prunes those drivers that are dominated by others, i.e., they provide higher price and higher waiting time (or arrival time) than other drivers. To realize its efficiency and scalability, SHAREK* employs a set of early pruning techniques that minimize the need for any actual shortest path computations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Agatz N, Erera A, Savelsbergh M, Wang X (2012) Optimization for dynamic ride-sharing: A review European Journal of Operational Research

  2. Asghari M., Deng D., Shahabi C., Demiryurek U., Li Y. (2016) Price-aware real-time ride-sharing at scale: an auction-based approach. In: SIGSPATIAL GIS, p 3

  3. Attanasio A, Cordeau JF, Ghiani G, Laporte G (2004) Parallel tabu search heuristics for the dynamic multi-vehicle dial-a-ride problem. Parallel Comput 30 (3):377–387

    Article  Google Scholar 

  4. Beaudry A, Laporte G, Melo T, Nickel S (2009) Dynamic transportation of patients in hospitals OR Spectrum

  5. Börzsönyi S., Kossmann D (2001) Stocker, k.: The Skyline Operator. In: ICDE. Heidelberg, Germany, pp 421–430

  6. Cao B, Alarabi L, Mokbel MF, Basalamah A (2015) Sharek: a scalable dynamic ride sharing system. In: MDM

  7. Chan EP, Lim H (2007) Optimization and evaluation of shortest path queries. VLDB J. 16(3):343–369

    Article  Google Scholar 

  8. Cici B, Markopoulou A, Laoutaris N (2015) Designing an on-line ride-sharing system. In: SIGSPATIAL. ACM, p 60

  9. Colorni A, Righini G (2001) Modeling and optimizing dynamic dial-a-ride problems. Int Trans Oper Res 8(2):155–166

    Article  Google Scholar 

  10. Cordeau JF, Laporte G (2007) The dial-a-ride problem: models and algorithms. Ann Oper Res 153(1):29–46

    Article  Google Scholar 

  11. What do we mean by dynamic ridesharing. http://dynamicridesharing.org/index.php

  12. Carpooling-the flinc carpooling service: flinc. https://flinc.org/

  13. Geisberger R, Luxen D, Neubauer S, Volker SP, Sanders P, Volker L (2010) Fast detour computation for ride sharing. In: ATMOS. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany, vol 14, pp 88–99

  14. Gidofalvi G, Aps G, Risch T, Pedersen TB, Zeitler E (2008) Highly scalable trip grouping for large scale collective transportation systems. In: EDBT, pp 678–689

  15. Hu Q, Ming L, Tong C, Zheng B (2019) An effective partitioning approach for competitive spatial-temporal searching (gis cup). In: SIGSPATIAL GIS, pp 620–623

  16. Huang Y, Jin R, Bastani F, Wang XS (2015) Large scale real-time ridesharing with service guarantee on road networks. In: PVLDB, pp 2017–2028

  17. Kalantari B, Hill AV, Arora SR (1985) An algorithm for the traveling salesman problem with pickup and delivery customers. Eur J Oper Res 22(3):377–386

    Article  Google Scholar 

  18. Kung HT, Luccio F (1975) On finding the maxima of a set of vectors. J. ACM 22(4):469–476

    Article  Google Scholar 

  19. Li RH, Qin L, Yu JX, Mao R (2016) Optimal multi-meeting-point route search. TKDE 28(3):770–784

    Google Scholar 

  20. Lyft On-demand ridesharing. http://www.lyft.me

  21. Ma S, Wolfson O (2013) Analysis and evaluation of the slugging form of ridesharing. In: SIGSPATIAL, pp 64–73

  22. Ma S, Zheng Y, Wolfson O (2013) T-share: a large-scale dynamic taxi ridesharing service. In: ICDE, pp 410–421

  23. Ma S, Zheng Y, Wolfson O (2015) Real-time city-scale taxi ridesharing. TKDE 27(7):1782–1795

    Google Scholar 

  24. Nievergelt J, Hinterberger H, Sevcik K (1984) The grid file: an adaptable, symmetric multikey file structure. TODS 9(1):38–71

    Article  Google Scholar 

  25. Papadias D, Zhang J, Mamoulis N, Tao Y (2003) Query processing in spatial network databases. In: VLDB, pp 802–813

  26. Slugging. http://en.wikipedia.org/wiki/Slugging

  27. Thomas Brinkhoff. Network-based generator of moving objects. http://iapg.jade-hs.de/personen/brinkhoff/generator/

  28. Tian C, Huang Y, Liu Z, Bastani F, Jin R (2013) Noah: a dynamic ridesharing system. In: SIGMOD, pp 985–988

  29. Yeung S, Miller E, Madria S (2016) A flexible real-time ridesharing system considering current road conditions. In: MDM, vol 1. IEEE, pp 186–191

  30. Zhang LG, Fang JY, Shen PW (2007) An improved dijkstra algorithm based on pairing heap [j]. J. Image Graphics 5

  31. Zhang X, Asano Y, Yoshikawa M (2016) Mutually beneficial confluent routing. TKDE 28(10):2681–2696

    Google Scholar 

  32. Zheng B, Huang C, Jensen CS, Chen L, Hung NQV, Liu G, Li G, Zheng K (2020) Online trichromatic pickup and delivery scheduling in spatial crowdsourcing ICDE

  33. Zheng B, Su H, Hua W, Zheng K, Zhou X, Li G (2017) Efficient clue-based route search on road networks. TKDE 29(9):1846–1859

    Google Scholar 

Download references

Acknowledgment

This research was partially supported by following foundations: Zhejiang Provincial Natural Science Foundation of China (LY19F020030).

Author information

Authors and Affiliations

  1. Zhejiang University of Technology, HangZhou, China

    Bin Cao, Chenyu Hou, Liwei Zhao & Jing Fan

  2. Department of Computer Science, Umm Al-Qura University, Mecca, Kingdom of Saudi Arabia

    Louai Alarabi

  3. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA

    Mohamed F. Mokbel

  4. Computer Engineering Department and KACST GIS Technology Innovation Center, Umm Al-Qura University, Makkah, Saudi Arabia

    Anas Basalamah

Authors
  1. Bin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chenyu Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liwei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Louai Alarabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohamed F. Mokbel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Anas Basalamah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Fan.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, B., Hou, C., Zhao, L. et al. SHAREK*: A Scalable Matching Method for Dynamic Ride Sharing. Geoinformatica 24, 881–913 (2020). https://doi.org/10.1007/s10707-020-00411-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-020-00411-0

Keywords

Search

Navigation