Learning User Preferences in Matching for Ridesharing

Mojtaba Montazery, Nic Wilson


Sharing car journeys can be very beneficial, since it can save travel costs, as well as reducing traffic congestion and pollution. The process of matching riders and drivers automatically at short notice, is referred to as dynamic ridesharing, which has attracted a lot of attention in recent years. In this paper, amongst the wide range of challenges in dynamic ridesharing, we consider the problem of ride-matching. While existing studies mainly consider fixed assignments of participants in the matching process, our main contribution is focused on the learning of the user preferences regarding the desirability of a choice of matching; this could then form an important component of a system that can generate robust matchings that maintain high user satisfaction, thus encouraging repeat usage of the system. An SVM inspired method is exploited which is able to learn a scoring function from a set of preferences; this function measures the predicted satisfaction degree of the user regarding specific matches. To the best of our knowledge, we are the first to present a model that is able to implicitly learn individual preferences of participants. Our experimental results, which are conducted on a real ridesharing data set, show the effectiveness of our approach.


  1. Agatz, N., Erera, A., Savelsbergh, M., and Wang, X. (2012). Optimization for dynamic ride-sharing: A review. European Journal of Operational Research, 223(2):295 - 303.
  2. Agatz, N. A., Erera, A. L., Savelsbergh, M. W., and Wang, X. (2011). Dynamic ride-sharing: A simulation study in metro atlanta. Transportation Research Part B: Methodological, 45(9):1450-1464.
  3. Aiolli, F. and Sperduti, A. (2011). A preference optimization based unifying framework for supervised learning problems. In F ürnkranz, J. and Hüllermeier, E., editors, Preference Learning, pages 19-42. Springer Berlin Heidelberg.
  4. Aizerman, A., Braverman, E. M., and Rozoner, L. I. (1964). Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning. Automation and Remote Control, 25:821-837.
  5. Amey, A., Attanucci, J., and Mishalani, R. (2011). Real-time ridesharing the opportunities and challenges of utilizing mobile phone technology to improve rideshare services. Transportation Research Record: Journal of the Transportation Research Board, 2217(1):103-110.
  6. Armant, V. and Brown, K. N. (2014). Minimizing the driving distance in ride sharing systems. In Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on, pages 568-575. IEEE.
  7. Baldacci, R., Maniezzo, V., and Mingozzi, A. (2004). An exact method for the car pooling problem based on lagrangean column generation. Operations Research, 52(3):422-439.
  8. Ben-Hur, A. and Weston, J. (2010). A users guide to support vector machines. In Carugo, O. and Eisenhaber, F., editors, Data Mining Techniques for the Life Sciences, volume 609 of Methods in Molecular Biology, pages 223-239. Humana Press.
  9. Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 13(1):281-305.
  10. Boyd, S. and Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.
  11. Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2):121-167.
  12. Chan, N. D. and Shaheen, S. A. (2012). Ridesharing in north america: Past, present, and future. Transport Reviews, 32(1):93-112.
  13. Chaube, V., Kavanaugh, A. L., and Perez-Quinones, M. A. (2010). Leveraging social networks to embed trust in rideshare programs. In System Sciences (HICSS), 2010 43rd Hawaii International Conference on, pages 1-8. IEEE.
  14. Duggan, M. and Smith, A. (2013). Cell internet use 2013. Pew Research Center, http://www. pewinternet.org/2013/09/16/cell-internet-use-2013/.
  15. Emarketer (2014). 2 billion consumers worldwide to get smart(phones) by 2016. http://www.emar keter.com/Article/2-Billion-Consumers-WorldwideSmartphones-by-2016/1011694.
  16. F ürnkranz, J. and H üllermeier, E. (2010). Preference learning. Springer.
  17. Furuhata, M., Dessouky, M., Ord ón˜ez, F., Brunet, M.-E., Wang, X., and Koenig, S. (2013). Ridesharing: The state-of-the-art and future directions. Transportation Research Part B: Methodological, 57:28-46.
  18. Ghoseiri, K., Haghani, A. E., and Hamedi, M. (2011). Realtime rideshare matching problem.
  19. Herbawi, W. M. and Weber, M. (2012). A genetic and insertion heuristic algorithm for solving the dynamic ridematching problem with time windows. In Proceedings of the 14th annual conference on Genetic and evolutionary computation, pages 385-392. ACM.
  20. Hwang, M., Kemp, J., Lerner-Lam, E., Neuerburg, N., and Okunieff, P. (2006). Advanced public transportation systems: state of the art update 2006. Technical report.
  21. Joachims, T. (2002). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133-142. ACM.
  22. Saranow, J. (2006). Carpooling for grown-ups-high gas prices, new services give ride-sharing a boost; rating your fellow rider. Wall Street Journal.
  23. Simonin, G. and O'Sullivan, B. (2014). Optimisation for the ride-sharing problem: a complexity-based approach. In ECAI, pages 831-836.
  24. Smith, A. (2015). Us smartphone use in 2015. Pew Research Center, http://www.pewinternet. org/2015/04/01/us-smartphone-use-in-2015/.
  25. Zickuhr, K. (2012). Three-quarters of smartphone owners use location-based services. Pew Research Center, http://www.pewinternet.org/2012/05/11/threequarters-of-smartphone-owners-use-location-basedservices/.

Paper Citation

in Harvard Style

Montazery M. and Wilson N. (2016). Learning User Preferences in Matching for Ridesharing . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 63-73. DOI: 10.5220/0005694700630073

in Bibtex Style

author={Mojtaba Montazery and Nic Wilson},
title={Learning User Preferences in Matching for Ridesharing},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Learning User Preferences in Matching for Ridesharing
SN - 978-989-758-172-4
AU - Montazery M.
AU - Wilson N.
PY - 2016
SP - 63
EP - 73
DO - 10.5220/0005694700630073