A 1.5-Approximation Route Finding for a Ride-sharing considering Movement of Passengers
Pages 59 - 69
Abstract
MaaS (stands for Mobility as a Service) is a concept that aims to integrate different transportation services into a unified and seamless mobility solution. It encourages a shift away from personally owned modes of transportation, such as private cars, towards a more comprehensive approach that combines public transportation, such as ride-sharing, bike-sharing, carpooling, and other modes of transport into a single and user-centric service. One of the services offered within MaaS is a ride-sharing, which provides several advantages such as cost savings, reduced traffic congestion, or environmental benefits. However, to make a ride-sharing efficient, a sophisticated route finding (i.e., planning) is required to avoid redundantly long routes when picking up or dropping off passengers.
In this paper, we consider an efficient ride-sharing route finding for large-vehicles such as buses that starts at the determined location and returns after visiting all the locations, when a set of the locations of the passengers is given. Moreover, we allow passengers to move within a short distance to find more efficient traversal plan. Note that this problem can be reduced to the traveling salesperson problem (with some constraints) which is a well-known NP-hard problem. Hence, we employ a 1.5-approximation algorithm to find an efficient route within a reasonable computational time, moreover, use the Viterbi algorithm to improve the efficiency of the route when allowing each passenger to move.
References
[1]
Kamel Aissat and Ammar Oulamara. 2014. A Priori Approach Of Real-Time Ridesharing Problem With Intermediate Meeting Locations. J. Artif. Intell. Soft Comput. Res. 4, 4 (2014), 287--299.
[2]
Javier Alonso-Mora et al. 2017. On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc. Natl. Acad. Sci. USA 114, 3 (2017), 462--467.
[3]
Mohammad Asghari et al. 2016. Price-aware real-time ride-sharing at scale: an auction-based approach. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2016. ACM, 3:1--3:10.
[4]
Mohammad Asghari and Cyrus Shahabi. 2017. An On-line Truthful and Individually Rational Pricing Mechanism for Ride-sharing. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2017. ACM, 7:1--7:10.
[5]
Kanika Bathla et al. 2018. Real-Time Distributed Taxi Ride Sharing. In 21st International Conference on Intelligent Transportation Systems, ITSC 2018, Maui, HI, USA, November 4--7, 2018. IEEE, 2044--2051.
[6]
Richard Bellman. 1962. Dynamic Programming Treatment of the Travelling Salesman Problem. J. ACM 9, 1 (1962), 61--63.
[7]
Mandell Bellmore and George L. Nemhauser. 1968. The Traveling Salesman Problem: A Survey. Oper. Res. 16, 3 (1968), 538--558.
[8]
Houssem E. Ben-Smida et al. 2016. Mixed Integer Linear Programming Formulation for the Taxi Sharing Problem. In Smart Cities - First International Conference, Smart-CT 2016 (Lecture Notes in Computer Science, Vol. 9704). Springer, 106--117.
[9]
Jon Louis Bentley. 1990. K-d Trees for Semidynamic Point Sets. In Proceedings of the Sixth Annual Symposium on Computational Geometry, Berkeley, CA, USA, June 6--8, 1990. ACM, 187--197.
[10]
Rainer E. Burkard et al. 1998. Well-Solvable Special Cases of the Traveling Salesman Problem: A Survey. SIAM Rev. 40, 3 (1998), 496--546.
[11]
Nicos Christofides. 2022. Worst-Case Analysis of a New Heuristic for the Travelling Salesman Problem. Oper. Res. Forum 3, 1 (2022).
[12]
Jack Edmonds. 1965. Paths, Trees, and Flowers. Canadian Journal of Mathematics 17 (1965), 449--467.
[13]
Robert W. Floyd. 1962. Algorithm 97: Shortest path. Commun. ACM 5, 6 (1962), 345.
[14]
OpenStreetMap Foundation. 2004--2023. OpenStreetMap. https://openstreet.map.jp/map.
[15]
Michael T. Goodrich and Roberto Tamassia. 2014. Algorithm Design and Applications. Wiley. https://www.wiley.com/en-us/Algorithm+Design+and+Applications-p-9781118335918
[16]
Gregory Z. Gutin, Anders Yeo, and Alexey Zverovich. 2002. Traveling salesman should not be greedy: domination analysis of greedy-type heuristics for the TSP. Discret. Appl. Math. 117, 1--3 (2002), 81--86.
[17]
Hirotaka Irie et al. 2017. Quantum Annealing of Vehicle Routing Problem with Time, State and Capacity. In Quantum Technology and Optimization Problems - First International Workshop, QTOP@NetSys 2019 (Lecture Notes in Computer Science, Vol. 11413). Springer, 145--156.
[18]
A. K. M. Mustafizur Rahman Khan et al. 2017. Ride-sharing is About Agreeing on a Destination. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2017. ACM, 6:1--6:10.
[19]
P. J. M. Laarhoven and E. H. L. Aarts. 1987. Simulated Annealing: Theory and Applications. Kluwer Academic Publishers, USA.
[20]
Shuo Ma et al. 2013. T-share: A large-scale dynamic taxi ridesharing service. In 29th IEEE International Conference on Data Engineering, ICDE 2013. IEEE Computer Society, 410--421.
[21]
L.A. Rastrigin. 1963. The Convergence of the Random Search Method in the External Control of Many-Parameter System. Automation and Remote Control 24 (1963), 1337--1342.
[22]
Massobrio Renzo et al. 2014. A parallel micro evolutionary algorithm for taxi sharing optimization. In VIII ALIO/EURO Workshop on Applied Combinatorial Optimization.
[23]
Kazuhiro Saito et al. 2021. Evaluation of Quantum Annealing for Vehicle Routing Problem (in Japanese). IPSJ-TOD 14, 1 (03 2021), 8--17.
[24]
Mitja Stiglic et al. 2015. The benefits of meeting points in ride-sharing systems. Transportation Research Part B: Methodological 82 (2015), 36--53.
[25]
C. Storey. 1962. Applications of a hill climbing method of optimization. Chemical Engineering Science 17, 1 (1962), 45--52.
[26]
Chi-Chung Tao and Chun-Ying Chen. 2007. Heuristic Algorithms for the Dynamic Taxipooling Problem Based on Intelligent Transportation System Technologies. In Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, Volume 3. IEEE Computer Society, 590--595.
[27]
Andrew J. Viterbi. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory 13, 2 (1967), 260--269.
[28]
Takato Yoshida et al. 2019. Modeling and evaluating taxi ride-sharing for event trips (in Japanese). Transaction on Mathematical Modeling and its Applications: TOM 12, 2 (07 2019), 1--11. https://cir.nii.ac.jp/crid/1050845763318147456
[29]
Yuki Yoshizuka et al. 2018. Performance Evaluation of Path Finding Algorithm for Ride-sharing Service (in Japanese). The 32nd Annual Conference of the Japanese Society for Artificial Intelligence JSAI2018 (2018), 4F1OS11c02--4F1OS11c02.
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- A 1.5-Approximation Route Finding for a Ride-sharing considering Movement of Passengers
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November 2023
74 pages
ISBN:9798400703614
DOI:10.1145/3615899
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Published: 19 December 2023
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