A Generic Multi-Agent Model for Resource Allocation Strategies in Online On-Demand Transport with Autonomous Vehicles
Pages 1489 - 1491
Abstract
The introduction of driver-less technologies can improve on-demand transport (ODT) systems and help make passenger transportation and logistics more efficient. Here, we aim to provide a generic model of the online ODT with autonomous vehicles problem and a multi-agent model specific to resource allocation and scheduling in vehicle fleets. Our model considers autonomous vehicles that communicate via peer-to-peer radio channels to meet passenger requirements and satisfy trip requests in an online ODT system. We experiment this model with several allocation mechanisms (mathematical programming, greedy heuristic, distributed constraint optimization, and auctions) and compare their performance on synthetic scenarios on a real-world city road network.
References
[1]
Nesrine Bessghaier, Mahdi Zargayouna, and Flavien Balbo. 2012. Online Localized Resource Allocation Application to Urban Parking Management. In 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. IEEE, Macau, China, 67--74. https://doi.org/10.1109/WI-IAT.2012.230
[2]
Alaa Daoud, Flavien Balbo, Paolo Gianessi, and Gauthier Picard. 2021. ORNInA: A decentralized, auction-based multi-agent coordination in ODT systems. AI Communications, Vol. (2021), 1--17. https://doi.org/10.3233/AIC-201579
[3]
Malcolm Egan and Michal Jakob. 2016. Market mechanism design for profitable on-demand transport services. Transportation Research Part B: Methodological, Vol. 89 (2016), 178--195.
[4]
Mohamad El Falou, Mhamed Itmi, Salah El Falou, and Alain Cardon. 2014. On demand transport system's approach as a multi-agent planning problem. In 2014 International Conference on Advanced Logistics and Transport (ICALT). IEEE, IEEE, Tunis, Tunisia, 53--58.
[5]
Ferdinando Fioretto, Enrico Pontelli, and William Yeoh. 2018. Distributed Constraint Optimization Problems and Applications: A Survey. Journal of Artificial Intelligence Research, Vol. 61 (March 2018), 623--698. https://doi.org/10.1613/jair.5565
[6]
Der-Horng Lee, Hao Wang, Ruey Cheu, and Siew Teo. 2004. Taxi Dispatch System Based on Current Demands and Real-Time Traffic Conditions. Transportation Research Record: Journal of the Transportation Research Board, Vol. 1882 (Jan. 2004), 193--200. https://doi.org/10.3141/1882--23
[7]
OpenStreetMap contributors. 2021. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org.
[8]
Jonathan P. Pearce and Milind Tambe. 2007. Quality Guarantees on K-Optimal Solutions for Distributed Constraint Optimization Problems. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (Hyderabad, India) (IJCAI'07). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1446--1451.
[9]
Mines Saint-Étienne. 2021. Plateforme Territoire. https://territoire.emse.fr/.
[10]
Rinde R.S. van Lon, Tom Holvoet, Greet Vanden Berghe, Tom Wenseleers, and Juergen Branke. 2012. Evolutionary synthesis of multi-agent systems for dynamic dial-a-ride problems. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12. ACM Press, Philadelphia, Pennsylvania, USA, 331. https://doi.org/10.1145/2330784.2330832
[11]
Li Yang, Zhao Jieru, Chen Jingxin, and Tang Zhiyong. 2017. Central Decision Intellective Taxi System and Multi Ride Algorithm. In Proceedings of the 2017 International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT '17). ACM, New York, NY, USA, 5:1--5:6. https://doi.org/10.1145/3080845.3080850 event-place: Wuhan, China.
[12]
Mahdi Zargayouna, Flavien Balbo, and Khadim Ndiaye. 2016. Generic model for resource allocation in transportation. Application to urban parking management. Transportation Research Part C: Emerging Technologies, Vol. 71 (2016), 538 -- 554. https://doi.org/10.1016/j.trc.2016.09.002
[13]
Weixiong Zhang, Guandong Wang, Zhao Xing, and Lars Wittenburg. 2005. Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks. Artificial Intelligence, Vol. 161, 1 (2005), 55 -- 87. https://doi.org/10.1016/j.artint.2004.10.004 Distributed Constraint Satisfaction.
Index Terms
- A Generic Multi-Agent Model for Resource Allocation Strategies in Online On-Demand Transport with Autonomous Vehicles
Recommendations
Route optimisation using evolutionary approaches for on-demand pickup problem
The development of information technologies realises an on-demand transport (pick-up) system. In this paper, we simulate transport situations for the system based on multi-agent model to find efficient strategies. We examine four types of driver agents; ...
Resource reconstruction algorithms for on-demand allocation in virtual computing resource pool
Resource reconstruction algorithms are studied in this paper to solve the problem of resource on-demand allocation and improve the efficiency of resource utilization in virtual computing resource pool. Based on the idea of resource virtualization and ...
Towards Explainable Recommendations of Resource Allocation Mechanisms in On-Demand Transport Fleets
Explainable and Transparent AI and Multi-Agent SystemsAbstractMulti-agent systems can be considered a natural paradigm when modeling various transportation systems, whose management involves solving hard, dynamic, and distributed allocation problems. Such problems have been studied for decades, and various ...
Comments
Information & Contributors
Information
Published In
May 2021
1899 pages
ISBN:9781450383073
- General Chairs:
- Frank Dignum,
- Alessio Lomuscio,
- Program Chairs:
- Ulle Endriss,
- Ann Nowé
Sponsors
Publisher
International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC
Publication History
Published: 03 May 2021
Check for updates
Author Tags
Qualifiers
- Extended-abstract
Conference
AAMAS '21
Sponsor:
AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems
May 3 - 7, 2021
Virtual Event, United Kingdom
Acceptance Rates
Overall Acceptance Rate 1,155 of 5,036 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 34Total Downloads
- Downloads (Last 12 months)8
- Downloads (Last 6 weeks)0
Reflects downloads up to 30 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in