A Utility Method for the Matching Optimization of Ride-Sharing Based on the E-CARGO Model in Internet of Vehicles
The Internet of Vehicles (IoV) is the extension of the Internet of Things (IoT) technology in the field of transportation systems. Ride-sharing is one of intelligent travel applications in IoV. Ride-sharing is aimed at taking passengers with similar itineraries and time arrangements to travel in the...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Hindawi-Wiley
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/03691792f5c546e99bc2bbf8d8e377fc |
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| Sumario: | The Internet of Vehicles (IoV) is the extension of the Internet of Things (IoT) technology in the field of transportation systems. Ride-sharing is one of intelligent travel applications in IoV. Ride-sharing is aimed at taking passengers with similar itineraries and time arrangements to travel in the same car according to a certain matching rule. To effectively integrate transport capacity resources and reduce the number of cars on the road, ride-sharing has become a popular and economical way of travel. The matching and optimizing of drivers and passengers are the core contents of a ride-sharing application system. This paper mainly studies the dynamic real-time matching of passengers and drivers in IoV, considering the main factors such as travel cost, car capacity, and utility. The matching problem is formulated in a ride-sharing system as a Role-Based Collaboration (RBC). A new utility method for the matching optimization of ride-sharing is present. In this paper, we establish a model for simulating the assignment of ride-sharing with the help of the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. The objective function and formal definitions are proposed. The utility and time of optimal matching are obtained by using the Kuhn-Munkres algorithm on the revenue matrix. The experimental results show that the proposed formal method based on the E-CARGO model and utility theory can be applied in the ride-sharing problem. Numerical experiments show that the matching time cost increases with the increase of the number of drivers and passengers participating in the ride-sharing system. When the number of drivers and passengers is different, one-to-many matching takes the least time, and one-to-one matching takes more time. When the number of drivers and passengers is the same, the time cost of one-to-one matching increases sharply with a certain value (bigger than 800). Compared with other matching methods, the time spent by the one-to-many method is reduced by 30%. The results show that the proposed solution can be applied to the matching and pricing in a ride-sharing system. |
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