Abstract
The high use of private cars increases the load on the environment and raises issues of high levels of air pollution in cities, parking problems, congestion and low transfer velocity. Car pooling is a collective transportation model based on shared use of private cars to reduce the number of cars in use by grouping people. By exploiting car pooling model, it can significantly reduce congestion, fuel consumption, parking demands and commuting costs. An important issue in car pooling systems is to develop a car pooling algorithm to match passengers and drivers. The goals of this paper are to propose a model and a solution methodology that is seamlessly integrated with existing geographic information system to facilitate determination of drivers/passengers for ride sharing. In this paper, we formulate a car pooling problem and propose a solution algorithm for it based on a meta-heuristic approach. We have implemented our solution algorithm and conduct experiments to illustrate the effectiveness of our proposed method by examples.
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Acknowledgement
This paper was supported in part by Ministry of Science and Technology, Taiwan, under Grant MOST-105-2410-H-324-005.
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Hsieh, FS., Zhan, FM., Guo, YH. (2017). Car Pooling Based on a Meta-heuristic Approach. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_4
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DOI: https://doi.org/10.1007/978-3-319-60042-0_4
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