Improving transportation efficiency without increasing urban traffic congestion, requires to carry out new services such as ridesharing which contributes to reduce operating cost and to save road resources. In this paper, we provide users of a ridesharing system greater flexibility: given a set of drivers’ offers already in the system, and a new rider’s request, we determine a best driver, a best pick-up and drop-off locations, and a sharing cost rate between rider and driver for their common path. The main idea of our approaches consists in labelling interesting nodes of a geographical map with information about drivers, in so-called buckets. Based on the information contained in these buckets, when a rider enters the system we determine a best driver, as well as a best pick-up and drop-off locations that minimize the total travel cost of rider and driver. Exact and heuristic approaches to identify a best driver, as well as a best pick-up and drop-off locations are proposed. Finally, we perform a comparative evaluation using real road network of the Lorraine region (FR) and real data provided by a local company. Experimental analysis shows a running time of a few seconds while improving participants’ cost-savings and matching rate compared to the recurring ridesharing.

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http://dx.doi.org/10.1007/978-3-319-27680-9_5 under the license http://www.springer.com/tdm
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Published on 01/01/2015

Volume 2015, 2015
DOI: 10.1007/978-3-319-27680-9_5
Licence: Other

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