Demand Supply Oriented Taxi Suggestion System for Vehicular Social Networks with Fuel Charging Mechanism

Authors(2) :-Selvi C, Keerthana D

Data mining depends on large-scale taxi traces is an important research concepts. A vital direction for analyzing taxi GPS dataset is to suggest cruising areas for taxi drivers. The project first investigates the real-time demand-supply level for taxis, and then makes an adaptive tradeoff between the utilities of drivers and passengers for different hotspots. This project constructs a recommendation system by jointly considering the profits of both drivers and passengers. At last, the qualified candidates are suggested to drivers based on analysis. The project also provides a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. By combining each EV taxiís historical recharging actions and real-time GPS trajectories, the present operational state of each taxi is predicted. Based on this information, for an EV taxi requesting a recommendation, recommend a charging station that leads to the minimal total time before its recharging starts.

Authors and Affiliations

Selvi C
Computer Science and Engineering, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India
Keerthana D
ME-CSE , Velalar College of Engineering and Technology, Erode, Tamil Nadu, India

Vehicular Social Networks, Hotspot location, Trajectory data mining, Supply-demand level, Electronic Vehicle.

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Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2018-12-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 38-44
Manuscript Number : CSEIT19515
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Selvi C, Keerthana D, "Demand Supply Oriented Taxi Suggestion System for Vehicular Social Networks with Fuel Charging Mechanism", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.38-44, January-February-2019. Available at doi :
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