【摘 要】
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On-demand one-way carsharing systems are gaining increasing popularity nowadays.Operating techniques including vehicle surveillance and fleet management are still inadequate,especially when facing wit
【机 构】
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Tongji University
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On-demand one-way carsharing systems are gaining increasing popularity nowadays.Operating techniques including vehicle surveillance and fleet management are still inadequate,especially when facing with the fast growth of the market and the rapid expansion of system scale.Practice on EVCARD – an on-demand one-way electric vehicle carsharing system operated in Shanghai – raises an issue on dynamically predicting the users destinations,in order to support decisions on dynamic fleet management.This paper presents a GPS-data-driven method to solve the problem.The historical vehicl e GPS data is enabled to match the users current trajectories and infer their possible destinations.Based on GPS trajectory similarity measurement,we proposed a four-step procedure including similarity calculation,most-similar track detection,adjustment,and sorting.The method also takes the stations correlations and the users historical destinations into account.A case study is given to demonstrate the dynamic prediction procedure of this method.Experiment on 96,821 valid test tracks shows that the positive prediction rate can be above 92%if the test trip has been completed over 70%.Factors that may influence the prediction result are additionally discussed,which include the existance of round-trips,the coverage of samples,and the alternatives of destinations.
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