GPS‐data‐driven dynamic destination prediction for on‐demand one‐way carsharing system

Abstract

On-demand one-way carsharing systems are increasingly gaining popularity nowadays and the market is growing unprecedentedly. However, operating techniques such as vehicle surveillance and fleet management fall behind the industrial development. Practice on EVCARD - an on-demand one-way electric vehicle carsharing system operating in Shanghai - raises an issue on dynamically predicting the user destinations, in order to support decisions on dynamic fleet management. This study presents a global positioning system (GPS)-data-driven method to solve the problem. The historical vehicle GPS data is enabled to match the user current trajectories and infer their possible destinations. Based on the GPS trajectory similarity measurement, the study presents a four-step procedure, including (i) similarity calculation, (ii) most-similar track detection, (iii) adjustment, and (iv) sorting. The method also takes the station correlations and the user 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 existence of round-trips, the coverage of samples, and the alternatives of destinations.