Abstract
Metropolitan development has motivated car sharing into an attractive type of car leasing with the help of information technologies. In this paper, we propose a new approach based on deep learning techniques to assess the operation of a station-based car sharing system. First, we analyse the pick-up and drop-off operations of the station-based car sharing system, capturing the operational features of car sharing service and the behaviours of vehicle use from a temporal perspective. Then, we introduced an analytical system to detect the system operation concerning the spontaneous deviations derived from user demands from service provisions. We employed Long Short-Term Memory (LSTM) structure to forecast short-term future vehicle uses. An experimental case based on real-world data is reported to demonstrate the effectiveness of this approach. The results prove that the proposed structure generates high-quality predictions and the operation status derived from user demands.
Highlights
We have learned from the market that car sharing is still in a development trend, and the fundamental problem of car sharing profitability is related to the demand side
Is paper considers the demand management of station-based car sharing service systems, whose customers are charged for the time used. e operation of car sharing systems can be analysed based on the usage of vehicles in the system, which we formulate as a discrete event model
Even though the concept of car sharing is no longer new, there still lacks a general understanding of the emerging features of its operation, such as the spontaneous deviating process caused by vehicle
Summary
One of the earliest car sharing initiatives, “Sefage” (Selbstfahrergenossenscha ), operated in Zurich, Switzerland, from 1948 to 1998 [1, 9,10,11]. e car sharing market in China started in 2010. In terms of nonAI prediction methods, Wang et al [20] proposed a new method to forecast and relocate car sharing service vehicles based on an inventory management model consisting of three main components: focus forecasting, inventory replenishment, and microscopic traffic simulation. Zhang et al [29] proposed a short-term traffic flow prediction model based on the Convolutional Neural Network (CNN) deep learning framework. To bridge the short-term forecast of a supplydemand gap for online car-hailing services, Gu et al [33] proposed a novel spatio-temporal deep learning model (S-TDL). We attempt to combine deep learning techniques with car sharing operation modelling so that the predicted data can be directly involved in detecting the deviations of servicing vehicles from market demands caused by system operation
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