Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems

Dongbo Liu,Wanjing Ma,Jian Lu,Meaad Saberi

doi:10.1155/2021/6654909

Dongbo Liu, Wanjing Ma + Show 2 more

Open Access

https://doi.org/10.1155/2021/6654909

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Abstract

One-way carsharing system has been widely adopted in the carsharing field due to its flexible services. However, as one of the main limitations of the one-way carsharing system, the imbalance between supply and demand needs to be solved. Predicting pick-up demand has been studied to achieve the goal, but using returned vehicles to reduce unnecessary relocation is also one of the important methods. Nowadays, trajectory data and other data are available for real-time prediction for return demand. Based on the return demand prediction, the relocation response can be more reasonable. Thus, the balance of demand and supply can be largely improved. The multisource data include trajectory data, user application log data, order data, station data, and user characteristic data. Based on these data, a return demand prediction model was used to predict whether the user will return the vehicle in 15 min in real time, and a destination station prediction model was applied to forecast which station the user will park at. Finally, a case study using ten stations’ one-week field data was conducted to test the benefit of the dynamic return demand prediction. The results showed that the return demand prediction improves the efficiency of the relocations by mitigating the condition that the station parking space is full or empty. The potential application of this study would effectively reduce unnecessary relocation and further formulate an active operation optimization strategy to reduce the system’s operational cost and improve the service quality of the system.

Highlights

With the vigorous development of mobile Internet technology and the emergence of the new business model represented by the sharing economy, electric carsharing systems play an increasingly important role in the transportation field. ey can improve travel convenience, increase vehicle utilization, and reduce the demand for parking spaces
To solve the above problems, this paper aims to establish a real-time dynamic return demand prediction model for a oneway electric carsharing system based on the data of the EVCARD system which is a large carsharing provider and has a fleet of more than 5,000 electric vehicles in Shanghai, China
To select the most significant and not highly correlated variables, firstly the correlation tests between dependent and independent variables are conducted to find the insignificant independent variables, and these variables are all significantly correlated with the dependent variable

Summary

Introduction

With the vigorous development of mobile Internet technology and the emergence of the new business model represented by the sharing economy, electric carsharing systems play an increasingly important role in the transportation field. ey can improve travel convenience, increase vehicle utilization, and reduce the demand for parking spaces. It is important to use an advanced, dynamic, and real-time management system to improve the relocation efficiency in order to bring higher profit to managers [7,8,9]. Predicting pick-up demand has been studied to achieve the goal [10,11,12], but using return demand to reduce unnecessary relocation is one of the important methods [13]. E main goal of user pick-up demand prediction for the carsharing system is to obtain potential user demand and increase the number of orders, and the main goal of user return demand prediction is to make full use of other users’ pick-up or return behavior within the system and reduce unnecessary relocation and costs. The study targets are different. e main goal of user pick-up demand prediction for the carsharing system is to obtain potential user demand and increase the number of orders, and the main goal of user return demand prediction is to make full use of other users’ pick-up or return behavior within the system and reduce unnecessary relocation and costs. erefore, it becomes more

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