Abstract
The car‐sharing system is a popular rental model for cars in shared use. It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road and fewer in the parking stations. To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k‐nearest neighbors (kNN), and deep learning models specifically long short‐time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN‐LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. These features include the past usage levels, Chongqing’s environmental conditions, and temporal information. After comparing the obtained results using different metrics, we found that CNN‐LSTM outperformed other methods to predict the future car usage. Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a time
Highlights
Predicting the future is considered as one of the most challenging tasks in applied sciences
Data Set. e experiments were performed on the preprocessed Chongqing’s car-sharing operator data set combined with Chongqing’s weather data set, to extract features that help to predict car usage, and to demonstrate the effectiveness of deep learning, more precisely the CNNLSTM comparing to other models
Mean Absolute Error (MAE) is calculated as the mean of the absolute predicted error values. e MAE is popular as it is easy to both understand and compute
Summary
Predicting the future is considered as one of the most challenging tasks in applied sciences. Computational and statistical methods are used for deducting dependencies between past and future observed values in order to build effective predictors from historical data. People are more dependent on cars for both intercity and intracity transit, causing traffic congestion and parking difficulties [3]. Many rental models are emerged to solve these parking problems as one of them is the car-sharing model, which aims to distribute cars within a city for use at a low cost. In this fashion, individuals can exploit all the benefits of a private vehicle without the hassles of lease payments, maintenance, or parking. Individuals can exploit all the benefits of a private vehicle without the hassles of lease payments, maintenance, or parking. e program comprises one-way or round-trip, depending on whether the pick-up and the drop-off stations are the same or not [4]
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