4.5 Article

Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning

Journal

COMPLEXITY
Volume 2022, Issue -, Pages -

Publisher

WILEY-HINDAWI
DOI: 10.1155/2022/8843000

Keywords

-

Funding

  1. Beijing Municipal Natural Science Foundation [4212026]
  2. National Science Foundation of China [61772075]

Ask authors/readers for more resources

The car-sharing system is a popular rental model that allows multiple users to share a car. This research aims to predict the car usage in parking stations and improve the prediction accuracy. Using various machine learning models and different features, it was found that the CNN-LSTM model outperforms other methods in predicting future car usage. Additionally, using all feature categories results in more precise predictions compared to using a single feature category.
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

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available