Back to all available

Hybrid parking space prediction model: integrating arima, lstm, and bpnn for smart city development

artificial-intelligence
autonomous-systems
visual-analytics
neural-networks

Abstract

Introduction: Parking space prediction is essential for addressing traffic congestion challenges and low parking availability in urban areas. It is a significant aspect of smart city development and a sustainable environment. The present research mainly focuses on predicting parking space using smart devices that collect time-series data, which is complex and unpredictable. Smart cities require an efficient parking prediction system. Methods: We present a smart parking space prediction model that combines Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The novelty of the proposed model is that it is hybridized using a back propagation neural network (BPNN). In contrast to the conventional assumption of a linear relationship between the predicted results of ARIMA and LSTM models, this approach employs BPNN to uncover the unidentified objective and establish the connection among the predicted values. The model utilizes the ARIMA model for handling linear values and the LSTM model for handling non-linear values of the (Internet of Things) IoT dataset. Results: The "Melbourne" dataset is used for evaluation. The proposed hybrid model achieves the minimum MSE, MAE, and RMSE values of 0.32, 0.48, and 0.56, respectively. Conclusion: This model potentially improves parking space prediction, contributes to sustainable and economically smart cities, and enhances citizens' quality of life.
Ask to review this manuscript

Notes for potential reviewers

  • Volunteering is not a guarantee that you will be asked to review. There are many reasons: reviewers must be qualified, there should be no conflicts of interest, a minimum of two reviewers have already accepted an invitation, etc.
  • This is NOT OPEN peer review. The review is single-blind, and all recommendations are sent privately to the Academic Editor handling the manuscript. All reviews are published and reviewers can choose to sign their reviews.
  • What happens after volunteering? It may be a few days before you receive an invitation to review with further instructions. You will need to accept the invitation to then become an official referee for the manuscript. If you do not receive an invitation it is for one of many possible reasons as noted above.
  • PeerJ Computer Science does not judge submissions based on subjective measures such as novelty, impact or degree of advance. Effectively, reviewers are asked to comment on whether or not the submission is scientifically and technically sound and therefore deserves to join the scientific literature. Our Peer Review criteria can be found on the "Editorial Criteria" page - reviewers are specifically asked to comment on 3 broad areas: "Basic Reporting", "Experimental Design" and "Validity of the Findings".
  • Reviewers are expected to comment in a timely, professional, and constructive manner.
  • Until the article is published, reviewers must regard all information relating to the submission as strictly confidential.
  • When submitting a review, reviewers are given the option to "sign" their review (i.e. to associate their name with their comments). Otherwise, all review comments remain anonymous.
  • All reviews of published articles are published. This includes manuscript files, peer review comments, author rebuttals and revised materials.
  • Each time a decision is made by the Academic Editor, each reviewer will receive a copy of the Decision Letter (which will include the comments of all reviewers).

If you have any questions about submitting your review, please email us at peer.review@peerj.com.