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Applied Sciences
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Intelligent Transportation System in Smart City
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Intelligent Transportation System in Smart City

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 809

Special Issue Editors

Prof. Dr. Boliang Lin

E-Mail Website
Guest Editor
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Interests: transportation planning and management
Prof. Dr. Shaoquan Ni

E-Mail Website
Guest Editor
School of Transportation and Logistics, Southwest Jiaotong University, Chendu 610031, China
Interests: transportation planning and management

Special Issue Information

Dear Colleagues,

Intelligent Transportation Systems (ITS) play a crucial role in the development of smart cities. These systems provide significant improvements in transportation efficiency, safety, and sustainability in contrast to traditional transportation systems. By integrating advanced technologies, such as complex network theories, neural network algorithms, and combinatorial optimization models, ITS offers transformative approaches for understanding and managing urban traffic. In the future, the development of ITS will become increasingly rapid, and the construction of smart cities will also continue to expand and improve with the support of ITS, creating a safer, more convenient, and higher-quality urban life for people. We are interested in articles that explore the potential and practical applications of ITS in smart cities. Potential topics include, but are not limited to, the following:

  • Integration of AI and IoT in ITS for traffic prediction and management;
  • Truck platooning routing and logistics transportation optimization;
  • Driverless technology and reservation travel strategy in smart cities.

Prof. Dr. Boliang Lin
Prof. Dr. Shaoquan Ni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent transportation system
  • smart city
  • traffic management
  • internet of things
  • neural network
  • artificial intelligence

Published Papers (1 paper)

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Research

18 pages, 6924 KiB  
Article
Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction
by Liangpeng Gao, Wenli Fan and Wenliang Jian
Appl. Sci. 2024, 14(13), 5927; https://doi.org/10.3390/app14135927 - 7 Jul 2024
Viewed by 498
Abstract
The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics [...] Read more.
The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics of different parking lots within the transportation network. This is mainly due to the lack of direct physical connections between parking lots, making it challenging to quantify the spatio-temporal features among them. To address this issue, we propose a dynamic spatio-temporal adaptive graph convolutional recursive network (DSTAGCRN) for VPS prediction. Specifically, DSTAGCRN divides VPS data into seasonal and periodic trend components and combines daily and weekly information with node embeddings using the dynamic parameter-learning module (DPLM) to generate dynamic graphs. Then, by integrating gated recurrent units (GRUs) with the parameter-learning graph convolutional recursive module (PLGCRM) of DPLM, we infer the spatio-temporal dependencies for each time step. Furthermore, we introduce a multihead attention mechanism to effectively capture and fuse the spatio-temporal dependencies and dynamic changes in the VPS data, thereby enhancing the prediction performance. Finally, we evaluate the proposed DSTAGCRN on three real parking datasets. Extensive experiments and analyses demonstrate that the DSTAGCRN model proposed in this study not only improves the prediction accuracy but can also better extract the dynamic spatio-temporal characteristics of available parking space data in multiple parking lots. Full article
(This article belongs to the Special Issue Intelligent Transportation System in Smart City)
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