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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 1185

Special Issue Editors


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Guest Editor
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Interests: transportation planning and management

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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

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Keywords

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

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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 791
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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