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Artificial Intelligence in Intelligent Transportation Systems and Traffic Control for Smart Cities

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 25 May 2026 | Viewed by 1426

Special Issue Editor

Dr. Daniele Cenni

E-Mail Website
Guest Editor
Area for Innovation and Management of Information and Computer Systems, University of Florence, 50139 Firenze, Italy
Interests: deep learning; cloud computing; information retrieval; social networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of smart city technologies is transforming urban transportation via the integration of Intelligent Transportation Systems (ITS) and advanced Traffic Control Systems (TCS). These innovations aim to address critical challenges such as traffic congestion, environmental sustainability, and safety in increasingly dense urban environments. This Special Issue welcomes the submission of innovative research on the design, implementation, and evaluation of ITS and TCS within the context of smart cities. The scope of this Special Issue includes, but is not limited to, the role of artificial intelligence (AI), machine learning, and the Internet of Things (IoT) in real-time traffic management, predictive analytics for congestion mitigation, and adaptive traffic signal control. Additionally, this Special Issue seeks contributions that address connected and autonomous vehicle systems, multimodal transportation integration, and the environmental impacts of ITS deployments. Article that explore the societal dimensions of ITS, including equity in access, data privacy, and scalability, are particularly welcome. This Special Issue aims to advance our understanding of how ITS and TCS can contribute to the development of sustainable, efficient, and resilient urban mobility systems. We encourage researchers, practitioners, and policymakers to submit innovative studies that can guide the future of transportation in smart cities, fostering a balance between technological advancement and societal well-being.

Dr. Daniele Cenni
Guest Editor

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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. Sensors 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 2600 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

  • deep learning
  • cloud computing
  • information retrieval
  • social networks
  • intelligent transportation systems
  • traffic control systems
  • smart city
  • traffic management

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Published Papers (1 paper)

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Research

26 pages, 3237 KB  
Article
Deep Learning-Driven Bus Short-Term OD Demand Prediction via a Physics-Guided Adaptive Graph Spatio-Temporal Attention Network
by Zhichao Cao, Longfei Song, Silin Zhang and Jingxuan Sun
Sensors 2025, 25(21), 6739; https://doi.org/10.3390/s25216739 - 4 Nov 2025
Viewed by 631
Abstract
This study develops a recent model proposed by Zhang et al. to predict bus short-term origin-destination (OD) demand based on a small-scale dataset (i.e., one week’s data per 30 mins’ collecting interval). We distinctively use sole input sequence by introducing a multi-head attention [...] Read more.
This study develops a recent model proposed by Zhang et al. to predict bus short-term origin-destination (OD) demand based on a small-scale dataset (i.e., one week’s data per 30 mins’ collecting interval). We distinctively use sole input sequence by introducing a multi-head attention mechanism while simultaneously ensuring prediction accuracy. Extensive experiments demonstrate that one-layer bidirectional LSTMs (BiLSTMs) perform better than multi-layer ones. A modified deep learning model integrating physics-guided mechanisms, adaptive graph convolution, attention networks, and spatiotemporal encoder–decoder is constructed. We retained the original name, i.e., physics-guided adaptive graph spatio-temporal attention network (PAG-STAN) model. The model uses an encoder–decoder architecture, where the encoder captures spatiotemporal correlations via an adaptive graph convolutional LSTM (AGC-LSTM), enhanced by an attention mechanism that adjusts the importance of different spatiotemporal features. The decoder utilizes bidirectional LSTM to reconstruct the periodic patterns and predict the full OD matrix for the next interval. A masked physics-guided loss function, which embeds the quantitative relationship between boarding passenger volume and OD demand, is adopted for training. The Adam optimizer and early stopping technique are used to enhance training efficiency and avoid overfitting. Experimental results show that PAG-STAN outperforms other deep learning models in prediction accuracy. Compared with the suboptimal model, the proposed model achieved reductions of 6.19% in RMSE, 6.59% in MAE, and 8.20% in WMAPE, alongside a 1.13% improvement in R2. Full article
(This article belongs to the Special Issue Artificial Intelligence in Intelligent Transportation Systems and Traffic Control for Smart Cities)
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