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Applied Sciences
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Advancements in Intelligent Transportation Systems and Traffic Analysis: 2nd Edition
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Advancements in Intelligent Transportation Systems and Traffic Analysis: 2nd Edition

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 July 2026 | Viewed by 2517

Special Issue Editor

Prof. Dr. Jian Zhang

E-Mail Website
Guest Editor
1. School of Transportation, Southeast University, Nanjing 211189, China
2. Engineering College, Tibet University, Tibet 850000, China
Interests: intelligent scheduling for public transit (analysis, modeling and simulation); traffic information system (data platform system design, highway traffic operation)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of intelligent transportation systems and traffic analysis has made remarkable progress in recent years, and these advances have attracted attention on a global scale. As one of the main components of intelligent transportation systems, the development of intelligent vehicles is also progressing. Pure visual perception and vehicle–road collaborative autonomous driving have gradually enhanced the function of current vehicles. From an industry perspective, these advances demonstrate the huge potential of intelligent transportation systems and traffic analysis, as well as how they can play a key role in improving traffic efficiency, safety, and environmental friendliness.

This Special Issue seeks to propose innovative control and analysis methods based on the new generation of intelligent transportation systems, as well as articles focusing on the latest advances in urban transportation planning, data mining, and vehicle engineering that address the most relevant challenges facing current and future intelligent transportation systems.

Dr. Jian Zhang
Guest Editor

Manuscript Submission Information

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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. 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

  • Internet of Vehicles
  • ITS
  • intelligent and connected vehicles
  • cooperative vehicle infrastructure system
  • traffic analysis

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Related Special Issue

Published Papers (5 papers)

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Research

29 pages, 3435 KB  
Article
Passenger-Oriented Interim-Period Train Timetable Synchronization Optimization for Urban Rail Transit Network
by Yan Xu, Haoran Liang, Ziwei Jia, Minghua Li, Jiaxin Bai and Qiyu Liang
Appl. Sci. 2026, 16(2), 1103; https://doi.org/10.3390/app16021103 - 21 Jan 2026
Viewed by 76
Abstract
Interim periods between peak and off-peak operations in urban rail transit networks often suffer from mismatched headways across lines, which increases passenger transfer waiting and operating costs. This paper proposes a passenger-oriented timetable synchronization method for network-wide interim period train service. In this [...] Read more.
Interim periods between peak and off-peak operations in urban rail transit networks often suffer from mismatched headways across lines, which increases passenger transfer waiting and operating costs. This paper proposes a passenger-oriented timetable synchronization method for network-wide interim period train service. In this study, based on the AFC data, passengers are assigned to the shortest travel time paths, and passenger transfer flows are linked to connecting train pairs by consideration of the maximum acceptable waiting time. As a result, the transfer waiting time is accurately calculated by matching passengers’ platform arrival times with the departures of feasible connecting trains. A mixed integer nonlinear programming model then jointly optimizes departure headways at each line’s first station, arrival and departure times at transfer stations, subject to safety headways and time bounds. The objective minimizes total cost, combining transfer waiting time cost and train operating cost (depreciation and distance-related cost). A simulated-annealing-based genetic algorithm (SA-GA) is designed to solve the NP-hard problem. A case study on the Nanjing rail transit network from 6:30 to 7:30 reduces total cost by 6.88%, including 3.77% lower transfer waiting time cost and 14.49% lower operating cost, and shows stable results under typical transfer demand fluctuations. Full article
(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis: 2nd Edition)
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22 pages, 2074 KB  
Article
Traffic Flow Prediction Model Based on Attention Mechanism Spatio-Temporal Graph Convolutional Network on U.S. Highways
by Ruiying Zhang and Yin Han
Appl. Sci. 2026, 16(1), 559; https://doi.org/10.3390/app16010559 - 5 Jan 2026
Viewed by 270
Abstract
Traffic flow prediction is a fundamental component of intelligent transportation systems and plays a critical role in traffic management and autonomous driving. However, accurately modeling highway traffic remains challenging due to dynamic congestion propagation, lane-level heterogeneity, and non-recurrent traffic events. To address these [...] Read more.
Traffic flow prediction is a fundamental component of intelligent transportation systems and plays a critical role in traffic management and autonomous driving. However, accurately modeling highway traffic remains challenging due to dynamic congestion propagation, lane-level heterogeneity, and non-recurrent traffic events. To address these challenges, this paper proposes an improved attention-mechanism spatio-temporal graph convolutional network, termed AMSGCN, for highway traffic flow prediction. AMSGCN introduces an adaptive adjacency matrix learning mechanism to overcome the limitations of static graphs and capture time-varying spatial correlations and congestion propagation paths. A hierarchical multi-scale spatial attention mechanism is further designed to jointly model local congestion diffusion and long-range bottleneck effects, enabling an adaptive spatial receptive field under congested conditions. To enhance temporal modeling, a gating-based fusion strategy dynamically balances periodic patterns and recent observations, allowing effective prediction under both regular and abnormal traffic scenarios. In addition, direction-aware encoding is incorporated to suppress interference from opposite-direction lanes, which is essential for directional highway traffic systems. Extensive experiments on multiple benchmark datasets, including PeMS and PEMSF, demonstrate the effectiveness and robustness of AMSGCN. In particular, on the I-24 MOTION dataset, AMSGCN achieves an RMSE reduction of 11.0% compared to ASTGCN and 17.4% relative to the strongest STGCN baseline. Ablation studies further confirm that dynamic and multi-scale spatial attention provides the primary performance gains, while temporal gating and direction-aware modeling offer complementary improvements. These results indicate that AMSGCN is a robust and effective solution for highway traffic flow prediction. Full article
(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis: 2nd Edition)
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25 pages, 2492 KB  
Article
Distant and Recent Historical Data Fusion for Improving Short- and Medium-Term Traffic Forecasting
by Metin Usta, H. Irem Turkmen and M. Amac Guvensan
Appl. Sci. 2025, 15(24), 13130; https://doi.org/10.3390/app152413130 - 13 Dec 2025
Viewed by 225
Abstract
Traffic became a major issue in large and crowded metropolitan cities and might cause people to waste in the order of days within a year. It is notable that traffic speed estimation problems were addressed in three main horizons: short term, medium term, [...] Read more.
Traffic became a major issue in large and crowded metropolitan cities and might cause people to waste in the order of days within a year. It is notable that traffic speed estimation problems were addressed in three main horizons: short term, medium term, and long term. In this paper, we both introduce a novel network feeding strategy improving short- and medium-term traffic forecasting and define the aforementioned horizons by evaluating the prediction results up to 6 h. We combined the advantages of both distant and recent historical data by developing two different Recurrent Neural Network (RNN)-based methods, H-LSTM and H-GRU, that employ Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. The proposed Historical Average Long Short-Term Memory (H-LSTM) model demonstrates superior performance compared to traditional methods, as it is capable of integrating both the typical long-term traffic patterns observed in a specific location and the daily fluctuations, such as accidents, unanticipated events, weather conditions, and human activities on particular days. We achieve up to 20% improvement, especially for rush hours, compared to the traditional approach, i.e., exploiting only recent historical data. H-LSTM could make predictions with an average of ±7.5 km/h error margin up to 6 h for a given location. Full article
(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis: 2nd Edition)
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26 pages, 898 KB  
Article
Super-Resolution Task Inference Acceleration for In-Vehicle Real-Time Video via Edge–End Collaboration
by Liming Zhou, Yafei Li, Yulong Feng, Dian Shen, Hui Wang and Fang Dong
Appl. Sci. 2025, 15(21), 11828; https://doi.org/10.3390/app152111828 - 6 Nov 2025
Viewed by 774
Abstract
As intelligent transportation systems continue to advance, on-board surveillance video has become essential for train safety and intelligent scheduling. However, high-resolution video transmission faces bandwidth limitations, and existing deep learning-based super-resolution models find it difficult to meet real-time requirements due to high computational [...] Read more.
As intelligent transportation systems continue to advance, on-board surveillance video has become essential for train safety and intelligent scheduling. However, high-resolution video transmission faces bandwidth limitations, and existing deep learning-based super-resolution models find it difficult to meet real-time requirements due to high computational complexity. To address this, this paper proposes an “edge–end” collaborative multi-terminal task inference framework, which improves inference speed by integrating resources of in-vehicle end devices and edge servers. The framework establishes a real-time-priority mathematical model, uses game theory to solve the problem of minimizing multi-terminal task inference latency, and proposes a multi-terminal task model partitioning strategy and an adaptive adjustment mechanism. It can dynamically partition the model according to device performance and network status, prioritizing real-time performance and minimizing the maximum inference delay. Experimental results show that the dynamic model partitioning mechanism can adaptively determine the optimal partition point, effectively reducing the inference delay of each end device in high-speed mobile and bandwidth-constrained scenarios and providing high-quality video data support for safety monitoring and intelligent analysis. Full article
(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis: 2nd Edition)
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20 pages, 1550 KB  
Article
Real-Time Traffic Arrival Prediction for Intelligent Signal Control Using a Hidden Markov Model-Filtered Dynamic Platoon Dispersion Model and Automatic License Plate Recognition Data
by Hanwu Qin, Dianhai Wang, Zhengyi Cai and Jiaqi Zeng
Appl. Sci. 2025, 15(21), 11537; https://doi.org/10.3390/app152111537 - 29 Oct 2025
Viewed by 751
Abstract
Accurate prediction of downstream vehicle arrivals is pivotal for intelligent signal control, yet many advanced controllers depend on high-resolution trajectories that are rarely available outside connected-vehicle settings. We present a deployable alternative that converts ubiquitous Automatic License Plate Recognition (ALPR) timestamps into the [...] Read more.
Accurate prediction of downstream vehicle arrivals is pivotal for intelligent signal control, yet many advanced controllers depend on high-resolution trajectories that are rarely available outside connected-vehicle settings. We present a deployable alternative that converts ubiquitous Automatic License Plate Recognition (ALPR) timestamps into the predictive inputs required by modern controllers. The method couples a Hidden Markov Model (HMM) for separating free-flow samples from signal-induced delays with a dynamic platoon-dispersion model that is re-estimated online in a rolling window to forecast downstream arrival profiles in real time. In a Simulation of Urban Mobility (SUMO) corridor testbed, the proposed framework consistently outperforms fixed-kernel dispersion and fixed-travel-time baselines, reducing RMSE by 57–75% and MAE by 53–73% across demand levels; ablation results confirm that HMM-based filtering is the dominant contributor to the gains. Robustness experiments further show stable parameter estimation under low ALPR matching rates, indicating suitability for real-world conditions where data quality fluctuates. Because it operates with existing roadside cameras and lightweight inference, the framework is readily integrable into adaptive signal strategies and broader smart-city traffic management. By turning discrete ALPR events into reliable arrival predictions, it bridges the gap between advanced signal control and today’s sensing infrastructure, enabling cost-effective real-time signal optimization in data-constrained urban networks. Full article
(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis: 2nd Edition)
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