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Applied Sciences
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Traffic Emergency: Forecasting, Control and Planning
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Traffic Emergency: Forecasting, Control and Planning

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

Deadline for manuscript submissions: 20 July 2024 | Viewed by 879

Special Issue Editors

Prof. Dr. Ping Wang

E-Mail Website
Guest Editor
School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China
Interests: rehabilitation robotics; control systems engineering; transportation; intelligent transport systems; deep learning
Dr. Linlin You

E-Mail Website
Guest Editor
School of Intelligent System Engineering, Sun Yat-Sen University, Guangzhou, China
Interests: urban big data; multi-source heterogeneous data fusion; machine learning; federated learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traffic emergencies are suscept to regular transportation systems, which increase complexity, posing challenges in terms of efficiency and safety. The surge in vehicle volume coupled with the rise in the frequency and intensity of traffic accidents have heightened the need for comprehensive strategies to forecast, control, and plan for traffic emergencies.

Traditional systems struggle with dynamic emergencies, leading to prolonged disruptions and safety compromises. Innovative solutions using advanced technologies, data analytics, and interdisciplinary approaches are in demand to enhance responsiveness. Emerging technologies like connected and automatic vehicles and real-time data analytics provide opportunities to revolutionize traffic emergency responses. They offer new tools for prediction, control, and effective communication, ensuring timely information dissemination.

This Special Issue aims to highlight new opportunities and challenges for traffic emergency issues and explore innovative methodologies, as well as practical solutions, focusing on improving traffic performance under emergencies with traffic forecasting, control, and planning.

Prof. Dr. Ping Wang
Dr. Linlin You
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

  • emergency responses
  • traffic evacuation
  • traffic predictive modeling
  • emergency traffic management and control
  • emergency resource dispatch and optimization
  • rescue vehicle route planning
  • automated and connected vehicles
  • data analytics

Published Papers (2 papers)

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19 pages, 6295 KiB  
Article
A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
by Jing Zhao, Wenchen Yang and Feng Zhu
Appl. Sci. 2024, 14(11), 4934; https://0-doi-org.brum.beds.ac.uk/10.3390/app14114934 - 6 Jun 2024
Viewed by 288
Abstract
To enhance traffic safety on mountainous roads, this study proposes an innovative CNN-LSTM-Attention model designed for the identification of near-crash events, utilizing naturalistic driving data from the challenging terrains in Yunnan, China. A combination of a threshold method complemented by manual verification is [...] Read more.
To enhance traffic safety on mountainous roads, this study proposes an innovative CNN-LSTM-Attention model designed for the identification of near-crash events, utilizing naturalistic driving data from the challenging terrains in Yunnan, China. A combination of a threshold method complemented by manual verification is used to label and annotate near-crash events within the dataset. The importance of vehicle motion features is evaluated using the random forest algorithm, revealing that specific variables, including x-axis acceleration, y-axis acceleration, y-axis angular velocity, heading angle, and vehicle speed, are particularly crucial for identifying near-crash events. Addressing the limitations of existing models in accurately detecting near-crash scenarios, this study combines the strengths of convolutional neural networks (CNN), long short-term memory (LSTM) networks, and an attention mechanism to enhance model sensitivity to crucial temporal and spatial features in naturalistic driving data. Specifically, the CNN-LSTM-Attention model leverages CNN to extract local features from the driving data, employs LSTM to track temporal dependencies among feature variables, and uses the attention mechanism to dynamically fine-tune the network weights of feature parameters. The efficacy of the proposed model is extensively evaluated against six comparative models: CNN, LSTM, Attention, CNN-LSTM, CNN-Attention, and LSTM-Attention. In comparison to the benchmark models, the CNN-LSTM-Attention model achieves superior overall accuracy at 98.8%. Moreover, it reaches a precision rate of 90.1% in detecting near-crash events, marking an improvement of 31.6%, 14.8%, 63.5%, 8%, 23.5%, and 22.6% compared to the other six comparative models, respectively. Full article
(This article belongs to the Special Issue Traffic Emergency: Forecasting, Control and Planning)
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20 pages, 3211 KiB  
Article
Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
by Liguo Zhao, Zhipeng Fu, Jingwen Yang, Ziqiao Zhao and Ping Wang
Appl. Sci. 2024, 14(11), 4593; https://0-doi-org.brum.beds.ac.uk/10.3390/app14114593 - 27 May 2024
Viewed by 320
Abstract
Fast detection of the trajectory is the key point to improve the further emergency proposal. Especially for ultra-long highway, prompt detection is labor-intensive. However, automatic detection relies on the accuracy and speed of vehicle detection, and tracking. In multi-camera surveillance system for ultra-long [...] Read more.
Fast detection of the trajectory is the key point to improve the further emergency proposal. Especially for ultra-long highway, prompt detection is labor-intensive. However, automatic detection relies on the accuracy and speed of vehicle detection, and tracking. In multi-camera surveillance system for ultra-long highways, it is often difficult to capture the same vehicle without intervals, which makes vehicle re-recognition crucial as well. In this paper, we present a framework that includes vehicle detection and tracking using improved DeepSORT, vehicle re-identification, feature extraction based on trajectory rules, and behavior recognition based on trajectory analysis. In particular, we design a network architecture based on DeepSORT with YOLOv5s to address the need for real-time vehicle detection and tracking in real-world traffic management. We further design an attribute recognition module to generate matching individuality attributes for vehicles to improve vehicle re-identification performance under multiple neighboring cameras. Besides, the use of bidirectional LSTM improves the accuracy of trajectory prediction, demonstrating its robustness to noise and fluctuations. The proposed model has a high advantage from the cumulative matching characteristic (CMC) curve shown and even improves above 15.38% compared to other state-of-the-art methods. The model developed on the local highway vehicle dataset is comprehensively evaluated, including abnormal trajectory recognition, lane change detection, and speed anomaly recognition. Experimental results demonstrate the effectiveness of the proposed method in accurately identifying various vehicle behaviors, including lane changes, stops, and even other dangerous driving behavior. Full article
(This article belongs to the Special Issue Traffic Emergency: Forecasting, Control and Planning)
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