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Electronics
Special Issues
Emerging Traffic Safety Research Based on Multi-Source Data
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Emerging Traffic Safety Research Based on Multi-Source Data

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 5583

Special Issue Editors

Dr. Lei Chen

E-Mail Website
Guest Editor
Department of Mobility and systems, Research Institutes of Sweden, Box 857, 501 15 Borås, Sweden
Interests: cooperative intelligent transport systems
Dr. Lingtao Wu

E-Mail Website
Guest Editor
Center for Transportation Safety, Texas A&M Transportation Institute, College Station, TX, USA
Interests: road safety analysis theories and methodologies; proactive road safety analysis techniques
Dr. Yajie Zou

E-Mail Website
Guest Editor
Department of Traffic Engineering, College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: traffic operation; transportation data mining; statistical analysis; traffic safety
Special Issues, Collections and Topics in MDPI journals
Dr. Yue Zhang

E-Mail Website
Guest Editor
Department of Traffic Engineering, College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: traffic safety; automated vehicles; driving behaviors; trajectory data mining

Special Issue Information

Dear Colleagues, 

With the rapid development of automatic detection technology, abundant data are generated from the transportation system such as trajectory data, vehicle movement data, physiological data, and mobile phone data. There is a great need to integrate these multi-source data for use in the quantitative analysis of traffic safety. The goal of this Special Issue is to advance emerging data analytics to address traffic safety challenges, find new insights for understanding traffic safety risks, provide safety solutions for transportation management agencies, and devise traffic safety policies.

This Special Issue aims to solicit cutting-edge ideas, knowledge, methodologies, techniques, and practices in mitigating the issue of road safety with the support of big data. Potential topics include but are not limited to the following:

  • Extracting and reconstructing risky driving scenarios from multi-source data;
  • Advanced data analysis methods related to modeling of vehicle interaction patterns;
  • Simulation of traffic safety risk and analysis of dynamic scenario;
  • Extreme weather-related traffic safety modeling;
  • Investigation of emerging connected automated vehicles (CAV) technologies for traffic safety analysis;
  • Advanced road-crash risk prediction and identification methods.

Dr. Lei Chen
Dr. Lingtao Wu
Dr. Yajie Zou
Dr. Yue Zhang
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. Electronics 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.

Published Papers (2 papers)

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Research

29 pages, 1904 KiB  
Article
Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network
by Mamoona Humayun, Farzeen Ashfaq, Noor Zaman Jhanjhi and Marwah Khalid Alsadun
Electronics 2022, 11(17), 2748; https://doi.org/10.3390/electronics11172748 - 01 Sep 2022
Cited by 41 | Viewed by 3099
Abstract
Detecting and counting on road vehicles is a key task in intelligent transport management and surveillance systems. The applicability lies both in urban and highway traffic monitoring and control, particularly in difficult weather and traffic conditions. In the past, the task has been [...] Read more.
Detecting and counting on road vehicles is a key task in intelligent transport management and surveillance systems. The applicability lies both in urban and highway traffic monitoring and control, particularly in difficult weather and traffic conditions. In the past, the task has been performed through data acquired from sensors and conventional image processing toolbox. However, with the advent of emerging deep learning based smart computer vision systems the task has become computationally efficient and reliable. The data acquired from road mounted surveillance cameras can be used to train models which can detect and track on road vehicles for smart traffic analysis and handling problems such as traffic congestion particularly in harsh weather conditions where there are poor visibility issues because of low illumination and blurring. Different vehicle detection algorithms focusing the same issue deal only with on or two specific conditions. In this research, we address detecting vehicles in a scene in multiple weather scenarios including haze, dust and sandstorms, snowy and rainy weather both in day and nighttime. The proposed architecture uses CSPDarknet53 as baseline architecture modified with spatial pyramid pooling (SPP-NET) layer and reduced Batch Normalization layers. We also augment the DAWN Dataset with different techniques including Hue, Saturation, Exposure, Brightness, Darkness, Blur and Noise. This not only increases the size of the dataset but also make the detection more challenging. The model obtained mean average precision of 81% during training and detected smallest vehicle present in the image Full article
(This article belongs to the Special Issue Emerging Traffic Safety Research Based on Multi-Source Data)
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15 pages, 2351 KiB  
Article
An Optimized Algorithm for Dangerous Driving Behavior Identification Based on Unbalanced Data
by Shengxue Zhu, Chongyi Li, Kexin Fang, Yichuan Peng, Yuming Jiang and Yajie Zou
Electronics 2022, 11(10), 1557; https://doi.org/10.3390/electronics11101557 - 13 May 2022
Cited by 5 | Viewed by 1833
Abstract
It is of great significance to identify dangerous driving behavior by extracting vehicle trajectory through video monitoring to ensure highway traffic safety. At present, there is no suitable method to identify dangerous driving vehicles accurately based on trajectory data. This paper aims to [...] Read more.
It is of great significance to identify dangerous driving behavior by extracting vehicle trajectory through video monitoring to ensure highway traffic safety. At present, there is no suitable method to identify dangerous driving vehicles accurately based on trajectory data. This paper aims to develop a detection algorithm for identifying dangerous driving behavior based on the road scene, which is mainly composed of imbalanced dangerous driver detection and labeling, extraction of driving behavior characteristics and the establishment of a recognition model about dangerous driving behavior. Firstly, this paper defines the risk index of the vehicle related to five types of dangerous driving behavior: dangerous following, lateral deviation, frequent acceleration and deceleration, frequent lane change, and forced insertion. Then, a variety of methods, including K-means clustering, local factor anomaly algorithm, isolation forest and OneClassSVM, are used to carry out anomaly detection on the risk indicators of drivers, and the optimal method is proposed to identify dangerous drivers. Then, the speed and acceleration of each vehicle are Fourier transformed to obtain the characteristics of the driver’s driving behavior. Finally, considering the imbalanced characteristic of the analyzed dataset with a very small proportion of dangerous drivers, this paper compares a variety of imbalanced classification algorithms to optimize the recognition performance of dangerous driving behavior. The results show that the OneClassSVM detection algorithm can be effectively applied to the identification of dangerous driving behavior. The improved Xgboost algorithm performs best for the extremely imbalanced data of dangerous drivers. Full article
(This article belongs to the Special Issue Emerging Traffic Safety Research Based on Multi-Source Data)
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