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Frontiers in Road Safety Research

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: closed (1 March 2022) | Viewed by 10854

Special Issue Editors

School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
Interests: roadway transportation safety; traffic data analysis; sustainable transportation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Civil Engineering, The University of Hong Kong, Hong Kong 999077, China
Interests: data mining; spatial analysis; Bayesian inference

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Guest Editor
School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China
Interests: traffic safety; driving behavior; transportation planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of techniques such as connected and autonomous vehicles (CAV), digital twins, and virtual reality has substantially shaped our cities. Despite their potential use toward a more efficient and sustainable transportation system, the associated challenges to road safety have not been fully validated and remain under investigation.

This Special Issue therefore focuses on the frontiers in road safety research in the era of emerging technologies and data. Specifically, it aims to invite stakeholders from all levels, including urban planners, traffic engineers, environmentalists, epidemiologists, behavioral psychologists, ergonomists, and policymakers to envisage, discuss, untangle, and define the role played by advanced technologies and big data in road safety and to develop cutting-edge and technically sound methods to achieve a safer, smarter, and more intelligent transportation system. Potential topics include but are not limited to:

  • Safety evaluation of various on-road and in-vehicle technologies;
  • Advanced approaches for crash reconstruction and causation analysis;
  • Advanced statistical and machine learning methods for crash data modeling;
  • Real-time crash prediction by virtue of ensemble deep learning methods;
  • Automatic extraction of travel trajectories, traffic conflicts, and violations;
  • Tailor-made characterization of surrogate safety measures and crash precursors;
  • Proactive identification of risky behaviors, such as speeding, fatigue driving, and drunk driving, driving in the wrong direction, and riding without helmets based on crowdsourced datasets;
  • Safety-oriented scenario design for CAV tests.

Dr. Qiang Zeng
Dr. Pengpeng Xu
Dr. Feng Chen
Prof. Dr. Zhongxiang Feng
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. Sustainability 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

  • safety evaluation
  • crash prediction
  • crash reconstruction
  • surrogatie safety measures
  • risky behaviors
  • CAV safety

Published Papers (4 papers)

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Research

21 pages, 1375 KiB  
Article
Risk Levels Classification of Near-Crashes in Naturalistic Driving Data
by Hasan A. H. Naji, Qingji Xue, Nengchao Lyu, Xindong Duan and Tianfeng Li
Sustainability 2022, 14(10), 6032; https://doi.org/10.3390/su14106032 - 16 May 2022
Cited by 4 | Viewed by 1613
Abstract
Identifying dangerous events from driving behavior data has become a vital challenge in intelligent transportation systems. In this study, we compared machine and deep learning-based methods for classifying the risk levels of near-crashes. A dataset was built for the study by considering variables [...] Read more.
Identifying dangerous events from driving behavior data has become a vital challenge in intelligent transportation systems. In this study, we compared machine and deep learning-based methods for classifying the risk levels of near-crashes. A dataset was built for the study by considering variables related to naturalistic driving, temporal data, participants, and road geometry, among others. Hierarchical clustering was applied to categorize the near-crashes into several risk levels based on high-risk driving variables. The adaptive lasso variable model was adopted to reduce factors and select significant driving risk factors. In addition, several machine and deep learning models were used to compare near-crash classification performance by training the models and examining the model with testing data. The results showed that the deep learning models outperformed the machine learning and statistical models in terms of classification performance. The LSTM model achieved the highest performance in terms of all evaluation metrics compared with the state-of-the-art models (accuracy = 96%, recall = 0.93, precision = 0.88, and F1-measure = 0.91). The LSTM model can improve the classification accuracy and prediction of most near-crash events and reduce false near-crash classification. The finding of this study can benefit transportation safety in predicting and classifying driving risk. It can provide useful suggestions for reducing the incidence of critical events and forward road crashes. Full article
(This article belongs to the Special Issue Frontiers in Road Safety Research)
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19 pages, 3842 KiB  
Article
An Exploration of Characteristics and Time Series Forecast of Fatal Road Crashes in Manipal, India
by Kumar Sumit, Veerle Ross, Robert A. C. Ruiter, Evelien Polders, Geert Wets and Kris Brijs
Sustainability 2022, 14(5), 2851; https://doi.org/10.3390/su14052851 - 1 Mar 2022
Cited by 3 | Viewed by 2426
Abstract
Road crashes are the sixth leading cause of death in India. There has been a fourfold increase in the number of road traffic crashes in India in the last four decades and an increase of 9.8 times in the fatalities associated with that [...] Read more.
Road crashes are the sixth leading cause of death in India. There has been a fourfold increase in the number of road traffic crashes in India in the last four decades and an increase of 9.8 times in the fatalities associated with that exponential increase. Manipal is a coastal place with a population of approximately 50,000 inhabitants lying in between the western Mountain range and the Arabian sea. The study’s objective is to explore the characteristics of fatal road crashes in Manipal from 2008–2018 using the data pertaining to fatal crashes retrieved from the office of the superintendent of police. Furthermore, it aims to forecast crashes by time series analysis prediction. The results show that most of the fatal crashes are due to exceeding the lawful speed limit, followed by driving under the influence of alcohol and going ahead and overtaking. The time series analysis forecasted the number of fatal crashes until the year 2025 and predicted that there will be an increase in the number of fatal road crashes by 4.5%. The results also provide essential leads for initiating specific intervention programmes targeting the causes of fatal road crashes. Full article
(This article belongs to the Special Issue Frontiers in Road Safety Research)
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15 pages, 2949 KiB  
Article
Impacts of Real-Time Traffic State on Urban Expressway Crashes by Collision and Vehicle Type
by Chen Wang, Ming Zhong, Hui Zhang and Siyao Li
Sustainability 2022, 14(4), 2238; https://doi.org/10.3390/su14042238 - 16 Feb 2022
Cited by 2 | Viewed by 1654
Abstract
With the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. Variation of traffic flow is considered to have significant impact on the safety performance of expressways. Therefore, the motivation of this study is [...] Read more.
With the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. Variation of traffic flow is considered to have significant impact on the safety performance of expressways. Therefore, the motivation of this study is to explore the mechanism of how the variation of traffic flow measurements such as average speed, speed variation and traffic volume impact the crash risk. Firstly, the crashes were classified according to crash type and vehicles involved: and they are labeled with rear-end collisions or side-impact collisions, they are labeled with heavy-vehicle related collisions or light-vehicle related collisions as well. Then, the corresponding crash data were aggregated based on the similarity of traffic flow conditions and types of crashes. Finally, a random effect negative binomial model was introduced to consider the heterogeneity of the crash risk due to the variance within the traffic flow and crash types. The results show that the significant influencing factors of each type of crashes are not consistent. Specifically, the percentage of heavy vehicles within traffic flow is found to have a negative impact on rear-end collisions and light-vehicle-related collisions, but it has no obvious correlation with side-impact collisions and heavy-vehicle-related collisions. Average speed, speed variation and traffic volume have an interactive effect on the crash rate. In conclusion, if the traffic flow is with higher speed variation within lanes and is with lower average speed, the risk of all types of crashes tends to be higher. If the speed variation within lanes decreases and the average speed increases, the crash risk will also increase. In addition, if the traffic flow is under the conditions of higher speed variation between lanes and lower traffic volume, the risk of rear-end collisions, side-impact collisions and heavy-vehicles related collisions tend to be higher. Meanwhile, if the speed variation between lanes decreases and the traffic volume increases, the crash risk is found to increase as well. Full article
(This article belongs to the Special Issue Frontiers in Road Safety Research)
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21 pages, 1100 KiB  
Article
Urban Road Accident Black Spot Identification and Classification Approach: A Novel Grey Verhuls–Empirical Bayesian Combination Method
by Yan Wan, Wenqiang He and Jibiao Zhou
Sustainability 2021, 13(20), 11198; https://doi.org/10.3390/su132011198 - 11 Oct 2021
Cited by 8 | Viewed by 3817
Abstract
The identification and classification of accident black spots on urban roads is a key element of road safety research. To solve the problems caused by the randomness of accident occurrences and the unclear classification of accident black spots by the traditional model, we [...] Read more.
The identification and classification of accident black spots on urban roads is a key element of road safety research. To solve the problems caused by the randomness of accident occurrences and the unclear classification of accident black spots by the traditional model, we propose a method that can quickly identify and classify accident black spots on urban roads: a combined grey Verhuls–Empirical Bayesian method. The grey Verhuls model is used to obtain the predicted/expected numbers of accidents at accident hazard locations, and the empirical Bayesian approach is used to derive two accident black spot discriminators, a safety improvement space and a safety index (SI), and to classify the black spots into two, three, four and five levels according to the range of the SI. Finally, we validate this combined method on examples. High-quality and high-accuracy data are obtained from the accident collection records of the Ningbo Jiangbei District from March to December 2020, accounting for 90.55% of the actual police incidents during this period. The results show that the combined grey Verhuls–Empirical Bayesian method can identify accident black spots quickly and accurately due to the consideration of accident information from the same types of accident locations. The accident black point classification results show that the five-level rating of accident black points is most reasonable. Our study provides a new idea for accident black spot identification and a feasible method for accident black spot risk level classification. Full article
(This article belongs to the Special Issue Frontiers in Road Safety Research)
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