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Traffic Accident Analyses and Road Safety for Sustainable Transportation
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Traffic Accident Analyses and Road Safety for Sustainable Transportation

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 11834

Special Issue Editors

Dr. Griselda López

E-Mail Website
Guest Editor
Dpto. de Ingeniería e Infraestructura de los Transportes, Universitat Politècnica de València, Valencia, Spain
Interests: crash Injuries; road accidents; cyclists; vulnerable users
Dr. Randa Oqab Mujalli

E-Mail Website
Guest Editor
Department of Civil Engineering, The Hashemite University, P.O. Box 150459, Zarqa City 13115, Jordan
Interests: transportation planning and management; traffic operation and management; traffic safety; intelligent systems applications in transportation engineering

Special Issue Information

Dear Colleagues,

Road traffic crashes are considered a serious and life-threatening problem worldwide, causing an estimated 1.3 million fatalities and between 20 and 50 million injuries per year, according to WHO data in 2019. Road accidents are not limited to motor vehicle drivers, but affect all road users, such as vulnerable road users (i.e., pedestrians, cyclists and motorcyclists and their passengers). In addition, new forms of mobility, such as Personal Mobility Vehicles or Automated and Connected Vehicles, generate conflicts and accidents that represent a new challenge to be investigated.

The resulting road traffic injuries place a significant financial burden on the victims and their families in the form of medical expenses for those who are injured, and lost wages for those who are killed or disabled. Consequently, this affects the nations’ economies due to the loss of productivity and cost of treatment.

Therefore, it is crucial to analyse traffic crashes in order to identify the factors that lead to their occurrence. In general, a number of factors, including road geometry, human characteristics, vehicle design, travel patterns, urban planning, and exposure to risk factors, contribute to traffic crashes. Understanding these factors can assist with the implementation of countermeasures and reduce the frequency of road crashes and their severity, in order to provide a safe and sustainable transport system.

This Special Issue provides researchers and readers with an insight into some key issues in road safety and sustainable transportation, ranging from road design to driver behaviour and human factor challenges that are associated with progress in the field of sustainable and safe mobility. Of particular interest are original and/or review papers addressing (but not limited to) the following topics:

  • Innovative methods in traffic crashes data analysis;
  • Data mining techniques in traffic crashes analysis;
  • Countermeasures to reduce traffic crashes injuries and fatalities;
  • Methods to improve the collection and maintenance of road traffic crashes data;
  • Human factors for motorized and vulnerable road users;
  • Innovative road infrastructure to increase vulnerable road users’ safety;
  • Automated and Connected Vehicles and road safety;
  • Concepts and approaches for sustainable mobility;
  • Sustainable concepts in road safety design;
  • Road safety in sustainable mobility.

Dr. Griselda López
Dr. Randa Oqab Mujalli
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

  • traffic crashes
  • road safety
  • sustainable mobility
  • Automated and Connected Vehicles

Published Papers (5 papers)

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Research

20 pages, 11520 KiB  
Article
Pedestrian Road Traffic Accidents in Metropolitan Areas: GIS-Based Prediction Modelling of Cases in Mashhad, Iran
by Alireza Mohammadi, Behzad Kiani, Hassan Mahmoudzadeh and Robert Bergquist
Sustainability 2023, 15(13), 10576; https://doi.org/10.3390/su151310576 - 5 Jul 2023
Cited by 3 | Viewed by 1754
Abstract
This study utilised multi-year data from 5354 incidents to predict pedestrian–road traffic accidents (PTAs) based on twelve socioeconomic and built-environment factors. The research employed the logistic regression model (LRM) and the fuzzy-analytical hierarchy process (FAHP) techniques to evaluate and assign weights to each [...] Read more.
This study utilised multi-year data from 5354 incidents to predict pedestrian–road traffic accidents (PTAs) based on twelve socioeconomic and built-environment factors. The research employed the logistic regression model (LRM) and the fuzzy-analytical hierarchy process (FAHP) techniques to evaluate and assign weights to each factor. The susceptibility map for PTAs is generated using the “Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)”. Subsequently, the probability of accidents in 2020 was predicted using real multi-year accident data and the Markov chain (MC) and cellular automata Markov chain (CA-MC) models, with the prediction accuracy assessed using the Kappa index. Building upon promising results, the model was extrapolated to forecast the probability of accidents in 2023. The findings of the LRM demonstrated the significance of the selected variables as predictors of accident likelihood. The prediction approaches identified areas prone to high-risk accidents. Additionally, the Kappa for no information (KNO) statistical value was calculated for both the MC and CA-MC models, which yielded values of 0.94 and 0.88, respectively, signifying a high level of accuracy. The proposed methodology is generalizable, and the identification of high-risk locations can aid urban planners in devising appropriate preventive measures. Full article
(This article belongs to the Special Issue Traffic Accident Analyses and Road Safety for Sustainable Transportation)
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23 pages, 9928 KiB  
Article
Self-Paced Ensemble-SHAP Approach for the Classification and Interpretation of Crash Severity in Work Zone Areas
by Roksana Asadi, Afaq Khattak, Hossein Vashani, Hamad R. Almujibah, Helia Rabie, Seyedamirhossein Asadi and Branislav Dimitrijevic
Sustainability 2023, 15(11), 9076; https://doi.org/10.3390/su15119076 - 4 Jun 2023
Cited by 3 | Viewed by 1390
Abstract
The identification of causative factors and implementation of measures to mitigate work zone crashes can significantly improve overall road safety. This study introduces a Self-Paced Ensemble (SPE) framework, which is utilized in conjunction with the Shapley additive explanations (SHAP) interpretation system, to predict [...] Read more.
The identification of causative factors and implementation of measures to mitigate work zone crashes can significantly improve overall road safety. This study introduces a Self-Paced Ensemble (SPE) framework, which is utilized in conjunction with the Shapley additive explanations (SHAP) interpretation system, to predict and interpret the severity of work-zone-related crashes. The proposed methodology is an ensemble learning approach that aims to mitigate the issue of imbalanced classification in datasets of significant magnitude. The proposed solution provides an intuitive way to tackle issues related to imbalanced classes, demonstrating remarkable computational efficacy, praiseworthy accuracy, and extensive adaptability to various machine learning models. The study employed work zone crash data from the state of New Jersey spanning a period of two years (2017 and 2018) to train and evaluate the model. The study compared the prediction outcomes of the SPE model with various tree-based machine learning models, such as Light Gradient Boosting Machine, adaptive boosting, and classification and regression tree, along with binary logistic regression. The performance of the SPE model was superior to that of tree-based machine learning models and binary logistic regression. According to the SHAP interpretation, the variables that exhibited the highest degree of influence were crash type, road system, and road median type. According to the model, on highways with barrier-type medians, it is expected that crashes that happen in the same direction and those that happen at a right angle will be the most severe crashes. Additionally, this study found that severe injuries were more likely to result from work zone crashes that happened at night on state highways with localized street lighting. Full article
(This article belongs to the Special Issue Traffic Accident Analyses and Road Safety for Sustainable Transportation)
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16 pages, 554 KiB  
Article
Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal
by Paulo Infante, Gonçalo Jacinto, Anabela Afonso, Leonor Rego, Pedro Nogueira, Marcelo Silva, Vitor Nogueira, José Saias, Paulo Quaresma, Daniel Santos, Patrícia Góis and Paulo Rebelo Manuel
Sustainability 2023, 15(3), 2352; https://doi.org/10.3390/su15032352 - 27 Jan 2023
Cited by 4 | Viewed by 3487
Abstract
Road traffic accidents (RTAs) are a problem with repercussions in several dimensions: social, economic, health, justice, and security. Data science plays an important role in its explanation and prediction. One of the main objectives of RTA data analysis is to identify the main [...] Read more.
Road traffic accidents (RTAs) are a problem with repercussions in several dimensions: social, economic, health, justice, and security. Data science plays an important role in its explanation and prediction. One of the main objectives of RTA data analysis is to identify the main factors associated with a RTA. The present study aims to contribute to the identification of the determinants for the type of RTA: collision, crash, or pedestrian running-over. These factors are essential for identifying specific countermeasures because there is a relevant relationship between the type of RTA and its severity. Daily RTA data from 2016 to 2019 in a district of Portugal were analyzed. A statistical multinomial logit model was fitted. The identified determinants for the type of RTA were geographical (municipality, location, and parking areas), meteorological (air temperature and weather), time of the day (hour, day of the week, and month), driver’s characteristics (gender and age), vehicle’s features (type and age) and road characteristics (road layout and type). The multinomial model results were compared with several machine learning algorithms, since the original data of the type of RTA are severely imbalanced. All models showed poor performance. However, when combining these models with ROSE for class balancing, their performance improved considerably, with the random forest algorithm showing the best performance. Full article
(This article belongs to the Special Issue Traffic Accident Analyses and Road Safety for Sustainable Transportation)
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11 pages, 1391 KiB  
Article
Influence of Built Environment on Micromobility–Pedestrian Accidents
by Songhyeon Shin and Sangho Choo
Sustainability 2023, 15(1), 582; https://doi.org/10.3390/su15010582 - 29 Dec 2022
Cited by 5 | Viewed by 1744
Abstract
The use of micromobility (MM), a form of sustainable urban mobility which has expected effects such as reducing traffic congestion and greenhouse gases, has been rapidly increasing across the world. However, this growth has resulted in a considerable number of MM-related accidents. Most [...] Read more.
The use of micromobility (MM), a form of sustainable urban mobility which has expected effects such as reducing traffic congestion and greenhouse gases, has been rapidly increasing across the world. However, this growth has resulted in a considerable number of MM-related accidents. Most previous studies have explored MM user injuries to improve the safety of MM users, but the threat to pedestrians by MM is not yet fully understood. Therefore, this study aims to identify built environment factors which contribute to MM–pedestrian accidents by using MM–pedestrian crash data in Seoul, Korea from 2020 to 2021. Setting the spatial unit of analysis as a hexagonal grid with an apothem of 150 m, we developed the SZINB (spatial zero-inflated negative binomial) models for the accidents, controlling spatial autocorrelation, zero-inflated, and overdispersion. The model results showed that road intersections, sidewalks, and subway entrances have significant impacts on MM–pedestrian accidents. Thus, it should be suggested that safety measures for both MM and pedestrians are reducing MM speed limits in intersections, preventing MM use on sidewalks through modified sidewalk designs, and installing MM stations near subway stations. Full article
(This article belongs to the Special Issue Traffic Accident Analyses and Road Safety for Sustainable Transportation)
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18 pages, 6389 KiB  
Article
Statistical Analysis of Major and Extra Serious Traffic Accidents on Chinese Expressways from 2011 to 2021
by Xiangyu Wei, Shixiang Tian, Zhangyin Dai and Peng Li
Sustainability 2022, 14(23), 15776; https://doi.org/10.3390/su142315776 - 27 Nov 2022
Cited by 3 | Viewed by 2116
Abstract
In order to explore the law of major and extra serious traffic accidents on expressways in China, a total of 802 cases of major and extra serious traffic accidents on expressways in the past 10 years were collected, and statistical analysis was conducted [...] Read more.
In order to explore the law of major and extra serious traffic accidents on expressways in China, a total of 802 cases of major and extra serious traffic accidents on expressways in the past 10 years were collected, and statistical analysis was conducted from the aspects of time distribution, spatial distribution, accident form, and accident cause. The results show that in the past 10 years, the incidence and casualties of major and extra-serious traffic accidents on expressways have shown a fluctuating downward trend. In January, May, August, and November every year, between 6: 00 and 8: 00 every day is the highest incidence of accidents. The Guangxi Zhuang Autonomous Region, Heilongjiang Province, Fujian Province, and Anhui Province have more accidents. Vehicle collisions have the highest number of deaths, and rollover injuries have the highest rate. Human factors accounted for 72.1% of the causes of accidents, among which improper measures and speeding accounted for the largest proportion. Finally, according to the results of data statistical analysis, the corresponding control measures should be put forward in order to provide reference and technical support for the current highway traffic safety situation and safety management policy. Full article
(This article belongs to the Special Issue Traffic Accident Analyses and Road Safety for Sustainable Transportation)
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