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Sustainable Transportation and Road Safety
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Sustainable Transportation and Road Safety

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 16273

Special Issue Editors

Prof. Dr. Wafa Elias

E-Mail Website
Guest Editor
Civil Engineering, Shamoon College of Engineering, Ashdod 77245, Israel
Interests: travel behavior; sustainable modes of transport; road user behavior; sustainable road infrastructure; vulnerable road users; human factors and safety
Prof. Dr. Shalom Hakkert

E-Mail Website
Assistant Guest Editor
Technion-Transportation Research Institute, Haifa 3200003, Israel
Interests: accident analysis; evaluation research; the effects of new; sustainable modes of transport on road safety (walking; cycling; ride sharing)
Dr. Victoria Gitelman

E-Mail Website
Assistant Guest Editor
Transportation Research Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
Interests: accident analysis; safety evaluation; road user behavior; sustainable road infrastructure; vulnerable road users; micromobility; public transport safety

Special Issue Information

Dear Colleagues,

Road traffic deaths and injuries represent a global public health epidemic. This epidemic has reached crisis proportions and is set to worsen over the years ahead. Therefore, huge efforts are being invested in order to improve road safety. 

It is widely recognized that road safety is a complex phenomenon depending on many factors including road infrastructure, human factors, vehicle design, travel model, urban planning, and exposure to risk factors, which can represent the daily activity patterns.

Involvement in road crashes derives from the need to travel for various purposes: the more traveling, the more risk exposure, while the risk exposure varies by the amount of travel , the types of road users or traffic modes, and their composition in traffic.  For example, given the same amount of travel, the injury rates of motorcycle riders, cyclists, and pedestrians are substantially higher than those of car drivers or passengers, and this finding is common for many countries. In addition, given the same amount of travel, the injury rates by public transport are substantially lower than those of car drivers or passengers. Consequently, the more we use private cars and active modes, the higher the risks. Sustainable transportation seeks to decrease dependence on the private car and increase the dependence on  active modes, such as cycling and walking and public transportation. Ride sharing is another mode that can decrease the dependence on individual car use. Encouraging the use of active modes requires an appropriate and safe built environment, and preferring public transport over private cars depends on the extent of the attractiveness of the PT, which is measured by many factors, including travel time, cost, comfort, reliability, price, and others. While human factors are widely considered to be responsible for the majority of road crashes, the development of  modern transportation technologies, “Smart transportation”,  will control the influence of the human factor, and as a result will lead to improvements in road safety and decrease the rate of road crashes.

These technologies aim to provide innovative services relating to different modes of transport and traffic management, and enable users to be better informed and make safer and “smarter” use of transport networks. A “safer” transportation system implies a lower accident and injury risk. Smart transportation is expected to impose changes on traffic exposure, and possibly on road user behavior, vehicle performance, and road infrastructure features. Changes in these factors may impact the transportation system's safety.

Transportation is a major influencer in reaching sustainability. It can lead to major reductions in our dependence on fossil fuels through the move to electric modes. A move towards shared rides and active modes of transport will reduce the discharge of pollutants. It will also have a major impact on health through the increased share of cycling and walking. At the same time, the road safety impacts of such changes should be monitored and evaluated.

This Special Issue aims to present state-of-the-art research related to recent advances in sustainable transportation and road safety. Topics of interest include, but are not limited to, the following:

  • Urban mobility patterns and road safety
  • Road safety in a sustainable urban mobility
  • Innovative services and the system’s safety
  • Safety impacts of vehicle automation
  • Level of vehicle  automation and road safety
  • Safety and automation for vulnerable road users
  • safety challenges and technological solutions for the human operator (drivers) in their interaction with AV systems, at various stages of transition to automation.
  • Connectivity, automation and road safety

Prof. Dr. Wafa Elias
Prof. Dr. Shalom Hakkert
Dr. Victoria Gitelman
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

  • road safety
  • sustainability
  • automation
  • technology
  • urban mobility
  • road infrastructure
  • active modes
  • exposure to risk
  • urban planning

Published Papers (6 papers)

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Research

15 pages, 2823 KiB  
Article
Speed Behavior of Heterogeneous Traffic on Two-Lane Rural Roads in Malaysia
by Rizwan Ullah Faiz, Nordiana Mashros and Sitti Asmah Hassan
Sustainability 2022, 14(23), 16144; https://doi.org/10.3390/su142316144 - 2 Dec 2022
Viewed by 1448
Abstract
Highway geometry is a significant factor that affects the efficiency and safety of highway systems. The present study aims to investigate the speed behavior of various vehicle classes on the horizontal alignment of two-lane rural roads. An automatic data collection system based on [...] Read more.
Highway geometry is a significant factor that affects the efficiency and safety of highway systems. The present study aims to investigate the speed behavior of various vehicle classes on the horizontal alignment of two-lane rural roads. An automatic data collection system based on a pressure sensor was employed to collect the speed of each individual vehicle, vehicle type, and headway at seven sites in each travel direction. The 85th percentile speed under free-flow conditions was used to observe the relationship between the operating speeds of various vehicle classes at consecutive curve points and the effect of the travel direction, time of day, and curve radius on the operating speed of the vehicle. A one-way ANOVA was employed to evaluate whether there is a significant difference in speed on horizontal curves. Then, a Tukey post hoc test was used to assess the significance of the difference in speed across four classes of vehicles. The results revealed that the horizontal curve affects the operating speed for all vehicle classes. A curve radius of less than 500 m, the travel direction, and the time of day are significant variables that affect the speed of all vehicle classes. The findings from this study can provide insight to transportation engineers for safer road design of horizontal curves and to assess traffic safety based on actual speed behavior. Full article
(This article belongs to the Special Issue Sustainable Transportation and Road Safety)
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18 pages, 2534 KiB  
Article
Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5
by Afaq Khattak, Hamad Almujibah, Ahmed Elamary and Caroline Mongina Matara
Sustainability 2022, 14(19), 12340; https://doi.org/10.3390/su141912340 - 28 Sep 2022
Cited by 16 | Viewed by 1854
Abstract
Road traffic accidents are among the top ten major causes of fatalities in the world, taking millions of lives annually. Machine-learning ensemble classifiers have been frequently used for the prediction of traffic injury severity. However, their inability to comprehend complex models due to [...] Read more.
Road traffic accidents are among the top ten major causes of fatalities in the world, taking millions of lives annually. Machine-learning ensemble classifiers have been frequently used for the prediction of traffic injury severity. However, their inability to comprehend complex models due to their “black box” nature may lead to unrealistic traffic safety judgments. First, in this research, we propose three state-of-the-art Dynamic Ensemble Learning (DES) algorithms including Meta-Learning for Dynamic Ensemble Selection (META-DES), K-Nearest Oracle Elimination (KNORAE), and Dynamic Ensemble Selection Performance (DES-P), with Random Forest (RF), Adaptive Boosting (AdaBoost), Classification and Regression Tree (CART), and Binary Logistic Regression (BLR) as the base learners. The DES algorithm automatically chooses the subset of classifiers most likely to perform well for each new test instance to be classified when generating a prediction, making it more efficient and flexible. The META-DES model using RF as the base learner outperforms other models with accuracy (75%), recall (69%), precision (71%), and F1-score (72%). Afterwards, the risk factors are analyzed with SHapley Additive exPlanations (SHAP). The driver’s age, month of the year, day of the week, and vehicle type influence SHAP estimation the most. Young drivers are at a heightened risk of fatal accidents. Weekends and summer months see the most fatal injuries. The proposed novel META-DES-RF algorithm with SHAP for predicting injury severity may be of interest to traffic safety researchers. Full article
(This article belongs to the Special Issue Sustainable Transportation and Road Safety)
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24 pages, 4612 KiB  
Article
A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways
by Zhaoshi Geng, Xiaofeng Ji, Rui Cao, Mengyuan Lu and Wenwen Qin
Sustainability 2022, 14(18), 11212; https://doi.org/10.3390/su141811212 - 7 Sep 2022
Cited by 4 | Viewed by 1753
Abstract
Collision risk identification and prediction is an effective means to prevent truck accidents. However, most existing studies focus only on highways, not on two-lane rural highways. To predict truck collision probabilities and identify high-risk scenes on two-lane rural highways, this study first calculated [...] Read more.
Collision risk identification and prediction is an effective means to prevent truck accidents. However, most existing studies focus only on highways, not on two-lane rural highways. To predict truck collision probabilities and identify high-risk scenes on two-lane rural highways, this study first calculated time to collision and post-encroachment time using high-precision trajectory data and combined them with extreme value theory to predict the truck collision probability. Subsequently, a traffic feature parameter system was constructed with the driving behavior risk parameter. Furthermore, machine learning algorithms were used to identify critical feature parameters that affect truck collision risk. Eventually, extreme value theory based on time to collision and post-encroachment time incorporated a machine learning algorithm to identify high-risk truck driving scenes. The experiments showed that bivariate extreme value theory integrates the applicability of time to collision and post-encroachment time for different driving trajectories of trucks, resulting in significantly better prediction performances than univariate extreme value theory. Additionally, the horizontal curve radius has the most critical impact on truck collision; when a truck is driving on two-lane rural highways with a horizontal curve radius of 227 m or less, the frequency and probability of collision will be higher, and deceleration devices and central guardrail barriers can be installed to reduce risk. Second is the driving behavior risk: the driving behavior of truck drivers on two-lane rural highways has high-risk, and we recommend the installation of speed cameras on two-lane rural roads to control the driving speed of trucks and thus avoid dangerous driving behaviors. This study extends the evaluation method of truck collisions on two-lane rural highways from univariate to bivariate and provides a basis for the design of two-lane rural highways and the development of real-time dynamic warning systems and enforcement for trucks, which will help prevent and control truck collisions and alleviate safety problems on two-lane rural highways. Full article
(This article belongs to the Special Issue Sustainable Transportation and Road Safety)
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20 pages, 730 KiB  
Article
Prediction of Crash Severity as a Way of Road Safety Improvement: The Case of Saint Petersburg, Russia
by Maria Rodionova, Angi Skhvediani and Tatiana Kudryavtseva
Sustainability 2022, 14(16), 9840; https://doi.org/10.3390/su14169840 - 9 Aug 2022
Cited by 5 | Viewed by 1782
Abstract
This article investigates factors that explain road crash severity levels in Saint Petersburg, Russia, during the 2015–2021 period. The research takes into account factors such as lighting conditions, weather conditions, infrastructure factors, human factors, accident types, and vehicle category and color to assess [...] Read more.
This article investigates factors that explain road crash severity levels in Saint Petersburg, Russia, during the 2015–2021 period. The research takes into account factors such as lighting conditions, weather conditions, infrastructure factors, human factors, accident types, and vehicle category and color to assess their influence on crash severity. The most influential accident type is run-off-road crashes, which are associated with an 11.2% increase in fatal accidents. The biggest reason for the increase in fatal accidents due to road infrastructure conditions is road barrier shortcomings (2.8%). Road infrastructure conditions, such as a lack of road lighting, have a significant effect on fatal outcomes, increasing them by 12.6%, and this is the most influential factor in the analysis. The obtained results may serve as a basis for Saint Petersburg authorities to develop new road safety policies. Full article
(This article belongs to the Special Issue Sustainable Transportation and Road Safety)
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22 pages, 4750 KiB  
Article
Analysis of the Causes of Traffic Accidents and Identification of Accident-Prone Points in Long Downhill Tunnel of Mountain Expressways Based on Data Mining
by Fu Wang, Jing Wang, Xianfeng Zhang, Dengjun Gu, Yang Yang and Hongbin Zhu
Sustainability 2022, 14(14), 8460; https://doi.org/10.3390/su14148460 - 11 Jul 2022
Cited by 7 | Viewed by 2881
Abstract
China has a large vehicle base, uneven road conditions, and the highest rate of traffic accidents in the world. Particularly on the long downhill sections of expressway tunnels in mountainous areas with harsh geographical conditions, traffic accidents are densely distributed, and once a [...] Read more.
China has a large vehicle base, uneven road conditions, and the highest rate of traffic accidents in the world. Particularly on the long downhill sections of expressway tunnels in mountainous areas with harsh geographical conditions, traffic accidents are densely distributed, and once a traffic accident occurs, the consequences are serious, which poses a large threat to people’s lives and property. This paper mined and analyzed the traffic accident data collected by the project on the Baoding section of Zhangshi Expressway. SPSS software was used to analyze the traffic accident data characteristics of the long downhill tunnel of the mountain expressways. The time, space, accident form, vehicle type, and road alignment distribution characteristics of the traffic accident in the long downhill tunnel section of mountain expressways were obtained. The decision tree algorithm was used to construct the cause analysis model of traffic accidents in the long downhill tunnel of mountain expressways, and the five primary influencing factors were obtained: horizontal curve radius, week, slope length, time, and cart ratio. The improved cumulative frequency curve method was used to study the accident-prone points of mountain expressways, and the accident-prone points and potential accident-prone points were obtained. Full article
(This article belongs to the Special Issue Sustainable Transportation and Road Safety)
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17 pages, 641 KiB  
Article
Identification of Factors Affecting Road Traffic Injuries Incidence and Severity in Southern Thailand Based on Accident Investigation Reports
by Nuntaporn Klinjun, Matthew Kelly, Chanita Praditsathaporn and Rewwadee Petsirasan
Sustainability 2021, 13(22), 12467; https://doi.org/10.3390/su132212467 - 11 Nov 2021
Cited by 23 | Viewed by 5348
Abstract
Thailand has the second-highest rates of road traffic mortality globally. Detailed information on the combination of human, vehicle, and environmental risks giving rise to each incident is important for addressing risk factors holistically. This paper presents the result of forensic road traffic investigation [...] Read more.
Thailand has the second-highest rates of road traffic mortality globally. Detailed information on the combination of human, vehicle, and environmental risks giving rise to each incident is important for addressing risk factors holistically. This paper presents the result of forensic road traffic investigation reports in Thailand and determines risk factor patterns for road traffic injuries. Detailed forensic reports were extracted for 25 serious traffic accident events. The Haddon matrix was used to analyze risk factors in three phases stratified by four agents. The 25 events analyzed involved 407 victims and 47 vehicles. A total of 65.8% of victims were injured, including 14.5% who died. The majority (66.1%) of deaths occurred at the scene. Human-error-related factors included speeding and drowsiness. Passenger risks included not using the seat belt, sitting in the cargo area and the cab of pickups. Overloaded vehicles, unsafe car modifications, no occupant safety equipment and having unfixed seats were vehicular risks. Environmental risks included fixed objects on the roadside, no traffic lights, no guard rails, no traffic signs, and road accident black spots. At present, traffic accidents cause much avoidable severe injury and death. The outcome of this paper identifies a number of preventable risk factors for traffic injury, and importantly examines them in conjunction. Road traffic safety measures need to consider how human, vehicle, and environmental risks intersect to influence injury likelihood and severity. The Haddon matrix is useful in identifying these pre- and post-accident risk factors. Furthermore, the sustainable preventions of road traffic injury need to address these risks together with active law enforcement. Full article
(This article belongs to the Special Issue Sustainable Transportation and Road Safety)
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