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Challenges and Strategies for Sustainable Transportation and Traffic Safety

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

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 23373

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Special Issue Editors

Dr. Athanasios (Akis) Theofilatos

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Guest Editor

Department of Civil Engineering, University of Thessaly, 38334 Volos, Greece
Interests: transport safety; statistical and econometric methods; machine learning; deep learning; traffic engineering; sustainable urban mobility
Special Issues, Collections and Topics in MDPI journals

Dr. Apostolos Ziakopoulos

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Guest Editor

Department of Transportation Planning and Engineering, National Technical University of Athens, GR-15773 Athens, Greece
Interests: traffic engineering; transport safety; crash analysis; statistical and econometric methods; spatial analyses; machine learning; data science

Dr. Ioanna Pagoni

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Guest Editor

Transportation and Decision Making Laboratory (TRANSDEM), University of the Aegean, Korai 2Α, 82100 Chios, Greece
Interests: transportation system analysis; intelligent transport systems; demand modelling; travel behavior analysis; sustainable mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is a growing interest in sustainability, sustainable development and sustainable transportation. Several factors contribute to the great interest in the importance of these issues. There is also a huge need and enormous potential for improving road safety policies, practices, decision-making and strategies, in order to achieve a safe, efficient and sustainable transportation system. Due to the recent trends towards alternative and sustainable urban mobility modes (e.g. e-scooters) as well as the introduction of Connected and Autonomous Vehicles (CAVs) in future traffic, urban systems are expected to become even more complex. Hence, efforts by scholars and policy makers need to become more intense and be set under a framework. This Special Issue aims to present state-of-the-art research related to challenges and strategies for Sustainable Transportation and Traffic Safety.

Topics of interest include, but are not limited to:

Traffic safety impact assessment of sustainability measures;
Cost-Benefit effects of sustainable safety interventions;
Policies for the integration of the dimension of sustainability in traffic safety interventions;
New data sources and KPIs for sustainable traffic safety;
Combining sustainability, traffic safety and smart urban mobility;
Impacts of new transport modes (e.g. e-scooters) on sustainable traffic safety;
Interactions between connected and automated vehicles (CAVs) and road users;
Policies to promote sustainable and safe urban mobility;
Innovative mobility services (e.g. shared mobility, MaaS, micromobility) within sustainable and safe systems.

Dr. Athanasios (Akis) Theofilatos
Dr. Apostolos Ziakopoulos
Dr. Ioanna Pagoni
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

transport data analytics
road/traffic safety
sustainability
urban mobility

Published Papers (11 papers)

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Research

Jump to: Review

18 pages, 4858 KiB

Open AccessArticle

Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

by Surjeet Dalal, Bijeta Seth, Magdalena Radulescu, Teodor Florin Cilan and Luminita Serbanescu

Sustainability 2023, 15(7), 6070; https://doi.org/10.3390/su15076070 - 31 Mar 2023

Cited by 4 | Viewed by 1912

Abstract

Unfortunately, accidents caused by bad weather have regularly made headlines throughout history. Some of the more catastrophic events to recently make news include a plane crash, ship collision, railway derailment, and several vehicle accidents. The public’s attention has been directed to the severe issue of safety and security under extreme weather conditions, and many studies have been conducted to highlight the susceptibility of transportation services to environmental factors. An automated method of determining the weather’s state has gained importance with the development of new technologies and the rise of a new industry: intelligent transportation. Humans are well-suited for determining the temperature from a single photograph. Nevertheless, this is a more challenging problem for a fully autonomous system. The objective of this research is developing a good weather classifier that uses only a single image as input. To resolve quality-of-life challenges, we propose a modified deep-learning method to classify the weather condition. The proposed model is based on the Yolov5 model, which has been hyperparameter tuned with the Learning-without-Forgetting (LwF) approach. We took 1499 images from the Roboflow data repository and divided them into training, validation, and testing sets (70%, 20%, and 10%, respectively). The proposed model has gained 99.19% accuracy. The results demonstrated that the proposed model gained a much higher accuracy level in comparison with existing approaches. In the future, this proposed model may be implemented in real-time. Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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17 pages, 2090 KiB

Open AccessArticle

Road Junction Configurations and the Severity of Traffic Accidents in Japan

by Yoshifumi Wada, Yasushi Asami, Kimihiro Hino, Hayato Nishi, Shino Shiode and Narushige Shiode

Sustainability 2023, 15(3), 2722; https://doi.org/10.3390/su15032722 - 02 Feb 2023

Cited by 1 | Viewed by 2225

Abstract

In many countries, 40–60% of the traffic accidents occur at junctions, making the reduction of junction accidents paramount to achieving UN Sustainable Development Goals. In Japan, the road safety guidelines specify the proximity between junctions and non-perpendicular angles at junctions as the two main risk factors behind junction accidents, yet their impact remains understudied. Using binomial logistic regression models, this study investigates the impact of junction intervals and junction angles on the severity of traffic accidents. The study found that, in general, (1) shorter intervals between adjacent junctions helps reduce the risk of serious accidents, which is the opposite of the current road safety guidelines in Japan, and (2) results from the junction angle analysis were mixed but there was no evidence that the roads should meet at a right angle to reduce traffic accidents. Some types of accidents also returned a non-linear curve, e.g., vehicle-to-vehicle collisions at four-armed junctions involving a driver aged 65 years and over have the highest risk of fatal/serious accidents when adjacent junctions were 32 m apart, and the risk reduces at a shorter or longer interval. These results suggest that the current road safety guidelines require updating to improve road safety around junctions. Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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13 pages, 2801 KiB

Open AccessArticle

Analysis of the Effect of Providing Pedestrian Crossing Information at the Blind Spots of Intersections on Vehicle Traffic

by Ki-Man Hong, Jong-Hoon Kim, Jung-Ah Ha, Gwang-Ho Kim and Jong-Hoon Kim

Sustainability 2023, 15(3), 2718; https://doi.org/10.3390/su15032718 - 02 Feb 2023

Viewed by 1725

Abstract

In this study, we conducted an analysis of the pedestrian safety system for crosswalks introduced in Korea to improve sustainable traffic safety. The pedestrian crossing information provision system provides information to a driver in advance when a pedestrian is detected in the driver’s blind spot when the latter is turning right at an intersection. The location analyzed was the three-way intersection in front of Yungheung Elementary School in Jeollabuk-do, and vehicle speed information for 150–160 min before and after system installation was collected. As a result of comparing and analyzing the change in the compliance rate of the spot speed and the speed limit, it was found that there was no statistical difference in the change in the spot speed, but in the absence of pedestrians, the speed increased slightly compared with that before installation. The change in the speed limit compliance rate was found to improve when pedestrian crossing information was provided. In addition, a chi-square test found that there was a difference in the speed limit compliance rate before and after system installation where pedestrians existed (when information was provided), while there was no difference in the situation where pedestrians did not exist (when information was not provided). Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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19 pages, 2720 KiB

Open AccessArticle

Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector

by Mireille Megnidio-Tchoukouegno and Jacob Adedayo Adedeji

Sustainability 2023, 15(3), 2014; https://doi.org/10.3390/su15032014 - 20 Jan 2023

Cited by 12 | Viewed by 3938

Abstract

Despite the measures put in place in different countries, road traffic fatalities are still considered one of the leading causes of death worldwide. Thus, the reduction of traffic fatalities or accidents is one of the contributing factors to attaining sustainability goals. Different factors such as the geometric structure of the road, a non-signalized road network, the mechanical failure of vehicles, inexperienced drivers, a lack of communication skills, distraction and the visual or cognitive impairment of road users have led to this increase in traffic accidents. These factors can be categorized under four headings that are: human, road, vehicle factors and environmental road conditions. The advent of machine learning algorithms is of great importance in analysing the data, extracting hidden patterns, predicting the severity level of accidents and summarizing the information in a useful format. In this study, three machine learning algorithms for classification, such as Decision Tree, LightGBM and XGBoost, were used to model the accuracy of road traffic accidents in the UK for the year 2020 using their default and hyper-tuning parameters. The results show that the high performance of the Decision Tree algorithm with default parameters can predict traffic accident severity and provide reference to the critical variables that need to be monitored to reduce accidents on the roads. This study suggests that preventative strategies such as regular vehicle technical inspection, traffic policy strengthening and the redesign of vehicle protective equipment be implemented to reduce the severity of road accidents caused by vehicle characteristics. Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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13 pages, 3812 KiB

Open AccessArticle

Analysis of the Relationship between Age and Violation of Traffic Laws and Ordinances in Traffic Accidents on Children

by Hiroki Onishi, Makoto Fujiu, Yuma Morisaki and Junichi Takayama

Sustainability 2022, 14(19), 12778; https://doi.org/10.3390/su141912778 - 07 Oct 2022

Cited by 3 | Viewed by 1294

Abstract

In Japan, where the birthrate continues to decline, various initiatives are underway to promote traffic safety for children. Although these efforts have helped reduce the number of traffic accidents involving children, an examination of the circumstances under which children were killed or injured in traffic accidents in recent years shows that accidents in which children were riding bicycles accounted for the highest percentage of accidents. We investigated the relationship between the ages of children involved in traffic accidents and violations of traffic laws using traffic accident statistics maintained by the Ishikawa Prefectural Police Headquarters. These records revealed that 16-year-olds were most likely to be involved in traffic accidents. Our analysis of the violations of laws and ordinances with respect to the ages of bicyclists involved in car accidents at intersections found that, of all the age categories, elementary school students had the lowest rate of accidents with no violations and the highest rate of accidents involving a failure to stop. Junior and senior high school students had lower rates of accidents involving a failure to stop than elementary school students. Moreover, at non-intersections, driving safety violations were notably higher for accidents involving bicycling elementary school students than for those in other age groups. Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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21 pages, 5598 KiB

Open AccessArticle

Smart Traffic Data for the Analysis of Sustainable Travel Modes

by Zoi Christoforou, Christos Gioldasis, Yeltsin Valero and Grigoris Vasileiou-Voudouris

Sustainability 2022, 14(18), 11150; https://doi.org/10.3390/su141811150 - 06 Sep 2022

Viewed by 1298

Abstract

We present and validate the image analysis algorithm μ-scope to capture personal mobility devices’ (PMDs) movement characteristics and extract their movement dynamics even when they interact with each other and with pedestrians. Experimental data were used for validation of the proposed algorithm. Data were collected through a large-scale, semicontrolled, real-track experiment at the University of Patras campus. Participants (N = 112) included pedestrians, cyclists, and e-scooter drivers. The experiment was video recorded, and μ-scope was used for trajectory extraction. Some of the participants had installed, beforehand, the Phyphox application in their smartphones. Phyphox accurately measures x-y-z acceleration rates and was used, in our case, as the baseline measurement (i.e., “ground truth”). Statistical comparison between Phyphox and camera-based measurements shows very low difference in most cases. High pedestrian densities were the only case where relatively high root mean square errors were registered. The proposed algorithm can be thus considered capable of producing reliable speed and acceleration estimates. Low-quality conventional smartphone cameras were used in this experiment. As a result, the proposed method can be easily applied to all urban contexts under normal traffic conditions, but eventually not in the case of special or emergency events generating very high pedestrian densities. Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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14 pages, 912 KiB

Open AccessArticle

The Impact of Flashing on the Efficacy of Variable Message Signs: A Vehicle-by-Vehicle Approach

by Franco Basso, Pedro Maldonado, Raúl Pezoa, Nicolás Szoloch and Mauricio Varas

Sustainability 2022, 14(15), 9705; https://doi.org/10.3390/su14159705 - 06 Aug 2022

Cited by 1 | Viewed by 1628

Abstract

A great deal of research has examined the efficacy of variable message signs (VMS) to induce driver behavior changes, improve safety conditions, and decongest the traffic network. However, there is little literature regarding the most effective ways to display this information on VMS. Furthermore, none of the previous contributions have concentrated on analyzing what impact flashing VMS have on drivers by using real traffic data. This article seeks to bridge this gap, analyzing the effect of incorporating intermittent light stimulation to messages on drivers’ behavior on a Chilean highway, using vehicle-by-vehicle data obtained in a non-intrusive way. In order to do so, an experiment was carried out to measure the responses of drivers when faced with two types of messages: (1) those intended to induce a speed reduction and (2) those aimed at generating lane changes. From the statistical models we obtained several insights. Our results show that flashing messages may increase the effectiveness of VMS depending on environmental and traffic conditions. In particular, for speed moderation messages, we found 12 significant effects, showing, for example, that a flashing message is most effective in the hours of darkness, with low congestion, small spacing, and low average speeds. Additionally, it has a more significant impact on experienced drivers. On the other hand, for lane change messages, we found five significant effects, showing that flashing messaging reduces its effectiveness in situations where a high cognitive load is required, such as in high flow and high average speeds. No particular effects were identified in either case for specific vehicle types. Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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15 pages, 2467 KiB

Open AccessArticle

Characteristics of Cyclist Crashes Using Polytomous Latent Class Analysis and Bias-Reduced Logistic Regression

by Yuta Sekiguchi, Masayoshi Tanishita and Daisuke Sunaga

Sustainability 2022, 14(9), 5497; https://doi.org/10.3390/su14095497 - 03 May 2022

Cited by 4 | Viewed by 1312

Abstract

Although the number of cyclist crashes is decreasing in Japan, the fatality rate is not. Thus, reducing their severity is a major challenge. We used a polytomous latent class analysis to understand their characteristics and bias-reduced logistic regression to analyze their severity. Specifically, 90,696 combinations and 139,955 cyclist accidents were divided into 17 classes. The variable contributing the most to the classification was the crash location. Common fatality risks included older age groups and rural areas, whereas other factors differed among crash locations. Median strips, stop signs, and boundaries between the sidewalk and roadway affected the severity of crashes at intersections. Moreover, the existence of a median strip, collision partner, and time period affected the severity of crashes between intersections. On the sidewalks, the fatality risk was higher when the front part of the bicycle was subjected to the collision. Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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12 pages, 1982 KiB

Open AccessArticle

An Analysis of Driving Behavior of Educated Youth in Bangladesh Considering Physiological, Cultural and Socioeconomic Variables

by Ashraf Mahmud Rayed, Muhammad Atiq Ur Rehman Tariq, Mizanur Rahman, A. W. M. Ng, Md. Khairul Alam Nahid, Mahibuzzaman Mridul, Wazed Al Islam and Muhammad Mohiuddin

Sustainability 2022, 14(9), 5134; https://doi.org/10.3390/su14095134 - 24 Apr 2022

Cited by 2 | Viewed by 2107

Abstract

One of the alarming aspects of Bangladesh’s traffic safety is the massive growth in the number of drivers without previous driving instruction or licenses. Proper traffic safety is defined as systems and techniques used to safeguard road users against dying or being severely injured. A driving simulator policy and an environmental model are validated in this research. It aims to create a safe mass transit system with a minimal number of fatalities and injuries. The study focuses on current road and transportation strategies. Educated and internet-using Bangladeshi drivers took part in a questionnaire about their emotional stability on an online platform with more than 100 questions comprising two parts. While one of the part outlines the physiological, cultural, and socioeconomic factors and driver education, in another part, an 18-point Driver’s Behavior Questionnaire was introduced to the responders. About 40% of the surveyed drivers in the poll were inexperienced. However, 49% of people prefer to ride two-wheelers. Moreover, 70% of surveyed drivers hold valid driver’s licenses. At the same time, 35.2% of those were college graduates. Even 34.8% of accidents were caused by excessive speed and non-aggressive driving. In addition, age and degree of education were significant indicators of distracted driving violations. The study’s findings will raise awareness about the country’s undesirable driving patterns, resulting in a safer transit system with fewer accidents and deaths. In addition, the findings may be utilized to improve present road and transit policies and lead to the development of a driving simulator program for Bangladeshis. Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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Review

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19 pages, 1449 KiB

Open AccessReview

A Review of Surrogate Safety Measures Uses in Historical Crash Investigations

by Dimitrios Nikolaou, Apostolos Ziakopoulos and George Yannis

Sustainability 2023, 15(9), 7580; https://doi.org/10.3390/su15097580 - 05 May 2023

Cited by 3 | Viewed by 2081

Abstract

Historical road crash data are the main indicator for measuring road safety outcomes. Over the past few decades, significant efforts have been made in obtaining and exploiting Surrogate Safety Measures (SSMs). SSMs have the potential to provide excellent sustainable road safety indicators and proxy measurements which can complement traditional historical crash analyses or even substitute them. By using SSMs, crash data collection demands can be bypassed and areas can be investigated before crashes occur. Due to such advantages, the objective of the present research is to provide a review of the scientific literature regarding studies exploiting SSMs for historical crash record investigations. Specifically, 34 studies were examined, providing insights on the different types of SSMs collected under real road environment conditions, the way they are collected, their connection with specific road crash types, and the type of the developed statistical models are examined and discussed. Particular focus is also placed on the temporal dimension of the collection period of both SSMs and road crashes. Finally, the overall trends deriving from the reviewed studies are summarized and future research directions are provided. Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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14 pages, 771 KiB

Open AccessReview

Slow and Steady Wins the Race: A Comparative Analysis of Standing Electric Scooters’ European Regulations Integrated with the Aspect of Forensic Traumatology

by Luigi Buongiorno, Alessandra Stellacci, Gerardo Cazzato, Pierluigi Caricato, Benedetta Pia De Luca, Francesca Tarantino, Stefania Lonero Baldassarra, Giuseppe Ingravallo and Maricla Marrone

Sustainability 2022, 14(10), 6160; https://doi.org/10.3390/su14106160 - 19 May 2022

Cited by 4 | Viewed by 1878

Abstract

Fuel-driven cars are widely considered unsustainable and contrary to the new paradigm of smart growth planning. The need to reform transport behavior, policies, and infrastructure is among the priorities in urban policies around the world. Electric vehicles are an emerging technology that could advance sustainability programs. In the past year, there has been a rapid increase in the diffusion of electric scooters in several European cities, but various states have been unprepared for the rapid spread of green micro-mobility from a regulatory point of view. In addition, in parallel with the spread, there have been numerous road collisions involving standing electric scooters. The aim of this study was to obtain a detailed view of this phenomenon. We focused on the current legislation on electric micro-mobility at the European level to study and summarize the different attitudes adopted by various states whose regulations are present on the web. (It was not possible to evaluate the regulations of all European countries because they are not all available on online platforms.) The elements assessed in the various regulation were age limits, speed limits, compulsory use of helmets, administrative penalties, and the obligation to insure the new e-vehicle (standing scooter). In this study, we analyze the state of the art in electric micro-mobility, highlight the current situation’s limits, and propose new strategies to adequately integrate this new smart vehicle into the urban transport network. Full article

(This article belongs to the Special Issue Challenges and Strategies for Sustainable Transportation and Traffic Safety)

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