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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Global Health".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 20045

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Special Issue Editors

Dr. Gen Li

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Guest Editor

College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: traffic safety; traffic simulation and optimization; traffic big data and machine learning; driving behavior

Dr. Chenming Jiang

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Guest Editor

China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
Interests: traffic safety; roadway crash analysis and prevention; traffic network analysis and modelling

Special Issue Information

Dear Colleagues,

With the rapid development of motorization, the number of casualties related to road traffic accidents is increasing. Therefore, road traffic safety is one of the most important concerns at present. Road traffic accidents may not only endanger people's lives, resulting in the significant loss of property, but also affects the operational status of the road, causing traffic congestion, reducing road capacity and service levels. The factors affecting road traffic safety mainly include drivers, vehicles, road, environment, and road traffic management, etc. In fact, most road traffic accidents are caused by a single factor, but by the coupling effect of multiple factors. In addition, with the development of intelligent networked vehicles, traffic safety management is also facing significant opportunities and challenges. At this time, it is important to transform the technical advantages in intelligence and network connectivity into advantages in road traffic management and traffic safety governance. Therefore, in order to develop reasonable road traffic safety precautions and improve road traffic safety to ensure the future traffic efficiency and traffic safety in the intelligent networked environment, it is necessary to explore the influence mechanism of road traffic safety from a multi-factor perspective. This Special Issue aims to present methods for analyzing and modeling the factors influencing road traffic safety at different levels. We welcome original research articles and comprehensive reviews of research areas, including, but not limited to: study of road or road network traffic accident data and severity modeling.; modeling the impact of drivers' physiological and psychological states on road traffic safety; modeling and comparing the effects of vehicle interaction behavior and vehicle types on road traffic safety; analysis and modeling the impact of road design (engineering design, facility design, etc.) on traffic safety; identification and comprehensive evaluation of road traffic safety impact factors; modeling the propagation and evolutionary mechanism of road traffic safety risks; study on traffic safety of heterogeneous traffic flows in an intelligent networked environment; research on road traffic safety characteristics under the interaction of intelligent networked vehicles; and road traffic safety management techniques and prevention strategies.

Dr. Gen Li
Dr. Chenming Jiang
Guest Editors

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Keywords

road safety
traffic accident
severity analysis
data mining
heterogeneous traffic flow

Published Papers (11 papers)

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Research

14 pages, 4056 KiB

Open AccessArticle

Mapping Grip Force Characteristics in the Measurement of Stress in Driving

by Yotam Sahar, Tomer Elbaum, Oren Musicant, Michael Wagner, Leon Altarac and Shraga Shoval

Int. J. Environ. Res. Public Health 2023, 20(5), 4005; https://doi.org/10.3390/ijerph20054005 - 23 Feb 2023

Cited by 1 | Viewed by 1797

Abstract

Reducing drivers’ stress can potentially increase road safety. However, state-of-the-art physiological stress indices are intrusive and limited by long time lags. Grip force is an innovative index of stress that is transparent to the user and, according to our previous findings, requires a two- to five-second time window. The aim of this study was to map the various parameters affecting the relationship between grip force and stress during driving tasks. Two stressors were used: the driving mode and the distance from the vehicle to a crossing pedestrian. Thirty-nine participants performed a driving task during either remote driving or simulated driving. A pedestrian dummy crossed the road without warning at two distances. The grip force on the steering wheel and the skin conductance response were both measured. Various model parameters were explored, including time window parameters, calculation types, and steering wheel surfaces for the grip force measurements. The significant and most powerful models were identified. These findings may aid in the development of car safety systems that incorporate continuous measurements of stress. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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28 pages, 3395 KiB

Open AccessArticle

Temporal Instability of Motorcycle Crash Fatalities on Local Roadways: A Random Parameters Approach with Heterogeneity in Means and Variances

by Thanapong Champahom, Chamroeun Se, Sajjakaj Jomnonkwao, Tassana Boonyoo, Amphaphorn Leelamanothum and Vatanavongs Ratanavaraha

Int. J. Environ. Res. Public Health 2023, 20(5), 3845; https://doi.org/10.3390/ijerph20053845 - 21 Feb 2023

Cited by 6 | Viewed by 1461

Abstract

Motorcycle accidents can impede sustainable development due to the high fatality rate associated with motorcycle riders, particularly in developing countries. Although there has been extensive research conducted on motorcycle accidents on highways, there is a limited understanding of the factors contributing to accidents involving the most commonly used motorcycles on local roads. This study aimed to identify the root causes of fatal motorcycle accidents on local roads. The contributing factors consist of four groups: rider characteristics, maneuvers prior to the crash, temporal and environmental characteristics, and road characteristics. The study employed random parameters logit models with unobserved heterogeneity in means and variances while also incorporating the temporal instability principle. The results revealed that the data related to motorcycle accidents on local roads between 2018 and 2020 exhibited temporal variation. Numerous variables were discovered to influence the means and variances of the unobserved factors that were identified as random parameters. Male riders, riders over 50 years old, foreign riders, and accidents that occurred at night with inadequate lighting were identified as the primary factors that increased the risk of fatalities. This paper presents a clear policy recommendation aimed at organizations and identifies the relevant stakeholders, including the Department of Land Transport, traffic police, local government organizations, and academic groups. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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10 pages, 1415 KiB

Open AccessArticle

Fractures of the Lower Extremity after E-Bike, Bicycle, and Motorcycle Accidents: A Retrospective Cohort Study of 624 Patients

by Thomas Rauer, Andrin Aschwanden, Benjamin B. Rothrauff, Hans-Christoph Pape and Julian Scherer

Int. J. Environ. Res. Public Health 2023, 20(4), 3162; https://doi.org/10.3390/ijerph20043162 - 10 Feb 2023

Cited by 3 | Viewed by 1442

Abstract

Electric bicycles (e-bikes) have gained enormous popularity in recent years, and as a result, they have successively become more involved in traffic accidents. The aim of the present study was to assess differences in severity and localization of injuries to the lower extremities after accidents with e-bikes, conventional bicycles, and motorcycles. A retrospective cohort-analysis of patients who sustained traumatic accidents with two-wheeled vehicles transferred to a level 1 trauma center in Switzerland was performed. We assessed patient demographics, injury pattern, and trauma severity (ISS), with a subgroup analysis of outcomes stratified by vehicle. In total, 624 patients (71% male) with injuries to the lower extremities after bicycle (n = 279), electric bike (n = 19), and motorcycle (n = 326) accident were included. The mean age of all assessed patients was 42.4 years (SD 15.8), with a significantly higher age in the e-bike cohort (p = 0.0001). High-velocity injuries were found significantly more often in the motorcycle and e-bike group. The motorcycle group had a significantly higher mean ISS (17.6) than the other groups (p = 0.0001). E-bike accidents produce a different injury profile to the lower extremities compared to motorcycle or bicycle accidents. Higher age, higher velocity, and different protective equipment seem to have an impact on these fracture patterns. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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15 pages, 3325 KiB

Open AccessArticle

Applying OHSA to Detect Road Accident Blackspots

by Zhuang-Zhuang Wang, Yi-Ning Lu, Zi-Hao Zou, Yu-Han Ma and Tao Wang

Int. J. Environ. Res. Public Health 2022, 19(24), 16970; https://doi.org/10.3390/ijerph192416970 - 17 Dec 2022

Cited by 1 | Viewed by 2101

Abstract

With increasing numbers of crashes and injuries, understanding traffic accident spatial patterns and identifying blackspots is critical to improve overall road safety. This study aims at detecting blackspots using optimized hot spot analysis (OHSA). Traffic accidents were classified by their participants and severity to explore the relationship between blackspots and different types of accidents. Based on the outputs of incremental spatial autocorrelation, OHSA was then implemented on different types of accidents. Finally, the performance of OHSA in evaluating the road safety level of the proposed RBT index are examined using a binary correlation analysis (i.e., R² = 0.89). The results show that: (1) The optimal scale distance varies from 0.6 km to 2.8 km and is influenced by the distance of the travel mode. (2) Central cities, with 54.6% of the total accidents, experiences more rigorous challenges regarding traffic safety than satellite cities. (3) There are many types of black spots in vulnerable communities, but in some specific areas, there are only black spots of non-motor vehicle accidents. Considering the practical significance of the above results, policy makers and traffic engineers are expected to give higher attention to central cities and vulnerable communities or prioritize the implementation of relevant optimization measures. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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13 pages, 2096 KiB

Open AccessArticle

Identifying Risk Factors for Autos and Trucks on Highway-Railroad Grade Crossings Based on Mixed Logit Model

by Lan Wu, Qi Shen and Gen Li

Int. J. Environ. Res. Public Health 2022, 19(22), 15075; https://doi.org/10.3390/ijerph192215075 - 16 Nov 2022

Cited by 4 | Viewed by 1170

Abstract

This study aimed to determine different influencing factors associated with the injury outcomes of heavy vehicle and automobile drivers at highway–rail grade crossings (HRGCs). A mixed logit model was adopted using the Federal Railroad Administration (FRA) dataset (n = 194,385 for 2011–2020). The results show that drivers’ injury severities at HRGCs are enormously different between automobile and truck/truck–trailer drivers. It was found that vehicle speed and train speed significantly affect the injury severity in automobile and truck drivers. Driver characteristics such as gender and driver actions significantly impact the injury severity in automobile drivers, while HRGC attributes such as open space, rural areas, and type of warning device become significant factors in truck models. This study gives us a better understanding of the differences in the types of determinants between automobiles and trucks and their implications on differentiated policies for car and truck drivers. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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23 pages, 3643 KiB

Open AccessArticle

Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction

by Bo Wang, Chi Zhang, Yiik Diew Wong, Lei Hou, Min Zhang and Yujie Xiang

Int. J. Environ. Res. Public Health 2022, 19(20), 13693; https://doi.org/10.3390/ijerph192013693 - 21 Oct 2022

Cited by 2 | Viewed by 1629

Abstract

Road infrastructure has significant effects on road traffic safety and needs further examination. In terms of traffic crash prediction, recent studies have started to develop deep learning classification algorithms. However, given the uncertainty of traffic crashes, predicting the traffic risk potential of different road sections remains a challenge. To bridge this knowledge gap, this study investigated a real-world expressway and collected its traffic crash data between 2013 and 2020. Then, according to the time-spatial density ratio (Pts), road sections were assigned into three classes corresponding to low, medium, and high risk levels of traffic. Next, different classifiers were compared that were trained using the transformed and resampled feature data to construct a traffic crash risk prediction model. Last, but not least, partial dependence plots (PDPs) were employed to interpret the results and analyze the importance of individual features describing the geometry, pavement, structure, and weather conditions. The results showed that a variety of data balancing algorithms improved the performance of the classifiers, the ensemble classifier superseded the others in terms of the performance metrics, and the combined SMOTEENN and random forest algorithms improved the classification accuracy the most. In the future, the proposed traffic crash risk prediction method will be tested in more road maintenance and design safety assessment scenarios. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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15 pages, 1372 KiB

Open AccessArticle

Comparative Analysis of Influencing Factors on Crash Severity between Super Multi-Lane and Traditional Multi-Lane Freeways Considering Spatial Heterogeneity

by Junxiang Zhang, Bo Yu, Yuren Chen, You Kong and Jianqiang Gao

Int. J. Environ. Res. Public Health 2022, 19(19), 12779; https://doi.org/10.3390/ijerph191912779 - 6 Oct 2022

Viewed by 1494

Abstract

With the growth of traffic demand, the number of newly built and renovated super multi-lane freeways (i.e., equal to or more than a two-way ten-lane) is increasing. Compared with traditional multi-lane freeways (i.e., a two-way six-lane or eight-lane), super multi-lane freeways have higher design speeds and more vehicle interweaving movements, which may lead to higher traffic risks. However, current studies mostly focus on the factors that affect crash severity on traditional multi-lane freeways, while little attention is paid to those on super multi-lane freeways. Therefore, this study aims to explore the impacting factors of crash severity on two kinds of freeways and make a comparison with traditional multi-lane freeways. The crash data of the Guangzhou-Shenzhen freeway in China from 2016 to 2019 is used in the study. This freeway contains both super multi-lane and traditional multi-lane road sections, and data on 2455 crashes on two-way ten-lane sections and 13,367 crashes on two-way six-lane sections were obtained for further analysis. Considering the effects of unobserved spatial heterogeneity, a hierarchical Bayesian approach is applied. The results show significant differences that influence the factors of serious crashes between these two kinds of freeways. On both two types of freeways, heavy-vehicle, two-vehicle, and multi-vehicle involvements are more likely to lead to serious crashes. Still, their impact on super multi-lane freeways is much stronger. In addition, for super multi-lane freeways, vehicle-to-facility collisions and rainy weather can result in a high possibility of serious crashes, but their impact on traditional multi-lane freeways are not significant. This study will contribute to understanding the impacting factors of crash severity on super multi-lane freeways and help the future design and safety management of super multi-lane freeways. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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19 pages, 5501 KiB

Open AccessArticle

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

by Qiang Shang, Tian Xie and Yang Yu

Int. J. Environ. Res. Public Health 2022, 19(17), 10903; https://doi.org/10.3390/ijerph191710903 - 1 Sep 2022

Cited by 4 | Viewed by 1713

Abstract

Traffic accidents causing nonrecurrent congestion and road traffic injuries seriously affect public safety. It is helpful for traffic operation and management to predict the duration of traffic incidents. Most of the previous studies have been in a certain area with a single data source. This paper proposes a hybrid deep learning model based on multi-source incomplete data to predict the duration of countrywide traffic incidents in the U.S. The text data from the natural language description in the model were parsed by the latent Dirichlet allocation (LDA) topic model and input into the bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) hybrid network together with sensor data for training. Compared with the four benchmark models and three state-of-the-art algorithms, the RMSE and MAE of the proposed method were the lowest. At the same time, the proposed model performed best for durations between 20 and 70 min. Finally, the data acquisition was defined as three phases, and a phased sequential prediction model was proposed under the condition of incomplete data. The results show that the model performance was better with the update of variables. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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24 pages, 732 KiB

Open AccessArticle

Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances

by Muhammad Ijaz, Lan Liu, Yahya Almarhabi, Arshad Jamal, Sheikh Muhammad Usman and Muhammad Zahid

Int. J. Environ. Res. Public Health 2022, 19(17), 10526; https://doi.org/10.3390/ijerph191710526 - 24 Aug 2022

Cited by 10 | Viewed by 2149

Abstract

Not wearing a helmet, not properly strapping the helmet on, or wearing a substandard helmet increases the risk of fatalities and injuries in motorcycle crashes. This research examines the differences in motorcycle crash injury severity considering crashes involving the compliance with and defiance of helmet use by motorcycle riders and highlights the temporal variation in their impact. Three-year (2017–2019) motorcycle crash data were collected from RESCUE 1122, a provincial emergency response service for Rawalpindi, Pakistan. The available crash data include crash-specific information, vehicle, driver, spatial and temporal characteristics, roadway features, and traffic volume, which influence the motorcyclist’s injury severity. A random parameters logit model with heterogeneity in means and variances was evaluated to predict critical contributory factors in helmet-wearing and non-helmet-wearing motorcyclist crashes. Model estimates suggest significant variations in the impact of explanatory variables on motorcyclists’ injury severity in the case of compliance with and defiance of helmet use. For helmet-wearing motorcyclists, key factors significantly associated with increasingly severe injury and fatal injuries include young riders (below 20 years of age), female pillion riders, collisions with another motorcycle, large trucks, passenger car, drivers aged 50 years and above, and drivers being distracted while driving. In contrast, for non-helmet-wearing motorcyclists, the significant factors responsible for severe injuries and fatalities were distracted driving, the collision of two motorcycles, crashes at U-turns, weekday crashes, and drivers above 50 years of age. The impact of parameters that predict motorcyclist injury severity was found to vary dramatically over time, exhibiting statistically significant temporal instability. The results of this study can serve as potential motorcycle safety guidelines for all relevant stakeholders to improve the state of motorcycle safety in the country. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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11 pages, 1933 KiB

Open AccessArticle

Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization

by Yongqiang Zhang, Zhuang Hu, Min Zhang, Wenting Ba and Ying Wang

Int. J. Environ. Res. Public Health 2022, 19(16), 10295; https://doi.org/10.3390/ijerph191610295 - 18 Aug 2022

Cited by 4 | Viewed by 1302

Abstract

Western China is a sparsely populated area with dispersed transportation infrastructure, making it challenging to meet people’s accessibility and mobility requirements. Rescue efficiency in sparse networks is severely hampered by the difficulty rescue teams have in getting to the scene of abrupt traffic accidents. This paper develops a layout optimization model for multiple rescue points to address this issue, suggests an improved particle swarm algorithm by including a variation that can reduce optimization time and avoid the disadvantage of precocity, and designs a MATLAB program using an adaptive variation algorithm. The proposed approach increases the effectiveness of rescue in a sparse network and optimizes the distribution of emergency resources. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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15 pages, 3993 KiB

Open AccessArticle

The Situation of Hazardous Materials Accidents during Road Transportation in China from 2013 to 2019

by Shengxue Zhu, Shiwen Zhang, Hong Lang, Chenming Jiang and Yingying Xing

Int. J. Environ. Res. Public Health 2022, 19(15), 9632; https://doi.org/10.3390/ijerph19159632 - 5 Aug 2022

Cited by 4 | Viewed by 2078

Abstract

The safety situation of hazardous materials (hazmat) accidents during road transportation in China is severe and very serious accidents occurred frequently. Such accidents not only have a huge impact on the environment but also have serious consequences for people and the economy, such as fires and explosions. Therefore, it is necessary to understand the characteristics and laws of road transport accidents of hazmat systematically. This paper investigated 2777 hazmat transportation accidents in China from 2013 to 2019 to identify the characteristics, consequences, and causes of the accident. The results show that August (10.05%) and December (9.76%) are the peak periods of hazmat transportation accidents, while most hazmat transportation accidents occurred in the early morning (6:00–9:00 a.m.) and at noon (9:00 a.m.–12:00 p.m.) hours. For the geographical location, the accidents mainly occurred in the east China (34.35%) and the northwest China areas (14.87%). The main types of hazmat transportation accidents were rollover (35.36%), rear-end (22.58%), and collision (14.87%), where the probability of a major leak was high. The most common hazmat transportation accidents involve gas (17.79%), flammable liquid (56.07%), and corrosive substance (12.28%). The most common consequences of the hazmat transportation accidents were leakage (80.34%), followed by fire release (8.32%) and explosion release (2.34%). Human factor (26.74%) is the main cause of hazmat transportation accidents. These findings could help hazmat transportation managers and planners develop appropriate measures for improving hazmat transportation safety. Full article

(This article belongs to the Special Issue Road Traffic Safety Risk Analysis)

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