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21 pages, 5530 KiB

Open AccessArticle

Will Smart Improvements to Child Restraints Increase Their Popularity?

by Li Jiang, Mei Zhao, Hao Lin, Haiyuan Xu, Xiaojiao Chen and Jing Xu

Int. J. Environ. Res. Public Health 2022, 19(23), 15727; https://doi.org/10.3390/ijerph192315727 - 25 Nov 2022

Cited by 1 | Viewed by 1394

Abstract

In developing countries, child safety seat use remains low, which contributes to the consistently high rate of child injuries and deaths in traffic accidents. In order to protect the safety of child passengers, it is necessary to improve the public acceptance of child [...] Read more.

In developing countries, child safety seat use remains low, which contributes to the consistently high rate of child injuries and deaths in traffic accidents. In order to protect the safety of child passengers, it is necessary to improve the public acceptance of child restraints. We improved the shortcomings of the traditional child restraints by adding some new features: 1, tightening Isofix automatically; 2, using temperature sensing, a high-temperature alarm, automatic ventilation, and cooling; 3, using pressure sensing, if the child is left alone it will set off the car alarm; 4, voice control to adjust the angle of the backrest; 5, the seat can be folded into the trunk. These functions make human-computer interaction more humane. The authors collected changes in parental acceptance of child restraints using the interview method and questionnaires. We found that acceptance increased significantly after making intelligent improvements to the child restraints. The authors used the Technology Acceptance Model to identify the key caveats influencing users’ use of intelligent child restraints. Performance expectations, effort expectations, social influence, convenience, and hedonic motivation positively and significantly impacted the willingness to use intelligent child restraints, so the authors suggest that these points should be emphasized when promoting the product. The current study findings have theoretical and practical implications for smart child restraint designers, manufacturers, sellers, and government agencies. To better understand and promote child restraint, researchers and marketers can analyze how people accept child restraint based on our research model. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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19 pages, 1746 KiB

Open AccessArticle

A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach

by Suparshya Babu Sukhavasi, Susrutha Babu Sukhavasi, Khaled Elleithy, Ahmed El-Sayed and Abdelrahman Elleithy

Int. J. Environ. Res. Public Health 2022, 19(5), 3085; https://doi.org/10.3390/ijerph19053085 - 06 Mar 2022

Cited by 17 | Viewed by 3211

Abstract

Machine and deep learning techniques are two branches of artificial intelligence that have proven very efficient in solving advanced human problems. The automotive industry is currently using this technology to support drivers with advanced driver assistance systems. These systems can assist various functions [...] Read more.

Machine and deep learning techniques are two branches of artificial intelligence that have proven very efficient in solving advanced human problems. The automotive industry is currently using this technology to support drivers with advanced driver assistance systems. These systems can assist various functions for proper driving and estimate drivers’ capability of stable driving behavior and road safety. Many studies have proved that the driver’s emotions are the significant factors that manage the driver’s behavior, leading to severe vehicle collisions. Therefore, continuous monitoring of drivers’ emotions can help predict their behavior to avoid accidents. A novel hybrid network architecture using a deep neural network and support vector machine has been developed to predict between six and seven driver’s emotions in different poses, occlusions, and illumination conditions to achieve this goal. To determine the emotions, a fusion of Gabor and LBP features has been utilized to find the features and been classified using a support vector machine classifier combined with a convolutional neural network. Our proposed model achieved better performance accuracy of 84.41%, 95.05%, 98.57%, and 98.64% for FER 2013, CK+, KDEF, and KMU-FED datasets, respectively. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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13 pages, 2320 KiB

Open AccessArticle

Assessing Driving Risk at the Second Phase of Overtaking on Two-Lane Highways for Young Novice Drivers Based on Driving Simulation

by Jie Pan and Yongjun Shen

Int. J. Environ. Res. Public Health 2022, 19(5), 2691; https://doi.org/10.3390/ijerph19052691 - 25 Feb 2022

Cited by 2 | Viewed by 1607

Abstract

Overtaking on two-lane highways is a complex and multi-phase maneuver associated with high collision risk, especially for young novice drivers. Most of the relevant studies, however, focused mainly on the first phase, i.e., the lane-changing phase, such as willingness to overtake, while the [...] Read more.

Overtaking on two-lane highways is a complex and multi-phase maneuver associated with high collision risk, especially for young novice drivers. Most of the relevant studies, however, focused mainly on the first phase, i.e., the lane-changing phase, such as willingness to overtake, while the second phase, i.e., the back-to-lane phase, has not been investigated systematically. It is a risky phase in which a driver faces the risk of collision with not only the approaching vehicle on the opposite lane but also the impeding vehicle at the original lane. In this study, by designing and conducting a driving simulator experiment, we assess the driving risk of 47 young novice drivers during their second phase of overtaking on two-lane highways. The time-to-collision (TTC) values at the two critical positions are calculated from a micro-geometric point of view, based on which a two-dimensional risk index is proposed and the fuzzy C-means clustering algorithm is applied to group all the samples and to assess their overtaking risk. Furthermore, a multi-class logistic model is developed to understand the potential factors related to the risky overtaking maneuvers at this phase. The results show that most of the young novice drivers cannot make accurate judgments during their second phase of overtaking. When turning back to the original lane, they are more likely to be aware of the opposite vehicle that is approaching them, while how to correctly avoid the collision risk with the impeding vehicle at this phase is probably a more critical issue for young novice drivers. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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23 pages, 5254 KiB

Open AccessArticle

Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection

by Susrutha Babu Sukhavasi, Suparshya Babu Sukhavasi, Khaled Elleithy, Ahmed El-Sayed and Abdelrahman Elleithy

Int. J. Environ. Res. Public Health 2022, 19(4), 2352; https://doi.org/10.3390/ijerph19042352 - 18 Feb 2022

Cited by 6 | Viewed by 2604

Abstract

Monitoring drivers’ emotions is the key aspect of designing advanced driver assistance systems (ADAS) in intelligent vehicles. To ensure safety and track the possibility of vehicles’ road accidents, emotional monitoring will play a key role in justifying the mental status of the driver [...] Read more.

Monitoring drivers’ emotions is the key aspect of designing advanced driver assistance systems (ADAS) in intelligent vehicles. To ensure safety and track the possibility of vehicles’ road accidents, emotional monitoring will play a key role in justifying the mental status of the driver while driving the vehicle. However, the pose variations, illumination conditions, and occlusions are the factors that affect the detection of driver emotions from proper monitoring. To overcome these challenges, two novel approaches using machine learning methods and deep neural networks are proposed to monitor various drivers’ expressions in different pose variations, illuminations, and occlusions. We obtained the remarkable accuracy of 93.41%, 83.68%, 98.47%, and 98.18% for CK+, FER 2013, KDEF, and KMU-FED datasets, respectively, for the first approach and improved accuracy of 96.15%, 84.58%, 99.18%, and 99.09% for CK+, FER 2013, KDEF, and KMU-FED datasets respectively in the second approach, compared to the existing state-of-the-art methods. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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23 pages, 3316 KiB

Open AccessArticle

Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines

by Ward Ahmed Al-Hussein, Lip Yee Por, Miss Laiha Mat Kiah and Bilal Bahaa Zaidan

Int. J. Environ. Res. Public Health 2022, 19(3), 1470; https://doi.org/10.3390/ijerph19031470 - 27 Jan 2022

Cited by 16 | Viewed by 4391

Abstract

The process of collecting driving data and using a computational model to generate a safety score for the driver is known as driver behavior profiling. Existing driver profiles attempt to categorize drivers as either safe or aggressive, which some experts say is not [...] Read more.

The process of collecting driving data and using a computational model to generate a safety score for the driver is known as driver behavior profiling. Existing driver profiles attempt to categorize drivers as either safe or aggressive, which some experts say is not practical. This is due to the “safe/aggressive” categorization being a state that describes a driver’s conduct at a specific point in time rather than a continuous state or a human trait. Furthermore, due to the disparity in traffic laws and regulations between countries, what is considered aggressive behavior in one place may differ from what is considered aggressive behavior in another. As a result, adopting existing profiles is not ideal. The authors provide a unique approach to driver behavior profiling based on timeframe data segmentation. The profiling procedure consists of two main parts: row labeling and segment labeling. Row labeling assigns a safety score to each second of driving data based on criteria developed with the help of Malaysian traffic safety experts. Then, rows are accumulated to form timeframe segments. In segment labeling, generated timeframe segments are assigned a safety score using a set of criteria. The score assigned to the generated timeframe segment reflects the driver’s behavior during that time period. Following that, the study adopts three deep-learning-based algorithms, namely, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), to classify recorded driving data according to the established profiling procedure, and selects the most suitable one for a proposed recognition system. Various techniques were used to prevent the classification algorithms from overfitting. Using gathered naturalistic data, the validity of the modulated algorithms was assessed on various timeframe segments ranging from 1 to 10 s. Results showed that the CNN, which achieved an accuracy of 96.1%, outperformed the other two classification algorithms and was therefore recommended for the recognition system. In addition, recommendations were outlined on how the recognition system would assist in improving traffic safety. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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17 pages, 3671 KiB

Open AccessArticle

Investigating the Effect of School Bus Stopping Process on Driver Behavior of Surrounding Vehicles Based on a Driving Simulator Experiment

by Yanyan Chen, Yinjia Guo, Xin Gu, Yuntong Zhou, Yao Tong and Bingxin Cao

Int. J. Environ. Res. Public Health 2021, 18(23), 12538; https://doi.org/10.3390/ijerph182312538 - 28 Nov 2021

Cited by 1 | Viewed by 1799

Abstract

School bus safety has attracted widespread attention with economic development and the improvement of overall quality of the population. However, violations of school bus regulations and school bus-related crashes often occur. Limited research has been conducted on the impact of the school bus [...] Read more.

School bus safety has attracted widespread attention with economic development and the improvement of overall quality of the population. However, violations of school bus regulations and school bus-related crashes often occur. Limited research has been conducted on the impact of the school bus stopping process on surrounding drivers’ behavior. This study provides a driving simulator experiment to explore drivers’ behaviors during the school bus stopping process under different traffic law awareness status, traffic volume status, and initial location status. Eight variables about behavior decision and kinetic parameters were assessed for analysis by a logistic regression model and multivariate analysis of variance (MANOVA). Results show that the mean speed decreases and the number of people complying with the regulations increases after publicizing the regulations. The proportion of surrounding vehicles in the acceleration state increases, especially under the scenario that the traffic volume is large and the initial distance is far. This indicates that the enforcement of the regulations may stimulate unsafe driving behavior. The findings of this study could help policy makers to better understand the prevalence and compliance of current school bus stopping regulations among drivers and support improvements in the practical application of the regulations. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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15 pages, 2775 KiB

Open AccessArticle

Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method

by Qiong Bao, Hanrun Tang and Yongjun Shen

Int. J. Environ. Res. Public Health 2021, 18(23), 12452; https://doi.org/10.3390/ijerph182312452 - 26 Nov 2021

Cited by 5 | Viewed by 1773

Abstract

Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of [...] Read more.

Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers’ behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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18 pages, 4571 KiB

Open AccessArticle

Investigating the Effect of Social and Cultural Factors on Drivers in Malaysia: A Naturalistic Driving Study

by Ward Ahmed Al-Hussein, Miss Laiha Mat Kiah, Lip Yee Por and Bilal Bahaa Zaidan

Int. J. Environ. Res. Public Health 2021, 18(22), 11740; https://doi.org/10.3390/ijerph182211740 - 09 Nov 2021

Cited by 6 | Viewed by 3123

Abstract

Road accidents are increasing every year in Malaysia, and it is always challenging to collect reliable pre-crash data in the transportation community. Existing studies relied on simulators, police crash reports, questionnaires, and surveys to study Malaysia’s drivers’ behavior. Researchers previously criticized such methods [...] Read more.

Road accidents are increasing every year in Malaysia, and it is always challenging to collect reliable pre-crash data in the transportation community. Existing studies relied on simulators, police crash reports, questionnaires, and surveys to study Malaysia’s drivers’ behavior. Researchers previously criticized such methods for being biased and unreliable. To fill in the literature gap, this study presents the first naturalistic driving study in Malaysia. Thirty drivers were recruited to drive an instrumented vehicle for 750 km while collecting continuous driving data. The data acquisition system consists of various sensors such as OBDII, lidar, ultrasonic sensors, IMU, and GPS. Irrelevant data were filtered, and experts helped identify safety criteria regarding multiple driving metrics such as maximum acceptable speed limits, safe accelerations, safe decelerations, acceptable distances to vehicles ahead, and safe steering behavior. These thresholds were used to investigate the influence of social and cultural factors on driving in Malaysia. The findings show statistically significant differences between drivers based on gender, age, and cultural background. There are also significant differences in the results for those who drove on weekends rather than weekdays. The study presents several recommendations to various public and governmental sectors to help prevent future accidents and improve traffic safety. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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20 pages, 4370 KiB

Open AccessArticle

Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach

by Shengxue Zhu, Ke Wang and Chongyi Li

Int. J. Environ. Res. Public Health 2021, 18(21), 11564; https://doi.org/10.3390/ijerph182111564 - 03 Nov 2021

Cited by 4 | Viewed by 2244

Abstract

In many related works, nominal classification algorithms ignore the order between injury severity levels and make sub-optimal predictions. Existing ordinal classification methods suffer rank inconsistency and rank non-monotonicity. The aim of this paper is to propose an ordinal classification approach to predict traffic [...] Read more.

In many related works, nominal classification algorithms ignore the order between injury severity levels and make sub-optimal predictions. Existing ordinal classification methods suffer rank inconsistency and rank non-monotonicity. The aim of this paper is to propose an ordinal classification approach to predict traffic crash injury severity and to test its performance over existing machine learning classification methods. First, we compare the performance of the neural network, XGBoost, and SVM classifiers in injury severity prediction. Second, we utilize a severity category-combination method with oversampling to relieve the class-imbalance problem prevalent in crash data. Third, we take advantage of probability calibration and the optimal probability threshold moving to improve the prediction ability of ordinal classification. The proposed approach can satisfy the rank consistency and rank monotonicity requirement and is proved to be superior to other ordinal classification methods and nominal classification machine learning by statistical significance test. Important factors relating to injury severity are selected based on their permutation feature importance scores. We find that converting severity levels into three classes, minor injury, moderate injury, and serious injury, can substantially improve the prediction precision. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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21 pages, 4424 KiB

Open AccessArticle

Lives Saved in Low- and Middle-Income Countries by Road Safety Initiatives Funded by Bloomberg Philanthropies and Implemented by Their Partners between 2007–2018

by Delia Hendrie, Greg Lyle and Max Cameron

Int. J. Environ. Res. Public Health 2021, 18(21), 11185; https://doi.org/10.3390/ijerph182111185 - 25 Oct 2021

Cited by 1 | Viewed by 4177

Abstract

Over the past 12 years, Bloomberg Philanthropies (BP) and its partner organisations have implemented a global road safety program in low- and middle-income countries. The program was implemented to address the historically increasing number of road fatalities and the inadequate funding to reduce [...] Read more.

Over the past 12 years, Bloomberg Philanthropies (BP) and its partner organisations have implemented a global road safety program in low- and middle-income countries. The program was implemented to address the historically increasing number of road fatalities and the inadequate funding to reduce them. This study evaluates the performance of the program by estimating lives saved from road safety interventions implemented during the program period (2007–2018) through to 2030. We estimated that 311,758 lives will have been saved by 2030, with 97,148 lives saved up until 2018 when the evaluation was conducted and a further 214,608 lives projected to be saved if these changes are sustained until 2030. Legislative changes alone accounted for 75% of lives saved. Concurrent activities related to reducing drink driving, implementing legislative changes, and social marketing campaigns run in conjunction with police enforcement and other road safety activities accounted for 57% of the total estimated lives saved. Saving 311,758 lives with funding of USD $259 million indicates a cost-effectiveness ratio of USD $831 per life saved. The potential health gains achieved through the number of lives saved from the road safety initiatives funded by Bloomberg Philanthropies represent a considerable return on investment. This study demonstrates the extent to which successful, cost-effective road safety initiatives can reduce road fatalities in low- and middle-income countries. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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9 pages, 329 KiB

Open AccessArticle

Drinking and Night-Time Driving May Increase the Risk of Severe Health Outcomes: A 5-Year Retrospective Study of Traffic Injuries among International Travelers at a University Hospital Emergency Center in Thailand

by Vorapot Sapsirisavat and Wiriya Mahikul

Int. J. Environ. Res. Public Health 2021, 18(18), 9823; https://doi.org/10.3390/ijerph18189823 - 17 Sep 2021

Cited by 7 | Viewed by 2618

Abstract

Road traffic injury (RTI) is a leading cause of death in developing countries. This burden affects not only locals, but also international travelers. Data on international travelers with RTIs in Thailand, especially from a medical perspective, are limited. This study aimed to analyze [...] Read more.

Road traffic injury (RTI) is a leading cause of death in developing countries. This burden affects not only locals, but also international travelers. Data on international travelers with RTIs in Thailand, especially from a medical perspective, are limited. This study aimed to analyze the factors associated with severe health outcomes following RTIs among international travelers at a university hospital emergency center in Thailand from January 2015 to December 2019. The retrieved data consisted of demographics, risks, preventive factors, and health outcomes. The severity of outcome was classified as fatality, hospitalization, or non-severe. A multinomial logistic regression model was used to identify the possible determinants of severity of health outcome among international travelers with RTI. A total of 720 travelers with RTIs (69% males; 82.5% were Southeast Asian) were included, with a mean age of 28.5 years. Of these, 144 (20%) had severe health outcomes: 64 (9%) fatalities and 80 (11%) hospitalizations. The level of severity of outcome was not associated with travelers’ demographics, but was associated with conventional risk factors, i.e., motorcycle use, alcohol/drug use, night-time driving, and less use of seatbelt/helmet. In a multinomial logistic regression analysis, alcohol drinking (adjusted odds ratio (AOR) 2.53, 95% confidence interval (CI) 1.41–4.55) and night-time driving (AOR 2.54, 95% CI 1.36–4.75) were associated with hospitalization. Patients who had a history of tetanus vaccination were less likely to die (AOR 0.37, 95% CI 0.17–0.81). In conclusion, one-fifth of RTIs resulted in severe health outcomes, and 9% were fatal. Road safety campaigns in Thailand should target travelers of all nationalities. Interventions that enhance travelers’ safety practices and proper preparation for road accidents should be explored further. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

21 pages, 4729 KiB

Open AccessArticle

Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain

by Paúl Narváez-Villa, Blanca Arenas-Ramírez, José Mira and Francisco Aparicio-Izquierdo

Int. J. Environ. Res. Public Health 2021, 18(16), 8327; https://doi.org/10.3390/ijerph18168327 - 06 Aug 2021

Cited by 5 | Viewed by 2672

Abstract

Knowledge of the kilometers traveled by vehicles is essential in transport and road safety studies as an indicator of exposure and mobility. Its application in the determination of user risk indices in a disaggregated manner is of great interest to the scientific community [...] Read more.

Knowledge of the kilometers traveled by vehicles is essential in transport and road safety studies as an indicator of exposure and mobility. Its application in the determination of user risk indices in a disaggregated manner is of great interest to the scientific community and the authorities in charge of ensuring road safety on highways. This study used a sample of the data recorded during passenger vehicle inspections at Vehicle Technical Inspection stations and housed in a data warehouse managed by the General Directorate for Traffic of Spain. This study has three notable characteristics: (1) a novel data source is explored, (2) the methodology developed applies to other types of vehicles, with the level of disaggregation the data allows, and (3) pattern extraction and the estimate of mobility contribute to the continuous and necessary improvement of road safety indicators and are aligned with goal 3 (Good Health and Well-Being: Target 3.6) of The United Nations Sustainable Development Goals of the 2030 Agenda. An Operational Data Warehouse was created from the sample received, which helped in obtaining inference values for the kilometers traveled by Spanish fleet vehicles with a level of disaggregation that, to the knowledge of the authors, was unreachable with advanced statistical models. Three machine learning methods, CART, random forest, and gradient boosting, were optimized and compared based on the performance metrics of the models. The three methods identified the age, engine size, and tare weight of passenger vehicles as the factors with greatest influence on their travel patterns. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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18 pages, 3816 KiB

Open AccessArticle

Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework

by Ke Wang, Qingwen Xue and Jian John Lu

Int. J. Environ. Res. Public Health 2021, 18(14), 7534; https://doi.org/10.3390/ijerph18147534 - 15 Jul 2021

Cited by 8 | Viewed by 2316

Abstract

Identifying high-risk drivers before an accident happens is necessary for traffic accident control and prevention. Due to the class-imbalance nature of driving data, high-risk samples as the minority class are usually ill-treated by standard classification algorithms. Instead of applying preset sampling or cost-sensitive [...] Read more.

Identifying high-risk drivers before an accident happens is necessary for traffic accident control and prevention. Due to the class-imbalance nature of driving data, high-risk samples as the minority class are usually ill-treated by standard classification algorithms. Instead of applying preset sampling or cost-sensitive learning, this paper proposes a novel automated machine learning framework that simultaneously and automatically searches for the optimal sampling, cost-sensitive loss function, and probability calibration to handle class-imbalance problem in recognition of risky drivers. The hyperparameters that control sampling ratio and class weight, along with other hyperparameters, are optimized by Bayesian optimization. To demonstrate the performance of the proposed automated learning framework, we establish a risky driver recognition model as a case study, using video-extracted vehicle trajectory data of 2427 private cars on a German highway. Based on rear-end collision risk evaluation, only 4.29% of all drivers are labeled as risky drivers. The inputs of the recognition model are the discrete Fourier transform coefficients of target vehicle’s longitudinal speed, lateral speed, and the gap between the target vehicle and its preceding vehicle. Among 12 sampling methods, 2 cost-sensitive loss functions, and 2 probability calibration methods, the result of automated machine learning is consistent with manual searching but much more computation-efficient. We find that the combination of Support Vector Machine-based Synthetic Minority Oversampling TEchnique (SVMSMOTE) sampling, cost-sensitive cross-entropy loss function, and isotonic regression can significantly improve the recognition ability and reduce the error of predicted probability. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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15 pages, 698 KiB

Open AccessArticle

Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity

by Susana García-Herrero, Juan Diego Febres, Wafa Boulagouas, José Manuel Gutiérrez and Miguel Ángel Mariscal Saldaña

Int. J. Environ. Res. Public Health 2021, 18(13), 7155; https://doi.org/10.3390/ijerph18137155 - 04 Jul 2021

Cited by 15 | Viewed by 4000

Abstract

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data [...] Read more.

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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17 pages, 1150 KiB

Open AccessArticle

Modelling the Relationship between the Nature of Work Factors and Driving Performance Mediating by Role of Fatigue

by Al-Baraa Abdulrahman Al-Mekhlafi, Ahmad Shahrul Nizam Isha, Nicholas Chileshe, Mohammed Abdulrab, Anwar Ameen Hezam Saeed and Ahmed Farouk Kineber

Int. J. Environ. Res. Public Health 2021, 18(13), 6752; https://doi.org/10.3390/ijerph18136752 - 23 Jun 2021

Cited by 49 | Viewed by 3927

Abstract

Driving fatigue is a serious issue for the transportation sector, decreasing the driver’s performance and increasing accident risk. This study aims to investigate how fatigue mediates the relationship between the nature of work factors and driving performance. The approach included a review of [...] Read more.

Driving fatigue is a serious issue for the transportation sector, decreasing the driver’s performance and increasing accident risk. This study aims to investigate how fatigue mediates the relationship between the nature of work factors and driving performance. The approach included a review of the previous studies to select the dimensional items for the data collection instrument. A pilot test to identify potential modification to the questionnaire was conducted, then structural equation modelling (SEM) was performed on a stratified sample of 307 drivers, to test the suggested hypotheses. Based on the results, five hypotheses have indirect relationships, four of which have a significant effect. Besides, the results show that driving fatigue partially mediates the relationship between the work schedule and driving performance and fully mediates in the relationship between work activities and driving performance. The nature of work and human factors is the most common reason related to road accidents. Therefore, the emphasis on driving performance and fatigue factors would thereby lead to preventing fatal crashes and life loss. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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14 pages, 331 KiB

Open AccessArticle

Profiles of Risky Driving Behaviors in Adolescent Drivers: A Cluster Analysis of a Representative Sample from Tuscany Region (Italy)

by Vieri Lastrucci, Francesco Innocenti, Chiara Lorini, Alice Berti, Caterina Silvestri, Marco Lazzeretti, Fabio Voller and Guglielmo Bonaccorsi

Int. J. Environ. Res. Public Health 2021, 18(12), 6362; https://doi.org/10.3390/ijerph18126362 - 11 Jun 2021

Cited by 5 | Viewed by 2287

Abstract

(1) Background: Research on patterns of risky driving behaviors (RDBs) in adolescents is scarce. This study aims to identify distinctive patterns of RDBs and to explore their characteristics in a representative sample of adolescents. (2) Methods: this is a cross-sectional study of a [...] Read more.

(1) Background: Research on patterns of risky driving behaviors (RDBs) in adolescents is scarce. This study aims to identify distinctive patterns of RDBs and to explore their characteristics in a representative sample of adolescents. (2) Methods: this is a cross-sectional study of a representative sample of Tuscany Region students aged 14–19 years (n = 2162). The prevalence of 11 RDBs was assessed and a cluster analysis was conducted to identify patterns of RDBs. ANOVA, post hoc pairwise comparisons and multivariate logistic regression models were used to characterize cluster membership. (3) Results: four distinct clusters of drivers were identified based on patterns of RDBs; in particular, two clusters—the Reckless Drivers (11.2%) and the Careless Drivers (21.5%)—showed high-risk patterns of engagement in RDBs. These high-risk clusters exhibited the weakest social bonds, the highest psychological distress, the most frequent participation in health compromising and risky behaviors, and the highest risk of a road traffic accident. (4) Conclusion: findings suggest that it is possible to identify typical profiles of RDBs in adolescents and that risky driving profiles are positively interrelated with other risky behaviors. This clustering suggests the need to develop multicomponent prevention strategies rather than addressing specific RDBs in isolation. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

17 pages, 2164 KiB

Open AccessArticle

Exploring Factors Impacting on the Lane Choice of Riders of Non-Motorized Vehicles at Exit Legs of Signalized At-Grade Intersections

by Guoqiang Zhang, Qiqi Zhou and Jun Chen

Int. J. Environ. Res. Public Health 2021, 18(12), 6327; https://doi.org/10.3390/ijerph18126327 - 11 Jun 2021

Cited by 1 | Viewed by 2064

Abstract

For most signalized at-grade intersections, exclusive lanes for non-motorized vehicles have been applied to improve the level of service, capacity and safety of both motorized vehicles and non-motorized vehicles. However, because of various factors, riders of non-motorized vehicles have been observed using lanes [...] Read more.

For most signalized at-grade intersections, exclusive lanes for non-motorized vehicles have been applied to improve the level of service, capacity and safety of both motorized vehicles and non-motorized vehicles. However, because of various factors, riders of non-motorized vehicles have been observed using lanes for motorized vehicles instead of lanes for non-motorized vehicles, which usually negatively influences the performance of signalized intersections and sometimes may cause serious problems such as traffic congestion and accidents. The objective of this paper is to explore factors influencing the lane choice of riders of non-motorized vehicles at exit legs of signalized at-grade intersections and develop a prediction model for riders’ lane choice. Data concerning the lane choice of riders of non-motorized vehicles and other impacting factors were collected at exit legs of four typical signalized at-grade intersections. Applying binary logistic regression, a probability prediction model was developed to explain how various factors influence the lane choice of riders of non-motorized vehicles. The prediction model indicates that female riders of non-motorized vehicles have a higher probability of choosing the lane for non-motorized vehicles than male riders. Compared with riders of non-motorized vehicles powered by electricity, riders of traditional man-powered bicycles are more likely to choose the lane for non-motorized vehicles. Right-turning riders of non-motorized vehicles are more likely to choose the lane for non-motorized vehicles than straight-going riders, who in turn, are more likely to choose the lane for non-motorized vehicles than left-turning riders. Decreasing the volume of non-motorized vehicles, increasing the volume of motorized vehicles, and widening the lane for non-motorized vehicles will increase the probability of the correct choice of lane for non-motorized vehicles. The predictions of the model are in good agreement with the observed facts. The model is meaningful for guidance on the design and management of signalized at-grade intersections. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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14 pages, 387 KiB

Open AccessArticle

Electrophysiological Brain-Cardiac Coupling in Train Drivers during Monotonous Driving

by Ty Lees, Taryn Chalmers, David Burton, Eugene Zilberg, Thomas Penzel, Shail Lal and Sara Lal

Int. J. Environ. Res. Public Health 2021, 18(7), 3741; https://doi.org/10.3390/ijerph18073741 - 02 Apr 2021

Cited by 7 | Viewed by 2511

Abstract

Electrophysiological research has previously investigated monotony and the cardiac health of drivers independently; however, few studies have explored the association between the two. As such the present study aimed to examine the impact of monotonous train driving (indicated by electroencephalogram (EEG) activity) on [...] Read more.

Electrophysiological research has previously investigated monotony and the cardiac health of drivers independently; however, few studies have explored the association between the two. As such the present study aimed to examine the impact of monotonous train driving (indicated by electroencephalogram (EEG) activity) on an individual’s cardiac health as measured by heart rate variability (HRV). Sixty-three train drivers participated in the present study, and were required to complete a monotonous train driver simulator task. During this task, a 32 lead EEG and a three-lead electrocardiogram were recorded from each participant. In the present analysis, the low (LF) and high frequency (HF) HRV parameters were associated with delta (p < 0.05), beta (p = 0.03) and gamma (p < 0.001) frequency EEG variables. Further, total HRV was associated with gamma activity, while sympathovagal balance (i.e., LF:HF ratio) was best associated fronto-temporal delta activity (p = 0.02). HRV and EEG parameters appear to be coupled, with the parameters of the delta and gamma EEG frequency bands potentially being the most important to this coupling. These relationships provide insight into the impact of a monotonous task on the cardiac health of train drivers, and may also be indicative of strategies employed to combat fatigue or engage with the driving task. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

15 pages, 1610 KiB

Open AccessArticle

Drivers’ Attention Strategies before Eyes-off-Road in Different Traffic Scenarios: Adaptation and Anticipation

by Zhuofan Liu, Wei Yuan and Yong Ma

Int. J. Environ. Res. Public Health 2021, 18(7), 3716; https://doi.org/10.3390/ijerph18073716 - 02 Apr 2021

Cited by 6 | Viewed by 2238

Abstract

The distribution of drivers’ visual attention prior to diverting focus from the driving task is critical for safety. The object of this study is to investigate drivers’ attention strategy before they occlude their vision for different durations under different driving scenarios. A total [...] Read more.

The distribution of drivers’ visual attention prior to diverting focus from the driving task is critical for safety. The object of this study is to investigate drivers’ attention strategy before they occlude their vision for different durations under different driving scenarios. A total of 3 (scenarios) × 3 (durations) within-subjects design was applied. Twenty-three participants completed three durations of occlusion (0, 1, and 2 s) test drive in a motion-based driving simulator under three scenarios (urban, rural, motorway). Drivers’ occlusion behaviour, driving behaviour, and visual behaviour in 6 s before occlusion was analyzed and compared. The results showed that drivers tended to slow down and increased their attention on driving task to keep safety in occlusion 2 s condition. The distribution of attention differed among different driving scenarios and occlusion durations. More attention was directed to Forward position and Speedometer in occlusion conditions, and a strong shift in attention from Forward position to Road users and Speedometer was found in occlusion 2 s condition. Road users was glanced more frequently in urban road with a higher percentage of attention transitions from Forward position to Road users. While gaze switching to Speedometer with a higher intensity was found on motorway. It suggests that drivers could adapt their visual attention to driving demand and anticipate the development of upcoming situations by sampling enough driving-related information before eyes-off-road. Moreover, the adaptation and anticipation are in accordance with driving situation and expected eyes-off-road duration. Better knowledge about attentional strategies before attention away from road contributes to more efficient and safe interaction with additional tasks. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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19 pages, 4368 KiB

Open AccessArticle

Study on the Relationship between Drivers’ Personal Characters and Non-Standard Traffic Signs Comprehensibility

by Antoni Wontorczyk and Stanislaw Gaca

Int. J. Environ. Res. Public Health 2021, 18(5), 2678; https://doi.org/10.3390/ijerph18052678 - 07 Mar 2021

Cited by 11 | Viewed by 3879

Abstract

Drivers’ incorrect perception and interpretation of the road space are among reasons for human errors. Proper road markings are elements improving perception of road space. Their effectiveness relies on traffic participants receiving the provided information correctly. The range of signs used is constantly [...] Read more.

Drivers’ incorrect perception and interpretation of the road space are among reasons for human errors. Proper road markings are elements improving perception of road space. Their effectiveness relies on traffic participants receiving the provided information correctly. The range of signs used is constantly expanding and unusual situations in traffic require use of non-standard signs or an unusual combination of existing standard signs. The aim of this study was to explore the level of comprehensibility of four different types of non-standard signs. The relationship between the level of comprehensibility of these signs and personality traits of the drivers was also studied. A total of 369 drivers were tested using a questionnaire to analyze the traffic signs comprehensibility and Five Factor Inventory (NEO-FFI). The obtained results indicate that symbolic signs, unlike symbolic and text ones, are much better comprehended by drivers. Men comprehend the significance of non-standard symbolic regulatory signs better than women. Higher level of comprehensibility of symbolic and text regulatory signs is shown by older, better educated drivers and professional drivers. The study found there is a link between personality traits of the driver and the comprehensibility of symbolic regulatory signs. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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21 pages, 756 KiB

Open AccessArticle

Road Safety Perception Questionnaire (RSPQ) in Latin America: A Development and Validation Study

by Fabricio Esteban Espinoza Molina, Blanca del Valle Arenas Ramirez, Francisco Aparicio Izquierdo and Diana Carolina Zúñiga Ortega

Int. J. Environ. Res. Public Health 2021, 18(5), 2433; https://doi.org/10.3390/ijerph18052433 - 02 Mar 2021

Cited by 11 | Viewed by 7493

Abstract

Background: Although public bodies need to know drivers’ perception of road safety, in Latin America there are no valid and reliable instruments that propose an integral dimensionality. The objective of this study was to design and validate a Road Safety Perception Questionnaire [...] Read more.

Background: Although public bodies need to know drivers’ perception of road safety, in Latin America there are no valid and reliable instruments that propose an integral dimensionality. The objective of this study was to design and validate a Road Safety Perception Questionnaire (RSPQ). Methodology: The design included a review of the available evidence and expert knowledge to select the dimensional items for the instrument. A pilot test was carried out to determine possible corrections and adjustments to the questionnaire, after which a Confirmatory Factor Analysis was performed on a stratified sample of 736 Ecuadorian drivers to determine its reliability and construct validity. Results: The results suggest that the RSPQ has a clear factorial structure with high factorial weight items and good internal consistency. The results of the 41-item model grouped into six dimensions (human, vehicle, road infrastructure, regulatory framework and intervention measures, socioeconomic and driving precautions) obtained the best adjustment indexes at the absolute, incremental and parsimonious levels. Conclusions: The preliminary RSPQ evidence can be considered a valid and reliable instrument to assess drivers’ perception of road safety. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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15 pages, 1253 KiB

Open AccessFeature PaperArticle

Where to Ride? An Explorative Study to Investigate Potential Risk Factors of Personal Mobility Accidents

by Jihun Oh and Jeongseob Kim

Int. J. Environ. Res. Public Health 2021, 18(3), 965; https://doi.org/10.3390/ijerph18030965 - 22 Jan 2021

Cited by 5 | Viewed by 2686

Abstract

As a mobility of future, the popularity of personal mobility vehicles (PMs) is rapidly increasing worldwide. However, this boom in the use of PMs has resulted in a substantial number of accidents involving not only PM users but also other road users including [...] Read more.

As a mobility of future, the popularity of personal mobility vehicles (PMs) is rapidly increasing worldwide. However, this boom in the use of PMs has resulted in a substantial number of accidents involving not only PM users but also other road users including pedestrians, bicyclists, and motor vehicle drivers. This study aims to explore the potential risk factors for the occurrence of PM-related accidents and the resulting injury severity using the Traffic Accident Analysis System (TAAS) of South Korea between 2017 and 2019. We found that PM–pedestrian accidents tend to occur on roads with wider sidewalks and bike lanes, possibly because the pedestrian–PM conflict increases in this road condition. There is still ongoing debate on whether it is appropriate for PMs to share the sidewalk with pedestrians. Some countries, including Korea, prohibit the use of PMs on sidewalks; however, in reality, this regulation is not well-observed because using PMs on roadways involves higher crash risk with motor vehicles. This study suggests one potential solution to ensure safety of PM users: expansion of bike lane infrastructure having physically separated bike lanes and sidewalks/motorways in addition to the formation and strict enforcement of appropriate safety rules for PM users. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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17 pages, 4673 KiB

Open AccessArticle

The Influence of Individual Differences on Diverging Behavior at the Weaving Sections of an Urban Expressway

by Zhanji Zheng, Qiaojun Xiang, Xin Gu, Yongfeng Ma and Kangkang Zheng

Int. J. Environ. Res. Public Health 2021, 18(1), 25; https://doi.org/10.3390/ijerph18010025 - 22 Dec 2020

Cited by 8 | Viewed by 2072

Abstract

Urban expressway weaving sections suffer from a high crash risk in urban transportation systems. Studying driving behavior is an important approach to solve safety and efficiency issues at expressway weaving sections. This study aimed to investigate the influence of drivers’ individual differences on [...] Read more.

Urban expressway weaving sections suffer from a high crash risk in urban transportation systems. Studying driving behavior is an important approach to solve safety and efficiency issues at expressway weaving sections. This study aimed to investigate the influence of drivers’ individual differences on diverging behavior at expressway weaving sections. First, a k-means cluster analysis of 650 questionnaires was performed, to classify drivers into three categories: aggressive, conservative and normal. Then, the driving behavior of 45 drivers from the three categories was recorded in a driving simulator and analyzed by an analysis of variance. The results show that different types of drivers have different driving behaviors at weaving sections. Aggressive drivers have a higher mean speed and mean longitudinal deceleration, followed by normal and conservative drivers. Significant differences in the range of lane-change positions were found between 100, 150 and 200 m of weaving length for the same type of drivers, and the duration of weaving for aggressive drivers was significantly smaller than for normal and conservative drivers. A significant correlation was found between lane-change position and weaving duration. These results can help traffic engineers to propose effective control strategies for different types of drivers, to improve the safety of weaving sections. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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12 pages, 4814 KiB

Open AccessArticle

Effects of Smoking Cannabis on Visual Function and Driving Performance. A Driving-Simulator Based Study

by Sonia Ortiz-Peregrina, Carolina Ortiz, José J. Castro-Torres, José R. Jiménez and Rosario G. Anera

Int. J. Environ. Res. Public Health 2020, 17(23), 9033; https://doi.org/10.3390/ijerph17239033 - 03 Dec 2020

Cited by 12 | Viewed by 4283

Abstract

Cannabis is the most widely used illegal drug in the world. Limited information about the effects of cannabis on visual function is available, and more detail about the possible impact of visual effects on car driving is required. This study investigated the effects [...] Read more.

Cannabis is the most widely used illegal drug in the world. Limited information about the effects of cannabis on visual function is available, and more detail about the possible impact of visual effects on car driving is required. This study investigated the effects of smoking cannabis on vision and driving performance, and whether these effects are correlated. Twenty drivers and occasional users were included (mean (SE) age, 23.3 (1.0) years; five women). Vision and simulated driving performance were evaluated in a baseline session and after smoking cannabis. Under the influence of cannabis, certain visual functions such as visual acuity (p < 0.001), contrast sensitivity (p = 0.004) and stereoacuity (far, p < 0.001; near, p = 0.013) worsened. In addition, there was an overall deterioration of driving performance, with the task of keeping the vehicle in the lane proving more difficult (p < 0.05). A correlation analysis showed significant associations between driving performance and visual function. Thus, the strongest correlations were found between the distance driven onto the shoulder and stereoacuity, for near (ρ = 0.504; p = 0.001) and far distances (ρ = 0.408; p = 0.011). This study provides the first evidence to show that the visual effects of cannabis could impact driving performance, compromising driving safety. The results indicate that information and awareness campaigns are essential for reducing the incidence of driving under the influence of cannabis. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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18 pages, 1483 KiB

Open AccessArticle

Assessing Fitness-To-Drive among Older Drivers: A Comparative Analysis of Potential Alternatives to on-Road Driving Test

by Yongjun Shen, Onaira Zahoor, Xu Tan, Muhammad Usama and Tom Brijs

Int. J. Environ. Res. Public Health 2020, 17(23), 8886; https://doi.org/10.3390/ijerph17238886 - 29 Nov 2020

Cited by 3 | Viewed by 2522

Abstract

To enable older drivers to maintain mobility without endangering public safety, it is necessary to develop more effective means of assessing their fitness-to-drive as alternatives to an on-road driving test. In this study, a functional ability test, simulated driving test, and on-road driving [...] Read more.

To enable older drivers to maintain mobility without endangering public safety, it is necessary to develop more effective means of assessing their fitness-to-drive as alternatives to an on-road driving test. In this study, a functional ability test, simulated driving test, and on-road driving test were carried out for 136 older drivers. Influencing factors related to fitness-to-drive were selected based on the correlation between the outcome measure of each test and the pass/fail outcome of the on-road driving test. Four potential alternatives combining different tests were considered and three modeling techniques were compared when constructing the fitness-to-drive assessment model for the elderly. As a result, 92 participants completed all of the tests, of which 61 passed the on-road driving test and the remaining 31 failed. A total of seven influencing factors from all types of tests were selected. The best model was trained by the technique of gradient boosted machine using all of the seven factors, generating the highest accuracy of 92.8%, with sensitivity of 0.94 and specificity of 0.90. The proposed fitness-to-drive assessment method is considered an effective alternative to the on-road driving test, and the results offer a valuable reference for those unfit-to-drive older drivers to either adjust their driving behavior or cease driving. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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22 pages, 2721 KiB

Open AccessArticle

Explaining Sex Differences in Motorcyclist Riding Behavior: An Application of Multi-Group Structural Equation Modeling

by Savalee Uttra, Napat Laddawan, Vatanavongs Ratanavaraha and Sajjakaj Jomnonkwao

Int. J. Environ. Res. Public Health 2020, 17(23), 8797; https://doi.org/10.3390/ijerph17238797 - 26 Nov 2020

Cited by 16 | Viewed by 2974

Abstract

Road accidents are caused by humans, vehicles, and road environments. Human attitudes affect behavioral changes and can lead to unsafe riding behavior. The sex of an individual is a key factor that affects their riding behavior. We aimed to use structural equation modeling [...] Read more.

Road accidents are caused by humans, vehicles, and road environments. Human attitudes affect behavioral changes and can lead to unsafe riding behavior. The sex of an individual is a key factor that affects their riding behavior. We aimed to use structural equation modeling (SEM) by analyzing the multi-group SEM between men and women and applying the theory of planned behavior (TPB) and the locus of control (LC) theory. The data used in the research were collected from all over Thailand, consisting of 1516 motorcycle riders (903 men and 613 women) aged over 20 years. A self-administered questionnaire was designed for data collection of the riding behavior using the Motorcycle Rider Behavior Questionnaire (MRBQ), including traffic errors, control errors, stunt frequency, and safety equipment. We found that riding behaviors between men and women were significantly different in both theories. For men, TPB showed that the main factors that highly influenced motorcycle riding behavior (MRB) were the attitudes based on health motivation (AHM) and perceived behavior control (PC); for women, AHM produced a stronger effect than in men. However, for the subjective norms (SN) factor, we found no direct effect on MRB, but did find an indirect effect through the attitudes based on severity (ASE) in both sexes. Particularly for women, the indirect influence value of the SN factor was higher. For women, the LC showed that internal factors had more influence than external factors. The same was found for men, but the effect in women was significantly stronger. We found that sex significantly affected the MRB. Therefore, policies must be implemented that address each group specifically as their attitudes and behaviors are different. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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16 pages, 3554 KiB

Open AccessArticle

Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models

by Khaled Assi

Int. J. Environ. Res. Public Health 2020, 17(20), 7598; https://doi.org/10.3390/ijerph17207598 - 19 Oct 2020

Cited by 20 | Viewed by 3323

Abstract

The accurate prediction of road traffic crash (RTC) severity contributes to generating crucial information, which can be used to adopt appropriate measures to reduce the aftermath of crashes. This study aims to develop a hybrid system using principal component analysis (PCA) with multilayer [...] Read more.

The accurate prediction of road traffic crash (RTC) severity contributes to generating crucial information, which can be used to adopt appropriate measures to reduce the aftermath of crashes. This study aims to develop a hybrid system using principal component analysis (PCA) with multilayer perceptron neural networks (MLP-NN) and support vector machines (SVM) in predicting RTC severity. PCA shows that the first nine components have an eigenvalue greater than one. The cumulative variance percentage explained by these principal components was found to be 67%. The prediction accuracies of the models developed using the original attributes were compared with those of the models developed using principal components. It was found that the testing accuracies of MLP-NN and SVM increased from 64.50% and 62.70% to 82.70% and 80.70%, respectively, after using principal components. The proposed models would be beneficial to trauma centers in predicting crash severity with high accuracy so that they would be able to prepare for appropriate and prompt medical treatment. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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13 pages, 877 KiB

Open AccessArticle

The Predictors of Driving Cessation among Older Drivers in Korea

by SeolHwa Moon and Kyongok Park

Int. J. Environ. Res. Public Health 2020, 17(19), 7206; https://doi.org/10.3390/ijerph17197206 - 01 Oct 2020

Cited by 9 | Viewed by 2322

Abstract

Background: As the elderly population and the number of older drivers grow, public safety concerns about traffic accidents involving older drivers are increasing. Approaches to reduce traffic accidents involving older drivers without limiting their mobility are needed. This study aimed to investigate the [...] Read more.

Background: As the elderly population and the number of older drivers grow, public safety concerns about traffic accidents involving older drivers are increasing. Approaches to reduce traffic accidents involving older drivers without limiting their mobility are needed. This study aimed to investigate the driving cessation (DC) rate among older Korean adults and predictors of DC based on the comprehensive mobility framework. Method: In this cross-sectional study, data from 2970 to 10,062 older adults over 65 years old from the 2017 National Survey of Elderly People were analyzed in April 2020. Multivariate logistic regression analyses were conducted to identify the predictors of DC. Results: Residential area, an environmental factor, was a strong predictor of DC (Odds Ratio (OR) 2.21, 95% Confidential Interval (CI) 1.86–2.62). Older drivers living in an area with a metro system were 2.21 more likely to stop driving than those living in an area without a metro system. Other demographic, financial, psychosocial, physical, and cognitive variables also predicted DC. Conclusion: Environmental factors were strong predictors of older adults’ DC. Therefore, political and environmental support, such as the provision of accessible public transportation, is essential to increase the DC rate among older adults to increase public safety without decreasing their mobility. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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20 pages, 2334 KiB

Open AccessArticle

Driving Performance and Technology Acceptance Evaluation in Real Traffic of a Smartphone-Based Driver Assistance System

by Gheorghe-Daniel Voinea, Cristian Cezar Postelnicu, Mihai Duguleana, Gheorghe-Leonte Mogan and Radu Socianu

Int. J. Environ. Res. Public Health 2020, 17(19), 7098; https://doi.org/10.3390/ijerph17197098 - 28 Sep 2020

Cited by 22 | Viewed by 3400

Abstract

Technological advances are changing every aspect of our lives, from the way we work, to how we learn and communicate. Advanced driver assistance systems (ADAS) have seen an increased interest due to the potential of ensuring a safer environment for all road users. [...] Read more.

Technological advances are changing every aspect of our lives, from the way we work, to how we learn and communicate. Advanced driver assistance systems (ADAS) have seen an increased interest due to the potential of ensuring a safer environment for all road users. This study investigates the use of a smartphone-based ADAS in terms of driving performance and driver acceptance, with the aim of improving road safety. The mobile application uses both cameras of a smartphone to monitor the traffic scene and the driver’s head orientation, and offers an intuitive user interface that can display information in a standard mode or in augmented reality (AR). A real traffic experiment consisting of two driving conditions (a baseline scenario and an ADAS scenario), was conducted in Brasov, Romania. Objective and subjective data were recorded from twenty-four participants with a valid driver’s license. Results showed that the use of the ADAS influences the driving performance, as most of them adopted an increased time headway and lower mean speeds. The technology acceptance model (TAM) questionnaire was used to assess the users’ acceptance of the proposed driver assistance system. The results showed significant interrelations between acceptance factors, while the hierarchical regression analysis indicates that the variance of behavioral intention (BI) can be predicted by attitude toward behavior. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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19 pages, 3667 KiB

Open AccessArticle

Research on the Comfort of Vehicle Passengers Considering the Vehicle Motion State and Passenger Physiological Characteristics: Improving the Passenger Comfort of Autonomous Vehicles

by Chang Wang, Xia Zhao, Rui Fu and Zhen Li

Int. J. Environ. Res. Public Health 2020, 17(18), 6821; https://doi.org/10.3390/ijerph17186821 - 18 Sep 2020

Cited by 19 | Viewed by 5199

Abstract

Comfort is a significant factor that affects passengers’ choice of autonomous vehicles. The comfort of an autonomous vehicle is largely determined by its control algorithm. Therefore, if the comfort of passengers can be predicted based on factors that affect comfort and the control [...] Read more.

Comfort is a significant factor that affects passengers’ choice of autonomous vehicles. The comfort of an autonomous vehicle is largely determined by its control algorithm. Therefore, if the comfort of passengers can be predicted based on factors that affect comfort and the control algorithm can be adjusted, it can be beneficial to improve the comfort of autonomous vehicles. In view of this, in the present study, a human-driven experiment was carried out to simulate the typical driving state of a future autonomous vehicle. In the experiment, vehicle motion parameters and the comfort evaluation results of passengers with different physiological characteristics were collected. A single-factor analysis method and binary logistic regression analysis model were used to determine the factors that affect the evaluation results of passenger comfort. A passenger comfort prediction model was established based on the bidirectional long short-term memory network model. The results demonstrate that the accuracy of the passenger comfort prediction model reached 84%, which can provide a theoretical basis for the adjustment of the control algorithm and path trajectory of autonomous vehicles. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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18 pages, 2005 KiB

Open AccessArticle

A Comparative Study of Accident Risk Related to Speech-Based and Handheld Texting during a Sudden Braking Event in Urban Road Environments

by Rui Fu, Yunxing Chen, Qingjin Xu, Yuxi Guo and Wei Yuan

Int. J. Environ. Res. Public Health 2020, 17(16), 5675; https://doi.org/10.3390/ijerph17165675 - 06 Aug 2020

Cited by 7 | Viewed by 2424

Abstract

The use of mobile phones while driving is a very common phenomenon that has become one of the main causes of traffic accidents. Many studies on the effects of mobile phone use on accident risk have focused on conversation and texting; however, few [...] Read more.

The use of mobile phones while driving is a very common phenomenon that has become one of the main causes of traffic accidents. Many studies on the effects of mobile phone use on accident risk have focused on conversation and texting; however, few studies have directly compared the impacts of speech-based texting and handheld texting on accident risk, especially during sudden braking events. This study aims to statistically model and quantify the effects of potential factors on accident risk associated with a sudden braking event in terms of the driving behavior characteristics of young drivers, the behavior of the lead vehicle (LV), and mobile phone distraction tasks (i.e., both speech-based and handheld texting). For this purpose, a total of fifty-five licensed young drivers completed a driving simulator experiment in a Chinese urban road environment under five driving conditions: baseline (no phone use), simple speech-based texting, complex speech-based texting, simple handheld texting, and complex handheld texting. Generalized linear mixed models were developed for the brake reaction time and rear-end accident probability during the sudden braking events. The results showed that handheld texting tasks led to a delayed response to the sudden braking events as compared to the baseline. However, speech-based texting tasks did not slow down the response. Moreover, drivers responded faster when the initial time headway was shorter, when the initial speed was higher, or when the LV deceleration rate was greater. The rear-end accident probability respectively increased by 2.41 and 2.77 times in the presence of simple and complex handheld texting while driving. Surprisingly, the effects of speech-based texting tasks were not significant, but the accident risk increased if drivers drove the vehicle with a shorter initial time headway or a higher LV deceleration rate. In summary, these findings suggest that the effects of mobile phone distraction tasks, driving behavior characteristics, and the behavior of the LV should be taken into consideration when developing algorithms for forward collision warning systems. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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19 pages, 3466 KiB

Open AccessArticle

Drivers’ Visual Attention Characteristics under Different Cognitive Workloads: An On-Road Driving Behavior Study

by Yanli Ma, Shouming Qi, Yaping Zhang, Guan Lian, Weixin Lu and Ching-Yao Chan

Int. J. Environ. Res. Public Health 2020, 17(15), 5366; https://doi.org/10.3390/ijerph17155366 - 25 Jul 2020

Cited by 14 | Viewed by 3588

Abstract

In this study, an on-road driving experiment was designed to investigate the visual attention fixation and transition characteristics of drivers when they are under different cognitive workloads. First, visual attention was macroscopically analyzed through the entropy method. Second, the Markov glance one- and [...] Read more.

In this study, an on-road driving experiment was designed to investigate the visual attention fixation and transition characteristics of drivers when they are under different cognitive workloads. First, visual attention was macroscopically analyzed through the entropy method. Second, the Markov glance one- and two-step transition probability matrices were constructed, which can study the visual transition characteristics under different conditions from a microscopic perspective. Results indicate that the fixation entropy value of male drivers is 23.08% higher than that of female drivers. Under the normal driving state, drivers’ fixation on in-vehicle systems is not continuous and usually shifts to the front and left areas quickly after such fixation. When under cognitive workload, drivers’ vision transition is concentrated only in the front and right areas. In mild cognitive workload, drivers’ sight trajectory is mainly focused on the distant front area. As the workload level increases, the transition trajectory shifts to the junction near the front and far sides. The current study finds that the difference between an on-road test and a driving simulation is that during the on-road driving process, drivers are twice as attentive to the front area than to the driving simulator. The research provides practical guidance for the improvement of traffic safety. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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14 pages, 1382 KiB

Open AccessArticle

P300 Measures and Drive-Related Risks: A Systematic Review and Meta-Analysis

by Chao Fang, Yamei Zhang, Mingyi Zhang and Qun Fang

Int. J. Environ. Res. Public Health 2020, 17(15), 5266; https://doi.org/10.3390/ijerph17155266 - 22 Jul 2020

Cited by 9 | Viewed by 2598

Abstract

Detecting signs for an increased level of risk during driving are critical for the effective prevention of road traffic accidents. The current study searched for literature through major databases such as PubMed, EBSCO, IEEE, and ScienceDirect. A total of 14 articles that measured [...] Read more.

Detecting signs for an increased level of risk during driving are critical for the effective prevention of road traffic accidents. The current study searched for literature through major databases such as PubMed, EBSCO, IEEE, and ScienceDirect. A total of 14 articles that measured P300 components in relation to driving tasks were included for a systematic review and meta-analysis. The risk factors investigated in the reviewed articles were summarized in five categories, including reduced attention, distraction, alcohol, challenging situations on the road, and negative emotion. A meta-analysis was conducted at both behavioral and neural levels. Behavioral performance was measured by the reaction time and driving performance, while the neural response was measured by P300 amplitude and latency. A significant increase in reaction time was identified when drivers were exposed to the risk factors. In addition, the significant effects of a reduced P300 amplitude and prolonged P300 latency indicated a reduced capacity for cognitive information processing. There was a tendency of driving performance decrement in relation to the risk factors, however, the effect was non-significant due to considerable variations and heterogeneity across the included studies. The results led to the conclusion that the P300 amplitude and latency are reliable indicators and predictors of the increased risk in driving. Future applications of the P300-based brain–computer interface (BCI) system may make considerable contributions toward preventing road traffic accidents. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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15 pages, 1710 KiB

Open AccessArticle

Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study

by Muhammad Zahid, Yangzhou Chen, Arshad Jamal, Khalaf A. Al-Ofi and Hassan M. Al-Ahmadi

Int. J. Environ. Res. Public Health 2020, 17(14), 5193; https://doi.org/10.3390/ijerph17145193 - 18 Jul 2020

Cited by 23 | Viewed by 3655

Abstract

Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is [...] Read more.

Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to investigate different traffic violations (overspeeding, wrong-way driving, illegal parking, non-compliance traffic control devices, etc.) using spatial analysis and different machine learning methods. Georeferenced violation data along two expressways (S308 and S219) for the year 2016 was obtained from the traffic police department, in the city of Luzhou, China. Detailed descriptive analysis of the data showed that wrong-way driving was the most common violation type observed. Inverse Distance Weighted (IDW) interpolation in the ArcMap Geographic Information System (GIS) was used to develop violation hotspots zones to guide on efficient use of limited resources during the treatment of high-risk sites. Lastly, a systematic Machine Learning (ML) framework, such as K Nearest Neighbors (KNN) models (using k = 3, 5, 7, 10, and 12), support vector machine (SVM), and CN2 Rule Inducer, was utilized for classification and prediction of each violation type as a function of several explanatory variables. The predictive performance of proposed ML models was examined using different evaluation metrics, such as Area Under the Curve (AUC), F-score, precision, recall, specificity, and run time. The results also showed that the KNN model with k = 7 using manhattan evaluation had an accuracy of 99% and outperformed the SVM and CN2 Rule Inducer. The outcome of this study could provide the practitioners and decision-makers with essential insights for appropriate engineering and traffic control measures to improve the safety of road-users. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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21 pages, 3200 KiB

Open AccessArticle

Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?

by Muhammad Zahid, Yangzhou Chen, Sikandar Khan, Arshad Jamal, Muhammad Ijaz and Tufail Ahmed

Int. J. Environ. Res. Public Health 2020, 17(11), 3937; https://doi.org/10.3390/ijerph17113937 - 02 Jun 2020

Cited by 57 | Viewed by 5045

Abstract

Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to [...] Read more.

Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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18 pages, 795 KiB

Open AccessArticle

A Multilevel Model Approach for Investigating Individual Accident Characteristics and Neighborhood Environment Characteristics Affecting Pedestrian-Vehicle Crashes

by Seunghoon Park and Dongwon Ko

Int. J. Environ. Res. Public Health 2020, 17(9), 3107; https://doi.org/10.3390/ijerph17093107 - 29 Apr 2020

Cited by 10 | Viewed by 2935

Abstract

Walking is the most basic movement of humans and the most fundamental mode of transportation. To promote walking, it is necessary to create a safe environment for pedestrians. However, pedestrian-vehicle crashes still remain relatively high in South Korea. This study employs a multilevel [...] Read more.

Walking is the most basic movement of humans and the most fundamental mode of transportation. To promote walking, it is necessary to create a safe environment for pedestrians. However, pedestrian-vehicle crashes still remain relatively high in South Korea. This study employs a multilevel model to examine the differences between the lower-level individual characteristics of pedestrian crashes and the upper-level neighborhood environmental characteristics in Seoul, South Korea. The main results of this study are as follows. The individual characteristics of pedestrian-vehicle crashes are better at explaining pedestrian injury severity than built environment characteristics at the neighborhood level. Older pedestrians and drivers suffer more severe pedestrian injuries. Larger vehicles such as trucks and vans are more likely to result in a high severity of pedestrian injuries. Pedestrian injuries increase during inclement weather and at night. The severity of pedestrian injuries is lower at intersections and crosswalks without traffic signals than at crosswalks and intersections with traffic signals. Finally, school zones and silver zones, which are representative policies for pedestrian safety in South Korea, fail to play a significant role in reducing the severity of pedestrian injuries. The results of this study can guide policymakers and planners when making decisions on how to build neighborhoods that are safer for pedestrians. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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13 pages, 2504 KiB

Open AccessArticle

Quantifying the Effects of Visual Road Information on Drivers’ Speed Choices to Promote Self-Explaining Roads

by Yuting Qin, Yuren Chen and Kunhui Lin

Int. J. Environ. Res. Public Health 2020, 17(7), 2437; https://doi.org/10.3390/ijerph17072437 - 03 Apr 2020

Cited by 11 | Viewed by 2760

Abstract

Roads should deliver appropriate information to drivers and thus induce safer driving behavior. This concept is also known as “self-explaining roads” (SERs). Previous studies have demonstrated that understanding how road characteristics affect drivers’ speed choices is the key to SERs. Thus, in order [...] Read more.

Roads should deliver appropriate information to drivers and thus induce safer driving behavior. This concept is also known as “self-explaining roads” (SERs). Previous studies have demonstrated that understanding how road characteristics affect drivers’ speed choices is the key to SERs. Thus, in order to reduce traffic casualties via engineering methods, this study aimed to establish a speed decision model based on visual road information and to propose an innovative method of SER design. It was assumed that driving speed is determined by road geometry and modified by the environment. Lane fitting and image semantic segmentation techniques were used to extract road features. Field experiments were conducted in Tibet, China, and 1375 typical road scenarios were picked out. By controlling variables, the driving speed stimulated by each piece of information was evaluated. Prediction models for geometry-determined speed and environment-modified speed were built using the random forest algorithm and convolutional neural network. Results showed that the curvature of the right boundary in “near scene” and “middle scene”, and the density of roadside greenery and residences play an important role in regulating driving speed. The findings of this research could provide qualitative and quantitative suggestions for the optimization of road design that would guide drivers to choose more reasonable driving speeds. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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15 pages, 2414 KiB

Open AccessArticle

Global Incidence and Mortality Patterns of Pedestrian Road Traffic Injuries by Sociodemographic Index, with Forecasting: Findings from the Global Burden of Diseases, Injuries, and Risk Factors 2017 Study

by Moien A. B. Khan, Michal Grivna, Javaid Nauman, Elpidoforos S. Soteriades, Arif Alper Cevik, Muhammad Jawad Hashim, Romona Govender and Salma Rashid Al Azeezi

Int. J. Environ. Res. Public Health 2020, 17(6), 2135; https://doi.org/10.3390/ijerph17062135 - 23 Mar 2020

Cited by 20 | Viewed by 4035

Abstract

(1) Background: Pedestrian injuries (PIs) represent a significant proportion of road traffic injuries. Our aim was to investigate the incidence and mortality of PIs in different age groups and sociodemographic index (SDI) categories between 1990 and 2017. (2) Method: Estimates of age-standardized incidence [...] Read more.

(1) Background: Pedestrian injuries (PIs) represent a significant proportion of road traffic injuries. Our aim was to investigate the incidence and mortality of PIs in different age groups and sociodemographic index (SDI) categories between 1990 and 2017. (2) Method: Estimates of age-standardized incidence and mortality along with trends of PIs by SDI levels were obtained from the Global Burden of Disease from 1990 to 2017. We also forecasted the trends across all the SDI categories until 2040 using the Statistical Package for the Social Sciences (SPSS Statistics for Windows, version 23.0, Chicago, IL, USA) time series expert modeler. (3) Results: Globally, the incidence of PIs increased by 3.31% (−9.94 to 16.56) in 2017 compared to 1990. Men have higher incidence of PIs than women. Forecasted incidence was 132.02 (127.37 to 136.66) per 100,000 population in 2020, 101.52 (65.99 to 137.05) in 2030, and reduced further to 71.02 (10.62 to 152.65) by 2040. Globally across all SDI categories, there was a decreasing trend in mortality due to PIs with the global estimated percentage reduction of 37.12% (−45.19 to −29.04). (4) Conclusions: The results show that PIs are still a burden for all SDI categories despite some variation. Although incidence and mortality are expected to decrease globally, some SDI categories and specific vulnerable age groups may require particular attention. Further studies addressing incidence and mortality patterns in vulnerable SDI categories are needed. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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14 pages, 1831 KiB

Open AccessArticle

Analyzing the Importance of Driver Behavior Criteria Related to Road Safety for Different Driving Cultures

by Danish Farooq, Sarbast Moslem, Rana Faisal Tufail, Omid Ghorbanzadeh, Szabolcs Duleba, Ahsen Maqsoom and Thomas Blaschke

Int. J. Environ. Res. Public Health 2020, 17(6), 1893; https://doi.org/10.3390/ijerph17061893 - 14 Mar 2020

Cited by 32 | Viewed by 4431

Abstract

Driver behavior has been considered as the most critical and uncertain criteria in the study of traffic safety issues. Driver behavior identification and categorization by using the Fuzzy Analytic Hierarchy Process (FAHP) can overcome the uncertainty of driver behavior by capturing the ambiguity [...] Read more.

Driver behavior has been considered as the most critical and uncertain criteria in the study of traffic safety issues. Driver behavior identification and categorization by using the Fuzzy Analytic Hierarchy Process (FAHP) can overcome the uncertainty of driver behavior by capturing the ambiguity of driver thinking style. The main goal of this paper is to examine the significant driver behavior criteria that influence traffic safety for different traffic cultures such as Hungary, Turkey, Pakistan and China. The study utilized the FAHP framework to compare and quantify the driver behavior criteria designed on a three-level hierarchical structure. The FAHP procedure computed the weight factors and ranked the significant driver behavior criteria based on pairwise comparisons (PCs) of driver’s responses on the Driver Behavior Questionnaire (DBQ). The study results observed “violations” as the most significant driver behavior criteria for level 1 by all nominated regions except Hungary. While for level 2, “aggressive violations” is observed as the most significant driver behavior criteria by all regions except Turkey. Moreover, for level 3, Hungary and Turkey drivers evaluated the “drive with alcohol use” as the most significant driver behavior criteria. While Pakistan and China drivers evaluated the “fail to yield pedestrian” as the most significant driver behavior criteria. Finally, Kendall’s agreement test was performed to measure the agreement degree between observed groups for each level in a hierarchical structure. The methodology applied can be easily transferable to other study areas and our results in this study can be helpful for the drivers of each region to focus on highlighted significant driver behavior criteria to reduce fatal and seriously injured traffic accidents. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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20 pages, 6234 KiB

Open AccessArticle

The Impact of LED Color Rendering on the Dark Adaptation of Human Eyes at Tunnel Entrances

by En-Zhong Zhao, Li-Li Dong, Yang Chen, Qi Lou and Wen-Hai Xu

Int. J. Environ. Res. Public Health 2020, 17(5), 1566; https://doi.org/10.3390/ijerph17051566 - 28 Feb 2020

Cited by 9 | Viewed by 3874

Abstract

The dark adaptation of drivers’ eyes at a tunnel entrance seriously affects traffic safety. This can be improved by the design of tunnel lighting. Light-Emitting Diode (LEDs) have been applied as a new type of luminaire in tunnel lighting in recent years, but [...] Read more.

The dark adaptation of drivers’ eyes at a tunnel entrance seriously affects traffic safety. This can be improved by the design of tunnel lighting. Light-Emitting Diode (LEDs) have been applied as a new type of luminaire in tunnel lighting in recent years, but at present, there are few studies on the influence of color rendering of LEDs on tunnel traffic safety, and there is no explicit indicator for the selection of appropriate color rendering parameters in tunnel lighting specifications, which has aroused researchers’ concern. In this article, several new color rendering evaluation indexes were compared, and as a result, it is considered that CRI2012 (a color difference-based color rendering index) is more suitable for evaluating the color rendering of LEDs used at tunnel entrances. The dark adaptation phenomenon was simulated in the laboratory. Four CRI2012s, three color temperatures and eight colored targets were used in the experiments. The results showed that yellow, silver and white can provide shorter reaction times, while red and brown lead to longer reaction times, which can provide a reference for the design of road and warning signs at tunnel entrances. The effect of target color on reaction time was greater than that of color rendering. Under most target colors, the higher the CRI2012, the shorter the reaction time. When designing the color rendering of the LEDs at a tunnel entrance, the value should thus be as large as possible (close to 100), and a lower color temperature value (about 2800 K) should be selected. This paper provides technical support for tunnel lighting design and a reference for tunnel lighting specifications, which is of significance to improve driving safety and avoid traffic accidents in highway tunnels. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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Review

Jump to: Research, Other

38 pages, 31027 KiB

Open AccessReview

A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations

by Sarah Najm Abdulwahid, Moamin A. Mahmoud, Bilal Bahaa Zaidan, Abdullah Hussein Alamoodi, Salem Garfan, Mohammed Talal and Aws Alaa Zaidan

Int. J. Environ. Res. Public Health 2022, 19(6), 3552; https://doi.org/10.3390/ijerph19063552 - 17 Mar 2022

Cited by 8 | Viewed by 5049

Abstract

With the continuous emergence of new technologies and the adaptation of smart systems in transportation, motorcyclist driving behaviour plays an important role in the transition towards intelligent transportation systems (ITS). Studying motorcyclist driving behaviour requires accurate models with accurate and complete datasets for [...] Read more.

With the continuous emergence of new technologies and the adaptation of smart systems in transportation, motorcyclist driving behaviour plays an important role in the transition towards intelligent transportation systems (ITS). Studying motorcyclist driving behaviour requires accurate models with accurate and complete datasets for better road safety and traffic management. As accuracy is needed in modelling, motorcyclist driving behaviour analyses can be performed using sensors that collect driving behaviour characteristics during real-time experiments. This review article systematically investigates the literature on motorcyclist driving behaviour to present many findings related to the issues, problems, challenges, and research gaps that have existed over the last 10 years (2011–2021). A number of digital databases (i.e., IEEE Xplore^®, ScienceDirect, Scopus, and Web of Science) were searched and explored to collect reliable peer-reviewed articles. Out of the 2214 collected articles, only 174 articles formed the final set of articles used in the analysis of the motorcyclist research area. The filtration process consisted of two stages that were implemented on the collected articles. Inclusion criteria were the core of the first stage of the filtration process keeping articles only if they were a study or review written in English or were articles that mainly incorporated the driving style of motorcyclists. The second phase of the filtration process is based on more rules for article inclusion. The criteria of inclusion for the second phase of filtration examined the deployment of motorcyclist driver behaviour characterisation procedures using a real-time-based data acquisition system (DAS) or a questionnaire. The final number of articles was divided into three main groups: reviews (7/174), experimental studies (41/174), and social studies-based articles (126/174). This taxonomy of the literature was developed to group the literature into articles with similar types of experimental conditions. Recommendation topics are also presented to enable and enhance the pace of the development in this research area. Research gaps are presented by implementing a substantial analysis of the previously proposed methodologies. The analysis mainly identified the gaps in the development of data acquisition systems, model accuracy, and data types incorporated in the proposed models. Finally, research directions towards ITS are provided by exploring key topics necessary in the advancement of this research area. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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20 pages, 409 KiB

Open AccessReview

Identifying Interactive Factors That May Increase Crash Risk between Young Drivers and Trucks: A Narrative Review

by Melissa R. Freire, Cassandra Gauld, Angus McKerral and Kristen Pammer

Int. J. Environ. Res. Public Health 2021, 18(12), 6506; https://doi.org/10.3390/ijerph18126506 - 16 Jun 2021

Cited by 5 | Viewed by 3497

Abstract

Sharing the road with trucks is associated with increased risk of serious injury and death for passenger vehicle drivers. However, the onus for minimising risk lies not just with truck drivers; other drivers must understand the unique performance limitations of trucks associated with [...] Read more.

Sharing the road with trucks is associated with increased risk of serious injury and death for passenger vehicle drivers. However, the onus for minimising risk lies not just with truck drivers; other drivers must understand the unique performance limitations of trucks associated with stopping distances, blind spots, and turning manoeuverability, so they can suitably act and react around trucks. Given the paucity of research aimed at understanding the specific crash risk vulnerability of young drivers around trucks, the authors employ a narrative review methodology that brings together evidence from both truck and young driver road safety research domains, as well as data regarding known crash risks for each driving cohort, to gain a comprehensive understanding of what young drivers are likely to know about heavy vehicle performance limitations, where there may be gaps in their understanding, and how this could potentially increase crash risk. We then review literature regarding the human factors affecting young drivers to understand how perceptual immaturity and engagement in risky driving behaviours are likely to compound risk regarding both the frequency and severity of collision between trucks and young drivers. Finally, we review current targeted educational initiatives and suggest that simply raising awareness of truck limitations is insufficient. We propose that further research is needed to ensure initiatives aimed at increasing young driver awareness of trucks and truck safety are evidence-based, undergo rigorous evaluation, and are delivered in a way that aims to (i) increase young driver risk perception skills, and (ii) reduce risky driving behaviour around trucks. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

Other

Jump to: Research, Review

14 pages, 1138 KiB

Open AccessCommentary

Drink Driving as the Commonest Drug Driving—A Perspective from Europe

by Richard Allsop

Int. J. Environ. Res. Public Health 2020, 17(24), 9521; https://doi.org/10.3390/ijerph17249521 - 18 Dec 2020

Cited by 6 | Viewed by 2595

Abstract

People mixing driving motor vehicles with consuming alcohol increases deaths and injuries on the roads, as was established irrefutably in the mid-1960s. This commentary discusses how society across Europe has responded since then to this burden by managing drink driving in the interests [...] Read more.

People mixing driving motor vehicles with consuming alcohol increases deaths and injuries on the roads, as was established irrefutably in the mid-1960s. This commentary discusses how society across Europe has responded since then to this burden by managing drink driving in the interests of road safety. The principal response has been to set, communicate and enforce limits on the level of alcohol in the blood above which it is illegal to drive and to deal in various ways with drivers found to be exceeding the limits. Achieving reduction in drink-related road deaths has benefitted public health, though the aim to change behaviour of drinking drivers has been a challenge to the profession. Other achievements have included changes in public attitude to drink driving, and reduction in reoffending by convicted offenders through rehabilitation courses and use of the alcohol interlock, which prevents starting of a vehicle by a driver who has drunk too much. There is scope for improved recording of road deaths identified as drink-related, greater understanding of effectiveness in enforcement of the legal limit and improved availability of the alcohol interlock. Relevance of experience with drink driving to management of other drug driving and prospects for building on the achievements so far are discussed. Full article

(This article belongs to the Special Issue Driving Behaviors and Road Safety)

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