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Ergonomics and Human Factors in Transportation Systems

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 11353

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Special Issue Editors

Prof. Dr. Changxu Wu

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Guest Editor

Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
Interests: human cognition system; transportation safety; user-centered human-machine interaction

Dr. Jingyu Zhang

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Guest Editor

Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
Interests: human cognition and behaviors; emotion and cultural influences on human judgment and behavior

Dr. Lei Wang

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Guest Editor

College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
Interests: human factors in aviation; flight data analysis; ergonomics in flight deck; pilot selection and training; fatigue and stress

Special Issue Information

Dear Colleagues,

Human factors and ergonomics focus on the phenomena, theory, and methodologies behind the interplay between humans, machines, and the environment. Driven initially by the requirements to reduce accidents and improve the safety of complex systems such as the transportation system, the discipline has now gained acceptance in a broader sense as it can also help improve efficiency, health, and well-being. In this era marked by the quick development of intelligent and smart technologies, the transportation system has become unprecedentedly complex, and the human factor plays a more and more important role in designing new systems/HMIs and enacting proper regulations and policies.

To promote the “human-centered design” and improve safety in transportation systems, this Special Issue welcomes submissions that focus on the interplay between human and transportation systems, including highway, railway, aviation, aerospace, and maritime systems. It covers a wide range of topics including traffic behaviors, accident analysis, human-error analysis and prediction, situation awareness, stress, fatigue, mental workload, decision-making processes, social and motivational processes, human performance modelling, competency assessment and training, human–AI interaction, decision support systems, human-centered design, and the mental health of transportation practitioners during COVID-19, etc. Studies based on new techniques, theories, and new transportation systems including high speed-railways, autonomous vehicles, single-pilot operations, and remote operations, etc., are particularly encouraged.

Prof. Dr. Changxu Wu
Dr. Jingyu Zhang
Dr. Lei Wang
Guest Editors

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Keywords

human factors
big data and human behavior
human–AI interaction
human–machine function allocation
operator competency
human performance modelling
human decision making
human error prediction

Published Papers (10 papers)

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Research

14 pages, 2209 KiB

Open AccessArticle

Behavioral Patterns of Drivers under Signalized and Unsignalized Urban Intersections

by Sirou Qu and Fengxiang Guo

Appl. Sci. 2024, 14(5), 1802; https://doi.org/10.3390/app14051802 - 22 Feb 2024

Viewed by 505

Abstract

Under the general trend of mixed traffic flow, an in-depth understanding of the driving behaviors of traditional vehicles is of great significance for the design of autonomous vehicles and the improvement in the safety and acceptance of autonomous vehicles. This study first obtained microdata on the behaviors of drivers through driving simulation experiments and conducted research in stages. Then, generalized linear mixed-effects models were constructed to study the main effects and interaction effects of driver attributes and traffic conditions on driving behaviors. The data analysis shows that the overall speed of drivers passing through intersections follows a “deceleration acceleration” mode, but the fluctuations are more pronounced at signalized intersections, and the signal control significantly changes the position of the lowest speed when turning left. According to the different signal control and driving tasks, there are significant differences in a driver’s acceleration patterns between the entry and exit stages. A driver’s heart rate fluctuates greatly during the exit phase, especially during straight tasks. Compared with other indicators, the change in the gaze duration is not significant. In addition, interaction effects were observed between driver attributes and traffic conditions, with participants exhibiting different behavioral patterns based on their different attributes. The research results can provide a basis for the design of driving assistance systems and further improve the interactions between autonomous vehicles and traditional vehicles at intersections. Full article

(This article belongs to the Special Issue Ergonomics and Human Factors in Transportation Systems)

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17 pages, 1480 KiB

Open AccessArticle

Impacts of Flight Operations on the Risk of Runway Excursions

by Feiyin Wang, Jintong Yuan, Xiaochen Liu, Pengtao Wang, Mao Xu, Xiaoyu Li and Hang Li

Appl. Sci. 2024, 14(3), 975; https://doi.org/10.3390/app14030975 - 23 Jan 2024

Viewed by 629

Abstract

The Quick Access Recorder (QAR), as an onboard device used for monitoring and recording flight parameters, has been extensively installed on various types of aircraft. Recently, there has been a significant focus on studying the typical flight safety event of runway excursions based on QAR data. However, there is limited research that combines the analysis of runway excursion risks with flight operations, and there is also a scarcity of studies that divide the investigation of the landing phase into multiple key stages. In this paper, we propose a comparative analysis of operational characteristics and risks associated with runway excursions from the perspective of operational styles. A total of 2087 flights were classified on the basis of touchdown distance, taxiing distance, and magnetic heading changes and were divided into three styles based on these indicators. Subsequently, we analyze flight operations and attitudes at five key stages: runway threshold, flare, speed brake deployment, touchdown, and reverse thrust activation. Furthermore, we employ the selection criteria of pilot proficiency levels to filter out standard operational curves. The curve similarity is used to compare the difference between the actual operating curves and the standard curves. Finally, we employ typical correlation analysis to explore the relationship between touchdown distance and operational variances. The findings indicate that Style 1 pilots exhibit the lowest probability of runway excursions, yet their maneuvers potentially elevate the risk of hard landing events. Full article

(This article belongs to the Special Issue Ergonomics and Human Factors in Transportation Systems)

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35 pages, 7210 KiB

Open AccessArticle

Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics

by Tongqiang Ding, Lei Yuan, Zhiqiang Li, Jianfeng Xi and Kexin Zhang

Appl. Sci. 2024, 14(1), 279; https://doi.org/10.3390/app14010279 - 28 Dec 2023

Viewed by 897

Abstract

A city bus carries a large number of passengers, and any traffic accidents can lead to severe casualties and property losses. Hence, predicting the likelihood of accidents among bus drivers is paramount. This paper considered occupational driving characteristics such as cumulative driving duration, station entry and exit features, and peak driving times, and categorical boosting (CatBoost) was used to construct an accident probability prediction model. Its effectiveness was confirmed by the daily management data of a Chongqing bus company in June. For data processing, Multiple Imputation by Chained Equations for Random Forests (MICEForest) was used for data filling. In terms of prediction, a comparative analysis of four boosted trees revealed that CatBoost exhibited superior performance. To analyze the critical factors affecting the probability of bus driver accidents, SHapley Additive exPlanations (SHAP) was applied to visualize and interpret the results. In addition to the significant effects of age, rainfall, and azimuthal change, etc., we innovatively discovered that the proportion of driving duration during peak duration, the dispersion when entering and exiting stations, the proportion of driving duration within a week, and the accumulated driving duration of the previous week also had varying degrees of impact on accident probability. Our research and findings provide a new idea of accident prediction for professional drivers and direct theoretical support for the accident risk management of bus drivers. Full article

(This article belongs to the Special Issue Ergonomics and Human Factors in Transportation Systems)

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19 pages, 5404 KiB

Open AccessArticle

Effects of Fatigue and Tension on the Physical Characteristics and Abilities of Young Air Traffic Controllers

by Xingjian Zhang, Mingyuan Liu, Peng Bai and Yifei Zhao

Appl. Sci. 2023, 13(18), 10383; https://doi.org/10.3390/app131810383 - 17 Sep 2023

Viewed by 962

Abstract

The purpose of this study was to analyze the effects of fatigue and tension on the physical characteristics and abilities of air traffic controllers (ATCOs) and determine their influence mechanisms. A simulated experiment was designed to evaluate the responses of ATCOs in four states: alertness, fatigue, tension, and fatigue and tension. Thirty young male ATCOs participated in the experiment. Fifteen parameters of their physical characteristics and abilities were collected and analyzed to estimate the effects and the decreasing order of influence of fatigue and tension on the indicators. The results showed that most of the parameters of the ATCOs were significantly affected by fatigue and tension. The attention, perception, reaction time, decision-making ability, and comprehensive performance of the ATCOs were adversely affected by fatigue, and tension had negative effects on their attention, decision-making ability, and comprehensive performance. Fatigue and tension impair the physical characteristics and abilities of ATCOs. Both states initially affected the physical characteristics of the ATCOs and then impaired their abilities. However, the influence mechanisms involved were different. The primary effect of the fatigue state was slowing down, whereas the effect of the tense state was instability. These results provide a reference for the evaluation and management of fatigue and tension states in ATCOs. Full article

(This article belongs to the Special Issue Ergonomics and Human Factors in Transportation Systems)

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22 pages, 5090 KiB

Open AccessArticle

Core Competency Quantitative Evaluation of Air Traffic Controller in Multi-Post Mode

by Changmiao Duan, Minghua Hu, Lei Yang and Qi Gao

Appl. Sci. 2023, 13(18), 10246; https://doi.org/10.3390/app131810246 - 12 Sep 2023

Viewed by 1227

Abstract

In order to quantify the core competency of air traffic controllers (ATCO) in multi-post mode, and analyze the ATCO competency-post fit (CPF) degree, this paper proposes the AFE-TOPSIS evaluation method based on the analytic hierarchy process (AHP), factor analysis (FA), and entropy weight method (EWM). Through the analysis of job tasks, it preliminarily develops an evaluation index library of ATCO core competency. Then, it constructs an improved adaptive multi-dimension core competency model divided into four types of post modes. It proposes a method for determining the combined weight of core competency indexes, where the weights are determined subjectively through AHP, combined with the objective method of FA and EWM. Furthermore, it constructs a comprehensive evaluation model based on AFE-TOPSIS and analyzes the CPF degree by calculating the relative closeness degree of each evaluation index. The empirical analysis results show that the four types of core competency models in General, TWR, APP, and ACC post mode include seven dimensions, such as situation awareness, and workload management, which are also graded into the pyramid hierarchical structure of basic, advanced, and high-level. The calculation of the ATCO CPF degree based on the proposed AFE-TOPSIS method can scientifically assist in the fit of ATCO and multi-post mode. Full article

(This article belongs to the Special Issue Ergonomics and Human Factors in Transportation Systems)

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16 pages, 2027 KiB

Open AccessArticle

Comparing Response Behaviors to System-Limit and System-Malfunction Failures with Four Levels of Operational Proficiency

by Junmin Du, Padun Yunusi, Shuyang He and Peng Ke

Appl. Sci. 2023, 13(14), 8304; https://doi.org/10.3390/app13148304 - 18 Jul 2023

Viewed by 701

Abstract

Commercial aircraft are becoming highly automated, but pilots must take control if automation systems fail. Failures can be due to known limitations (system-limit failures) or unforeseen malfunctions (system-malfunction failures). This study quantifies the impact of these failures on response performance and monitoring behavior, considering four levels of operational proficiency. In a flight simulator with pitch, roll, and yaw, 24 participants experienced both types of failures at different proficiency levels. The results showed that system-malfunction failure response times were 3.644, 2.471, 2.604, and 4.545 times longer than system-limit failure response times at proficiency levels 1 to 4. Monitoring behaviors (fixation duration, saccade duration, fixation rate) differed between failure types and proficiency levels. Considering these differences in response performance and monitoring behavior between failure types, it is important to differentiate between system-limit and system-malfunction failures in the literature and not overlook the influence of proficiency. Furthermore, due to the unpredictability of system-malfunctions, it is crucial to develop pilots’ psychological models and training theories regarding the operation of automated systems, fostering their core competency to excel in handling unknown situations. Full article

(This article belongs to the Special Issue Ergonomics and Human Factors in Transportation Systems)

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19 pages, 2043 KiB

Open AccessArticle

Individual Differences in Signal Perception for Takeover Request in Autonomous Driving

by Okkeun Lee and Hyunmin Kang

Appl. Sci. 2023, 13(14), 8162; https://doi.org/10.3390/app13148162 - 13 Jul 2023

Cited by 1 | Viewed by 752

Abstract

In the context of partial autonomy, where autonomous vehicles and humans share control of the vehicle, bringing out-of-the-loop drivers back into the loop is a significant challenge. While warning signal design guidelines are commonly used to provide alerts, few studies have examined each signal in depth with an emphasis on the autonomous environment. This study aims to fill this gap by investigating visual, auditory, and tactile stimuli and modifying their sub-attributes to explore variations related to age, gender, and other individual backgrounds. For this objective, the research examined the correlations between age, gender, and individual backgrounds with reaction times to TOR signals, investigating the effects of sub-attribute variations on participants’ responses and exploring the subjective evaluations of the signals. A driving simulator was utilized to create a realistic driving environment and measure participants’ reaction times in takeover request situations. Analysis of the data revealed correlations between age and reaction times for auditory and tactile signals, with interaction effects observed between age and sub-attribute intensity. Additionally, participants exhibited varying reaction time patterns in response to different sub-attribute intensities. By evaluating individual differences in perception based on modality characteristics, often overlooked in prior research, this study serves as a foundational contribution to future research in the field. Full article

(This article belongs to the Special Issue Ergonomics and Human Factors in Transportation Systems)

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16 pages, 4375 KiB

Open AccessArticle

Generating Function Reallocation to Handle Contingencies in Human–Robot Teaming Missions: The Cases in Lunar Surface Transportation

by Yan Fu, Wen Guo, Haipeng Wang, Shuqi Xue and Chunhui Wang

Appl. Sci. 2023, 13(13), 7506; https://doi.org/10.3390/app13137506 - 25 Jun 2023

Viewed by 807

Abstract

On lunar missions, efficient and safe transportation of human–robot systems is essential for the success of human exploration and scientific endeavors. Given the fact that transportation constructs bottlenecks for numerous typical lunar missions, it is appealing to investigate what function allocation strategies can generate optimal task implementation paths for robots with low-human workloads when the situation changes. Thus, this paper presents a novel approach to dynamic human–robot function allocation explicitly designed for team transportation in lunar missions. The proposed dynamic allocation framework aims to optimize human–robot collaboration by responding to existing and potential contingencies. First, a fitness concept model is designed to quantify the factors that motivate the functional adaptation of each agent in dynamic lunar mission scenarios. A hierarchical reinforcement learning (HRL) algorithm with two layers is then employed for decision-making and optimization of human–robot function allocation. Finally, the validity of the framework and algorithm proposed is validated by a series of human–robot function allocation experiments on a simulated environment that mimics lunar transportation scenarios, and is compared with the performance of other algorithms. In the future, path-planning algorithms can be incorporated into the proposed framework to improve the adaptability and efficiency of the human–robot function allocation in lunar missions. Full article

(This article belongs to the Special Issue Ergonomics and Human Factors in Transportation Systems)

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21 pages, 5698 KiB

Open AccessArticle

Uncertainty Quantification of Ride Comfort Based on gPC Framework for a Fully Coupled Human–Vehicle Model

by Byoung-Gyu Song, Jong-Jin Bae and Namcheol Kang

Appl. Sci. 2023, 13(11), 6785; https://doi.org/10.3390/app13116785 - 2 Jun 2023

Cited by 1 | Viewed by 885

Abstract

We investigated the stochastic response of a person sitting in a driving vehicle to quantify the impact of an uncertain parameter important in controlling defect reduction in terms of ride comfort. Using CarSim software and MATLAB/Simulink, we developed a fully coupled model that simulates a driving vehicle combined with an analytical nonlinear human model. Ride comfort was evaluated as a ride index considering the frequency weights defined in BS 6841. Additionally, to investigate the uncertainty of the ride index, a framework for calculating the ride index was proposed using the generalized polynomial (gPC) method. Further, sensitivity analysis of the ride index was performed for each uncertainty parameter, such as stiffness and damping. The results obtained through the gPC method were in good agreement with those obtained via Monte Carlo simulation (MCS) and were excellent in terms of computation time without a loss of numerical accuracy. Through in-depth investigation, we found that the stochastic distribution of the ride index varies differently for each uncertain parameter in the human model. By comparing linear and nonlinear human models, we also found that the nonlinearity of the human model is an important concern in the stochastic estimation of ride comfort. Full article

(This article belongs to the Special Issue Ergonomics and Human Factors in Transportation Systems)

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15 pages, 3039 KiB

Open AccessArticle

Mental Health and Safety Assessment Methods of Bus Drivers

by Jianfeng Xi, Ping Wang, Tongqiang Ding, Jian Tian and Zhiqiang Li

Appl. Sci. 2023, 13(1), 100; https://doi.org/10.3390/app13010100 - 21 Dec 2022

Cited by 1 | Viewed by 2030

Abstract

To explore the influence of the health psychology characteristics of bus driver on the probability of traffic accidents, such as the severity of unhealthy psychology and negative and impulsive personality. Combined with the demographic questionnaire, SCL-90 scale, and Y-G scale, the psychological factors of drivers causing traffic accidents were evaluated. The key factors selected by binary logistic regression analysis are used as node variables, and the Bayesian network structure was established by combining the K2 algorithm and expert knowledge. The EM algorithm was used for parameter learning. The work identified seven key factors that made bus drivers prone to accidents. The most likely factors were moderate depression, mild anxiety, and mild somatization. Bus drivers in the accident group were significantly more anxious, depressed, and more hypersensitive and emotionally unstable than drivers in the non-accident group. The psychological scale and a Bayesian network model were used to evaluate the mental health and traffic safety of bus drivers. It shows that different degrees of depression, anxiety, and different degrees of subjective and cyclical personality of bus drivers had different effects on traffic safety. Full article

(This article belongs to the Special Issue Ergonomics and Human Factors in Transportation Systems)

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