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An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

Critical social marketing can play a vital role in countering the consequences of behaviours toxified by commercial marketing. This paper aims to hypothesise that auto sector brand activities may be associated with riskier driving.

In this paper, the authors hypothesised that auto sector brand activities may be associated with riskier driving. UK collision data was examined, focusing on collisions that occurred because of an “injudicious action” (risky or aggressive driving manoeuvres) and analysing this data set by comparing the incidence of vehicle brands involved.

After allowing for other effects, a gradient graph illustrated differing associations between vehicle brands and collision rates.

A discussion was offered, adopting the position that if such a problem exists the solutions cannot be left to the sector itself, and that socially responsible interventions may be required. A number of social marketing strategies are proposed including regulatory support, “Truth Campaign” style exposure of commercial damage, and counter-marketing that promotes safe driver behaviour.

This work provides valuable empirical support to the concerns raised by previous workers about the possible effects of automotive sector advertising on driving behaviour. The paper offers a concise discussion of ways forward, concluding with the novel possibility of regulating individual brands as an alternative to sector-wide regulation.

For autonomous vehicles, trajectory prediction of surrounding vehicles is beneficial to improving the situational awareness of dynamic and stochastic traffic environments, which is a crucial and indispensable element to realize highly automated driving.

In this paper, the overall framework consists of two parts: first, a novel driver characteristic and intention estimation (DCIE) model is built to indicate the higher-level information of the vehicle using its low-level motion variables; then, according to the estimation results of the DCIE model, a classified Gaussian process model is established for probabilistic vehicle trajectory prediction under different motion patterns.

The whole method is later applied and analyzed in the highway lane-change scenarios with the parameters of models learned from the public naturalistic driving data set. Compared with other traditional methods, the performance of this proposed approach is proved superior, demonstrated by the higher accuracy in the long prediction horizon and a more reasonable description of uncertainty.

This hierarchical approach is proposed to make trajectory prediction accurately both in the short term and long term, which can also deal with the uncertainties caused by the perception system or indeterminate vehicle behaviors.

Global issues on the environment, such as climate change, air pollution and carbon monoxide emission, are the primary concerns in any part of the world. The purpose of this paper is to construct a conceptual framework for the travel behavior performance of a commuter, and it is expected to mitigate air pollution from vehicle emission and to promote smart mobility on the road.

From the extensive literature review, the conceptual framework for the travel behavior performance of a commuter has been developed and is supported by the theory of interpersonal behavior (TIB), whose functions are attitude, social factor, affect and habit. In the present paper, attitude is conceptualized by four predictors, namely confidence in driving, green environment, social responsibility and deviation in driving. The social factor is characterized by subjective norms, social status and digitalization. Affect factor is conceptualized by accidents and damages, road infrastructure, and weather conditions. The mental block in following the ancestor’s way of owning a personal vehicle is the predictor for the habit.

One of the major contributors to environmental damages is road traffic. Notably, vehicle emissions are on the rise every year due to the increase of reliance on vehicles, and there is no alternative to this issue. Although Malaysia has a well-organized infrastructure with effective digitalized technology on the road for the transport system, there is severe traffic congestion in Klang Valley, Kuala Lumpur, because of a lack of travel plan behavior during peak hours. If the road commuters give the predictors constructed in the proposed conceptual framework the highest importance, then there will be much relief to traffic congestion on the road.

Since the present study focuses on the conceptualization of an urban travel behavior model (UTBM), and also highlights the synchronization of the proposed framework with the management theory, the results are expected after the primary survey based on the cross-sectional study will be conducted.

The identification of the suitable predictors for the UTBM toward the travel behavior performance of a commuter is the real novelty of the present study. Also, the cause and effect relationships of different predictors in terms of path directions of the proposed research framework are the highlights of the study. Further, the predictors in the proposed framework and the TIB have been synchronized with operational definitions, which are the original contributions of the present study, which will enhance the sustainable environmental development for the society as a whole.

Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency.

To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term.

The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages.

The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.

This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline.

Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms.

Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles.

To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.

The purpose of this study is to investigate the influence of institutional dynamics on road accidents and whether this relationship is moderated by information and communication technology (ICT).

The study adopted a quantitative approach with 133 respondents. Research hypotheses were tested in AMOS version 21. In addition, moderated regression analysis was used to test the moderating role of ICT on the relationship between institutional dynamics and road accidents.

The results show that vehicle maintenance, policy enforcement, safety culture, driver training and driver management positively influence road accidents. Moreover, the study established that ICT moderates the relationship between institutional dynamics and road accidents.

The results of this study serve as a practical guideline for policymakers in the road haulage sector. Managers may gain insights on how to design effective interventions to reduce road accidents.

This research contributes to the existing body of knowledge by exploring previously unexplored moderating paths in the relationship between institutional dynamics and road accidents. By highlighting the moderating role of ICT, the study sheds new light on the institutional dynamics that influence road accidents in the context of road haulage companies.

The purpose of this paper is to combine individual (personality traits and profiles) and organizational (perceived safety climate and work motivation) factors and look for a model that explains safety performance in a sample of professional drivers. The authors hypothesize that the effect of personality on risky driving is moderated by perceived organizational safety climate and work motivation.

The sample consisted of 166 professional drivers (males). The subjects completed the self‐reported questionnaire that consisted of the Big Five Inventory, Driver Behaviour Questionnaire, Work motivation and Safety Climate Questionnaires. Cross‐sectional methodology, analysis of variance, cluster analysis and structural equation modeling were used to predict the relationships between personality traits, organizational factors, and risky driving.

The results revealed that personality profile is very important in occupational setting, predicting work motivation, perceived safety climate in organization as well as risky or safe driving. Results encourage making a conclusion that “socially oriented” drivers drive less riskily if they have higher levels of work motivation and the perception of organizational climate being safe. “Emotionally unstable” professional drivers are probably driven by neuroticism and are non‐responsive to organizational factors.

The design does not allow making causal statements. In addition, the sample is quite small and may not be representative. Self‐report data may bias the results due to social desirability or lack of experience in self‐reflection.

The results of the present investigation have expanded understanding of the role of personality and organizational interaction in predicting occupational safety of professional drivers. The main implication for practitioners is to develop such selection procedures that could identify drivers with safe driving personalities.

The research contributes to the field of occupational safety by integrating individual attributes with organizational factors by providing empirical findings and theoretical interpretations.

This paper examines the relationship between personality characteristics - as indexed by the ICES Personality Inventory (Bartram, 1994; 1998) and the IBS Clinical Inventory (Mauger, Adkinson, Zoss, Firestone & Hook, 1980) - and bullying behavior. Although it proved to be difficult to obtain a large enough sample of bullies, the findings indicated that bullies are aggressive, hostile, and extraverted and independent. Furthermore, bullies are egocentric, selfish, and show little concern for the opinions of others. High levels of aggressiveness, assertiveness, competitiveness and independence are traits that are also associated with leadership.

The purpose of this paper is to deal with the practical challenge faced by modern logistics enterprises to accurately evaluate driving performance with high computational efficiency under the disturbance of road smoothness and to identify significantly associated performance influence factors.

The authors cooperate with a logistics server (G7) and establish a driving grading system by constructing real-time inertial navigation data-enabled indicators for both driving behaviour (times of aggressive speed change and times of lane change) and road smoothness (average speed and average vibration times of the vehicle body).

The developed driving grading system demonstrates highly accurate evaluations in practical use. Data analytics on the constructed indicators prove the significances of both driving behaviour heterogeneity and the road smoothness effect on objective driving grading. The methodologies are validated with real-life tests on different types of vehicles, and are confirmed to be quite effective in practical tests with 95% accuracy according to prior benchmarks. Data analytics based on the grading system validate the hypotheses of the driving fatigue effect, daily traffic periods impact and transition effect. In addition, the authors empirically distinguish the impact strength of external factors (driving time, rainfall and humidity, wind speed, and air quality) on driving performance.

This study has good potential for providing objective driving grading as required by the modern logistics industry to improve transparent management efficiency with real-time vehicle data.

This study contributes to the existing research by comprehensively measuring both road smoothness and driving performance in the driving grading system in the modern logistics industry.