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Purpose: This chapter synthesises a variety of findings on the topic of aggressive driving and delivers a suite of strategies for moderating such behaviours. Examples and formal definitions of aggressive driving acts are given, along with specific techniques for reducing excessive speed and other aggressive behaviours.

Methodology: Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings: Speeding is the most common type of aggressive driving, and speeding-related crashes represent a high share of traffic deaths. Speeding relates to many factors, including public attitudes, personal behaviours, vehicle performance capabilities, roadway design attributes, laws and policies. Anonymity, while encased in a vehicle, and driver frustration, due to roadway congestion or other issues, contribute to aggressive driving.

Research implications: More observational data are needed to quantify the effects of the contributing factors on aggressive driving.

Practical implications: Driver frustration, intoxication and stress can lead to serious crashes and other traffic problems. They can be addressed, to some extent, through practical enforcement, design decisions and education campaigns.

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.