Search results

The purpose of this paper is to identify the effect of people’s personality on driving behavior and traffic accidents and violations in the province of South Sulawesi.

This research was conducted in order to determine the moderating effects of age on the relationship between personality variables, driving behavior and driving outcomes. The research was conducted over two years. For the first year of this study, research was conducted in the city of Makassar, the capital of the South Sulawesi province, which has the highest volume of accidents compared to other districts/cities in South Sulawesi. The approach used in conducting the data analysis was a quantitative approach; the inferential statistical analysis method of analysis used to test the hypothesis of the research was structural equation modeling.

The results of the analysis show that age is a moderating variable in the relationship between personality, driving behavior and driving outcomes. The higher the age, the stronger the influence of personality on driving behavior and driving outcomes, and the stronger the effect of driving behavior on driving outcomes.

Originality for this paper is shown as follows: using age on personality has a moderating effect on the relationship between driving behavior and driving outcomes; and the research would implicate driving behavior and inclined factors from the eastern part of Indonesia, since most research works were conducted in the western part of Indonesia and they hardly considered the moderating effect of age.

Driving for work has been identified as potentially one of the riskiest activities performed by workers within the course of their working day. Jurisdictions around the world have passed legislation and adopted policy and procedures to improve the safety of workers. However, particularly within the work driving setting, complying with legislation and the minimum safety standards and procedures is not sufficient to improve work driving safety. This chapter outlines the manner in which safety citizenship behavior can offer further improvement to work-related driving safety by acting as a complementary paradigm to improve risk management and current models and applications of safety culture.

Research on concepts associated with risk management and theoretical frameworks associated with safety culture and safety citizenship behavior are reviewed, along with their practical application within the work driving safety setting. A model incorporating safety citizenship behavior as a complementary paradigm to safety culture is proposed. It is suggested that this model provides a theoretical framework to inform future research directions aimed at improving safety within the work driving setting.

This research examines what motivates professional truck drivers to engage in eco-driving by linking their self-reports with objective driving scores.

Theory of Planned Behavior (TPB) is illustrated in an embedded, single-case study of a Finnish carrier with 17 of its truck drivers. Data are obtained through in-depth interviews with drivers, their fuel-efficiency scores generated by fleet telematics and a focus group session with the management.

Discrepancies between drivers’ intentions and eco-driving behaviors are illustrated in a two-by-two matrix that classifies drivers into four categories: ideal eco-drivers, wildcards, wannabes and non-eco-drivers. Attitudes, subjective norms and perceived behavioral control are examined for drivers within each category, revealing that drivers’ perceptions did not always align with the reality of their driving.

This study strengthens the utility of TPB through data triangulation while also revealing the theory’s inherent limitations in elucidating the underlying causes of its three antecedents and their impact on the variance in driving behaviors.

Managerial insights are offered to fleet managers and eco-driving solution providers to stipulate the right conditions for drivers to enhance fuel-efficiency outcomes of transport fleets.

This is one of the first studies to give a voice to professional truck drivers about their daily eco-driving practice.

This paper aims to explore the link between dangerous driving and other criminal behaviour.

Arksey and O’Malley’s (2005) five-step process for scoping reviews to identify, summarise and classify identified literature was used. Within the 30-year timeframe (1990–2019), 12 studies met the inclusion criteria.

This review indicates that individuals who commit certain driving offences are more likely to also have a general criminal history. In particular, driving under the influence, driving unlicensed and high-range speeding offences were associated with other forms of criminal behaviour. Seven of the studies mentioned common criminological theories; however, they were not integrated well in the analysis. No studies used explanatory psychosocial theories that investigate social and contextual factors.

Future research in this area would benefit from exploring individual and social influences that contribute to criminal behaviour in both contexts.

There is the potential to develop an information-led policing approach to improve safety on the roads and reduce wider offending behaviour. However, it is critical that road policing officers continue to focus on ensuring the road system is as safe as possible for users.

Criminal behaviour on the roads is often seen as a separate from other types of offending. This paper explores if, and how, these two types of offending are linked.

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.

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.

This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.

A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.

The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.

The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.

This paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.