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- Vagioula Tsoutsi & Maria Papadakaki & George Yannis & Dimosthenis Pavlou & Maria Basta & Joannes Chliaoutakis & Dimitris Dikeos (2023): Driving Behaviour in Depression Based on Subjective Evaluation and Data from a Driving Simulator
Depression is characterized by mental, emotional and executive dysfunction, which may have an impact on driving behaviour. Patients with depression (N = 39) and healthy controls (N = 30) were asked to complete questionnaires and to drive on a driving simulator in different scenarios. ... Demographic and medical information, insomnia (Athens Insomnia Scale, AIS), sleepiness (Epworth Sleepiness Scale, ESS), fatigue (Fatigue Severity Scale, FSS), symptoms of sleep apnoea (StopBang Questionnaire) and driving (Driver Stress Inventory, DSI and Driver Behaviour Questionnaire, DBQ) were assessed. ... The group of patients with depression did not differ from controls regarding driving behaviour as assessed through questionnaires; on the driving simulator, patients kept a longer safety distance. ... It seems that, although certain symptoms of depression (insomnia, fatigue and somnolence) may affect driving performance, patients drive more carefully eliminating, thus, their impact.
RePEc:gam:jijerp:v:20:y:2023:i:8:p:5609-:d:1129694 Save to MyIDEAS - Ali Keyvanfar & Arezou Shafaghat & Nasiru Zakari Muhammad & M. Salim Ferwati (2018): Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited
Frequency aggregation indicates that the mitigation policies associated with driving behaviours adopted to curtail this consumption and decrease hazardous emissions, as well as a safety enhancement. ... Additionally, the influence of such driving behaviours as acceleration/deceleration and the compliance to speed limits on each approach was discussed. Other driving behaviours, such as gear shifting, compliance to traffic laws, choice of route, and idling and braking style, were also discussed. Likewise, the influence of aggression, anxiety, and motivation on driving behaviour of motorists was highlighted. The research determined that driving behaviours can lead to new adaptive driving behaviours and, thus, cause a significant decrease of vehicle fuel consumption and CO 2 emissions.
RePEc:gam:jsusta:v:10:y:2018:i:4:p:1152-:d:140611 Save to MyIDEAS - Javadreza Vahedi & Afshin Shariat Mohaymany & Zahra Tabibi & Milad Mehdizadeh (2018): Aberrant Driving Behaviour, Risk Involvement, and Their Related Factors Among Taxi Drivers
The current study aims to investigate the aberrant driving behaviour and risk involvement of Iranian taxi drivers. ... We contribute to the literature by understanding how and to what extent the socioeconomic, demographic, driving, and aberrant driving behaviours influence risk involvement (accident involvement and traffic tickets). The validated 27-item Driver Behaviour Questionnaire (DBQ) was applied to measure aberrant driving behaviour. ... The results also showed that being a single driver, having a high annual driving mileage, and a high number of daily taxi trips were positively associated with accident involvement.
RePEc:gam:jijerp:v:15:y:2018:i:8:p:1626-:d:161389 Save to MyIDEAS - Gupta, Akshay & Choudhary, Pushpa & Parida, Manoranjan (2024): Examining risky driving behaviours: A comparative analysis of SUVs and other car types
The aim of the study was to analyze whether the driving behaviour changes with different types of cars (Hatchback, Sedan and Sports Utility Vehicle (SUV)), particularly on expressways. ... With the help of structural equation modelling, individual risky driving score of each driver was calculated. ... Further, ANOVA test revealed that SUV drivers performed riskier behaviour more compared to other types of cars. ... The results indicated that drivers' age, crash history, and speeding behaviour were statistically significant predictors of risky driving behaviour. ... These findings would be helpful in understanding the differences among risky driving behaviour performed by drivers of different types of cars and to identify the potential road safety countermeasures.
RePEc:eee:trapol:v:152:y:2024:i:c:p:9-20 Save to MyIDEAS - Shaobo Ji & Ke Zhang & Guohong Tian & Zeting Yu & Xin Lan & Shibin Su & Yong Cheng (2022): Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car
Evaluation of driving behaviour is helpful for policy development, and for designing infrastructure and an intelligent safety system for a car. This study focused on a quantitative evaluation method of driving behaviour based on the shared-electrical car. ... Eleven types of NDS data were selected as the indexes for driving behaviour evaluation. ... The distribution of the main driving behaviour parameters was compared and the change trend of the parameters was analysed in conjunction with car speed to identify the threshold for recognition of aberrant driving behaviour. ... A score-based evaluation method was proposed and verified by the driving behaviour data collected from randomly chosen drivers.
RePEc:gam:jeners:v:15:y:2022:i:13:p:4625-:d:846789 Save to MyIDEAS - Tomer Toledo (2006): Driving Behaviour: Models and Challenges
Driving behaviour models capture drivers’ tactical manoeuvring decisions in different traffic conditions. ... The paper reviews the state‐of‐the‐art in the main areas of driving behaviour research: acceleration, lane changing and gap acceptance. Overall, the main limitation of current models is that in many cases they do not adequately capture the sophistication of drivers: they do not capture the interdependencies among the decisions made by the same drivers over time and across decision dimensions; they represent instantaneous decision‐making, which fails to capture drivers’ planning and anticipation capabilities; and only capture myopic considerations that do not account for extended driving goals and considerations. ... Hence, data availability poses a significant obstacle to the advancement of driving behaviour modelling.
RePEc:taf:transr:v:27:y:2006:i:1:p:65-84 Save to MyIDEAS - Jingqiu Guo & Yangzexi Liu & Lanfang Zhang & Yibing Wang (2018): Driving Behaviour Style Study with a Hybrid Deep Learning Framework Based on GPS Data
Innovative technologies and traffic data sources provide great potential to extend advanced strategies and methods in travel behaviour research. Considering the increasing availability of real-time vehicle trajectory data and stimulated by the advances in the modelling and analysis of big data, this paper developed a hybrid unsupervised deep learning model to study driving bahaviour and risk patterns. The approach combines Autoencoder and Self-organized Maps (AESOM), to extract latent features and classify driving behaviour.
RePEc:gam:jsusta:v:10:y:2018:i:7:p:2351-:d:156561 Save to MyIDEAS - Al-Wreikat, Yazan & Serrano, Clara & Sodré, José Ricardo (2021): Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving
This work evaluates driving behaviour, trip distance, ambient temperature, traffic condition and road grade effects on the specific energy consumption (SEC) of an electric vehicle (EV) under different operation modes according to a real driving cycle (RDC) test schedule. ... The driving behaviour was classified as aggressive, moderate and passive, according to dynamic operation limits, and the parameters representing the traffic conditions were stop time percentage and average vehicle speed in urban driving. ... Traffic conditions and driving behaviour also demonstrated a high influence on SEC, increasing it by as much as 40% and 16%, respectively, from the most favourable to the most unfavourable condition.
RePEc:eee:appene:v:297:y:2021:i:c:s0306261921005444 Save to MyIDEAS - Stella Roussou & Thodoris Garefalakis & Eva Michelaraki & Tom Brijs & George Yannis (2024): Machine Learning Insights on Driving Behaviour Dynamics among Germany, Belgium, and UK Drivers
The goal is to evaluate the safety levels of participants as they engage in natural driving experiences within the i-DREAMS on-road field trials. ... These trips were then input into the aforementioned machine learning methods to reveal the factors contributing to unsafe driving behaviour across various experiment stages. The results obtained highlight the significant positive impact of i-DREAMS’ real-time interventions and post-trip assessments on enhancing driving behaviour.
RePEc:gam:jsusta:v:16:y:2024:i:2:p:518-:d:1314551 Save to MyIDEAS - Alessandra Pizzo & Giulia Lausi & Jessica Burrai & Alessandro Quaglieri & Emanuela Mari & Ivan D’Alessio & Benedetta Barchielli & Pierluigi Cordellieri & Anna Maria Giannini & Clarissa Cricenti (2024): Emotion behind the Wheel: Unravelling the Impact of Emotional (dys)Regulation on Young Driving Behaviour—A Systematic Review
Young people engage in a variety of behaviours that can have an impact on their health and safety, including driving and road accidents, which represent a major health issue today. Emotions, and in particular emotional regulation (ER), interact significantly with key elements of driving behaviour, such as risk perception, decision-making, and attention. We carried out a systematic review considering the presence of an association between emotional (dys)regulation and driving behaviour of young adults (18–25 years). ... Two main findings were found: on the one hand, driving anger, unlike other emotional patterns, emerged as a well-defined cause of impairment among young drivers. ... Expressing one’s emotions adaptively, improving the ability to accept and be aware of negative emotions, and controlling impulsive behaviour could reduce driving risks in young drivers.
RePEc:gam:jsusta:v:16:y:2024:i:8:p:3384-:d:1377820 Save to MyIDEAS