Modeling anger and aggressive driving behavior in a dynamic choice–latent variable model

Danaf, Mazen; Abou-Zeid, Maya; Kaysi, Isam

doi:10.1016/j.aap.2014.11.012

Cited by 56 publications

(32 citation statements)

References 24 publications

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“…4, our driver gets angry early in the journey because of slow moving traffic (1). Their heart rate is lowest when speed is high and the road is clear (2). It then increases slightly, but still within the normal range, when they return to the city and speed decreases (3).…”

Section: Preliminary Data Analysismentioning

confidence: 99%

“…1 depicts the sensor hardware that has been used for the study. As it can be seen in this figure, our hardware configuration consists of two mobile Shimmer3™ sensors 1 that collect ECG signals, via a five-lead electrode (1), and acceleration via an accelerometer (2). Photographs have been captured using a Samsung™ Galaxy S5/S6 smartphone (3).…”

Section: Case Studymentioning

confidence: 99%

“…speeding and tailgating) [1,3]. Driver aggression is a common phenomenon on the road, with 90% of drivers in the United States having been involved in at least one incident of aggressive driving [1]; furthermore angry drivers are twice as likely to be involved in accidents [2,4]. Driver aggression is a risk to health as well as traffic safety.…”

Section: Introductionmentioning

confidence: 99%

“…This daily occurrence can be associated with high levels of anger, which can be significantly correlated to risky/aggressive driving behavior [1,2]. Driver aggression can range from extreme acts of violence (such as assault) to arguments, confrontations and behavior that deliberately endangers others (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A mobile lifelogging platform to measure anxiety and anger during real-life driving

Dobbins

Fairclough

2017

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Abstract-The experience of negative emotions in everyday life, such as anger and anxiety, can have adverse effects on longterm cardiovascular health. However, objective measurements provided by mobile technology can promote insight into this psychobiological process and promote self-awareness and adaptive coping. It is postulated that the creation of a mobile lifelogging platform can support this approach by continuously recording personal data via mobile/wearable devices and processing this information to measure physiological correlates of negative emotions. This paper describes the development of a mobile lifelogging system that measures anxiety and anger during real-life driving. A number of data streams have been incorporated in the platform, including cardiovascular data, speed of the vehicle and first-person photographs of the environment. In addition, thirteen participants completed five days of data collection during daily commuter journeys to test the system. The design of the system hardware and associated data streams are described in the current paper, along with the results of preliminary data analysis.

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Section: Preliminary Data Analysismentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A mobile lifelogging platform to measure anxiety and anger during real-life driving

Dobbins

Fairclough

2017

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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show abstract

“…Then, the findings are illustrated by probabilistic models. Similarly Danaf et al [6] use a simulator environment to collect data about driving behaviour and expresses anger (or aggressiveness) as a dynamic variable. Hamdar et al [7] define and develop a quantitative aggressiveness propensity index in order to model driving behaviour by testing its proposal with a driving simulator.…”

Section: Related Workmentioning

confidence: 99%

Driver aggressiveness detection via multisensory data fusion

Kumtepe

Akar

Yüncü³

2016

J Image Video Proc.

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Detection of driver aggressiveness is a significant method in terms of safe driving. Every year, a vast number of traffic accidents occur due to aggressive driving behaviour. These traffic accidents cause fatalities, severe disorders and huge economical cost. Therefore, detection of driver aggressiveness could help in reducing the number of traffic accidents by warning related authorities to take necessary precautions. In this work, a novel method is introduced in order to detect driver aggressiveness on vehicle. The proposed method is based on the fusion of visual and sensor features to characterize related driving session and to decide whether the session involves aggressive driving behaviour. Visual information is used to detect road lines and vehicle images, whereas sensor information provides data such as vehicle speed and engine speed. Both information is used to obtain feature vectors which represent a driving session. These feature vectors are obtained by modelling time series data by Gaussian distributions. An SVM classifier is utilized to classify the feature vectors in order for aggressiveness decision. The proposed system is tested by real traffic data, and it achieved an aggressive driving detection rate of 93.1 %.

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Identification of aggressive driving behavior of online car‐hailing drivers based on association classification

Wu,

Chen,

et al. 2023

Hum Ftrs & Erg Mfg Svc

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With the rapid development of online car‐hailing, the related crashes have become a key issue with public concern. Identifying and predicting aggressive driving behaviors is critical to reduce traffic crashes. In this study, we propose a method to recognize aggressive driving behavior based on association classification, with multisource features being employed, including driver emotion, vehicle kinematic characteristics, and road environment. The model performs best in a 10‐fold cross‐test when the minimum support and minimum confidence are set as 0.01 and 0.8, respectively. Besides, we also compare the performance of aggressive driving behavior recognition classifiers constructed using association classification with other rule‐based classification methods, including ID3, C4.5, CART, and Random Forest. The results show that association classification performs better than other classification competitors. Thirty‐six if–then rules generated by the association classification are used to analyze the influencing factors and associated mechanisms of aggressive driving behavior. It is found that aggressive driving behavior is highly correlated with driver anger and disgust emotions. Aggressive driving behavior is more likely to occur when no passengers are in the car than the case with passengers. Driver entertainment behavior and passenger interference also affect driving behavior. Moreover, drivers are prone to aggressive driving when making a U‐turn. This research not only proposed a new identification method for aggressive driving behavior but also provided a comprehensive understanding of the associated influencing factors which thus benefit the further research and development of safety assistance driving devices.

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Modeling anger and aggressive driving behavior in a dynamic choice–latent variable model

Cited by 56 publications

References 24 publications

A mobile lifelogging platform to measure anxiety and anger during real-life driving

A mobile lifelogging platform to measure anxiety and anger during real-life driving

Driver aggressiveness detection via multisensory data fusion

Identification of aggressive driving behavior of online car‐hailing drivers based on association classification

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