Gender and age‐related differences in the perception of in‐vehicle mobile phone usage among Portuguese drivers

Ferreira, Ana; Piccinini, Giulio Bianchi; Rôla, Susana; Simões, Anabela

doi:10.1049/iet-its.2012.0149

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…Moreover, senior drivers increased crash odds when engaged in cell phone dialing hand-held, texting, holding or browsing. One of the potential reasons could be senior drivers perceived a higher level of hazard for most mobile phone-related tasks when compared to the young and the middle-aged drivers (Ferreira et al, 2013). Generally, they need more time to text by hand, thus, they made longer internal glances which degraded the control of the vehicle to a greater extent for senior drivers (Owens et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

An investigation on the link between driver demographic characteristics and distracted driving by using the SHRP 2 naturalistic driving data

Cao

Zhang

Song

et al. 2020

JICV

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Purpose The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages. Design/methodology/approach A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research. The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage. The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics (age, driving experience or their combination) and the crash risk regarding cell phone engagements, as well as the correlation among the likelihood of the cell phone engagement during the driving, multiple driver demographic characteristics (gender, age and driving experience) and environment conditions. Findings Senior drivers face an extremely high crash risk when distracted by cell phone during driving, but they are not involved in crashes at a large scale. On the contrary, cell phone usages account for a far larger percentage of total crashes for young drivers. Similarly, experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving, and cell phone engagements are attributed to a lower percentage of total crashes for them. Furthermore, experienced, senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving. Originality/value The results provide support to guide countermeasures and vehicle design.

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Section: Discussionmentioning

confidence: 99%

An investigation on the link between driver demographic characteristics and distracted driving by using the SHRP 2 naturalistic driving data

Cao

Zhang

Song

et al. 2020

JICV

View full text Add to dashboard Cite

show abstract

“…[8] apply the theory of planned behavior to model the driver behavior, and develop a driver model that accounts for various human aspects such as driving experiences, emotions, age, gender etc. [9] and [10] apply control theory and Markov Decision Process (MDP) to model human behaviors specifically for the navigation process in a single lane. To extend the analysis to multi lane junctions, Hidden Markov Models are proposed to model human behaviors in [11].…”

Section: B Human Behavior-based Modelsmentioning

confidence: 99%

Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural Network

Rao,

Sundaram

2021

Preprint

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Prognostication of vehicle trajectories in unknown environments is intrinsically a challenging and difficult problem to solve. The behavior of such vehicles is highly influenced by surrounding traffic, road conditions, and rogue participants present in the environment. Moreover, the presence of pedestrians, traffic lights, stop signs, etc., makes it much harder to infer the behavior of various traffic agents. This paper attempts to solve the problem of spatio temporal look ahead trajectory prediction using a novel recurrent neural network called the Memory Neuron Network. The Memory Neuron Network (MNN) attempts to capture the input-output relationship between the past positions and the future positions of the traffic agents. The proposed model is computationally less intensive and has a simple architecture as compared to other deep learning models that utilize LSTMs and GRUs. It is then evaluated on the publicly available NGSIM dataset and its performance is compared with several state of art algorithms. Additionally, the performance is also evaluated on a custom synthetic dataset generated from the CARLA simulator. It is seen that the proposed model outperforms the existing state of art algorithms. Finally, the model is integrated with the CARLA simulator to test its robustness in real time traffic scenarios.

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“…Quantity of life 1) Accidents: It is becoming common that people are using their smart phones while driving. Despite many warnings and strict driving policies by the government in many countries it is difficult to control the use of smart phones while driving [13]. This distraction during driving can be dangerous because the driver can get into accident and put his life or other people' lives in danger [14].…”

Section: ) Limited Attention Spanmentioning

confidence: 99%

A Controlled Environment Model for Dealing with Smart Phone Addiction

Uddin¹,

Baig²,

Minhas³

2018

ijacsa

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Smart phones are commonly used in most parts of the world and it is difficult to find a society that is not affected by the smart phone culture. But the usage of smart phone is crossing the limit of being used as a facility towards high level of abnormal dependency on the phone. This dependency can reach to the point where we have no longer control on the over-use and hence the negative impacts it can cause to our lives. The worst situation is that people do not even consider that this dependency is actually a type of addiction and we need to find some solutions to deal with it. In this research paper, we identify symptoms that show the existence of smart phone addiction and demonstrate that this addiction has an effect on the quality and even quantity of people' lives and it can ultimately affect the whole society. We propose solutions to deal with smart phone addiction and propose the design of a smart phone application to reduce the level of abnormal dependency on smart phones.

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Gender and age‐related differences in the perception of in‐vehicle mobile phone usage among Portuguese drivers

Cited by 9 publications

References 18 publications

An investigation on the link between driver demographic characteristics and distracted driving by using the SHRP 2 naturalistic driving data

An investigation on the link between driver demographic characteristics and distracted driving by using the SHRP 2 naturalistic driving data

Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural Network

A Controlled Environment Model for Dealing with Smart Phone Addiction

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