Young females at risk while driving with a small child

Maasalo, Ida; Lehtonen, Esko; Summala, Heikki

doi:10.1016/j.aap.2017.09.012

Cited by 11 publications

(14 citation statements)

References 39 publications

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“…In short, the network of driving behaviors can be seen as being in two qualitatively different states distinguished by the connection strengths between the behaviors. As another example, it has been shown that young mothers have an elevated crash risk when driving with an infant passenger compared to driving alone (Maasalo, Lehtonen & Summala, 2017); the dynamics of their behavior in these contexts are likely to differ.…”

Section: Discussionmentioning

confidence: 99%

Network models of driver behavior

Mattsson

2019

PeerJ

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The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental/theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.

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Section: Discussionmentioning

confidence: 99%

Network models of driver behavior

Mattsson

2019

PeerJ

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“…In short, the network of driving behaviors can be seen as being in two qualitatively different states distinguished by the connection strengths between the behaviors. As another example, it has been shown that young mothers have an elevated crash risk when driving with an infant passenger compared to driving alone 58 ; the dynamics of their behavior in these contexts are likely to differ.…”

Section: Discussionmentioning

confidence: 99%

Peer Review #1 of "Network models of driver behavior (v0.2)"

2019

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The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation-and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental / theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.

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“…Current studies have revealed several findings concerning this area of study. Among social factors that influence driving behaviors, child occupancy is a crucial factor, especially for young females with a child ( 19 ). Children’s behavior can lead to driver distraction and sometimes can increase the potential of crashes ( 20 , 21 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Probing into Driver Speeding Patterns and Their Influence on Child Occupancy in Urban Areas

Das

Kong

Wei

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Many studies have investigated how child occupants lead to distraction-associated crashes. However, not many studies have focused on how the presence of child passengers influences drivers’ speeding behavior. This study examines how the presence of child passengers can affect driving behavior considering sociodemographic characteristics such as driver’s age, gender, ethnicity, employment, and driving aggressiveness. The current study used resourceful, open-source data based on urban areas in Texas to identify the key patterns of speeding behavior and associated safety surrogates in the presence or absence of child occupants. A unique data analysis procedure known as Taxicab Correspondence Analysis (TCA) was used to analyze the data. The results indicate that speeding patterns are affected by child occupancy during a trip. The results find that regardless of the presence of child passengers, female and unemployed drivers are more likely to drive defensively and male and employed drivers are more likely to drive aggressively. The findings also indicate that drivers from the young age group (20–29 years old) are strongly associated with speeding trips without child occupancy. However, young parent drivers from this age group are strongly associated with non-speeding trips and driving defensively. The study also finds that the impact of child occupancy on driving behaviors varies across different ethnicities. These findings will benefit transportation agencies in identifying aggressive driver groups and developing countermeasures to mitigate speeding behaviors, especially for trips with child occupancy.

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Young females at risk while driving with a small child

Cited by 11 publications

References 39 publications

Network models of driver behavior

Network models of driver behavior

Peer Review #1 of "Network models of driver behavior (v0.2)"

Probing into Driver Speeding Patterns and Their Influence on Child Occupancy in Urban Areas

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