Examination of drivers' cell phone use behavior at intersections by using naturalistic driving data

Xiong, Huimin; Bao, Shan; Sayer, James R.; Kato, Kazuma

doi:10.1016/j.jsr.2015.06.012

Cited by 17 publications

(13 citation statements)

References 7 publications

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“…For example, Christoph et al [33] restricted their study area to some specific intersections to analyze conflicts between drivers and riders. Wang et al [34] studied the occurrence of some incidents on horizontal curves along rural two-lane highways, and Xiong et al [35] analyzed how some drivers used their cell phone at crossings.…”

Section: Strategies and Toolsmentioning

confidence: 99%

GIS Mapping of Driving Behavior Based on Naturalistic Driving Data

Balsa-Barreiro

Valero‐Mora

Berné-Valero

et al. 2019

IJGI

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Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. It is quite common that these studies implement strategies for thinning and/or reducing the data volumes that have been initially collected. Thus, and unfortunately, the great potential of these datasets is significantly constrained to specific situations, events, and contexts. For this, to implement appropriate strategies for the visualization of these data is becoming increasingly necessary, at any scale. Mapping naturalistic driving data with Geographic Information Systems (GIS) allows for a deeper understanding of our driving behavior, achieving a smarter and broader perspective of the whole datasets. GIS mapping allows for many of the existing drawbacks of the traditional methodologies for the analysis of naturalistic driving data to be overcome. In this article, we analyze which are the main assets related to GIS mapping of such data. These assets are dominated by the powerful interface graphics and the great operational capacity of GIS software.Undoubtedly, one of the most interesting topics related to traffic and transportation issues is road safety [16]. This is highly complex and goes beyond the number of deaths and injuries in crashes within a certain period. Road fatalities have a huge impact, not only in terms of the number of casualties, but also in terms of the costs related to caring for the injured people, the disruption of traffic flows just after accidents, and so forth [17]. For this reason, it becomes increasingly important to deploy prevention policies to manage infrastructures and road flows as well as to provide a quick and efficient response in emergencies [18].Most studies analyze road safety by considering the different elements that are involved, such as the vehicle, driver, road, and infrastructure. However, they generally do it by isolating one of these elements separately or assessing the interaction of two elements at most. Some recent studies suggest the growing need for integrating the maximum number of elements that can affect driving performance to ensure a bigger picture of the whole driving scenario. Thus, more light is shed on why a certain incident happens and under what circumstances [19].In the last few years, the naturalistic driving method has gained more relevance. This research method aims to characterize the driving behavior of people in real-world situations. In order to do that, the different elements and factors involved in the driving performance are recorded using a large array of sensors and video cameras inconspicuously installed in the vehicle. This equipment allows data related to the driving performance to be collected at high temporal rates. These kinds of data are collected using massive and blind strategies. They are massive because their aim is to collec...

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Section: Strategies and Toolsmentioning

confidence: 99%

GIS Mapping of Driving Behavior Based on Naturalistic Driving Data

Balsa-Barreiro

Valero‐Mora

Berné-Valero

et al. 2019

IJGI

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“…Studies focusing on the effect of cell phones conversation whilst driving over the past decades have shown that driving performances were greatly degraded [11,12,13,14,15,16,17,18], for example, reaction time to events, lateral and longitudinal vehicle control, and glance behaviour. One of the most often studied performances, as a result of cell phone conversations, is the reaction time.…”

Section: Introductionmentioning

confidence: 99%

The Role of Situation Criticality in Affecting the Effect of Cognitive Load on Drivers’ Brake responses: A Driving Simulator Based Study

Xue

Yan

Zhao

et al. 2020

Journal of Advanced Transportation

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A dramatic increase in talking on the phone whilst driving has been seen over the past decades, which posed a significant safety threat on the whole society consequently. Studies on the topic regarding the effect of phone conversations on drivers’ driving performances have never come to a cease, especially on the studies of drivers’ brake response times. However, few studies focus on the relationship between situation criticality and the effect of cognitive load on drivers’ brake responses. To better understand it, a driving simulator experiment with two braking scenarios corresponding to two levels of situation criticality was conducted in this study. Participants were asked to follow a lead vehicle as they normally did and answer arithmetic problems (simple and complex) in three phone modes (baseline, hands-free, and handheld) in the meantime. Drivers’ brake response times to the lead vehicle under five conditions were collected and fitted in accumulator models, in which visual looming and brake lights onset were included as the sensory cues. Results demonstrated that the previously proposed mechanistically explicit simulation model was able to predict drivers’ brake response times on different levels of cognitive load and the increased effect of cognitive load on drivers’ brake response times in less critical situations was demonstrated in this paper as well.

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“…With the launch of large-scale natural driving projects, such as the Second Strategic Highwasy Research Plan Naturalistic Driving Study (SHRP2 NDS), UTDrive, and Shanghai NDS (SH-NDS), researchers began to conduct mobile phone distraction study based on natural driving data [7,8,10,15,31,32,33,34]. Natural driving research reduces the interference of experimental arrangements on driving behavior and provides the opportunity to observe the actual driving process with unobtrusive high precision data [35].…”

Section: Introductionmentioning

confidence: 99%

Effect of Using Mobile Phones on Driver’s Control Behavior Based on Naturalistic Driving Data

Zhang

Cui

Yang

et al. 2019

IJERPH

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Distracted driving behaviors are closely related to crash risk, with the use of mobile phones during driving being one of the leading causes of accidents. This paper attempts to investigate the impact of cell phone use while driving on drivers’ control behaviors. Given the limitation of driving simulators in an unnatural setting, a sample of 134 cases related to cell phone use during driving were extracted from Shanghai naturalistic driving study data, which provided massive unobtrusive data to observe actual driving process. The process of using mobile phones was categorized into five operations, including dialing, answering, talking and listening, hanging up, and viewing information. Based on the concept of moving time window, the variation of the intensity of control activity, the sensitivity of control operation, and the stability of control state in each operation were analyzed. The empirical results show strong correlation between distracted operations and driving control behavior. The findings contribute to a better understanding of drivers’ natural behavior changes with using mobiles, and can provide useful information for transport safety management.

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Examination of drivers' cell phone use behavior at intersections by using naturalistic driving data

Cited by 17 publications

References 7 publications

GIS Mapping of Driving Behavior Based on Naturalistic Driving Data

GIS Mapping of Driving Behavior Based on Naturalistic Driving Data

The Role of Situation Criticality in Affecting the Effect of Cognitive Load on Drivers’ Brake responses: A Driving Simulator Based Study

Effect of Using Mobile Phones on Driver’s Control Behavior Based on Naturalistic Driving Data

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