Impact of mobile phone use on car-following behaviour of young drivers

Saifuzzaman, Mohammad; Boyle, Linda Ng; Zheng, Zuduo; Washington, Simon

doi:10.1016/j.aap.2015.05.001

Cited by 123 publications

(74 citation statements)

References 30 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Saifuzzaman et al (55) found that mobile phone use while driving was a significant distraction, especially in young drivers, that impaired driving performance, thus becoming a leading cause of traffic motor vehicle crashes. For example, drivers were more likely to miss traffic signals (stop signs, traffic lights, etc.)…”

Section: Traffic Accidents Adolescence and Risk Factorsmentioning

confidence: 99%

Description of Various Factors Contributing to Traffic Accidents in Youth and Measures Proposed to Alleviate Recurrence

et al. 2017

View full text Add to dashboard Cite

Traffic accidents are the leading cause of hospitalization in adolescence, with the 18–24-year-old age group accounting for 23% of deaths by traffic accidents. Recurrence rate is also high. One in four teenagers will have a relapse within the year following the first accident. Cognitive impairments known in adolescence could cause risky behaviors, defined as repetitive engagement in dangerous situations such as road accidents. Two categories of factors seem to be associated with traffic accidents: (1) factors specific to the traffic environment and (2) “human” factors, which seem to be the most influential. Moreover, the establishment of a stronger relation to high speed driving increases traffic accident risks and can also be intensified by sensation seeking. Other factors such as substance use (alcohol, drugs, and “binge drinking”) are also identified as risk factors. Furthermore, cell phone use while driving and attention deficit disorder with or without hyperactivity also seem to be important risk factors for car accidents. The family environment strongly influences a young person’s driving behavior. Some interventional driving strategies and preventive measures have reduced the risk of traffic accidents among young people, such as the graduated driver licensing program and advertising campaigns. So far, few therapeutic approaches have been implemented. Reason why, we decided to set up an innovative strategy consisting of a therapeutic postaccident group intervention, entitled the ECARR2 protocol, to prevent recurrence among adolescents and young adults identified at risk, taking into account the multiple risk factors.

show abstract

Section: Traffic Accidents Adolescence and Risk Factorsmentioning

confidence: 99%

Description of Various Factors Contributing to Traffic Accidents in Youth and Measures Proposed to Alleviate Recurrence

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The SDLP (cm) was calculated according to the mathematical definition described in Verster and Roth (), larger values indicate poorer lane‐keeping performance with higher risks of lane departure and collisions with other vehicles (McLaughlin, Hankey, & Dingus, ). Recent studies have shown that both parameters were sensitive to reveal poorer performance of a driver engaged in phone conversation when followed a preceding vehicle (He et al., ; Saifuzzaman, Haque, Zheng, & Washington, ).…”

Section: Methodsmentioning

confidence: 99%

Primary insomnia patients’ performances during simulated car following and urban driving in the afternoon

Perrier

Amato

Berthelon³

et al. 2019

Journal of Sleep Research

View full text Add to dashboard Cite

According to epidemiological studies, insomnia is associated with an increase in risk of traffic accidents. Recent investigations revealed that patients with insomnia had driving performance impairment under monotonous conditions. However, it is unclear whether other driving abilities may be impacted by insomnia, especially those needing more attentional resources than those involved in monotonous driving. Other findings revealed that impaired performances are more likely to occur with the increase of cognitive demands. However, such tests did not reflect difficulties in situations of everyday life expressed by patients with insomnia, such as driving in an urban environment with traffic and critical situations. Therefore, behaviour in situations encountered in everyday life has to be explored. The aim of the present study was to assess driving performances of patients with insomnia in daily routine tasks such as urban and car‐following tests. For this purpose, 15 patients with insomnia and 16 good sleepers performed an urban test of driving with original risk scenarios and a car‐following test during the middle afternoon. No significant behavioural difference between patients with insomnia and good sleepers has been found in both the urban test and the car‐following test, showing that patients with insomnia have no impairment in behaviour implicated in daily contexts driving tasks of short period of time performed in the late afternoon. Although our results provide a first step towards knowledge of behavioural performance during daily routine driving tasks in patients with insomnia, future studies are needed using on‐the‐road driving tests and/or different population of patients with insomnia before generalisation.

show abstract

“…The speed is faster of the following vehicle, the more sensitive the acceleration/deceleration is. On this basis, Sheu [7], Yu [8], Liu [9], Tang [10,11], Yu [12], Zhu [13], Zheng [14], Saifuzzaman [15], Davoodi [16] calibrated parameters in the basic stimulation-reaction model from different perspectives to optimize the model. The study of these researchers helps the model greatly reflect the characteristic of car-following in actual traffic stream.…”

Section: Introductionmentioning

confidence: 99%

An extended car-following model considering the influence of bus

Shen

Qiu

et al. 2017

Teh. vjesn.

View full text Add to dashboard Cite

Original scientific paper In order to describe car-following behaviour of traffic flow which is composed of buses and passenger cars on freeway, an extended car-following model is proposed for single lane traffic in this paper. The proposed model discriminates four types of car-bus following combination, car-following-bus, busfollowing-bus, bus-following-car and car-following-car. The four combinations are considered in terms of following distance, following speed and following acceleration/deceleration. The proposed methodology is demonstrated using data collected from the combination of microwave radar detector and roadside laser detector on Xuanwu Avenue in the main urban area of Nanjing. Besides, the field data is divided into two data sets, one used for the training of the model, and the other for evaluation purpose. Gazis model and Edie model, the two most extensively used car-following models, are calibrated against the same training data sets and used as a reference benchmark. Finally, the performance of the model, proposed by this paper, was compared with the two classic models based on the evaluation data sets. The results show that buses have different characteristics and manoeuvrability compared with passenger cars. With the influence of buses in the car-following process, it could lead to uneven distribution of traffic flow on the lanes and become the main reason for traffic highway capacity decline. The model, proposed in this paper, is more accurate and stable when predicting acceleration/deceleration of different vehicles during car-following. It has better superiority to describe the car-following behaviours under the influence of bus on freeway. Keywords: bus; car-following model; model calibration; stimulus-response process; traffic flow Prošireni model kolone vozila koji uzima u obzir utjecaj autobusaIzvorni znanstveni članak Kako bi se opisalo ponašanje prometa kolone vozila, koja se sastoji od autobusa i osobnih automobila na autocesti, u ovom članku je predložen prošireni model kolone vozila za promet u jednoj traci. Predloženi model razlikuje četiri vrste kombinacija kolona automobil-autobus, automobil-nakon-autobusa, autobus-nakon-autobusa, autobus-nakon-automobila i automobil-nakon-automobila. Četiri kombinacije uzimaju u obzir udaljenost, brzinu i ubrzavanje/usporavanje kolone. Predložena metodologija je prikazana uporabom podataka prikupljenih kombinacijom mikrovalnog radarskog detektora i cestovnog laserskog detektora na aveniji Xuanwu u glavnom urbanom području Nanjinga. Osim toga, podaci o terenu podijeljeni su u dva seta podataka, jedan se koristi za uvježbavanje modela, a drugi je za ocjenu. Gazis model i Edie model, dva najčešće korištena modela kolone vozila, kalibrirani su prema istim skupovima podataka za vježbu, a koriste se kao referentna vrijednost. Konačno, izvedba modela, predložena ovim radom, uspoređena je s dva klasična modela na temelju evaluacijskih skupova podataka. Rezultati pokazuju da autobusi imaju različite karakteristike i manevriranost u usporedbi s osobnim ...

show abstract

Impact of mobile phone use on car-following behaviour of young drivers

Cited by 123 publications

References 30 publications

Description of Various Factors Contributing to Traffic Accidents in Youth and Measures Proposed to Alleviate Recurrence

Description of Various Factors Contributing to Traffic Accidents in Youth and Measures Proposed to Alleviate Recurrence

Primary insomnia patients’ performances during simulated car following and urban driving in the afternoon

An extended car-following model considering the influence of bus

Contact Info

Product

Resources

About