The fitness-to-drive of shift-work taxi drivers with obstructive sleep apnea: An investigation of self-reported driver behavior and skill

Zhang, Weihua; Zhang, Xuxin; Feng, Zhongxiang; Liu, Jing; Zhou, Muxiong; Wang, Kun

doi:10.1016/j.trf.2017.12.004

Cited by 30 publications

(19 citation statements)

References 37 publications

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“…In the first instance, the first shift ranges from 4:00 a.m. to 3:00 p.m. and the second shift ranges from 3:00 p.m. to 0:00 a.m. In the second instance, the first shift ranges from 2:00 a.m. to 12:00 a.m. and the second shift ranges from 2:00 p.m. to 0:00 a.m. rush hours [10]. Based on the rule of travel activities of citizens, the two-shift operation mode of taxi drivers may contain two possibilities because of the rest schedules, as shown in Figure 2.…”

Section: Schedule Of Taxi-driver Shift Activitiesmentioning

confidence: 95%

“…Taking Wuhan city as a case, a full operation cycle for a taxi takes 24 h and runs on a two-shift operation mode. Most taxi drivers work for 10 h per day, and they will choose to rest during non-rush hours [10]. Based on the rule of travel activities of citizens, the two-shift operation mode of taxi drivers may contain two possibilities because of the rest schedules, as shown in Figure 2.…”

Section: Schedule Of Taxi-driver Shift Activitiesmentioning

confidence: 99%

“…Most of the existing research for taxi-driver shift events intends to improve the taxi service performance by building models to explicitly consider taxi-driver shift schedule. For instance, researchers claimed that taxi drivers worked on a shift basis, with eight hours per shift, and most of the taxis operate on two or three shifts by more than one driver every day [9,10]. The taxi-driver shift schedule was usually used to divide the operation time of a taxi in a day into different shifts.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data

Cheng

Yang

Tang

et al. 2020

IJGI

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In taxi management, taxi-driver shift behaviors play a key role in arranging the operation of taxis, which affect the balance between the demand and supply of taxis and the parking spaces. At the same time, these behaviors influence the daily travel of citizens. An analysis of the distribution of taxi-driver shifts, therefore, contributes to transportation management. Compared to the previous research using the real shift records, this study focuses on the spatiotemporal analysis of taxi-driver shifts using big trace data. A two-step strategy is proposed to automatically identify taxi-driver shifts from big trace data without the information of drivers’ identities. The first step is to pick out the frequent spatiotemporal sequential patterns from all parking events based on the spatiotemporal sequence analysis. The second step is to construct a Gaussian mixture model based on prior knowledge for further identifying taxi-driver shifts from all frequent spatiotemporal sequential patterns. The spatiotemporal distribution of taxi-driver shifts is analyzed based on two indicators, namely regional taxi coverage intensity and taxi density. Taking the city of Wuhan as an example, the experimental results show that the identification precision and recall rate of taxi-driver shift events based on the proposed method can achieve about 95% and 90%, respectively, by using big taxi trace data. The occurrence time of taxi-driver shifts in Wuhan mainly has two high peak periods: 1:00 a.m. to 4:00 a.m. and 4:00 p.m. to 5:00 p.m. Although taxi-driver shift behaviors are prohibited during the evening peak hour based on the regulation issued by Wuhan traffic administration, experimental results show that there are still some drivers in violation of this regulation. By analyzing the spatial distribution of taxi-driver shifts, we find that most taxi-driver shifts distribute in central urban areas such as Wuchang and Jianghan district.

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Section: Schedule Of Taxi-driver Shift Activitiesmentioning

confidence: 95%

Section: Schedule Of Taxi-driver Shift Activitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data

Cheng

Yang

Tang

et al. 2020

IJGI

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“…e questionnaire included questions on the basic statistical information of the drivers, such as their gender, age, driving experience, annual average mileage, number of accidents, penalty points and working hours. In addition, to assess the drivers' driving behaviors and driving skills, the Driving Behavior Questionnaire (DBQ) (Cronbach's α = 0.852) and the Driving Skill Inventory (DSI) (Cronbach's α = 0.750) in Zhang et al [41], which were translated and revised, were selected, as shown in Tables 2 and 3, respectively. e DBQ contains 17 items, which are divided into four di erent dimensions: aggression (α = 0.763), violations (α = 0.731), lack of concentration (α = 0.669) and memory lapses (α = 0.644).…”

Section: Methodsmentioning

confidence: 99%

“…In the process of completing the questionnaire, the drivers could ask questions at any time if there was something that they did not understand, and experimenters provided clari cation. e questionnaire data on the taxi drivers' driving behaviors and driving skills come from the study by Zhang et al [41] on taxi drivers. All participants were randomly sampled from three dining centers for taxi drivers (where taxi drivers can stop to rest and have lunch or dinner) in Hefei, China.…”

Section: Methodsmentioning

confidence: 99%

A Study on the Differences in Driving Skills of Chinese Bus and Taxi Drivers

Zhang

et al. 2019

Journal of Advanced Transportation

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Professional drivers constitute an important group of drivers who shoulder the responsibility of safely transporting passengers and cargo. Bus drivers and taxi drivers are an important part of the urban public transport system, and their driving safety affects road traffic safety. Therefore, the purpose of this paper is to explore the differences between bus drivers and taxi drivers in their driving behaviors and driving skills and to predict their traffic accident involvement based on these behaviors and skills. We conducted a field survey of 274 bus drivers and 178 taxi drivers in Hefei, China. The results revealed significant differences between bus drivers and taxi drivers in terms of violations, lack of concentration and technical driving skills. Aggression and violations had significant predictive effects on bus drivers’ traffic accident involvement, and memory lapses and a lack of safety consciousness had significant predictive effects on taxi drivers’ accident involvement.

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Understanding Drivers’ Physiological Responses in Different Road Conditions

Mostowfi

Kim

2022

HCI in Mobility, Transport, and Automotive Systems

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The fitness-to-drive of shift-work taxi drivers with obstructive sleep apnea: An investigation of self-reported driver behavior and skill

Cited by 30 publications

References 37 publications

Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data

Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data

A Study on the Differences in Driving Skills of Chinese Bus and Taxi Drivers

Understanding Drivers’ Physiological Responses in Different Road Conditions

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