Performance analysis of urban drivers encountering pedestrian

Sheykhfard, Abbas; Haghighi, Farshidreza

doi:10.1016/j.trf.2018.12.019

Cited by 36 publications

(14 citation statements)

References 24 publications

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“…Finally, the comprehensive index weight (τ j ) can be calculated from AS j and EW j using Equation (13). Then, the normalisation can be applied to index layer, which is expressed as Ω ij .…”

Section: Confirmation Of Weight and Scoring Rulementioning

confidence: 99%

“…Video cameras were used to capture the drivers' facial expressions and eye movements to analyse the driving behaviour [12]. Video cameras were also used to record drivers' body movements, such as feet movements, to evaluate the reaction time of the drivers [13]. Independent sensors such as accelerometer [14], Radar sensor [15] and Inertial Measurement Unit (IMU) [16] were also used to obtain driving behaviour data.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car

Zhang

Tian

et al. 2022

Energies

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Evaluation of driving behaviour is helpful for policy development, and for designing infrastructure and an intelligent safety system for a car. This study focused on a quantitative evaluation method of driving behaviour based on the shared-electrical car. The data were obtained from the OBD interface via CAN bus and transferred to a server by 4G network. Eleven types of NDS data were selected as the indexes for driving behaviour evaluation. Kullback–Leibler divergence was calculated to confirm the minimum data quantity and ensure the effectiveness of the analysis. The distribution of the main driving behaviour parameters was compared and the change trend of the parameters was analysed in conjunction with car speed to identify the threshold for recognition of aberrant driving behaviour. The weights of indexes were confirmed by combining the analytic hierarchy process and entropy weight method. The scoring rule was confirmed according to the distribution of the indexes. A score-based evaluation method was proposed and verified by the driving behaviour data collected from randomly chosen drivers.

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Section: Confirmation Of Weight and Scoring Rulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car

Zhang

Tian

et al. 2022

Energies

View full text Add to dashboard Cite

show abstract

“…In another study, a set of drivers' actions to prevent pedestrians from colliding in divided and undivided roads was assessed. Evaluation of 701 conflicts through the NDS of 66 participants showed that drivers performed actions such as decelerating, accelerating, and braking to a complete stop and changing lanes to prevent pedestrians on both roads [25].…”

Section: Problem Importance and Research Goalmentioning

confidence: 99%

Analyzing Drivers’ Mental Patterns Using Q-Methodology

Sheykhfard¹,

Haghighi²,

Soltaninejad³

et al. 2020

JTTs

Self Cite

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The human factor is the most important cause of road accidents. Investigating the drivers' mental patterns can lead to a better understanding of the factors that affect drivers to make a mistake and thus increase the likelihood of an accident. In this study, mental patterns of drivers as a human characteristic are determined through a questionnaire survey. To do this, 166 participants (18 -65 years) were asked to express their opinion on the possible effect of 25 factors on the occurrence of accidents. These factors were selected through the investigation of the accident database during the last three years in different areas of the case study. To analyze the data extracted from the survey, Q-methodology was used. The results of the factor analysis showed that there are 5 mental patterns among the participants. Based on the driver's opinion, human factors and road conditions were the most and least influential accident-generating items, respectively. The most significant reason for accidents determined by drivers was human errors including 1) unauthorized overtaking, 2) unauthorized speed, 3) driver distractions (such as cell phone), and 4) driver physical disability (such as visual impairment). Moreover, the failure of the vehicle was mostly reported as another influential contributor to accidents. It is worth mentioning that the results of this study can be used to minimize accidents resulted from drivers' behavioral errors by suggesting strategies for enhancing their performance through new manuals which is a step towards a safer road.

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“…Yang and Lam [55] established a binary logistic model to analyze drivers' decision-making behavior of paying for intelligent parking information system. Sheykhfard and Haghighi [56] studied the performance of drivers during interaction (whether the driver yields or fails to yield) with pedestrians based on a binary logistic model. In this study, the binary logistic regression is applied to model the individual choice between OCS and TTS.…”

Section: Mode Choice Modelmentioning

confidence: 99%

How Do Passengers with Different Using Frequencies Choose between Traditional Taxi Service and Online Car-Hailing Service? A Case Study of Nanjing, China

Wang

Zhang

et al. 2019

Sustainability

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The rapid development of online car-hailing services (OCSs) has a huge impact on traditional taxi service (TTS) and is triggering a revolution in the taxi industry. Due to the differences in age, monthly income level, etc., travelers’ using frequencies of taxi service are different. It is necessary for online car-hailing platforms and traditional taxi companies to know the choice behavior of different types of passengers to enhance competitiveness. Based on the survey data of taxi passengers in Nanjing, China, the passengers are characterized by using frequency per week as infrequent passengers, moderately frequent passengers, and frequent passengers. The group characteristics and the differences among groups are analyzed. Further, three binary logit models are applied to analyze the taxi service choice behavior of different groups. The model results show the significant factors vary among three types of passengers. The result indicates that the impacts of safety level improvement, comfort level improvement and travel cost reduction for OCS on passengers’ choice behavior are higher than that of safety level decreasing, comfort level decreasing, and travel cost increasing. Moderately frequent passengers are more sensitive to comfort level than travel cost. The conclusions contribute to both the OCS and TTS business strategies. The results also provide insights into taxi industry management for governments.

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Performance analysis of urban drivers encountering pedestrian

Cited by 36 publications

References 24 publications

Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car

Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car

Analyzing Drivers’ Mental Patterns Using Q-Methodology

How Do Passengers with Different Using Frequencies Choose between Traditional Taxi Service and Online Car-Hailing Service? A Case Study of Nanjing, China

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