The Stochastic and Integrative Prediction Methodology and Modeling for Reliability of Pedestrian Crossing on Roads

Hojjati-Emami, Khashayar; Dhillon, B.S.; Jenab, Kouroush

doi:10.1080/19439962.2012.761316

Cited by 8 publications

(4 citation statements)

References 37 publications

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“…This is a powerful graphical approach to perform failure data analysis. The important advantage of the approach is that it indicates if the given data (Hojjati-Emami et al, 2013b) set whether complete or incomplete belongs to the tried distribution and if so, then it estimates the associated parameters (Nelson, 1969(Nelson, , 1970(Nelson, & 1972Dhillon, 1999).…”

Section: Hazard Plotting Methodsmentioning

confidence: 99%

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The HRA-Based Road Crash Data

Hojjati-Emami

Dhillon

Jenab

2014

International Journal of Strategic Decision Sciences

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Human error has played a critical role in the events precipitating the road accidents. Such accidents can be predicted and prevented by risk assessment, in particular assessing the human contribution to risk. As part of the Human Reliability Assessment (HRA) process, it is usually necessary not only to define what human errors can occur, but how often they will occur. Lack of understanding of the failure distribution characteristics of drivers on roads at any given time is a factor impeding the development of human reliability assessment and prediction of road accidents in order to take best proactive measures. The authors developed the complete investigation methodology for crash data collection. Furthermore, they have experimentally tested the proposed predictive behavioral characteristics of drivers in light of their instantaneous error rate over the course of driving period to assist processing and analysis of data collection as part of risk assessment. The findings of this research can assist road safety authorities to collect the necessary data, to better understand the behavioral characteristics of drivers on roads, to make more accurate risk assessments and finally to come up with right preventive measures.

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Section: Hazard Plotting Methodsmentioning

confidence: 99%

“…The proposed investigation procedure (Fig. 1) has been developed and extracted from an integrative fault tree model as illustrated in Figure 2 derived from the earlier works of authors (Hojjati-Emami et al, 2013b.…”

Section: The Drivers' Failure Data In Road Accidents and Proposed Inv...mentioning

confidence: 99%

The HRA-Based Road Crash Data

Hojjati-Emami

Dhillon

Jenab

2014

International Journal of Strategic Decision Sciences

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“…Knowledge of the variables influencing the incidence of highway crashes can be expanded by taking into account spatial and temporal correlations, according to the review [MB14]. For example, Hojjati-Emami [HEDJ13] presented the causes of vehicle accidents on roads using a fault tree model. Meanwhile, road safety indexes, the incidence of highway crashes, and other relative indexes were proposed to evaluate risk levels.…”

Section: Introductionmentioning

confidence: 99%

A Driving Risk Surrogate and Its Application in Car-Following Scenario at Expressway

Wu¹,

Li²,

Shi³

et al. 2022

Preprint

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Traffic safety is important in reducing death and building a harmonious society. In addition to studies of accident incidences, the perception of driving risk is significant in guiding the implementation of appropriate driving countermeasures. Risk assessment can be conducted in real-time for traffic safety due to the rapid development of communication technology and computing capabilities. This paper aims at the problems of difficult calibration and inconsistent thresholds in the existing risk assessment methods. It proposes a risk assessment model based on the potential field to quantify the driving risk of vehicles. Firstly, virtual energy is proposed as an attribute considering vehicle sizes and velocity. Secondly, the driving risk surrogate(DRS) is proposed based on potential field theory to describe the risk degree of vehicles. Risk factors are quantified by establishing submodels, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. To unify the risk threshold, acceleration for implementation guidance is derived from the risk field strength. Finally, a naturalistic driving dataset in Nanjing, China, is selected, and 3063 pairs of following naturalistic trajectories are screened out. Based on that, the proposed model and other models use for comparisons are calibrated through the improved particle optimization algorithm. Simulations prove that the proposed model performs better than other algorithms in risk perception and response, car-following trajectory, and velocity estimation. In addition, the proposed model exhibits better car-following ability than existing car-following models.

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“…In the post-crash stage, road traffic risk assessments are generally measured on the basis of historical accident data [2,3] and represented as the indexes of road safety or relative accident rates obtained using statistical models such as accident regression analyses [4,5]. For example, Hojjati-Emami et al [6] determined the causes of pedestrian-vehicle accidents on roads using a fault tree model. However, slight collisions are generally not included in traffic accident records, and traffic risk analysis methods that are based on previous accident data have low adaptability and accuracy limitations.…”

Section: Introductionmentioning

confidence: 99%

Modified Driving Safety Field Based on Trajectory Prediction Model for Pedestrian–Vehicle Collision

Zheng

et al. 2019

Sustainability

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Pedestrian–vehicle collision is an important component of traffic accidents. Over the past decades, it has become the focus of academic and industrial research and presents an important challenge. This study proposes a modified Driving Safety Field (DSF) model for pedestrian–vehicle risk assessment at an unsignalized road section, in which predicted positions are considered. A Dynamic Bayesian Network (DBN) model is employed for pedestrian intention inference, and a particle filtering model is conducted to simulate pedestrian motion. Driving data collection was conducted and pedestrian–vehicle scenarios were extracted. The effectiveness of the proposed model was evaluated by Monte Carlo simulations running 1000 times. Results show that the proposed risk assessment approach reduces braking times by 18.73%. Besides this, the average value of TTC−1 (the reciprocal of time-to-collision) and the maximum TTC−1 were decreased by 28.83% and 33.91%, respectively.

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The Stochastic and Integrative Prediction Methodology and Modeling for Reliability of Pedestrian Crossing on Roads

Cited by 8 publications

References 37 publications

The HRA-Based Road Crash Data

The HRA-Based Road Crash Data

A Driving Risk Surrogate and Its Application in Car-Following Scenario at Expressway

Modified Driving Safety Field Based on Trajectory Prediction Model for Pedestrian–Vehicle Collision

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