Investigating and modeling the influence of PET-types on crossing conflicts at urban unsignalized intersections in India

Paul, Aninda Bijoy; Gore, Ninad; Arkatkar, Shriniwas; Joshi, Gaurang

doi:10.1080/17457300.2022.2147194

Cited by 12 publications

(3 citation statements)

References 35 publications

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“…The macroscopic traffic variables can be collected using roadside detectors such as loop detectors, radar sensors, Bluetooth/Wi-Fi sensors, and other Intelligent Transportation System (ITS)-based technologies. The developed machine learning model can be employed to predict TSC (operational risk) in real time using the macroscopic traffic flow variables and, therefore, facilitate real-time traffic safety monitoring ( 29 , 40 ). The developed machine learning model can also facilitate identifying critical road segments and periods.…”

Section: Practical Implications Of the Studymentioning

confidence: 99%

“…Two approaches, (a) aggregated approach and (b) disaggregated approach, are most famous for traffic conflict studies. The aggregated approach relates the characteristics of conflicts like conflict frequency (25)(26)(27)(28)(29) or duration of a conflict (30), collision risk (31)(32)(33)(34), conflict rate (35), and conflict severity (36) to traffic flow characteristics. In the disaggregated approach, traffic conflict (binary variable, 1 if conflict, and 0 if non-conflict) is related to vehicle kinematics and traffic dynamics (37)(38)(39)(40)(41)(42)(43)(44).…”

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confidence: 99%

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Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

Patel

Gore

Easa

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The present study proposed a real-time traffic safety evaluation framework using macroscopic flow variables. To this end, open-access extended vehicle trajectories were employed. Rear-end traffic conflicts and macroscopic traffic flow variables were derived from the trajectory data and were integrated for real-time safety evaluation. The Proportion of Stopping distance ( PSD) accounts for all types of interactions (both safe and unsafe) in the traffic stream; therefore, the same was adopted to analyze the rear-end traffic conflicts. A macroscopic indicator termed “time spent in conflict ( TSC)” was derived to evaluate the rear-end traffic conflicts. Machine learning models, namely, Random Forest (RF), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGB), were employed to predict TSCs using macroscopic traffic flow variables. The results revealed that the TSC computed based on PSD exhibits a reliable and explainable relationship with the macroscopic traffic flow variables. TSC computed based on PSD revealed that intermediately congested traffic flow conditions are critical in traffic safety and can be attributed to complex traffic phenomena such as traffic hysteresis, traffic oscillations, and increased speed variance. Moreover, a stable relation between traffic safety and traffic flow was suggested for varying threshold values. Among different machine learning models, the RF model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. TSC quantifies the safety status of a given traffic flow condition, where a higher value of TSC for a particular traffic flow condition indicates that vehicles prevail in the conflicting scenario for a longer time and, therefore, reflect higher operational risk. The developed machine learning model can be employed to predict TSC (operational risk) in real time using the macroscopic traffic flow variables and, therefore, facilitate traffic safety monitoring.

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Section: Practical Implications Of the Studymentioning

confidence: 99%

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confidence: 99%

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

Patel

Gore

Easa

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…In India and other Asian-Pacific countries, the priority rules are not followed because of the ignorance and impatience of drivers. Intersections therefore operate at high risk, where drivers from the minor and major streams perceive equal priority, and as a result, they force their way into the intersection area by accepting and rolling over smaller gaps ( 3 ). With such aggressive driving behavior, the naturally available gap in the traffic flow is altered, which increases the risk substantially.…”

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confidence: 99%

Drivers’ Dilemma at High-Speed Unsignalized Intersections

Bhatt

Gore

Shah

et al. 2023

Transportation Research Record: Journal of the Transportation R

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At unsignalized intersections, drivers typically reject smaller gaps and accept larger gaps in traffic. However, drivers experience a dilemma or confusion over a wide range of gaps, since incorrect decisions by them can lead to crashes. The present study quantifies the drivers’ dilemma at high-speed unsignalized intersections. Traffic data on gap size (temporal and spatial), driver’s decision (acceptance or rejection), the waiting time of the offending vehicle, the offending and conflicting vehicle types, and the speed of the conflicting vehicle are extracted from recorded video. The decisions by drivers to accept or reject gaps are modeled as a function of the gap size, the waiting time of the offending vehicle, and the speed of the conflicting vehicle using binary logit regression. The results reveal that the probability of rejection decreases as the gap size and waiting time of the subject vehicle increases. On the contrary, as the speed of the conflicting vehicle increases, the probability of rejection increases. The length and location of the dilemma zone were investigated using vehicle type, right-turning movement, and the speed of the conflicting vehicle, and a significant effect was noted. Moreover, the length and location of the dilemma zone are analytically quantified based on drivers’ compliance with a driver assistance system (DAS). The length of the dilemma decreases as the proportion of drivers’ compliance with the DAS increases. Further, the location of the dilemma zone shifts upstream of the intersection with an increase in drivers’ compliance with the DAS.

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Bridging conventional and proactive approaches for road safety analytic modeling and future perspectives

Singh,

Das,

Ghosh

2024

Innov. Infrastruct. Solut.

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Investigating and modeling the influence of PET-types on crossing conflicts at urban unsignalized intersections in India

Cited by 12 publications

References 35 publications

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

Drivers’ Dilemma at High-Speed Unsignalized Intersections

Bridging conventional and proactive approaches for road safety analytic modeling and future perspectives

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