Critical Gap Estimation and Its Implication on Capacity and Safety of High-Speed Un-Signalised T-Intersection under Heterogeneous Traffic Conditions

Bhatt, Khushbu; Gore, Ninad; Shah, Jiten

doi:10.26552/com.c.2022.4.d215-d228

Cited by 4 publications

(2 citation statements)

References 28 publications

(52 reference statements)

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“…The reduction in risk with an increase in drivers’ compliance with the DAS can be corroborated by the increase in the value of the critical gap increase. A higher critical gap value indicates lower risk ( 15 , 32–35 ). Further, a significant reduction in the length of the dilemma zone can be noted as the drivers’ compliance with the DAS increases.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…The following section deals with the estimation of the critical gap, using the Binary Logit Model (BLM) and the evaluation of POR using the critical gap value [22].…”

Section: A) Gap Data Extractionmentioning

confidence: 99%

Reckoning Level of Risk and Severity (LORS) Based on the Gap Acceptance at a Three-Legged Uncontrolled Intersection of a Rural Highway

Bhatt¹,

Shah²

2023

Communications

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Traffic safety assessment is an essential part of traffic planning and engineering. At uncontrolled intersections, the severity of crashes has increased manifolds. This is due to the fact of not following the priority rule during the right-turning movements. In conventional methods, past accident records were used to analyze traffic safety. However, the drawbacks of the traditional method to estimate the severity require a lot of accident data for analysis to draw any conclusion and suggest safety measures. The accuracy of the record of accidents and details is highly uncertain. Therefore, surrogate safety measures are being used to analyze the safety. In the present paper, the gap acceptance time (GT) is used to examine the risk and the speed and gap values define the level of risk and severity (LORS). Moreover, using the value of the critical gap, the probability of risk is estimated. Further, the level of risk and severity is computed based on the gap and speed data of vehicles at an uncontrolled intersection. Hence, with the speed and gap data, the level of risk and severity is classified as low risk, high risk and low severe, and high severe. The methodology implemented is validated by using the actual accident data.

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