Estimation of Critical Gap for through Movement at Four Leg Uncontrolled Intersection

Maurya, Akhilesh Kumar; Amin, Harsh J.; Kumar, Arvind

doi:10.1016/j.trpro.2016.11.076

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…the right turn as compared to the vehicles with a lesser area [23]. Whereas for the crossing movements, major to minor turning vehicles accept the larger gap compared to the minor to major turning vehicles.…”

Section: Critical Gap Estimationmentioning

confidence: 92%

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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Section: Critical Gap Estimationmentioning

confidence: 92%

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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“…On the other hand, the probabilistic method takes into account the inconsistency and heterogeneity in the gap acceptance behavior of drivers in the minor stream and treats the critical gap as a random variable. There are different methods of calculating the critical gap in the literature, which includes Raff’s method, the clearing time method, the binary probit model, the maximum likelihood method, and so forth, as well as machine learning methods, such as the decision tree (DT), random forest (RF), support vector machine (SVM), and ANN methods ( 13 – 27 ). For example, Sangole and Patil ( 21 ) applied an adaptive neuro-fuzzy interface system (ANFIS) to model drivers’ gap acceptance behavior at a limited priority T-intersection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, Sangole and Patil ( 21 ) applied an adaptive neuro-fuzzy interface system (ANFIS) to model drivers’ gap acceptance behavior at a limited priority T-intersection. It should be noted that the estimated critical gap value may vary for each of these estimation approaches ( 22 – 25 ). For instance, Ashalatha and Chandra ( 5 ) proposed a new method to estimate the critical gap, known as the clearing time method, which is suitable for mixed traffic conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Dutta and Ahmed ( 27 ) concluded that the estimation of the critical gap at a three-legged unsignalized intersection obtained better results by the logit method than the clearing time method. In another study conducted by Maurya et al ( 25 ), 10 different methods for calculating the critical gap were compared, including Raff’s method, the logit model, the acceptance curve, and the clearing time approach. The results indicated that the most significant difference was between Raff’s method (2.65 s) and the clearing time method (5.0 s) for passenger cars.…”

Section: Literature Reviewmentioning

confidence: 99%

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Implementing Artificial Neural Network-Based Gap Acceptance Models in the Simulation Model of a Traffic Circle in SUMO

Bagheri

Bartin

Özbay

2023

Transportation Research Record: Journal of the Transportation R

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The impact of various operational and design alternatives at roundabouts and traffic circles can be evaluated using microscopic simulation tools. Most microscopic simulation software utilizes default underlying models for this purpose, which may not be generalized to specific facilities. Since the effectiveness of traffic operations at traffic circles and roundabouts is highly affected by the gap rejection–acceptance behavior of drivers, it is essential to accurately model drivers’ gap acceptance behavior using location-specific data. The objective of this paper was to evaluate the feasibility of implementing an artificial neural network (ANN)-based gap acceptance model in SUMO, using its application programming interface. A traffic circle in New Jersey was chosen as a case study. Separate ANN models for one stop-controlled and two yield-controlled intersections were trained based on the collected ground truth data. The output of the ANN-based model was then compared with that of the SUMO model, which was calibrated by modifying the default gap acceptance parameters to match the field data. Based on the results of the analyses it was concluded that the advantage of the ANN-based model lies not only in the accuracy of the selected output variables in comparison to the observed field values, but also in the realistic vehicle crossings at the uncontrolled intersections in the simulation model.

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“…Based on the data used, different methods frequently perform differently ( 17 – 19 ). For example, a previous NCHRP study ( 20 ) applied both the logit and Raff methods and showed that the results of the Raff method were approximately 0.2 s shorter than those of the logit method.…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

Hao

Gibbons

et al. 2021

Transportation Research Record

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Even though drivers disregarding a stop sign is widely considered a major contributing factor for crashes at unsignalized intersections, an equally important problem that leads to severe crashes at such locations is misjudgment of gaps. This paper presents the results of an effort to fully understand gap acceptance behavior at unsignalized intersections using SHPR2 Naturalistic Driving Study data. The paper focuses on the findings of two research activities: the identification of critical gaps for common traffic/roadway scenarios at unsignalized intersections, and the investigation of significant factors affecting driver gap acceptance behaviors at such intersections. The study used multiple statistical and machine learning methods, allowing a comprehensive understanding of gap acceptance behavior while demonstrating the advantages of each method. Overall, the study showed an average critical gap of 5.25 s for right-turn and 6.19 s for left-turn movements. Although a variety of factors affected gap acceptance behaviors, gap size, wait time, major-road traffic volume, and how frequently the driver drives annually were examples of the most significant.

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Estimation of Critical Gap for through Movement at Four Leg Uncontrolled Intersection

Cited by 14 publications

References 9 publications

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

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

Implementing Artificial Neural Network-Based Gap Acceptance Models in the Simulation Model of a Traffic Circle in SUMO

Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

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