Influence of major stream composition on critical gap at two-way stop-controlled intersections – a case study

Mohan, Mithun; Chandra, Satish

doi:10.1080/19427867.2018.1494896

Cited by 9 publications

(7 citation statements)

References 11 publications

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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%

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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Section: Literature Reviewmentioning

confidence: 99%

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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“…In contrast, the CTM and OTM are the methods developed in developing countries like India. The CTM is helpful in heterogeneous traffic conditions, and the OTM is useful in both homogeneous and heterogeneous traffic conditions (Mohan and Chandra, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Among the existing methods, the Raff method is the earliest method for estimating the critical gap and is used in many countries because of its simplicity. Many researchers have emphasized that the Maximum Likelihood Method (MLM) is more consistent, produces unbiased estimates of the mean critical gap, and provides the best results (Kyte et al, 1994;Brilon, Koenig and Troutbeck, 1999;Gavulová, 2012;Troutbeck, 2014Troutbeck, , 2016Sangole, 2015, 2016;Mohan and Chandra, 2016). Most of these methods, excluding the CTM and OTM, were incapable and inefficient in producing actual critical gaps for traffic in developing countries (Chandra, Mohan and Gates, 2014;Amin and Maurya, 2015;Chandra, 2016, 2018a).…”

Section: Introductionmentioning

confidence: 99%

“…Driver behaviour, intersection geometry, and vehicular and traffic characteristics influence the operational efficiency of uncontrolled intersections. The critical gaps vary among intersections even when they have similar geometric conditions (Mohan and Chandra, 2020b). This variation is due to the difference in the proportion of large-sized vehicles in conflicting traffic (Mohan and Chandra, 2020b).…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Critical Gap and Capacity at Skewed Uncontrolled Intersections

Arathi¹

2023

European Transport/Trasporti Europei

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Critical gaps and capacity of movements at uncontrolled intersections are influenced by intersection geometry, especially in mixed traffic conditions. However, existing models to compute the capacity of uncontrolled base intersections are only suitable for intersections with 00 to 100 skew angles. This study aims to bridge the gap by evaluating the effect of skew angle on the critical gap and capacity of uncontrolled intersections. The critical gap models are developed for different vehicle types. The capacity of uncontrolled intersections is determined for different skew angles (00 to 270) using simulation and Indo-HCM models. The comparison reveals that the Indo-HCM model over-predicts the capacities. Thus, new capacity models are proposed, and it is observed that the capacity varies as a quadratic function of the skew angle, where the constant indicates base capacity. This study also provides the adjustment factors for Indo-HCM capacity models to deduce the capacity of any skew-angled intersections.

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“…Analysis of unsignalized intersections is often based on the assumption that major street traffic passes through the intersections unhindered. However, an earlier study found that the priority rules are not observed at intersections in India and even the major street traffic is forced to slow down by the minor street vehicles as they approach an intersection 18 . Hence it would be erroneous to use PCE values obtained at mid-block sections for removing the heterogeneity of the major street through traffic at unsignalized intersections.…”

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confidence: 97%

Occupancy Time-Based Passenger Car Equivalents at Unsignalized Intersections in India

Mohan¹,

Chandra²

2018

Current Science

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In developing countries, one of the vital steps involved in analysing the capacity of any transportation facility is the estimation of passenger car equivalents (PCEs) for different types of vehicles to convert the heterogeneous traffic stream into an equivalent stream of passenger cars. This study proposes occupancy time method for the estimation of PCEs for different types of vehicles generally observed at unsignalized intersections in India. PCEs for vehicles executing different movements at unsignalized intersections have been estimated in this study and the dynamic nature of PCEs has also been explored. However, PCEs were found to be statistically similar across different movements and across intersections of different geometry.

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Influence of major stream composition on critical gap at two-way stop-controlled intersections – a case study

Cited by 9 publications

References 11 publications

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

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

Analysis of Critical Gap and Capacity at Skewed Uncontrolled Intersections

Occupancy Time-Based Passenger Car Equivalents at Unsignalized Intersections in India

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