Identification of Spatial and Temporal Dilemma Zone at Mid-Block Median Openings: A Gap Acceptance Based Approach

Khan, Tathagatha; Mohapatra, Smruti Sourava

doi:10.1177/03611981221114118

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…The U-turn is a complex process because the U-turning driver must judge the available gap in the opposing stream based on their perception and make a decision whether to proceed with the merging process or wait for the next available gap. The decision also varies for the same driver based on difference in traffic conditions, speed of the traffic in opposing stream, and the psychological character of the driver such as angry, happy, frustrated, and so on ( 15 ). Thus, MBMOs are considered as one of the potential hotspots for traffic congestion and conflict.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Liu et al ( 12 ) carried out a study in the United States of America on a six-lane road and reported conflicting traffic volume as 2.2 times approaching traffic volume in each lane. This finding for the estimation of conflicting traffic volume is not suitable under the heterogeneous traffic conditions found in developing economies, such as India, where different types of vehicles of varying physical and dynamic characteristics (size, age of vehicle, and efficiency of vehicle), use the same road section simultaneously without any physical separation ( 14 , 15 ). Also, driver characteristics, location geometry, and design vary significantly.…”

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confidence: 98%

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Realistic Approach for Capacity Estimation of U-Turns Under Heterogeneous Traffic Condition

Khan,

Vivek,

Mohapatra

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The main purpose of the present study is to estimate U-turn capacity at mid-block median openings (MBMO). Estimation of U-turn capacity requires three key parameters, namely: conflicting traffic volume, critical gap, and follow-up time. Conflicting traffic volume has been estimated by introducing a novel parameter, namely, spatiotemporal conflicting factor. Critical gap has been estimated using five different methods: modified Raff method, Ashworth method, logit model, occupancy time method, and support vector machine technique. The follow-up time has been estimated directly from recorded videos. Subsequently, capacity has been estimated for different types of vehicles along with the U-turning stream capacity. The estimated capacity has been validated using field capacity, using Kyte’s method. Further, a suitable method for the estimation of critical gap in the Indian context has been determined. This study could be used as a guideline for the U-turn capacity estimation for efficient design and management of traffic at MBMO.

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Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 98%

Realistic Approach for Capacity Estimation of U-Turns Under Heterogeneous Traffic Condition

Khan,

Vivek,

Mohapatra

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…A U -turn at a midblock median opening is a complicated maneuver that causes drivers to be unsure whether to accept or reject the available gap in order to prevent a collision with opposing traffic flow. The study by Khan and Mohapatra in 2022 analyzed the dilemma zone for U-turning vehicles at a midblock median opening for different vehicle categories, and they concluded that the dilemma zone would be highly affected by the vehicle size [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

Modeling Behavior of U-Turning Vehicles at the Median Opening Using a Merging Behavior Approach: A Case Study in Bahir Dar City, Ethiopia

Wolelaw

Tessema

Alene

2022

Computational Intelligence and Neuroscience

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Median openings are one of the most commonly used road features, which are mainly used to allow U-turning movement in urban areas, and this study focuses mainly on modeling the behavior of U-turning vehicles at the median opening using a merging behavior approach. The purpose of the study is to estimate and model the critical gap of u-turning vehicles at the median opening under mixed traffic conditions. Under this study, the accepted gap, rejected gap, driver waiting time, merging time, and critical gap are estimated, and the modified Raff’s method and modified INAFOGA method are used for the estimation of a critical gap. However, modified INAFOGA is used for the modeling of critical gaps under mixed traffic conditions. In this study, sixteen median openings were selected in Bahir Dar city, and data were collected using a video recording technique at each selected median opening during the peak hour of the day. The necessary data were extracted using Forevid analysis software tools. Different types of traffic are involved in the mixed traffic, and each vehicle type is categorized according to the Ethiopian Road Authority’s 2013 design guide into seven different classes, such as 2-wheeler, 3-wheeler, passenger car, minibus, small bus and truck, medium bus, and medium truck. Among those traffic types, three vehicle classes (three-wheeler, passenger car, and minibus) were only considered due to the prohibition of U-turning movement for medium and large vehicles. For the modeling of critical gaps, waiting time and conflicting traffic flow are used as independent variables using the regression technique. Driver waiting time and the critical gap were found to be power related to passenger cars and minibuses and exponentially to three-wheelers. Conflicting traffic flow and critical gaps were power related to passenger cars and minibuses and linearly related to three-wheelers.

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“…Studies have analyzed and quantified the pedestrian dilemma zone for the uncontrolled midblock section ( 15 , 16 ). It has been noted that different types of vehicles have an impact on the length and location of the dilemma zone ( 17 ). According to these studies, the speed of the approaching vehicle, vehicle type, pedestrian type, crossing speed, and pedestrian waiting time all affect the length and location of the pedestrian dilemma zone.…”

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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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Identification of Spatial and Temporal Dilemma Zone at Mid-Block Median Openings: A Gap Acceptance Based Approach

Cited by 7 publications

References 25 publications

Realistic Approach for Capacity Estimation of U-Turns Under Heterogeneous Traffic Condition

Realistic Approach for Capacity Estimation of U-Turns Under Heterogeneous Traffic Condition

Modeling Behavior of U-Turning Vehicles at the Median Opening Using a Merging Behavior Approach: A Case Study in Bahir Dar City, Ethiopia

Drivers’ Dilemma at High-Speed Unsignalized Intersections

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