Estimation of U-Turn Capacity at Median Openings

Mohapatra, Smruti Sourava; Dey, Partha Pratim

doi:10.1061/jtepbs.0000174

Cited by 14 publications

(14 citation statements)

References 15 publications

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“…The merging behavior approach is the most preferable method for the estimation of the critical gap when the traffic condition is mixed, lane discipline is not enforced, and the rule of priority is not obeyed or practiced [ 3 , 6 , 17 , 18 ]. The first step of the merging behavior approach is to identify the influence area for gap acceptance (INAFOGA), which is an area, where it is believed that all merging and gap acceptances occur.…”

Section: Methodsmentioning

confidence: 99%

“…Te gap acceptance behavior of U-turn vehicle drivers is highly afected by the waiting time, and the statistical result from the study by Shubber showed that when the driver's waiting time fell in a range between 21 and 30 sec, the driver was forced to accept a gap size less than that, which fell in the range of 11 to 20 sec at a confdence interval of 95%. On the other hand, there is a slight diference in the mean gap acceptance between an interval of (1-10) and (11)(12)(13)(14)(15)(16)(17)(18)(19)(20) sec at the same confdence interval [7]. In addition to the drivers' waiting time, another researcher [8] demonstrated that gender has an impact.…”

Section: Behavior Of U-turning Vehicle Movementmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Behavior Of U-turning Vehicle Movementmentioning

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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“…The calculation framework of HCM [53] delay was adopted in this paper to estimate the delay per vehicle, as shown in Eqs. (2) and (3). However, the vehicle retention problem in the mixed-usage area is an operational risk of the EFL intersection.…”

Section: B Constraints 1) Calculation Of the Vehicular Delaymentioning

confidence: 99%

Robust Signal Control of Exit Lanes for Left-Turn Intersections With the Consideration of Traffic Fluctuation

et al. 2020

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Traffic throughput at intersections can be improved by using exit lanes for left-turning (EFL), tidal flow lanes near an intersection, which has been recently introduced. This paper considers the operational robustness of the EFL intersection in relation to the control scheme applied to the traffic light setting. For safety and efficiency, it is important that the tidal lanes are emptied before traffic in the opposing direction uses these lanes. Hence, the signal control should not only be optimized for the mean value but be robust for all kinds of fluctuations. This paper formulates a traffic control scheme using robust optimization, i.e. an optimization scheme which explicitly accounts for extreme events. The fluctuation of traffic is considered from three aspects: the distribution of traffic demand, the distribution of base saturation flow rate, and the distribution of actual travel speed. Via a case study and extensive numerical analysis, we find that the established robust optimization method produces an efficient design of signal control and design speed at the EFL intersection under traffic demand and supply fluctuations. Though the optimization method is now applied to intersections with an EFL, it is considered useful for all intersections with high fluctuation of traffic demand and saturation flow rate. INDEX TERMS Exit-lanes for left-turn intersection, robust optimization, signal control, unconventional intersections.

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“…Methods to deal with these problems have been a concern of traffic engineers for a long time, including providing exclusive left-turn lanes and left-turn waiting areas (Ma et al 2017;Yang et al 2013;Zhou and Zhuang 2012;Dong et al 2016;Yang et al 2012) in the aspect of road layout, adding left-turn phases in the aspect of signal control (Al-Kaisy and Stewart 2001;Allsop 1971;Webster 1958;Heydecker and Dudgeon 1987;Improta and Cantarella 1984;Silcock 1997)., and prohibiting conflicting left-turns and setting detour routes in the aspect of road network organization (DePrator, Hitchcock, and Gayah 2017;Zhao, Liu, and Li 2016;Yu and Prevedouros 2013). With the increase of traffic demand, median U-turn intersections (Liu et al 2007;El Esawey and Sayed 2011;Mohapatra and Dey 2018;Zhao et al 2018), continuous flow intersections (Goldblatt, Mier, and Friedman 1994;Yang and Cheng 2017), exit lanes for left-turn intersections (Zhao et al 2013;Wu et al 2016), tandem intersections (Xuan, Daganzo, and Cassidy 2011;Yan, Jiang, and Xie 2014), uninterrupted flow intersections (Liu and Luo 2012; Xie and Turnquist 2011), special width approach lanes intersections (Zhao, Liu, and Wang 2016) and other series of unconventional intersection design methods have been proposed and applied in many cities all over the world, to further improve the capacity of intersections.…”

Section: Introductionmentioning

confidence: 99%

An innovative design for left turn bicycles at continuous flow intersections

Zhao

Gao

Knoop

2019

Transportmetrica B: Transport Dynamics

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An innovative design for left turn bicycles at continuous flow intersections To improve the practical capacity of the continuous flow intersections (CFI) and eliminate the conflict between left-turn bicycles and through vehicles at the mainsignal, an optimization design method for left-turn bicycles was proposed. From the perspective of the geometric design, the pre-stop line for through vehicles and the crossing passage for left-turn bicycles were set at the pre-signal points of the CFI. From the perspective of signal control, the control of the through vehicles at pre-signal was added in the proposed method. A linear programming model was established to achieve the maximum vehicular practical capacity. The optimization effectiveness and applicability for this design were validated by a case study and sensitivity analyses. The results show that this method can effectively avoid the conflict between left-turn bicycles and through vehicles at main-signal and enhance the practical capacity at continuous flow intersections. As for vehicles, the improvements in practical capacity obtained by the proposed method increase with the increase of the left-turn bicycles volume and through vehicle percentage, while decrease with longer cycle length. On average, a 5% increase and 3.5% increase in the improvement of practical capacity can be obtained for every 100 left-turn bicycles per hour increase and for every 10% through vehicle percentage increase, respectively. As for bicycles, the proposed method can reduce the delay in the case of high traffic demand.

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Estimation of U-Turn Capacity at Median Openings

Cited by 14 publications

References 15 publications

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

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

Robust Signal Control of Exit Lanes for Left-Turn Intersections With the Consideration of Traffic Fluctuation

An innovative design for left turn bicycles at continuous flow intersections

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