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contributor authorBing Li
contributor authorHongyu Yang
contributor authorWei Cheng
contributor authorMingwei Ma
date accessioned2022-05-07T20:45:27Z
date available2022-05-07T20:45:27Z
date issued2021-12-09
identifier otherJTEPBS.0000627.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4282855
description abstractThe use of channelized islands to optimize the operation of advance right-turn motor vehicles (ARTMVs) is an effective intersection design method. To overcome the limitations of previous models with respect to the characteristics of nonstrict priority crossing behaviors and mixed nonmotor vehicle (NMV) flow, we have constructed a new capacity estimation model of ARTMVs. This novel model is based on the driving force model for nonstrict priority crossing behaviors, and fully describes the mixed NMV flow with perceived density. Based on the driving force model for nonstrict priority crossing, the speed of ARTMVs crossing the NMV lane and the headways of ARTMVs are obtained. Finally, the number of ARTMVs crossing the NMV lane in a saturated traffic state, i.e., the capacity, is estimated. The method does not need to consider the gap probability of NMV flow, which makes up for the influence of NMVs passing ARTMVs side-by-side. The model was verified by data collected at validation sites in Kunming, China, and compared with the gap acceptance model; the accuracy of the proposed model with heterogeneous NMV flow improved by 22.2%. The construction method of the model provides a new idea for the capacity estimation of ARTMVs under mixed traffic conditions.
publisherASCE
titleCapacity Estimation of Advance Right-Turn Motor Vehicles Considering Nonstrict Priority Crossing Behaviors under Mixed-Traffic Conditions
typeJournal Paper
journal volume148
journal issue2
journal titleJournal of Transportation Engineering, Part A: Systems
identifier doi10.1061/JTEPBS.0000627
journal fristpage04021114
journal lastpage04021114-9
page9
treeJournal of Transportation Engineering, Part A: Systems:;2021:;Volume ( 148 ):;issue: 002
contenttypeFulltext


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