Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
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Capacity Estimation of Advance Right-Turn Motor Vehicles Considering Nonstrict Priority Crossing Behaviors under Mixed-Traffic Conditions
https://orcid.org/0000-0003-4369-6530
The 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.
Capacity Estimation of Advance Right-Turn Motor Vehicles Considering Nonstrict Priority Crossing Behaviors under Mixed-Traffic Conditions
https://orcid.org/0000-0003-4369-6530
The 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.
Capacity Estimation of Advance Right-Turn Motor Vehicles Considering Nonstrict Priority Crossing Behaviors under Mixed-Traffic Conditions
https://orcid.org/0000-0003-4369-6530
The 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.
Capacity Estimation of Advance Right-Turn Motor Vehicles Considering Nonstrict Priority Crossing Behaviors under Mixed-Traffic Conditions
J. Transp. Eng., Part A: Systems
Li, Bing (Autor:in) / Yang, Hongyu (Autor:in) / Cheng, Wei (Autor:in) / Ma, Mingwei (Autor:in)
01.02.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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