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Sensitivity analysis of key input parameters in conditional cell transmission model for oversaturated arterials
  • 时间:0
  • 分类:TP393.4[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] P642.22[天文地球—工程地质学;天文地球—地质矿产勘探;天文地球—地质学]
  • 作者机构:[1]Key Laboratory of Road and Traffic Engineering of Ministry of Education (Tongji University), Shanghai 201804, China, [2]Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa 35401, USA
  • 相关基金:Project(51108343) supported by the National Natural Science Foundation of China; Project(06121) supported by University of Transportation Center for Alabama, USA
中文摘要:

A novel conditional cell transmission model (CCTM) is a potential simulation tool because it accommodates all traffic conditions from light condition to oversaturated condition.To test the performance of the CCTM,a series of experiments for sensitivity analysis were designed and performed for a multilane,two-way,three-signal sample network.Experiment 1 shows that the model is performed in a logical and expected manner with variations in traffic demand with time and direction.Experiment 2 shows when the possibility of the occurrence of a useful gap increases to 60% and 100%,the delays in left turns decrease by 5% and 15%,respectively.In Experiment 3,comparing the possibility of a conditional cell of 0 with 100%,delay of left turn and delay of the entire network were underestimated by 58% and 11%,respectively.Hence,sensitivity analysis demonstrates that by reflecting local drivers’ behaviors properly,the CCTM provides an accurate representation of traffic flow in simulating oversaturated traffic conditions.更多还原

英文摘要:

A novel conditional cell transmission model (CCTM) is a potential simulation tool because it accommodates all traffic conditions from light condition to oversaturated condition. To test the performance of the CCTM, a series of experiments for sensitivity analysis were designed and performed for a multilane, two-way, three-signal sample network. Experiment 1 shows that the model is performed in a logical and expected manner with variations in traffic demand with time and direction. Experiment 2 shows when the possibility of the occurrence of a useful gap increases to 60% and 100%, the delays in left rams decrease by 5% and 15%, respectively. In Experiment 3, comparing the possibility of a conditional cell of 0 with 100%, delay of left turn and delay of the entire network were underestimated by 58% and 11%, respectively. Hence, sensitivity analysis demonstrates that by reflecting local drivers' behaviors properly, the CCTM provides an accurate representation of traffic flow in simulating oversaturated traffic conditions.

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