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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2019  >  19(3): 122-133
SUN Jian, YIN Ju-yuan, LI Tao-ning. Macroscopic traffic flow model of expressway on-ramp bottlenecks[J]. Journal of Traffic and Transportation Engineering, 2019, 19(3): 122-133. doi: 10.19818/j.cnki.1671-1637.2019.03.013
Citation: SUN Jian, YIN Ju-yuan, LI Tao-ning. Macroscopic traffic flow model of expressway on-ramp bottlenecks[J]. Journal of Traffic and Transportation Engineering, 2019, 19(3): 122-133. doi: 10.19818/j.cnki.1671-1637.2019.03.013
shu

Macroscopic traffic flow model of expressway on-ramp bottlenecks

doi: 10.19818/j.cnki.1671-1637.2019.03.013
  • 1.

    Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China

  • 2.

    Shanghai Bureau of Public Security Traffic Police Corps, Shanghai 200070, China

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  • Author Bio:

    SUN Jian(1979-), male, professor, PhD, sunjian@tongji.edu.cn

  • Received Date: 2018-12-26
  • Publish Date: 2019-06-25
    Abstract
  • Based on on-ramp merging mode and fundamental diagram form, an adjusted cell transmission model (CTM) was proposed. On-ramp state variables were introduced to track the traffic state of on-ramps, and new on-ramp merging rules were defined.The dual capacity fundamental diagram was introduced to the adjusted CTM in order to adapt to the varying capacities under different traffic conditions. The nelder-mead method and genetic algorithm were combined, and a hybrid multi-objective parameter optimization method was proposed. Three simulation scenarios were established, the performances of adjusted CTM and the hybrid multi-objective parameter optimization method were evaluated. Simulation result shows that for the prediction of the occurrence time and ending time of congestion on the upstream of on-ramp, compared with the original CTM, the adjusted CTM improves the accuracy by 22.3 and 10.8 min, respectively. For the simulation of the propagation and dissipation of congestion at the on-ramp merging section, the result of adjusted CTM is closer to the actual propagation/dissipation rules.As for the simulation of the early-onset breakdown traffic characteristic on the test segment, the fitting errors of adjusted CTM for the maximum pre-queue flow and queue discharge flow are below 4%, which are less than the values of original CTM. In the term of model simulation accuracy, compared with the original CTM, the various indexes of adjusted CTM are better, the simulated speed error of the former is 10.42 km·h-1, which is 25.4% lower than the value of the latter. Compared with the traditional genetic algorithm, the hybrid multi-objective parameter optimization method can reduce the total calculation times, and the total consumed time of parameter calibration shortens by 29.3%.

     

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