Volume 40 Issue 2
Apr.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(2): 98-107
CAO Shuchao, SUN Feiyang, LI Yang. A Cellular Automaton Simulation Model Considering Spatial-temporal Distribution for Mixed Bicycle Flows[J]. Journal of Transport Information and Safety, 2022, 40(2): 98-107. doi: 10.3963/j.jssn.1674-4861.2022.02.012
Citation: CAO Shuchao, SUN Feiyang, LI Yang. A Cellular Automaton Simulation Model Considering Spatial-temporal Distribution for Mixed Bicycle Flows[J]. Journal of Transport Information and Safety, 2022, 40(2): 98-107. doi: 10.3963/j.jssn.1674-4861.2022.02.012
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A Cellular Automaton Simulation Model Considering Spatial-temporal Distribution for Mixed Bicycle Flows

doi: 10.3963/j.jssn.1674-4861.2022.02.012

  • School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212000, Jiangsu, China

  • Received Date: 2021-09-21
    Available Online: 2022-05-18
    Abstract
  • Traditional cellular automata(CA)models provide inaccurate simulation results in modeling non-motorized traffic flow, due to the fact that they subjectively define spatial-temporal parameters and roughly represent bicycle lanes. With this, an improved CA model is proposed in this paper. Specifically, the grid density and time step of the proposed model are upgraded based on the updating rules of a NaSch model, which considers the conflict between heterogeneous bicycles and the dynamic lane-changing behavior in a two-dimensional space. In the proposed model, bicycles that need to make lane-changing can change to a lateral position which meets the condition of safe lateral and the forward movement. The bicycle can change lanes to the optimal position considering both the forward and lateral distance of each position. In addition, the influence of different spatial-temporal parameters on simulation results is quantified under the period boundary condition. Data from Zhengdong Road in Zhenjiang is obtained, and the spatial-temporal diagrams of trajectories are generated and with which the reliability of the proposed model is verified at both the macro and micro levels. Study results are supportive for the following conclusions. First, grid density and time step have a significant impact on the simulated flows and they are positively correlated with the longitudinal grid density but negatively correlated with the lateral grid density, and their global grid density is the compound effect of the two densities. Second, the flow is almost unaffected by the size of time step when the occupancy rate is around 0.1, but when the occupancy rates is around 0.3, 0.5, or 0.7, the bicycle flow shows similar trend that first increase and then decrease with the increment of time step. Third, moderate lane-changing behavior of bicycles can improve road capacity, while frequent lane-changing behavior would lead to congestion. Significant differences in the spatial-temporal diagrams of trajectories are found under different occupancy conditions, and bicycle flows with a high density would lead to stop-and-go condition. Fourth, when the global grid density is 5 and the time step is 0.5 s, the accuracy is highest, where the mean absolute percentage error(MAPE)between simulated results and observed data is only 14.84%.

     

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