考虑时空分布特征的混合自行车流元胞自动机模型仿真方法

曹淑超; 孙菲阳; 李阳

doi:10.3963/j.jssn.1674-4861.2022.02.012

考虑时空分布特征的混合自行车流元胞自动机模型仿真方法

doi: 10.3963/j.jssn.1674-4861.2022.02.012

江苏大学汽车与交通工程学院江苏镇江 212000

基金项目:

国家自然科学基金项目 72001095

中国博士后科学基金项目 2020M681507

详细信息

作者简介:
曹淑超（1989—），博士，副教授.研究方向：交通流建模、交通安全等.E-mail: sccao@ujs.edu.cn

中图分类号: U491
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出版历程
- 收稿日期: 2021-09-21
- 网络出版日期: 2022-05-18

A Cellular Automaton Simulation Model Considering Spatial-temporal Distribution for Mixed Bicycle Flows

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

摘要

摘要: 针对非机动车交通流中传统元胞自动机模型主观定义时空参数，粗略划分自行车虚拟车道，导致仿真精度偏差的问题，构建了精细元胞自动机模型。基于NaSch模型的更新规则，考虑二维空间内异质自行车间的错位冲突及动态换道行为特征，细化了模型网格密度及模拟时间步长。产生换道需求的自行车可以换至满足安全侧向换道条件及前行需求条件的横向位置，随后综合考虑各位置前向及侧向间隙选取最优的目标位置执行换道。在周期边界条件下，量化时空参数取值对混合自行车交通流仿真结果的影响。在镇江市正东路开展观测实验，并基于实测数据获得轨迹时空图，在宏观与微观层面验证模型的可靠性。结果表明：网格密度及时间步长取值对仿真流量影响显著，仿真流量与纵向网格密度呈正相关，与横向网格密度呈负相关，而全局网格密度的影响反映了横向与纵向网格密度的复合效应；当道路占有率为0.1时，仿真流量几乎不受时间步长的影响，而当道路占有率为0.3，0.5和0.7时，仿真流量随着时间步长的增大先增加后减小；自行车适度换道可以提高道路通行能力，但换道过于频繁则会导致交通拥堵，不同道路占有率条件下的轨迹时空特征存在明显差异，自行车流越密集，走停现象越显著；当全局网格密度为5，时间步长为0.5 s时，模拟与实测的交通流量平均绝对百分比误差为14.84%，此时拟合效果最优。
- 非机动车交通 /
- 混合自行车流 /
- 时空分布特征 /
- 元胞自动机模型 /
- 换道行为
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%.
- non-motorized traffic /
- mixed bicycle flow /
- spatial-temporal distribution /
- cellular automaton model /
- lane changing behavior

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图 1 NaSch模型及精细元胞自动机模型示意图

Figure 1. Schematic diagram of NaSch model and fine cellular automata model

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图 2 自行车位置更新流程图

Figure 2. Flow chart of bicycle position update

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图 3 网格密度对交通流量的影响（R_s =0.3）

Figure 3. Effect of different n_x and global grid density on traffic flow(R_s =0.3)

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图 4 网格密度示意图

Figure 4. Illustration of grid density

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图 5 时间步长对交通流量的影响

Figure 5. Influence of time step on traffic flow

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图 6 时间步长和换道次数的关系

Figure 6. Relation between time step and lane-changing times.

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图 7 换道次数对流量的影响

Figure 7. Influence of lane-changing times on flow

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图 8 道路坐标系

Figure 8. Coordinate system of road

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图 9 不同道路占有率下自行车仿真时空分布图

Figure 9. Spatio-temporal diagrams of bicycles under different road occupancy rates in the model

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图 10 观测路段拍摄示意图及PeTrack跟踪界面

Figure 10. Schematic diagram of observation road and PeTrack tracking interface

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图 11 R_s =0.3时的观测实验及仿真模拟自行车轨迹时空图

Figure 11. Spatio-temporal diagrams of experiment and simulation under R_s =0.3

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图 12 自行车换道示意图

Figure 12. Schematic diagram of lane-changing behavior of bicycles

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图 13 实测数据与仿真结果对比

Figure 13. Comparison between measured data and simulation results

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表 1 基于元胞自动机的自行车交通流建模参数

Table 1. Parameters of bicycle simulation based on CA model

文献	年份	模型	车辆类型	元胞长度/m	车辆尺寸/m	占用元胞数	时间步/s
邝先验等^[12]	2015	改进的NaSch模型	电动自行车	2×0.9	2×0.9	1×1	1
邝先验等^[12]	2015	改进的NaSch模型	电动三轮车	2×0.9	2×1.8	1×2	1
邝先验等^[13]	2016	改进的NaSch模型	人力自行车	2×1	2×1	1×1	1
邝先验等^[13]	2016	改进的NaSch模型	电动自行车	2×1	2×1	1×1	1
Tang等^[8]	2018	改进的NaSch模型	人力自行车	0.75×0.75	1.5×0.75	2×1	1
			行人		1×1	2×2
Liu等^[11]	2015	改进的NaSch模型	人力自行车	0.5×0.5	2×1	4×2	1
Liu等^[11]	2015	改进的NaSch模型	摩托车		3×1	6×2
	机动车		7×2.5		14×5
	人力自行车		2×0.8		1×1
Li等^[10]	2020	改进的NaSch模型	电动自行车	2×0.8	2×0.8	1×1	1
Li等^[10]	2020	改进的NaSch模型	机动车	2×0.8	6×2.4	3×3	1
冯雪等^[14]	2016	改进的NaSch模型	机动车	1×1	7×动态宽度	7×动态宽度	1
冯雪等^[14]	2016	改进的NaSch模型	非机动车	1×1	2×1	2×1	1
Jin等^[19]	2015	多值元胞自动机模型	人力自行车	2×^*	2×^*	1	1
Jin等^[19]	2015	多值元胞自动机模型	电动自行车	2×^*	2×^*	1	1
注：^*号表示相应参数无具体数值说明。

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表 2 全局网格密度n

Table 2. Global grid density n

n	cell size/m²	n_y	n_x
1	0.9×0.9	1	2
2	0.45×0.45	2	4
3	0.3×0.3	3	6
4	0.22×0.22	4	8
5	0.18×0.18	5	10
6	0.15×0.15	6	12

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