快速路入口匝道瓶颈宏观交通流模型

孙剑; 殷炬元; 黎淘宁

doi:10.19818/j.cnki.1671-1637.2019.03.013

快速路入口匝道瓶颈宏观交通流模型

doi: 10.19818/j.cnki.1671-1637.2019.03.013

孙剑¹,
殷炬元¹,
黎淘宁^{1, 2}

1.
同济大学道路与交通工程教育部重点实验室, 上海 201804
2.
上海市公安局交通警察总队, 上海 200070

基金项目:

国家自然科学基金项目 U1764261

国家自然科学基金项目 61773288

上海市曙光计划项目 18SG21

详细信息

作者简介:
孙剑(1979-), 男, 山东德州人, 同济大学教授, 工学博士, 从事智能交通与交通仿真研究

中图分类号: U491.112
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出版历程
- 收稿日期: 2018-12-26
- 刊出日期: 2019-06-25

Macroscopic traffic flow model of expressway on-ramp bottlenecks

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

More Information

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

摘要

摘要: 基于入口匝道汇入方式与基本图形态, 提出了一种调整型元胞传输模型; 增加了入口匝道状态变量以追踪入口匝道交通状态, 定义了新的入口匝道汇入规则; 将双通行能力基本图引入到调整型元胞传输模型中, 以适应不同交通状态下通行能力的变化; 将单纯形法与遗传算法相结合, 提出了混合多目标参数优化方法; 建立了3个仿真场景, 评价调整型元胞传输模型与混合多目标参数优化方法的效果。仿真结果表明: 在预测入口匝道上游主线拥堵发生与结束时间方面, 与经典元胞传输模型相比, 调整型元胞传输模型将时间预测准确性分别提升了22.3、10.8 min; 在模拟入口匝道汇入段主线拥堵传播与消散方面, 调整型元胞传输模型模拟结果更加符合实际的传播与消散规律; 在模拟试验路段早发性失效交通特性方面, 调整型元胞传输模型对于拥堵前最大流量与拥堵后消散流量的拟合误差在4%以内, 小于经典元胞传输模型; 在模型仿真精度方面, 调整型元胞传输模型各项评价指标均优于经典元胞传输模型, 前者的仿真速度误差为10.42 km·h^-1, 较后者降低了25.4%;与传统的遗传算法相比, 混合多目标参数优化方法的总计算次数更少, 参数标定过程总耗时缩短了29.3%。
- 智能交通系统 /
- 宏观交通流模型 /
- 元胞传输模型 /
- 入口匝道瓶颈 /
- 参数标定 /
- 精细化建模
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%.
- intelligent transportation system /
- macroscopic traffic flow model /
- cell transmission model /
- on-ramp bottleneck /
- parameter calibration /
- fine modeling

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图 1 快速路基本路段离散化

Figure 1. Discretization of expressway basic segments

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图 2 元胞传输模型中基本图

Figure 2. Fundamental diagram in CTM

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图 3 快速路入口匝道汇入

Figure 3. On-ramp merging of expressway

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图 4 混合多目标参数优化方法流程

Figure 4. Flow of hybrid multi-objective parameter optimization method

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图 5 元胞划分与线圈布设(单位: m)

Figure 5. Cell division and coil configuration (unit: m)

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图 6 早高峰速度分布

Figure 6. Speed distribution at morning peak

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图 7 实测速度与不同场景下的仿真速度分布

Figure 7. Distributions of measured speed and simulated speeds in different scenarios

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图 8 线圈64处实测速度与仿真速度时间序列

Figure 8. Time serials of measured speed and simulated speed at detector 64

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图 9 线圈61处实测速度与仿真速度时间序列

Figure 9. Time serials of measured speed and simulated speed at coil 61

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图 10 线圈58处实测速度与仿真速度时间序列

Figure 10. Time serials of measured speed and simulated speed at coil 58

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图 11 线圈55处实测速度与仿真速度时间序列

Figure 11. Time serials of measured speed and simulated speed at coil 55

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表 1 三个场景下的参数标定结果

Table 1. Parameter calibration results in three scenarios

场景	参数
场景	V/ (km·h^-1)	Q/ (veh·h^-1)	W/ (km·h^-1)	P/ (veh·km^-1)	P′/ (veh·km^-1)	W′/ (km·h^-1)	Q₁ / (veh·h^-1)	Q₂/ (veh·h^-1)	V′/ (km·h^-1)	p′/ (veh·km^-1)
1	69.45	1 965.5	12.13	188.24	228.98	7.97	/	/	/	/
2	68.11	1 929.5	11.93	192.52	228.11	7.72	/	/	/	/
3	67.20	/	17.22	150.23	233.91	8.80	1 951.3	2 153.6	54.72	39.90

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表 2 场景1、2中优化算法的表现

Table 2. Performances of optimization algorithms in scenarios 1 and 2

统计量	场景1	场景2
	以J₀为目标函数的GA	以J₁为目标函数的NM法	以J₂为目标函数的GA
总迭代次数	2 650	2 042	1 785
总函数检验次数	1 325 000	3 165	892 500
		895 665
标定总耗时/min	332.6	235.3

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表 3 不同场景下的评价指标

Table 3. Evaluation indexes in different scenarios

场景	C₁	C₂	C₃	C₄	计算时长/s	速度误差/ (km·h^-1)
1	0.828 9	0.483 5	5.653 4	0.183 8	3.48	12.71
2	0.832 2	0.505 0	5.581 0	0.186 4	3.51	13.96
3	0.896 8	0.737 4	4.429 7	0.124 9	5.76	10.42

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表 4 场景2、3模拟的PQF与QDF

Table 4. PQFs and QDFs simulated in scenarios 2 and 3

交通流统计量	实际值/ (veh·h^-1)	场景2		场景3
交通流统计量	实际值/ (veh·h^-1)	仿真值/ (veh·h^-1)	误差/%	仿真值/ (veh·h^-1)	误差/%
PQF	1 146.3	1 113.0	-2.90	1 172.4	2.27
QDF	1 241.0	1 153.1	-7.08	1 289.4	3.90

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