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
计量
- 文章访问数: 1130
- HTML全文浏览量: 217
- PDF下载量: 489
- 被引次数: 0
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

HTML全文

图 1 快速路基本路段离散化

Figure 1. Discretization of expressway basic segments

下载: 全尺寸图片幻灯片

图 2 元胞传输模型中基本图

Figure 2. Fundamental diagram in CTM

下载: 全尺寸图片幻灯片

图 3 快速路入口匝道汇入

Figure 3. On-ramp merging of expressway

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

图 5 元胞划分与线圈布设(单位: m)

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

下载: 全尺寸图片幻灯片

图 6 早高峰速度分布

Figure 6. Speed distribution at morning peak

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 导出CSV

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

下载: 导出CSV

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

下载: 导出CSV

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

下载: 导出CSV

参考文献(31)

[1]	KOTSIALOS A, PAPAGEORGIOU M. The importance of traffic flow modeling for motorway traffic control[J]. Networks and Spatial Economics, 2001, 1 (1/2): 179-203. doi: 10.1023/A:1011537329508
[2]	DAGANZO C F. The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory[J]. Transportation Research Part B: Methodological, 1994, 28 (4): 269-287. doi: 10.1016/0191-2615(94)90002-7
[3]	SZETO W Y. Enhanced lagged cell-transmission model for dynamic traffic assignment[J]. Transportation Research Record, 2008 (2085): 76-85.
[4]	MUÑOZ L, SUN Xiao-tian, HOROWITZ R, et al. Traffic density estimation with the cell transmission model[C]//IEEE. Proceedings of the 2003 American Control Conference. New York: IEEE, 2003: 3750-3755.
[5]	SUMALEE A, ZHONG R X, PAN T L, et al. Stochastic cell transmission model (SCTM): a stochastic dynamic traffic model for traffic state surveillance and assignment[J]. Transportation Research Part B: Methodological, 2011, 45 (3): 507-533. doi: 10.1016/j.trb.2010.09.006
[6]	MUÑOZ L, SUN Xiao-tian, SUN Deng-feng, et al. Methodological calibration of the cell transmission model[C]//IEEE. Proceedings of the 2004 American Control Conference. New York: IEEE, 2004: 798-803.
[7]	GOMES G, HOROWITZ R. Optimal freeway ramp metering using the asymmetric cell transmission model[J]. Transportation Research Part C: Emerging Technologies, 2006, 14 (4): 244-262. doi: 10.1016/j.trc.2006.08.001
[8]	RONCOLI C, PAPAGEORGIOU M, PAPAMICHAIL I. Traffic flow optimisation in presence of vehicle automation and communication systems—Part Ⅰ: a first-order multi-lane model for motorway traffic[J]. Transportation Research Part C: Emerging Technologies, 2015, 57: 241-259. doi: 10.1016/j.trc.2015.06.014
[9]	SHIOMI Y, TANIGUCHI T, UNO N, et al. Multilane first-order traffic flow model with endogenous representation of lane-flow equilibrium[J]. Transportation Research Procedia, 2015, 7: 398-419. doi: 10.1016/j.trpro.2015.06.021
[10]	杨泳, 严余松, 户佐安, 等. 城市快速路改进型元胞传输模型及仿真[J]. 公路交通科技, 2015, 32 (6): 135-141. doi: 10.3969/j.issn.1002-0268.2015.06.021 YANG Yong, YAN Yu-song, HU Zuo-an, et al. An improved cell transmission model for urban expressway and simulation[J]. Journal of Highway and Transportation Research and Development, 2015, 32 (6): 135-141. (in Chinese). doi: 10.3969/j.issn.1002-0268.2015.06.021
[11]	杨泳, 户佐安. 改进型CTM模型匝道控制下拥堵传播规律研究[J]. 武汉理工大学学报(交通科学与工程版), 2015, 39 (5): 915-919. doi: 10.3963/j.issn.2095-3844.2015.05.004 YANG Yong, HU Zuo-an. Research on ramp metering traffic congestion propagation mechanism with improved CTM model[J]. Journal of Wuhan University of Technology (Transportation Science and Engineering), 2015, 39 (5): 915-919. (in Chinese). doi: 10.3963/j.issn.2095-3844.2015.05.004
[12]	林琴, 龙科军. 基于改进CTM模型的城市快速路交通流仿真[J]. 长沙理工大学学报(自然科学版), 2018, 15 (4): 52-58. doi: 10.3969/j.issn.1672-9331.2018.04.008 LIN Qin, LONG Ke-jun. Urban expressway traffic flow simulation based on improved CTM model[J]. Journal of Changsha University of Science and Technology (Natural Science), 2018, 15 (4): 52-58. (in Chinese). doi: 10.3969/j.issn.1672-9331.2018.04.008
[13]	陈庚. 基于改进型CTM模型的城市快速路交通事件仿真[J]. 交通科技, 2016 (6): 146-149. https://www.cnki.com.cn/Article/CJFDTOTAL-SKQB201606047.htm CHEN Geng. Urban expressway traffic incident simulation based on improved CTM model[J]. Transportation Science and Technology, 2016 (6): 146-149. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SKQB201606047.htm
[14]	陈永恒, 刘鑫山, 熊帅, 等. 冰雪条件下快速路汇流区可变限速控制[J]. 吉林大学学报(工学版), 2018, 48 (3): 677-687. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201803005.htm CHEN Yong-heng, LIU Xin-shan, XIONG Shuai, et al. Variable speed limit control under snow and ice conditions for urban expressway in junction bottleneck area[J]. Journal of Jilin University (Engineering and Technology Edition), 2018, 48 (3): 677-687. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201803005.htm
[15]	黄超, 陈日强. 基于元胞传输模型的高速公路交通流仿真模型[J]. 中国交通信息化, 2017 (7): 127-130, 135. https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC201707020.htm HUANG Chao, CHEN Ri-qiang. Freeway traffic flow simulation model based on cellular transmission model[J]. China ITS Journal, 2017 (7): 127-130, 135. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC201707020.htm
[16]	邹娟. 基于改进元胞传输模型的交通信号优化控制[D]. 武汉: 武汉科技大学, 2018. ZOU Juan. Traffic signal optimization control based on improved cell transmission model[D]. Wuhan: Wuhan University of Science and Technology, 2018. (in Chinese).
[17]	龚, 李苏剑, 邢恩辉. 拥堵程度时变的随机路网可变元胞传输模型[J]. 计算机工程与应用, 2015, 51 (3): 6-11. doi: 10.3778/j.issn.1002-8331.1405-0448 GONG Yan, LI Su-jian, XING En-hui. Novel variable cell transmission model for stochastic road network with time-varying congestion degree[J]. Computer Engineering and Applications, 2015, 51 (3): 6-11. (in Chinese). doi: 10.3778/j.issn.1002-8331.1405-0448
[18]	SPILIOPOULOU A, KONTORINAKI M, PAPAGEORGIOU M, et al. Macroscopic traffic flow model validation at congested freeway off-ramp areas[J]. Transportation Research Part C: Emerging Technologies, 2014, 41: 18-29. doi: 10.1016/j.trc.2014.01.009
[19]	CASSIDY M J, BERTINI R L. Some traffic features at freeway bottlenecks[J]. Transportation Research Part B: Methodological, 1999, 33 (1): 25-42. doi: 10.1016/S0191-2615(98)00023-X
[20]	CASSIDY M J, RUDJANAKANOKNAD J. Increasing capacity of an isolated merge by metering its on-ramp[J]. Transportation Research Part B: Methodological, 2005, 39 (10): 896-913. doi: 10.1016/j.trb.2004.12.001
[21]	SUN Jian, ZHANG Juan, ZHANG H. Investigation of the early-onset breakdown phenomenon at urban expressway bottlenecks in Shanghai[J]. Transportmetrica B: Transport Dynamics, 2014, 2 (3): 215-228. doi: 10.1080/21680566.2014.932262
[22]	HU Jia-qi, SUN Jian, ZHAO Li. Some flow features at urban expressway on-ramp bottlenecks in Shanghai[C]//TRB. 93rd Annual Meeting of the Transportation Research Board. Washington DC: TRB, 2014: 1-17.
[23]	李宙峰, 城市快速路宏观交通流建模与仿真[D]. 上海: 同济大学, 2016. LI Zhou-feng. Macroscopic traffic flow modeling and simulation for urban expressway[D]. Shanghai: Tongji University, 2016. (in Chinese).
[24]	JIN Wen-long, GAN Qi-jian, LEBACQUE J P. A kinematic wave theory of capacity drop[J]. Transportation Research Part B: Methodological, 2015, 81: 316-329.
[25]	MUÑOZ L, SUN Xiao-tian, HOROWITZ R, et al. A piecewise-linearized cell transmission model and parameter calibration methodology[J]. Transportation Research Record, 2006 (1965): 183-191.
[26]	GREWAL M S, PAYNE H J. Identification of parameters in a freeway traffic model[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1976, 6 (3): 176-185.
[27]	KAMIYAMA D, TAMURA K, YASUDA K. Down-hill simplex method based differential evolution[C]//IEEE. Proceedings of SICE Annual Conference. New York: IEEE, 2010: 1641-1646.
[28]	POOLE A J, KOTSIALOS A. METANET model validation using a genetic algorithm[J]. IFAC Proceedings Volumes, 2012, 45 (24): 7-12. doi: 10.3182/20120912-3-BG-2031.00002
[29]	SPILIOPOULOU A, PAPAMICHAIL I, PAPAGEORGIOU M, et al. Macroscopic traffic flow model calibration using different optimization algorithms[J]. Operational Research, 2017, 17: 145-164.
[30]	BAN Xue-gang, CHU Lian-yu, BENOUAR H. Bottleneck identification and calibration for corridor management planning[J]. Transportation Research Record, 2007 (1999): 40-53.
[31]	SONG Rui, SUN Jian. Calibration of a micro-traffic simulation model with respect to the spatial-temporal evolution at expressway on-ramp bottlenecks[J]. Simulation, 2016, 92 (6): 535-546.