综合交通系统“多网合一”交通分析模型与算法

王炜; 华雪东; 郑永涛

doi:10.19818/j.cnki.1671-1637.2021.02.014

综合交通系统“多网合一”交通分析模型与算法

doi: 10.19818/j.cnki.1671-1637.2021.02.014

王炜^{1, 2,},
华雪东^{1, 2, ,},
郑永涛^{1, 2}

1.
东南大学交通学院，江苏南京 211189
2.
东南大学江苏省城市智能交通重点实验室，江苏南京 211189

基金项目:

国家自然科学基金项目 51878166

国家重点研发计划项目 2018YFE0102700

国家重点研发计划项目 2018YFB1600900

详细信息

作者简介:
王炜(1959-)，男，浙江绍兴人，东南大学教授，工学博士，从事交通规划与管理、交通仿真研究

通讯作者:
华雪东(1987-)，男，江苏扬州人，东南大学讲师，工学博士

中图分类号: U491
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- 被引次数: 0
出版历程
- 收稿日期: 2020-10-06
- 刊出日期: 2021-04-01

Multi-network integrated traffic analysis model and algorithm of comprehensive transportation system

WANG Wei^{1, 2
,},
HUA Xue-dong^{1, 2
, ,},
ZHENG Yong-tao^{1, 2}

1.
School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China
2.
Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, Jiangsu, China

Funds:

National Natural Science Foundation of China 51878166

National Key Research and Development Program of China 2018YFE0102700

National Key Research and Development Program of China 2018YFB1600900

More Information

Author Bio:
WANG Wei (1959-), male, professor, PhD, wangwei@seu.edu.cn

Corresponding author: HUA Xue-dong (1987-), male, assistant professor, PhD, qdurgk@163.com

摘要

摘要: 为解决综合交通体系中不同交通方式各自为政、条块分割的问题，研究了综合交通体系融合发展中缺乏一体化交通分析技术的瓶颈，提出了以交通枢纽为关键，覆盖铁路、公路、水运、航空、管道以及城市道路的“多网合一”的物理网络与虚拟网络拓扑结构模型; 构建了服务各交通运输方式、结果量化可比的交通阻抗函数模型与优势运输距离模型; 研发了异质交通网络环境下的一体化交通分配模型与算法，提出了综合交通系统客运组合出行与货运多式联运的交通量分析方法，形成了服务于综合交通系统一体化融合发展的交通分析模型与技术体系; 通过完全自主的“交运之星——TranStar”综合交通版交通仿真分析软件，搭建了综合交通系统虚拟仿真平台，实现了对大规模综合交通网络规划建设与运行管理的快速响应，并验证了分析模型与算法的可行性。研究结果表明：相比传统分析方法，提出的交通分析模型与算法可满足“多网合一”条件下综合交通系统的各类分析需求; 利用提出的交通分析模型与算法对综合交通网络的交通流量进行分析，相对误差不超过3%，平均误差不超过2%，分析结果精度高，满足工程实践要求。
- 综合交通系统 /
- 交通网络 /
- 交通方式 /
- 多网合一 /
- 交通分析模型
Abstract: To solve the problem of fragmentation in comprehensive transportation system, the technical bottlenecks in the integration of comprehensive transportation system were addressed. The topological models of multi-network integration consisting of the physical and virtual networks with comprehensive transportation hubs at their core, and considering railways, highways, waterways, airlines, pipelines, and urban roads, were proposed. Traffic impedance function model and advantage transport distance model serving each traffic mode and quantifying the results were constructed. Integrated traffic assignment model and algorithm under the condition of heterogeneous network traffic distribution were developed, and a analysis method of traffic volume for passenger combined travel and freight multimodal transport in integrated transport system was proposed. The traffic analysis model and technical system to serve the integrated development of comprehensive transportation system were built. TranStar (Comprehensive Transportation Version), an independently developed software, was implemented to build a virtual simulation platform for the comprehensive transportation system, enabling the rapid responses of large-scale comprehensive transport network's planning, construction, operation, and management to be realized. The feasibility of the models and algorithms were also verified. Research result shows that compared with the traditional analysis methods, the proposed traffic analysis model and algorithm satisfy the diverse analytical demands of a comprehensive transportation system under the condition of multi-network integration. The traffic flow of a comprehensive transportation network is verified by the proposed traffic analysis model and algorithm. The relative error is less than 3%, and the average error is less than 2%. The analysis result is of high precision and meets the requirements of engineering practice. 5 tabs, 10 figs, 31 refs.
- comprehensive transportation system /
- traffic network /
- transportation mode /
- multi-network integration /
- traffic analysis model

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图 1 综合交通系统分析框架

Figure 1. Analysis framework of comprehensive transportation system

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图 2 综合交通网络拓扑结构

Figure 2. Topology structure of comprehensive transportation network

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图 3 综合交通网络物理网络拓扑结构

Figure 3. Topology structure for physical network of comprehensive transportation network

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图 4 物理网络与虚拟网络的转换

Figure 4. Conversion of physical network and virtual network

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图 5 综合交通网络交通量分析框架

Figure 5. Analysis framework for traffic volume of comprehensive transportation networks

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图 6 运输距离与分担率曲线

Figure 6. Transport distance-sharing ration curves

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图 7 综合交通网络运输路径

Figure 7. Transport routes of comprehensive transportation network

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图 8 沈山高速交通量预测技术路线

Figure 8. Procedure of traffic volume prediction on Shenshan Highway

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图 9 不同运输方式的阻抗函数

Figure 9. Impedance functions of different transportation modes

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图 10 综合运输网络交通量分布

Figure 10. Traffic volume distribution of comprehensive transportation network

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表 1 点、线类成员

Table 1. Node and line class members

类名称	类成员
点类	编号、坐标、类型、转换阻抗
线类	起终点编号、类型、等级、长度、设计速度、断面规模、路段阻抗

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表 2 点、线类型成员

Table 2. Node and line type members

类型名称	类型成员
点类型	城市或集市、公路节点、火车站、铁路节点、码头、航道节点、飞机场、航线节点、管道站、管道节点
线类型	高速铁路、城际铁路、动车组铁路、特快铁路、普通铁路、货运专线铁路、高速公路、一级公路、二级公路、三级公路、四级公路、其他公路、城市道路、一级航道、二级航道、三级航道、四级航道、五级航道、六级航道、七级航道、主要干线航线、普通干线航线、普通支线航线、主要干线管道、普通干线管道、普通支线管道

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表 3 路段阻抗参数

Table 3. Impedance parameters of sections

运输方式	L_s/km	客运					货运
运输方式	L_s/km	κ_τ(s)⁰/(元·km^-1)	ω_τ(s)⁰/(元·h^-1)	t_s/h	α_τ(s)⁰	β_τ(s)⁰	κ_τ(s)¹/(元·km^-1)	ω_τ(s)¹/(元·h^-1)	t_s/h	α_τ(s)¹	β_τ(s)¹
公路(沈山高速)	361	1.20	30	3.80	1.08	1.88+4.90(q_s)³	0.83	18	4.00	1.13	1.67+4.35(q_s)³
铁路(京沈线)	710	0.43	48	4.50	1.30	7.50	0.33	27	9.00	1.35	7.80
航空(京沈线)	710	1.27	60	2.00	2.50	7.50	0.98	45	2.50	2.70	7.80
水运(大连—烟台)	150	0.70	30	7.00	1.00	8.00	0.28	10	8.50	1.00	8.35

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表 4 枢纽阻抗参数

Table 4. Impedance parameters of hubs

换乘类型	T_{(τ₁, τ₂)1}^σ/min		T_{(τ₁, τ₂)2}^σ/min		T_{(τ₁, τ₂)3}^σ/min		T_{(τ₁, τ₂)4}^σ/min		d_{(τ₁, τ₂)}^σ/元
换乘类型	客运	货运	客运	货运	客运	货运	客运	货运	客运	货运
公路—铁路	5.0	3.9	6.0	15.8	4.0	6.3	0.0	20.5	45.0	26.0
公路—水运	5.0	3.9	7.0	23.7	8.0	3.9	0.0	34.2	25.0	32.0
公路—航空	8.0	3.9	15.0	39.5	7.0	6.9	30.0	27.4	30.0	28.0
铁路—公路	15.0	5.4	0.0	18.4	0.0	4.5	0.0	13.7	15.0	0.0
铁路—水运	5.0	5.4	6.0	15.8	4.0	3.9	5.0	5.7	30.0	42.0
水运—公路	10.0	3.6	6.0	15.8	4.0	4.5	0.0	22.8	40.0	0.0
水运—铁路	10.0	3.6	0.0	18.4	0.0	6.3	0.0	14.8	15.0	19.0
航空—公路	30.0	6.0	0.0	10.5	0.0	4.5	0.0	10.3	15.0	0.0

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表 5 沈山高速仿真模型标定结果与推荐方案分析

Table 5. Simulation models calibration results and recommended schemes analysis of Shenshan Highway

路段	仿真模型标定结果			仿真分析结果
路段	现状交通量/(pcu·d^-1)	分配交通量/(pcu·d^-1)	相对误差/%	趋势交通量/(pcu·d^-1)	诱增交通量/(pcu·d^-1)	转移交通量/(pcu·d^-1)
沈阳—辽中	44 207	45 067	1.9	102 336	6 347	-8 013
辽中—盘锦	58 159	59 646	2.6	120 686	7 772	-10 212
盘锦—锦州	92 569	94 660	2.3	189 963	11 996	-16 894
锦州—葫芦岛	100 471	98 822	-1.6	196 935	12 125	-17 039
葫芦岛—省界	84 551	86 773	2.6	143 309	9 018	-12 638

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参考文献(31)

[1]	《隧道建设(中英文)》编辑部. 中国城市轨道交通2019年度数据统计[J]. 隧道建设(中英文), 2020, 40(5): 762-767. https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD202005023.htm Editorial Office of Tunnel Construction. Statistics on annual data of urban rail transit in China in 2019[J]. Tunnel Construction, 2020, 40(5): 762-767. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD202005023.htm
[2]	United Nations Conference on Trade and Development. Review of maritime transport 2019[R]. New York: United Nations Publications, 2019.
[3]	CHOWELL G, HYMAN J M, EUBANK S, et al. Scaling laws for the movement of people between locations in a large city[J]. Physical Review E, 2003, 68(6): 066102. doi: 10.1103/PhysRevE.68.066102
[4]	VON FERBER C, HOLOVATCH T, HOLOVATCH Y, et al. Public transport networks: empirical analysis and modeling[J]. The European Physical Journal B, 2009, 68(2): 261-275. doi: 10.1140/epjb/e2009-00090-x
[5]	LÄMMER S, GEHLSEN B, HELBING D. Scaling laws in the spatial structure of urban road networks[J]. Physica A: Statistical Mechanics and its Applications, 2006, 363(1): 89-95. doi: 10.1016/j.physa.2006.01.051
[6]	JIANG Bin, JIA Tao. Agent-based simulation of human movement shaped by the underlying street structure[J]. International Journal of Geographical Information Science, 2011, 25(1): 51-64. doi: 10.1080/13658811003712864
[7]	王庆云. 关于综合交通网规划的方法与实践[J]. 交通运输系统工程与信息, 2005, 5(1): 11-15. doi: 10.3969/j.issn.1009-6744.2005.01.003 WANG Qing-yun. Methodology and practice of comprehensive transport network planning[J]. Journal of Transportation Systems Engineering and Information Technology, 2005, 5(1): 11-15. (in Chinese) doi: 10.3969/j.issn.1009-6744.2005.01.003
[8]	陆锋, 周成虎, 万庆. 基于特征的城市交通网络非平面数据模型[J]. 测绘学报, 2000, 29(4): 333-340. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB200004009.htm LU Feng, ZHOU Cheng-hu, WAN Qing. A feature-based non-planar data model for urban traffic networks[J]. Acta Geodaetica et Cartographica Sinica, 2000, 29(4): 333-340. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CHXB200004009.htm
[9]	何南, 刘宁, 赵胜川. 基于BPR函数的道路阻抗研究[J]. 南京工程学院学报(自然科学版), 2013, 11(1): 6-11. https://www.cnki.com.cn/Article/CJFDTOTAL-NJGC201301003.htm HE Nan, LIU Ning, ZHAO Sheng-chuan. A study of road traffic impedance based on BPR function[J]. Journal of Nanjing Institute of Technology (Natural Science Edition), 2013, 11(1): 6-11. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NJGC201301003.htm
[10]	王炜, 张庆涛, 李伟, 等. 收费公路交通阻抗分析方法[J]. 中国公路学报, 1998, 11(S1): 49-55. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL1998S1007.htm WANG Wei, ZHANG Qing-tao, LI Wei, et al. A method of analyzing traffic impedance on toll roads[J]. China Journal of Highway and Transport, 1998, 11(S1): 49-55. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL1998S1007.htm
[11]	NABIL ALI SAFWAT K, MAGNANTI T L. A combined trip generation, trip distribution, modal split, and trip assignment model[J]. Transportation Science, 1988, 22(1): 14-30. doi: 10.1287/trsc.22.1.14
[12]	WILSON A G. A statistical theory of spatial distribution models[J]. Transportation Research, 1967, 1(3): 253-269. doi: 10.1016/0041-1647(67)90035-410.3969/j.issn.1009-6744.2005.01.003
[13]	AGAMEZ-ARIAS A M, MOYANO-FUENTES J. Intermodal transport in freight distribution: a literature review[J]. Transport Reviews, 2017, 37(6): 782-807. doi: 10.1080/01441647.2017.1297868
[14]	CARIS A, MACHARIS C, JANSSENS G K. Planning problems in intermodal freight transport: accomplishments and prospects[J]. Transportation Planning and Technology, 2008, 31(3): 277-302. doi: 10.1080/03081060802086397
[15]	SUN Yan, LANG Mao-xiang, WANG Dan-zhu. Optimization models and solution algorithms for freight routing planning problem in the multi-modal transportation networks: a review of the state-of-the-art[J]. Open Civil Engineering Journal, 2015, 9(1): 714-723. doi: 10.2174/1874149501509010714
[16]	ZIAEI Z, JABBARZADEH A. A multi-objective robust optimization approach for green location-routing planning of multi-modal transportation systems under uncertainty[J]. Journal of Cleaner Production, 2021, 291: 125293. doi: 10.1016/j.jclepro.2020.125293
[17]	PALLME D, LAMBERT B, MILLER C, et al. A review of public and private intermodal railroad development in the Memphis region[J]. Research in Transportation Business and Management, 2015, 14: 44-55. doi: 10.1016/j.rtbm.2014.10.011
[18]	LIU Zhi-yuan, MENG Qiang, WANG Shuai-an, et al. Global intermodal liner shipping network design[J]. Transportation Research Part E, 2014, 61: 28-39. doi: 10.1016/j.tre.2013.10.006
[19]	MUTLU A, KAYIKCI T, ÇATAY B. Planning multimodal freight transport operations: a literature review[C]//ISL. 22nd International Symposium on Logistics. Nottingham: ISL, 2017: 535-542.
[20]	徐凤, 朱金福, 杨文东. 复杂网络在交通运输网络中的应用研究综述[J]. 复杂系统与复杂性科学, 2013, 10(1): 18-25. doi: 10.3969/j.issn.1672-3813.2013.01.005 XU Feng, ZHU Jin-fu, YANG Wen-dong. The complex networks' application in transportation networks: a survey[J]. Complex Systems and Complexity Science, 2013, 10(1): 18-25. (in Chinese) doi: 10.3969/j.issn.1672-3813.2013.01.005
[21]	邓兴栋. 城市宏观交通仿真系统架构与关键技术研究[D]. 广州: 华南理工大学, 2010. DENG Xing-dong. Study on the framework and key technologies of macro simulation system for urban transportation[D]. Guangzhou: South China University of Technology, 2010. (in Chinese)
[22]	ALEXIADIS V, COLYAR J, HALKIAS J, et al. The next generation simulation program[J]. ITE Journal, 2004, 74(8): 22-26. http://www.zhangqiaokeyan.com/academic-journal-foreign_other_thesis/020418701818.html
[23]	DURAKU R, ATANASOVA V, KRSTANOSKI N. Building and calibration transport demand model in Anamorava region[J]. Tehnicki Vjesnik-Technical Gazette, 2019, 26(6): 1784-1793. http://www.morebooks.de/store/de/book/the-position-of-gifted-students-in-the-region-of-anamorava/isbn/978-613-9-99463-2
[24]	JO Y, KIM J, OH C, et al. Benefits of travel time savings by truck platooning in Korean freeway networks[J]. Transport Policy, 2019, 83: 37-45. doi: 10.1016/j.tranpol.2019.09.003
[25]	DONG Sheng, ZHOU Ji-biao, MA Chang-xi. Design of a network optimization platform for the multivehicle transportation of hazardous materials[J]. International Journal of Environmental Research and Public Health, 2020, 17(3): 1104. doi: 10.3390/ijerph17031104
[26]	王炜. 多路径交通分配模型的改进及节点分配算法[J]. 东南大学学报, 1994, 24(6): 21-26. doi: 10.3321/j.issn:1001-0505.1994.06.001 WANG Wei. Improved model of multipath assignment and quick algorithm—node assignment algorithm[J]. Journal of Southeast University, 1994, 24(6): 21-26. (in Chinese) doi: 10.3321/j.issn:1001-0505.1994.06.001
[27]	王炜. 一种动态的多路径交通分配模型[J]. 东南大学学报, 1990, 20(1): 63-68. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX199001009.htm WANG Wei. A dynamic model of multiple path traffic assignment[J]. Journal of Southeast University, 1990, 20(1): 63-68. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX199001009.htm
[28]	项昀, 王炜, 郑敦勇, 等. 区域综合网络货运交通方式的优势运距研究[J]. 交通运输系统工程与信息, 2016, 16(6): 33-39. doi: 10.3969/j.issn.1009-6744.2016.06.006 XIANG Yun, WANG Wei, ZHENG Dun-yong, et al. Dominant transportation distance for multi transportation modes in regional integrated freight network[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(6): 33-39. (in Chinese) doi: 10.3969/j.issn.1009-6744.2016.06.006
[29]	项昀, 徐铖铖, 于维杰, 等. 基于人口迁徙大数据的城市对外交通客运方式优势出行距离研究[J]. 交通运输系统工程与信息, 2020, 20(1): 241-246. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202001037.htm XIANG Yun, XU Cheng-cheng, YU Wei-jie, et al. Dominant trip distance of urban external passenger transport mode based on big data of migration[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(1): 241-246. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202001037.htm
[30]	XIANG Yun, XU Cheng-cheng, YU Wei-jie, et al. Investigating dominant trip distance for intercity passenger transport mode using large-scale location-based service data[J]. Sustainability, 2019, 11(19): 5325. doi: 10.3390/su11195325
[31]	HUA Xue-dong, XIE Wen-jie, WANG Wei, et al. The influence of travel distance on mode share for regional trips in China[C]//ASCE. 19th COTA International Conference of Transportation. Washington DC: ASCE, 2019: 5068-5079.