|Table of Contents|

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

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2021年02期
Page:
159-172
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Multi-network integrated traffic analysis model and algorithm of comprehensive transportation system
Author(s):
WANG Wei1; 2;  HUA Xue-dong1; 2;  ZHENG Yong-tao1; 2
(1. School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China; 2. Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, Jiangsu, China)
Keywords:
comprehensive transportation system;  traffic network;  transportation mode;  multi-network integration;  traffic analysis model
PACS:
U491
DOI:
10.19818/j.cnki.1671-1637.2021.02.014
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 ofmulti-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.

References:

[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.
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)
[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.
[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.
[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.
[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.
[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.
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)
[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.
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)
[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.
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)
[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.
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)
[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.
[12] WILSON A G. A statistical theory of spatial distribution
models[J]. Transportation Research, 1967, 1(3): 253-269.
[13] AGAMEZ-ARIAS A M, MOYANO-FUENTES J. Intermodal transport in freight distribution: a literature review[J]. Transport Reviews, 2017, 37(6): 782-807.
[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.
[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.
[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.
[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.
[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.
[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.
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)
[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.
[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.
[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.
[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.
[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.
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)
[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.
WANG Wei. A dynamic model of multiple path traffic assignment[J]. Journal of Southeast University, 1990, 20(1): 63-68.(in Chinese)
[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.
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)
[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.
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)
[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.
[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.

Memo

Memo:
-
Last Update: 2021-06-01