Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (1): 199-208.doi: 10.13229/j.cnki.jdxbgxb.20240712

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Characteristics analysis of port and city transportation network based on percolation theory

Yao SUN1,2(),Dong-xuan BAI1,Bao-zhen YAO1(),Zi-jian BAI2   

  1. 1.School of Mechanical Engineering，Dalian University of Technology，Dalian 116024，China
    2.Tianjin Municipal Engineering Design & Research Institute Co. ，Ltd. ，Tianjin 300051，China
  • Received:2024-06-26 Online:2026-01-01 Published:2026-02-03
  • Contact: Bao-zhen YAO E-mail:sunyao@mail.dlut.edu.cn;yaobaozhen@dlut.edu.cn

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Abstract:

To pinpoint bottleneck sections in the port-city transportation network and delineate the physical boundaries between urban zones and port zones， this paper analyzes the characteristics of the port-city transportation network based on percolation theory. Firstly， the Van-Genuchten soil permeability model was applied to transportation research for the first time， formulating a road section seepage probability model tailored for port-city transportation networks. Secondly， based on actual port-city transportation network data and penetration model parameters， the bottleneck roads and periodic characteristics of the network were analyzed， which categorized the road network into three characteristic regions： urban， port， and fusion areas. Finally， an optimization strategy for the port-city transportation network based on the characteristics of zonal network is established. Results revealed that the partition optimization strategy consistently surpassed the overall optimization strategy， achieving a higher average critical threshold when enhancing bottleneck capacity by 5%-30%. Notably， the partition strategy demonstrated remarkable improvements even with a 5% capacity boost， whereas the overall strategy's benefits became evident only at a 15% increase. This paper offers theoretical guidance for alleviating traffic congestion in port-city regions and shaping effective regional traffic enhancement plans.

Key words: transportation planning and management, percolation theory, port and city transportation network, bottleneck identification, regional optimization strategy

CLC Number: 

  • U491.1

Fig.1

Diagram of the seepage process"

Fig.2

Tianjin port-city road transportation network"

Table 1

Conversion coefficient of each vehicle type"

车型折算系数
小型客车1
小型货车1
中型货车1.5
大型客车1.5
大型货车3
集卡4
其他2

Table 2

Multi-lane saturation traffic volume with different road attributes"

道路属性单车道双车道三车道四车道
高/快速路1 3182 4913 5194 375
主干道1 1202 1162 9903 718
次干道7701 4552 0552 556
支路5049521 3451 673

Fig.3

Seepage probability distribution in uplink direction of road network"

Fig.4

Seepage cluster distribution under different seepage occupation probability"

Fig.5

Seepage cluster sizes under different seepage occupation probabilities"

Fig.6

Bottleneck section identification of road network is studied"

Fig.7

Comparison of seepage probability distributions in upstream and downstream directions of road network"

Fig.8

Analysis of regional network traffic travel characteristics results"

Table 3

Typical critical thresholds for workdays and rest days subregions"

临界阈值Pc城区工作日城区休息日港区工作日港区休息日融合区工作日融合区休息日

最大临界阈值

时间段

0.82

03：30 am

0.80

04：00 am

0.74

03：30 am

0.69

02：00 am

0.71

03：00 am

0.72

03：30 am

最小临界阈值

时间段

0.37

08：00 am

0.58

15：30 pm

0.36

19：00 pm

0.33

7：30 am

0.29

17：30 pm

0.35

19：30 pm

最大、最小临界阈值差值0.450.220.380.360.420.37
平均临界阈值0.620.670.580.570.500.55

Fig.9

Sub-regional and overall bottleneck road section identification results of the road network"

Table 4

Changes of average critical threshold of roadnetwork under different traffic capacityenhancements"

通行能力提升比例/%策略一（分区域）策略二（整体）
00.570.57
50.610.58
100.630.59
150.650.63
200.660.64
250.660.65
300.660.65
[1] 李勋高,李乃强,刘淑永,等. 青岛董家口港城公共交通安全规划探索与实践[J]. 城市公共交通, 2021(8): 46-50.
Li Xun-gao, Li Nai-qiang, Liu Shu-yong, et al. Exploration and practice of public transport safety planning in Dongjiakou port city of Qingdao[J]. Urban Public Transport, 2021(8): 46-50.
[2] 郑长江, 陶童统, 陈志超. 基于流量可调重分配的级联失效模型[J]. 吉林大学学报: 工学版, 2024, 54(9): 2441-2450.
Zheng Chang-jiang, Tao Tong-tong, Chen Zhi-chao. Cascading failure model based on adjustable redistribution of traffic flow[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(9): 2441-2450.
[3] 温晓岳, 钱国敏, 孔桦桦, 等. TrafficPro:一种针对城市信控路网的路段速度预测框架[J]. 吉林大学学报: 工学版, 2024, 54(8): 2214-2222.
Wen Xiao-yue, Qian Guo-min, Kong Hua-hua, et al. TrafficPro: a framework to predict link speeds on signalized urban traffic network[J]. Journal of Jilin University (Engineering and Technology Edition), 2024, 54(8): 2214-2222.
[4] 袁韵, 徐戈, 贾建民. 网约车流动行为的时空特征及网络分析[J]. 系统管理学报, 2021, 30(1): 28-39.
Yuan Yun, Xu Ge, Jia Jian-min. Spatiotemporal characteristics and network analysis of car-hailing mobility behavior[J]. Journal of Systems & Management, 2021, 30(1): 28-39.
[5] 丁祥颖, 胡尧, 刘智权. 基于交通数据分解和时空特征提取的车辆平均车速预测模型[J]. 运筹与模糊学, 2023, 13(2): 415-430.
Ding Xiang-ying, Hu Yao, Liu Zhi-quan. Vehicle average speed prediction model based on traffic data decomposition and spatio-temporal feature extraction[J]. Operations Research and Fuzziology, 2023, 13(2): 415-430.
[6] Zhang M Y, Huang T, Guo Z X, et al. Complex-network-based traffic network analysis and dynamics: A comprehensive review[J]. Physica A: Statistical Mechanics and its Applications, 2022, 607: No.128063.
[7] Latora V, Marchiori M. Is the Boston subway a small-world network?[J]. Physica A: Statistical Mechanics and its Applications, 2002, 314(1-4):109-113.
[8] Sen P, Dasgupta S, Chatterjee A, et al. Small-world properties of the Indian railway network[J]. Physical Review E, 2003, 67(3): No.036106.
[9] 吴样平, 郭飞, 曾明华. 基于复杂网络的城市综合交通网络特征分析与优化研究[J]. 江西师范大学学报: 自然科学版, 2015, 39(3): 326-330.
Wu Yang-ping, Guo Fei, Zeng Ming-hua. The characteristic analyses and optimization of urban integrated transport network based on complex network[J]. Journal of Jiangxi Normal University (Natural Science Edition), 2015, 39(3): 326-330.
[10] 王国明. 城市群道路网络特性及演化研究[D]. 长沙:中南大学交通运输工程学院, 2012.
Wang Guo-ming. A study on property and evolution of road network of urban agglomeration[D]. Changsha: School of Traffic & Transportation Engineering, Central South University, 2012.
[11] Zou H, Yue Y, Li Q. Urban traffic state explained by road networks and spatial variance approach using floating car data[J]. Transportation Research Record, 2014(2467):40-48.
[12] 胡垚, 吕斌. 城市地铁网络特征分析及类型划分[J]. 规划师, 2013, 29(5): 96-101.
Hu Yao, Lv Bin. Metro system character analysis and categorization[J]. Planners, 2013, 29(5): 96-101.
[13] 张李盈, 任景莉. 渗流理论、方法、进展及存在问题[J]. 自然杂志, 2019, 41(2): 119-131.
Zhang Li-ying, Ren Jing-li. Percolation theory, method, progress, and existing problems[J]. Chinese Journal of Nature, 2019, 41(2): 119-131.
[14] Flory P J. Molecular size distribution in three dimensional polymers. I. gelation[J]. Journal of the American Chemical Society, 1941, 63(11): 3083-3090.
[15] Broadbent S R, Hammersley J M. Percolation processes[J]. Mathematical Proceedings of the Cambridge Philosophical Society, 1957, 53(3): 629-641.
[16] Werner W, Schramm O, Lawler G F.Conformal invariance of planar loop-erased random walks and uniform spanning trees[J]. The Annals of Probability, 2004, 32(1B): 939-995.
[17] 于少波, 吴玲达, 张喜涛, 等. 面向空间信息网络的多特征可视化方法综述[J]. 中国电子科学研究院学报, 2018, 13(2): 201-208.
Yu Shao-bo, Wu Ling-da, Zhang Xi-tao, et al. Survey of multi-feature visualization for space information network[J]. Journal of China Academy of Electronics and Information Technology, 2018, 13(2): 201-208.
[18] Sander L M, Warren C P, Sokolov I M, et al. Percolation on heterogeneous networks as a model for epidemics[J]. Mathematical Biosciences, 2002, 180(1/2): 293-305.
[19] Guo C, Zhao Y D, David J H, et al. Attack structural vulnerability of power grids: a hybrid approach based on complex networks[J]. Physica A: Statistical Mechanics and its Applications, 2010, 389(3): 595-603.
[20] Glauche I, Krause W, Sollacher R, et al. Continuum percolation of wireless ad hoc communication networks[J]. Physica A: Statistical Mechanics and its Applications, 2003, 325(3/4): 577-600.
[21] Ferraro G D, Moreno A, Min B, et al. Finding influential nodes for integration in brain networks using optimal percolation theory[J]. Nature Communications, 2018, 9(1):No. 2274.
[22] Li D Q, Fu B W, Wang Y P,et al. Percolation transition in dynamical traffic network with evolving critical bottlenecks[J]. Proceedings of the National Academy of Sciences, 2015, 112(3):669-672.
[23] Zeng G W, Li D Q, Guo S M, et al. Switch between critical percolation modes in city traffic dynamics[J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116: 23-28.
[24] Chen Z, Yang C, Qian J H, et al. Recursive traffic percolation on urban transportation systems[J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023, 33(3): No.033132.
[25] 傅裕寿. 交通流理论[J]. 力学与实践, 1989(4): 5-16, 25.
Fu Yu-shou. Traffic flow theory[J]. Mechanics in Engineering, 1989(4): 5-16, 25.
[26] 林豪, 江竹, 李树彬. 基于机器学习的城市快速路速度-密度关系模型[J]. 西安科技大学学报, 2020, 40(6): 1109-1116.
Lin Hao, Jiang Zhu, Li Shu-bin. Speed-density relationship model of urban expressway based on machine learning[J]. Journal of Xi'an University of Science and Technology, 2020, 40(6): 1109-1116.
[27] Chen B Y, Lam W H K, Sumalee A, et al. Reliable shortest path finding in stochastic networks with spatial correlated link travel times[J]. International Journal of Geographical Information Science, 2012, 26(2): 365-386.
[28] 张平, 王聪, 谢长青, 等. 关于非饱和渗透理论中的几项基本问题的剖析[J]. 沈阳大学学报: 自然科学版, 2014, 26(5): 386-390.
Zhang Ping, Wang Cong, Xie Chang-qing, et al. Basic issues in unsaturated hydraulic theory[J]. Journal of Shenyang University(Natural Science), 2014, 26(5): 386-390.
[29] 彬楠, 樊贵盛. 土壤水分特征曲线Van-Genuchten 模型参数的土壤传输函数比选[J]. 农村水利水电, 2018(5): 33-36.
nan Bin, Fan Gui-sheng. Van-Genuchten model parameters of soil water characteristics curve are compared with the soil transfer[J]. China Rural Water and Hydropower, 2018(5): 33-36.
[30] 城市道路工程设计规范 .[S].
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