Detecting clusters over intercity transportation networks using K-shortest paths and hierarchical clustering: a case study of mainland China

Yue, Hanqiu; Guan, Qingfeng; Pan, Yongting; Chen, Lirong; Lv, Jianjun; Yao, Yao

doi:10.1080/13658816.2019.1566551

Cited by 18 publications

(6 citation statements)

References 46 publications

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“…Many scholars have applied and innovated around these fundamental tasks, and these studies mainly focus on complex network community discovery based on modularity and spectral clustering (Lei, Zhou, & Shi, 2019;Newman, 2003), link prediction and centrality analysis (Trouillon, Welbl, Riedel, Gaussier, & Bouchard, 2016), influence calculation and heterogeneous network alignment (Chauhan, Ram, Hari, & Jolly, 2021;. It also includes some work at the algorithmic level of graph space optimization, using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to extract and merge traffic flow networks to form better complex network graph layouts (Von Landesberger et al, 2015;Wang, Zhang, Zheng, & Hu, 2022) and combining K-shortest paths & hierarchical clustering to parse urban transportation network structure Yue et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

City2vec: Urban knowledge discovery based on population mobile network

Zhang

Zheng²,

Helbich³

et al. 2022

Sustainable Cities and Society

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Section: Related Workmentioning

confidence: 99%

City2vec: Urban knowledge discovery based on population mobile network

Zhang

Zheng²,

Helbich³

et al. 2022

Sustainable Cities and Society

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“…e comparison between PTN clusters and urban agglomerations can be used to estimate whether the PTNs are capable of supporting these human distributions [69]. Identifying under-and overserviced areas can also help in policy decisions, including infrastructure planning and local development [70].…”

Section: Link Building Process In Ptnsmentioning

confidence: 99%

Modeling the Public Transport Networks: A Study of Their Efficiency

López

2021

Complexity

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The public transportation network (PTN) provides mobility and access to community resources, employment, medical care, infrastructures, and other resources in the city. This research studies the process of the formation of links among nodes in different real-world PTNs. We have found that this process may be appropriately explained by a generalized linear model (GLM) using local, global, and quasilocal similarity indexes as explanatory variables. In modeling, the response variable was described by a binomial probability density function, and the logit function was used as a link function. In the crossvalidation process, utilising a downsampling approach, both average accuracy and area under the receiver operating characteristic curve (AUC) metrics presented higher values than 0.99. The kappa parameter had magnitudes larger than 0.93 for most of the PTNs. In the final validation stage, recall and specificity metrics took the value 1. Accuracy and precision parameters were larger than 0.99 and 0.87, respectively, for the majority of PTNs. Only one of the PTNs required utilising a smoothed bootstrap approach in order to achieve better results. The similarity measures with the greatest influence on the model were determined. We also assessed the impact of link removal on the global efficiency of PTNs, considering several similarity indexes. Additionally, we find that most of the networks show low local and global efficiencies (≤0.20), as well as travel times with a relevant variability, exhibiting standard deviations larger than 790 seconds. Significant similarities exist between the cumulative probability distributions of the local efficiency in all PTNs. With respect to the centrality measures, the eigenvector centrality presented a strong correlation with the hub/authority centralities (>0.80), while the pagerank showed a moderate, high, or very high correlation with the degree in all PTNs, >0.50.

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“…Among them, community detection models can more effectively and accurately reveal the grouping effect in urban networks, including Girvan-Newman [20], Walktrap [21], Label Propagation [22], Infomap [23], etc. However, these models only consider the network topological relationship, failing to consider the impact of the geographical distance on the connection strength [24]. For this reason, Chen et al [25] introduced the geographical distance into the calculation of modularity, i.e., geographical modularity.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Wan et al [29] employed the DASSCAN algorithm based on density clustering to merge spatially adjacent nodes with similar structures into communities, but this method was only used for homogeneous spatial networks within partitions. To address the aforementioned issues, Yue et al [24] proposed the Transportation Cluster Detection (TCD) community detection algorithm based on K-shortest paths and hierarchical clustering. The algorithm utilizes K-shortest paths to reflect real travel behaviors, employs a bottom-up hierarchical clustering approach to explore node-merging processes, and incorporates geographical modularity to identify the optimal communities.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Liang,

Xie,

Bao

2024

Sustainability

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Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

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Detecting clusters over intercity transportation networks using K-shortest paths and hierarchical clustering: a case study of mainland China

Cited by 18 publications

References 46 publications

City2vec: Urban knowledge discovery based on population mobile network

City2vec: Urban knowledge discovery based on population mobile network

Modeling the Public Transport Networks: A Study of Their Efficiency

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

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