On a Clustering-Based Approach for Traffic Sub-area Division

Zhu, Jiahui; Niu, Xinjian; Wu, Chase Q.

doi:10.1007/978-3-030-22999-3_45

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Bie et al [3], starting from the overall perspective of traffic control system and taking saturation as an indicator, established the target set of subarea division, put forward the idea of establishing the correlation model and the division algorithm, and formed the framework system of subarea dynamic division from three levels: strategic, theoretical, and algorithmic level. Zhu et al [21] proposed a subarea division method TSAD-HR (Traffic Subarea Division-Hot Region) based on hot spots according to the spatiotemporal trajectory of traffic flow. In these partitioning methods based on a variety of single or compound indicators, the compatibility of traffic control partitioning results to algorithms is rarely considered.…”

Section: Existing Workmentioning

confidence: 99%

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

Luo

Zhang

2021

Discrete Dynamics in Nature and Society

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In this paper, the traffic area of subzone division in urban road network is studied and a subzone division method based on the combination of static partition and dynamic partition is proposed. The static partition is carried out for the road network when the traffic flow is in a noncongested state, so as to provide the decision-making basis for the traffic green wave signal control strategy. At the same time, aiming at the road network when the traffic flow is congested, the dynamic partition is carried out on the basis of static partition to provide the decision-making basis for the traffic maximum flow signal control strategy. In view of the fact that it is difficult to determine the clustering center point during the initial division, this method proposes to determine the clustering center point according to the value of nodes of betweenness centrality. In order to solve the problem that it is difficult to collect traffic data, a method for estimating traffic flow density is proposed. In order to solve the problem of normalization of different probability distribution among various parameters, Mahalanobis distance is used as the fusion index of subzone division. Model verification shows that the method is feasible and effective.

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Section: Existing Workmentioning

confidence: 99%

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

Luo

Zhang

2021

Discrete Dynamics in Nature and Society

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“…The process of trajectory data analysis mainly consists of obtaining and preprocessing trajectory data, trajectory data management, and a variety of mining tasks, including trajectory pattern mining, privacy protection, outlier detection [8,9], and clustering trajectories on complex road networks [10,11]. Many studies have been published, and trajectory data analysis is a very active research field.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

Wang

Niu

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

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“…Comparatively, partitional approaches usually group the data points into a predetermined number of clusters, but Shen et al ( 22 ) divided a whole area into several subareas by studying the spatial distribution of boarding and alighting points using taxi GPS trajectories. Yuan et al ( 23 ) utilized the mobility and location information obtained from latent activity trajectory (LAT) to cluster the road segments into several functional subareas, Zheng et al ( 24 ) and Zhu et al ( 25 ) proposed grid-based K -means clustering algorithms with the utilization of taxi GPS data to get traffic subareas, and Lu et al ( 26 ) realized the dynamic partition of coordinated control subareas based on the clustering algorithm and correlation-degree theory, with the genetic algorithm then being adopted for fast optimization of the subarea partition results. The partitional approaches with a determined cluster number could result in imbalanced subareas.…”

mentioning

confidence: 99%

Subarea Partition Based on Correlation Analysis with Edge-Elimination Strategy Using Automatic License Plate Recognition Data

Liu²,

et al. 2022

Transportation Research Record: Journal of the Transportation R

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To partition an urban network into several subareas (i.e., subarea partition) is a vital step for regional coordinated signal control. The correlation between intersections must be analyzed for achieving reliable subarea partition results. However, because of the incompleteness of spatial–temporal information in traffic data, previous studies merely explored the relationship between any intersections. Subarea partition considering the correlation of any pair of intersections remains a challenge in a large-scale network. This paper proposes a subarea partition method that integrates a novel correlation-degree model and the Newman fast algorithm with an edge-elimination strategy using automatic license plate recognition (ALPR) data. First, vehicle trips are extracted and a correlation-degree model is developed for measuring the relationship of any pair of intersections. Second, an edge-elimination strategy is proposed to generate candidate subarea partition solutions under conditions with different proportions of correlated edges. Finally, an optimal solution of subarea partition is identified by the ratios of ideal connected intersections to total intersections of different partition solutions’ correlation index (CI). The proposed method was implemented in a real-world urban network in Kunshan, China. The results show that the optimal partition solution can be obtained when the top 33% of correlated edges are maintained, and the ratio of ideal connected intersections’ CI is 72.35% with most of the intersections being connected, which demonstrates the rationality of the proposed partition method in large-scale urban networks.

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On a Clustering-Based Approach for Traffic Sub-area Division

Cited by 7 publications

References 18 publications

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

Subarea Partition Based on Correlation Analysis with Edge-Elimination Strategy Using Automatic License Plate Recognition Data

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