On a clustering-based mining approach for spatially and temporally integrated traffic sub-area division

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

doi:10.1016/j.engappai.2020.103932

Cited by 14 publications

(6 citation statements)

References 25 publications

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“…A spatialtemporal model was established to facilitate the application of a clustering algorithm by combining traffic flow direction with real-time traffic status [19]. An integrated traffic sub-area division method, which converted trajectory information into a set of spatially and temporally co-located data points in the 3-dimensional space, was proposed in Niu et al's study [20]. In Feng et al's work [21], a spatial subarea updating method and a multi-period division method of multiple intersections were proceeded in an interleaved manner periodically, which achieves a spatial-temporal subarea division.…”

Section: Temporal Division Methodsmentioning

confidence: 99%

Spatial-temporal adaptive network partitioning for urban traffic signal control

Liu

Yan

et al. 2023

J. Phys.: Conf. Ser.

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In response to rapidly growing and diversifying traffic demand, it is necessary to develop a network partitioning method that could achieve real-time global optimal performance and adapt to traffic network evolution. In this paper, an adaptive partitioning method is presented, which achieves optimal partitions at runtime and determines appropriate Time-of-Day breakpoints to update partition results simultaneously. For each time interval, partitioning schemes are firstly assessed in terms of modularity by taking roadway geometry, real-time traffic flow information, and signal timing into account. Two values are attained from the assessment: the maximum modularity of the optimal partition and the modularity obtained from the existing partition. Then the existing partition is updated, provided that the relative deviation of these two values exceeds a given threshold for a certain number of successive time intervals. Experimental results show that the above-mentioned partitioning scheme outperforms some notable traffic control techniques in modularity in the spatial aspect. In the temporal aspect, the updating scheme can well respond to varying traffic conditions and yield significantly higher average modularity.

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Section: Temporal Division Methodsmentioning

confidence: 99%

Spatial-temporal adaptive network partitioning for urban traffic signal control

Liu

Yan

et al. 2023

J. Phys.: Conf. Ser.

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“…The algorithm has a good effect on hurricane data and animal migration data, but the results have not been very good on real road trajectory datasets, and there are problems such as many clustering parameters and parameter sensitivity. At present, there are a lot of researches to correct these shortcomings, such as the ATCGD algorithm (Mao et al [20]), NEAT algorithm (Binh Han et al [21]), and LBTC algorithm (Niu et al [22]). The ATCGD algorithm maps the divided subtrajectory segments to the grid cell space, then calculates the number of trajectory segments in the grid cell and the distance of the trajectory segments based on this mapping space, adaptively determines the parameters based on the DBSCAN method, and finally completes the clustering.…”

Section: Related Workmentioning

confidence: 99%

“…Then, the clusters are adjusted and updated based on the iterative process, and the clustering centers are updated after each iteration. If the conditions are met, the clustering process will stop, and the trajectory clusters and abnormal trajectory set will be output (lines [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Specifically, according to the outlier function, it is determined whether each trajectory can be clustered into a cluster or temporarily as an exception (lines [4][5][6][7][8][9][10][11][12].…”

Section: The Adjustment and Update Of Clustersmentioning

confidence: 99%

Grid‐Based Whole Trajectory Clustering in Road Networks Environment

Wang

Niu

et al. 2021

Wireless Communications and Mobile Computing

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In the data mining of road networks, trajectory clustering of moving objects plays an important role in many applications. Most existing algorithms for this problem are based on every position point in a trajectory and face a significant challenge in dealing with complex and length-varying trajectories. This paper proposes a grid-based whole trajectory clustering model (GBWTC) in road networks, which regards the trajectory as a whole. In this model, we first propose a trajectory mapping algorithm based on grid estimation, which transforms the trajectories in road network space into grid sequences in grid space and forms grid trajectories by recognizing and eliminating redundant, abnormal, and stranded information of grid sequences. We then design an algorithm to extract initial clustering centers based on density weight and improve a shape similarity measuring algorithm to measure the distance between two grid trajectories. Finally, we dynamically allocate every grid trajectory to the best clusters by the nearest neighbor principle and an outlier function. For the evaluation of clustering performance, we establish a clustering criterion based on the classical Silhouette Coefficient to maximize intercluster separation and intracluster homogeneity. The clustering accuracy and performance superiority of the proposed algorithm are illustrated on a real-world dataset in comparison with existing algorithms.

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“…Ding et al established a congested area range estimation model by using the density wave transfer velocity, and then based on the 3D macroscopic fundamental graph surface model, a dynamic boundary sliding mode control method was proposed to assess the entrance of the congested area, so as to determine the control boundary and dynamic changes [28]. e method proposed by Niu et al is similar to this article [29], which considers both time and space, and a spatiotemporal comprehensive traffic partitioning method is proposed based on clustering.…”

Section: Introductionmentioning

confidence: 96%

Urban Arterial Signal Coordination Using Spatial and Temporal Division Methods

Feng

Zhao

Lin

et al. 2022

Journal of Advanced Transportation

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Traffic signal coordination on urban arterials often requires that timing plans be divided either spatially into clusters of signalized intersections or temporally as time-of-day-based plans. This research proposes a method of dividing timing plans by both spatial and temporal factors simultaneously, in order to provide a dynamic coordinated signal control plan suitable for handling variations in intersection demands and fluctuations in traffic flow. The optimal coordination phase difference of adjacent space coordination subarea is obtained through the method of set operation, so that the spatial subareas can be connected. Similarly, timing plans are dynamically grouped into times of day using the concept of risk decision-making by solving the minimum value of the risk function. Divisions can be further adjusted in real time by changing the conditions, thus resulting in dynamic coordinated signal control. The proposed method was tested in a microscopic simulation of a real-world arterial based on empirical volumes and turning movements. The results showed that the proposed model produced greater reductions in delay and queue length when compared to the methods that subdivide by spatial or temporal thresholds alone. Sensitivity analysis revealed that the proposed method was better suited to imbalances in directional volumes when compared to spatial or temporal division methods alone.

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On a clustering-based mining approach for spatially and temporally integrated traffic sub-area division

Cited by 14 publications

References 25 publications

Spatial-temporal adaptive network partitioning for urban traffic signal control

Spatial-temporal adaptive network partitioning for urban traffic signal control

Grid‐Based Whole Trajectory Clustering in Road Networks Environment

Urban Arterial Signal Coordination Using Spatial and Temporal Division Methods

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