Detecting spatiotemporal extents of traffic congestion: a density-based moving object clustering approach

Shi, Yan; Wang, Da; Tang, Jianbo; Deng, Min; Liu, Huimin; Liu, Baoju

doi:10.1080/13658816.2021.1905820

Cited by 22 publications

(8 citation statements)

References 54 publications

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“…electrocardiograms 71 and MRI observations 72 ), are modeled as time series consisting of observations at regular time intervals. 73,74 In contrast, trajectory data is usefully considered as a group of moving objects, such as vehicles, 12,75 people, 76 or animals, 13 with each trajectory describing a particular evolution of associated spatial coordinates over time. 77,78 Considering the trajectories as a group of moving objects, the spatiotemporal approach to clustering involves applying a clustering algorithm to each independent time step of the trajectories, generating a database of independent spatial clusters at each time.…”

Section: Choice Of Methodologymentioning

confidence: 99%

“…Typically, data, such as stock prices, temperature measurements, and some specific types of medical data (e.g. electrocardiograms and MRI observations), are modeled as time series consisting of observations at regular time intervals. , In contrast, trajectory data is usefully considered as a group of moving objects, such as vehicles, , people, or animals, with each trajectory describing a particular evolution of associated spatial coordinates over time. , …”

Section: Methodsmentioning

confidence: 99%

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Automatic Clustering of Excited-State Trajectories: Application to Photoexcited Dynamics

Acheson,

Kirrander

2023

J. Chem. Theory Comput.

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We introduce automatic clustering as a computationally efficient tool for classifying and interpreting trajectories from simulations of photo-excited dynamics. Trajectories are treated as time-series data, with the features for clustering selected by variance mapping of normalized data. The L 2 -norm and dynamic time warping are proposed as suitable similarity measures for calculating the distance matrices, and these are clustered using the unsupervised density-based DBSCAN algorithm. The silhouette coefficient and the number of trajectories classified as noise are used as quality measures for the clustering. The ability of clustering to provide rapid overview of large and complex trajectory data sets, and its utility for extracting chemical and physical insight, is demonstrated on trajectories corresponding to the photochemical ring-opening reaction of 1,3-cyclohexadiene, noting that the clustering can be used to generate reduced dimensionality representations in an unbiased manner.

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Section: Choice Of Methodologymentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automatic Clustering of Excited-State Trajectories: Application to Photoexcited Dynamics

Acheson,

Kirrander

2023

J. Chem. Theory Comput.

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“…Then, vehicles will update their position based on the first-in-first-out (FIFO) formula within a single time step. Subsequently, road segments with evident traffic congestion are detected via the clustering method [28]. Finally, routing schemes are replanned in advance according to the bidding mechanism for vehicles that will be affected by congestion within each time step (see Section 3.1), while vehicles located on other uncongested road segments continue traveling along their planned routes.…”

Section: Dynamic Route-planning Methods Overviewmentioning

confidence: 99%

An Adaptive Route Planning Method of Connected Vehicles for Improving the Transport Efficiency

Long

Deng

Yang

et al. 2022

IJGI

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In recent years, the route-planning problem has gained increased interest due to the development of intelligent transportation systems (ITSs) and increasing traffic congestion especially in urban areas. An independent route-planning strategy for each in-vehicle terminal improves its individual travel efficiency. However, individual optimal routes pursue the maximization of individual benefit and may contradict the global benefit, thereby reducing the overall transport efficiency of the road network. To improve traffic efficiency while considering the travel time of individual vehicles, we propose a new dynamic route-planning method by innovatively introducing a bidding mechanism in the connected vehicle scenario for the first time. First, a novel bidding-based dynamic route planning is proposed to formulate vehicle routing schemes for vehicles affected by congestion via the bidding process. Correspondingly, a bidding price incorporating individual and global travel times was designed to balance the travel benefits of both objectives. Then, in the bidding process, a new local search algorithm was designed to select the winning routing scheme set with the minimum bidding price. Finally, the proposed method was tested and validated through case studies of simulated and actual driving scenarios to demonstrate that the bidding mechanism would be conducive to improving the transport efficiency of road networks in large-scale traffic flow scenarios. This study positively contributes to the research and development of traffic management in ITSs.

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“…The application of big data has served as a basic strategic digital resource in smart cities. Many researchers have analyzed the trajectory GPS data of transportation vehicles in order to mine the hidden information behind the data to reflect the urban operation status and define temporal and spatial change rules [1], in addition to use in traffic congestion status analysis [2][3][4][5][6][7], crowd movement distribution [8][9][10], traffic travel recommendation [11,12], and road planning [13,14], urban hotspot discovery [15][16][17][18], and so on. Such research results are directly applied to the construction of a smart city to elucidate more reasonable urban road planning and a more reasonable dispersion of vehicle flow and human flow.…”

Section: Introductionmentioning

confidence: 99%

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

et al. 2021

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With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots.

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Detecting spatiotemporal extents of traffic congestion: a density-based moving object clustering approach

Cited by 22 publications

References 54 publications

Automatic Clustering of Excited-State Trajectories: Application to Photoexcited Dynamics

Automatic Clustering of Excited-State Trajectories: Application to Photoexcited Dynamics

An Adaptive Route Planning Method of Connected Vehicles for Improving the Transport Efficiency

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

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