A DBSCAN-based framework to mine travel patterns from origin-destination matrices: Proof-of-concept on proxy static OD from Brisbane

Behara, Krishna N.S.; Bhaskar, Ashish; Chung, Edward

doi:10.1016/j.trc.2021.103370

Cited by 27 publications

(11 citation statements)

References 49 publications

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“…These time series were used as inputs for the DBSCAN to complete the clustering, and a total of 3 HSR station classification clusters were finally generated. As opposed to k-means (Laharotte et al, 2015), DBSCAN does not require one to specify the number of clusters in the data the prior (Behara et al, 2021), in which the resulting number of clusters is the optimal result. Therefore, the classes of HSR stations in this study are determined to be 3.…”

Section: Classification Results and Spatiotemporal Characteristics Of...mentioning

confidence: 99%

Classification and determinants of high-speed rail stations using multi-source data: A case study in Jiangsu Province, China

Yue

Chen

Feng

et al. 2023

Sustainable Cities and Society

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Section: Classification Results and Spatiotemporal Characteristics Of...mentioning

confidence: 99%

Classification and determinants of high-speed rail stations using multi-source data: A case study in Jiangsu Province, China

Yue

Chen

Feng

et al. 2023

Sustainable Cities and Society

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“…(3) Outliers: A sample that is neither a core point nor a border point [54,57]. Figure 4 illustrates the process of the DBSCAN clustering algorithm [31,58,59], where A is a randomly selected point in the sample points. First, set the radius and the minimum number of sample points contained within the circle and draw a circle around A.…”

Section: Density-based Clusteringmentioning

confidence: 99%

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

et al. 2023

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Clustering is an unsupervised learning technology, and it groups information (observations or datasets) according to similarity measures. Developing clustering algorithms is a hot topic in recent years, and this area develops rapidly with the increasing complexity of data and the volume of datasets. In this paper, the concept of clustering is introduced, and the clustering technologies are analyzed from traditional and modern perspectives. First, this paper summarizes the principles, advantages, and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms. Then, the core elements of clustering are presented, such as similarity measures and evaluation index. Considering that data processing is often applied in vehicle engineering, finally, some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted. The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use, especially in vehicles.

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“…The DBSCAN algorithm has been proven effective for extracting meaningful spatial clusters and performs better than other similar algorithms in the presence of data noise, which is often seen in traffic datasets. 20 Denote N min as the minimum number threshold of data samples required to be considered as a cluster and 𝜀 as the distance threshold for two data samples being in the same cluster. The basic process of the DBSCAN clustering algorithm is stated as follows.…”

Section: Spatial Clusteringmentioning

confidence: 99%

“…The destination coordinates in the training subset are used as the input for the DBSCAN clustering and the test subset is used for prediction performance evaluation. We adjust the threshold parameter of the minimum number of data samples required to be considered as a cluster in the DBSCAN clustering algorithm with values from (3,5,10,20), to obtain different cluster numbers for comparison. There are approximately 5800 vehicles in the dataset.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Vehicle destination prediction with spatial clustering and machine learning

Jiang

2022

Internet Technology Letters

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The Internet of Vehicles is the next-generation information and technology infrastructure for solving existing challenges. The ability to predict vehicle destinations is a prerequisite for providing more applications, for example, content caching and data dissemination. However, the data in a real-world system could be limited. In this letter, the vehicle destination prediction problem with limited data is defined, and a general clustering-then-classification framework is further proposed as the solution. Based on a real-world dataset, the experimental results show that the combination of the DBSCAN clustering algorithm and the XGBoost classifier outperforms the baselines.

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A DBSCAN-based framework to mine travel patterns from origin-destination matrices: Proof-of-concept on proxy static OD from Brisbane

Cited by 27 publications

References 49 publications

Classification and determinants of high-speed rail stations using multi-source data: A case study in Jiangsu Province, China

Classification and determinants of high-speed rail stations using multi-source data: A case study in Jiangsu Province, China

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Vehicle destination prediction with spatial clustering and machine learning

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