Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data

Chen, Huiyu; Yang, Chao; Xu, Xiangdong

doi:10.1155/2017/1738085

Cited by 34 publications

(26 citation statements)

References 23 publications

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“…To identify the commuting vehicles, a clustering technique was utilized to analyze temporal-spatial features extracted from ALPR data. Many clustering algorithms and strategies, such as K-means [24,31], DBSCAN [32], GMM (Gaussian Mixture Model) [33], nested clustering [34], online agglomerative clustering [35], hierarchical clustering [36], and other algorithms [37,38] had been proposed in the past decades. Hierarchical clustering, as a typical unsupervised machine learning algorithm, has been applied to a wide spectrum of transportation researches.…”

Section: Commuting Vehicles Identification Using Ward's Hierarchical mentioning

confidence: 99%

“…Recently, with the emerging big data technologies [13], the commuting pattern at an individual level can be efficiently derived using advanced data-driven methods (e.g., machine learning) [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Various kinds of data were utilized in these data-driven based methods, including Global Positioning System (GPS) data, mobile phone call detail records (CDRs), smart card data and remote sensing imagery [14][15][16], which provide new sights for traffic control-oriented applications.…”

Section: Introductionmentioning

confidence: 99%

“…Chang and Yang [23] analyzed the temporal and spatial distributions of the commuting vehicles based on the ALPR data. Chen et al [24] used a K-means clustering algorithm to extract the commuting travel vehicles based on ALPR data. Our previous study [25] estimated the O-D patterns using vehicle trajectory data collected by ALPR devices and investigated the temporal-spatial distribution patterns of trip generation and attraction, etc.…”

Section: Introductionmentioning

confidence: 99%

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Commuting Pattern Recognition Using a Systematic Cluster Framework

et al. 2020

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Identifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters' route feature is not fully considered or not accurately characterized. In this study, a systematic framework considering the route feature for commuting pattern recognition was developed for urban road networks. Three modules are included in the proposed framework. These modules were proposed based on automatic license plate recognition (ALPR) data. First, the temporal and spatial features of individual vehicles were extracted based on the trips detected by ALPR sensors, then a hierarchical clustering technique was applied to classify the detected vehicles and the ratio of commuting trips was derived. Based on the ratio of commuting trips, the temporal and spatial commuting patterns were investigated, respectively. The proposed method was finally implemented in a ring expressway of Kunshan, China. The results showed that the method can accurately extract the commuting patterns. Further investigations revealed the dynamic temporal-spatial features of commuting patterns. The findings of this study demonstrate the effectiveness of the proposed method in mining commuting patterns at urban traffic networks.

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Section: Commuting Vehicles Identification Using Ward's Hierarchical mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Commuting Pattern Recognition Using a Systematic Cluster Framework

et al. 2020

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“…Park [5] found that several clustering methods such as the K-means and Fuzzy clustering were effective in traffic volume forecasting. Chen et al [6] used the K-means clustering along with Davies-Bouldin Index and Silhouette Coefficient to capture the distinct groups in the vehicle temporal and spatial travel behaviors using license plate recognition data. Fuzzy C-means clustering, a probability-based clustering was found successful in recognizing congestion patterns on urban roads based on GPS trajectory [7].…”

Section: Review Of Clustering Applicationsmentioning

confidence: 99%

Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling

Saha

Tariq

Hadi

et al. 2019

Journal of Advanced Transportation

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There has been an increasing interest in recent years in using clustering analysis for the identification of traffic patterns that are representative of traffic conditions in support of transportation system operations and management (TSMO); integrated corridor management; and analysis, modeling, and simulation (AMS). However, there has been limited information to support agencies in their selection of the most appropriate clustering technique(s), associated parameters, the optimal number of clusters, clustering result analysis, and selecting observations that are representative of each cluster. This paper investigates and compares the use of a number of existing clustering methods for traffic pattern identifications, considering the above. These methods include the K-means, K-prototypes, K-medoids, four variations of the Hierarchical method, and the combination of Principal Component Analysis for mixed data (PCAmix) with K-means. Among these methods, the K-prototypes and K-means with PCs produced the best results. The paper then provides recommendations regarding conducting and utilizing the results of clustering analysis.

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“…However, scant attention has been paid to mining human travel patterns with AVI data in previous research. Chen et al clustered several travel characteristics such as travel distance, travel frequency, and total activity duration using the K-Means clustering algorithm based on AVI and presented a detailed analysis of each group [30]. Their result showed that it is possible to identify vehicle groups with similar travel behavior using AVI data.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China

et al. 2019

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Automatic vehicle identification (AVI) systems collect 24 h vehicle travel data for the efficient management of traffic flows. The automatic vehicle identification data collected by an overhead traffic monitoring system provides a means for understanding urban traffic flows and human mobility. This article explores the weekly travel patterns of private vehicles based on AVI data in Wuhan, a megacity in Central China. We extracted origin–destination information and applied the K-Means clustering algorithm to classify spatial traffic hot spots by camera locations. Subsequently, the Latent Dirichlet Allocation algorithm was used to mine the temporal travel patterns of individual vehicles. The cluster results are summarized in nine travel probability matrixes. The effectiveness of this approach is illustrated by a case study using a large set of AVI data collected from 19 to 24 November 2018, in Wuhan, China. The results revealed six variations of the travel demand on weekdays and weekends—the commuting behaviors of private drivers triggered a tidal change in traffic flows. This study also exposed nine weekly travel patterns for private cars, reflecting temporal similarities of human mobility patterns. We identified four types of commuters. These results can help city managers understand daily changes in urban travel demands.

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Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data

Cited by 34 publications

References 23 publications

Commuting Pattern Recognition Using a Systematic Cluster Framework

Commuting Pattern Recognition Using a Systematic Cluster Framework

Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling

Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China

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