Abstract
Exploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to implement soft constraints in the clustering method and evaluate the effectiveness quantitatively is still a challenge. In this study, we propose an improved trajectory clustering method based on fuzzy density-based spatial clustering of applications with noise (TC-FDBSCAN) to conduct classification on trajectory data. Firstly, we define the trajectory distance which considers the influence of different attributes and determines the corresponding weight coefficients to measure the similarity among trajectories. Secondly, membership degrees and membership functions are designed in the fuzzy clustering method as the extension of the classical DBSCAN method. Finally, trajectory data collected in Shenzhen city, China, are divided into two types (workdays and weekends) and then implemented in the experiment to explore different travel patterns. Moreover, three indices including Silhouette Coefficient, Davies–Bouldin index, and Calinski–Harabasz index are used to evaluate the effectiveness among the proposed method and other traditional clustering methods. The results also demonstrate the advantage of the proposed method.
Highlights
Travel patterns can be explored by analyzing the travel characteristics of moving objects, which reflects peoples’ travel regularity, traffic congestion regularity, and social activity pattern
There are some researchers who propose several modified clustering methods to obtain better clustering results: a modified bee colony optimization (MBCO) [31] is proposed which introduces the approach based on probability selection; a hybrid model-fused k-means and fuzzy c-means clustering with the modified cluster centroid (FKMFCM-MCC) [32]; Wang et al [33] presented a modified find density peaks (MFDP) algorithm to transform the high-dimensional points into two-dimensional, and it expressed good potential for application
E framework mainly contains three aspects: (1) after preprocessing the initial trajectory data, the trajectory distance can be calculated with the combination of spatial, temporal, and directional distance using the weight coefficients; (2) the TC-FDBSCAN is adopted to cluster trajectories, which need to determine the weight coefficients and other algorithm parameters; (3) three indices are used to evaluate the clustering results
Summary
Travel patterns can be explored by analyzing the travel characteristics of moving objects (vehicles and humans), which reflects peoples’ travel regularity, traffic congestion regularity, and social activity pattern. More and more researchers and scholars explore urban travel patterns using large-scale trajectory data, which contain huge hidden information about travel feature and regularity. (2) Spatial statistics: Ni et al [16] employed a spatial econometric model for travel flow analysis to explore factors that influence travel demand; Zhang et al [17] proposed a Bayesian hierarchical approach for modeling the destination choice behavior considering the unavailable factors and spatio-temporal correlations; Kamruzzaman et al [18] estimated the effects of urban form and spatial biases on residential mobility. By defining a reasonable similarity criterion, the clustering method can effectively excavate hidden information from massive trajectory data and reveal travel patterns. There are some researchers who propose several modified clustering methods to obtain better clustering results: a modified bee colony optimization (MBCO) [31] is proposed which introduces the approach based on probability selection; a hybrid model-fused k-means and fuzzy c-means clustering with the modified cluster centroid (FKMFCM-MCC) [32]; Wang et al [33] presented a modified find density peaks (MFDP) algorithm to transform the high-dimensional points into two-dimensional, and it expressed good potential for application
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