An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns

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.

[1]  Hang Li,et al.  Spatial–temporal travel pattern mining using massive taxi trajectory data , 2018, Physica A: Statistical Mechanics and its Applications.

[2]  Oded Cats,et al.  A data driven method for OD matrix estimation , 2020, Transportation Research Part C: Emerging Technologies.

[3]  Yong Wang,et al.  Green logistics location-routing problem with eco-packages , 2020 .

[4]  Hans-Peter Kriegel,et al.  Density-based clustering of uncertain data , 2005, KDD '05.

[5]  Eren Özceylan,et al.  A GIS-based MCDM approach for the evaluation of bike-share stations , 2018, Journal of Cleaner Production.

[6]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[7]  R. Karthi,et al.  Graph Similarity-based Hierarchical Clustering of Trajectory Data , 2020 .

[8]  Jinjun Tang,et al.  Exploring urban travel patterns using density-based clustering with multi-attributes from large-scaled vehicle trajectories , 2021 .

[9]  Yong Wang,et al.  Collaborative two-echelon multicenter vehicle routing optimization based on state–space–time network representation , 2020 .

[10]  Fang Liu,et al.  Inferring driving trajectories based on probabilistic model from large scale taxi GPS data , 2018, Physica A: Statistical Mechanics and its Applications.

[11]  Nikola K. Kasabov,et al.  An efficient greedy K-means algorithm for global gene trajectory clustering , 2006, Expert Syst. Appl..

[12]  Taha Mokfi,et al.  Evaluation and selection of clustering methods using a hybrid group MCDM , 2019, Expert Syst. Appl..

[13]  R. A. Acheampong.  Spatial structure, intra-urban commuting patterns and travel mode choice: Analyses of relationships in the Kumasi Metropolis, Ghana , 2020, Cities.

[14]  Josep Casanovas-Garcia,et al.  A Comparison of Deep Learning Methods for Urban Traffic Forecasting using Floating Car Data , 2020 .

[15]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Mohsen Ramezani,et al.  Dynamic modeling and control of taxi services in large-scale urban networks: A macroscopic approach , 2018, Transportation Research Part C: Emerging Technologies.

[17]  Sanjay Garg,et al.  Development and validation of OPTICS based spatio-temporal clustering technique , 2016, Inf. Sci..

[18]  Yingyuan Xiao,et al.  A novel next new point-of-interest recommendation system based on simulated user travel decision-making process , 2019, Future Gener. Comput. Syst..

[19]  Loukas Dimitriou,et al.  Dynamic Estimation of Optimal Dispatching Locations for Taxi Services in Mega-Cities based on Detailed GPS Information , 2016 .

[20]  Gloria Bordogna,et al.  Fuzzy extensions of the DBScan clustering algorithm , 2016, Soft Comput..

[21]  Yong Wang,et al.  Profit distribution in collaborative multiple centers vehicle routing problem , 2017 .

[22]  Changjiang Bu,et al.  Modified FDP cluster algorithm and its application in protein conformation clustering analysis , 2019, Digit. Signal Process..

[23]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[24]  Yan Chen,et al.  Adaptive entropy weighted picture fuzzy clustering algorithm with spatial information for image segmentation , 2020, Appl. Soft Comput..

[25]  Y. Zou,et al.  Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city—China , 2018 .

[26]  Yong Qi,et al.  Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach , 2018, Physica A: Statistical Mechanics and its Applications.

[27]  Toshiyuki Yamamoto,et al.  Identification of activity stop locations in GPS trajectories by DBSCAN-TE method combined with support vector machines , 2018 .

[28]  Hans-Peter Kriegel,et al.  Clustering Multi-represented Objects with Noise , 2004, PAKDD.

[29]  M. Punniyamoorthy,et al.  Development of new seed with modified validity measures for k-means clustering , 2020, Comput. Ind. Eng..

[30]  Dushmanta Kumar Das,et al.  A modified Bee Colony Optimization (MBCO) and its hybridization with k-means for an application to data clustering , 2018, Appl. Soft Comput..

[31]  Ickjai Lee,et al.  Hierarchical trajectory clustering for spatio-temporal periodic pattern mining , 2018, Expert Syst. Appl..

[32]  Pierpaolo D'Urso,et al.  Robust fuzzy clustering of multivariate time trajectories , 2018, Int. J. Approx. Reason..

[33]  Xiaofei Wang,et al.  Understanding characteristics in multivariate traffic flow time series from complex network structure , 2017 .

[34]  Simone Bassis,et al.  Discovering regression data quality through clustering methods , 2009, WIRN.

[35]  Hani S. Mahmassani,et al.  Spatial and Temporal Characterization of Travel Patterns in a Traffic Network Using Vehicle Trajectories , 2015 .

[36]  Bui Anh Tuan,et al.  Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries , 2020, Chaos, Solitons & Fractals.

[37]  Vania Bogorny,et al.  A clustering-based approach for discovering interesting places in trajectories , 2008, SAC '08.

[38]  Aminah Robinson Fayek,et al.  A fuzzy clustering algorithm for developing predictive models in construction applications , 2020, Appl. Soft Comput..

[39]  Linglin Ni,et al.  A spatial econometric model for travel flow analysis and real-world applications with massive mobile phone data , 2017 .

[40]  Ray A. Jarvis,et al.  Clustering Using a Similarity Measure Based on Shared Near Neighbors , 1973, IEEE Transactions on Computers.

[41]  Jinjun Tang,et al.  Trip destination prediction based on multi-day GPS data , 2019, Physica A: Statistical Mechanics and its Applications.

[42]  Hong Wang,et al.  Shared-nearest-neighbor-based clustering by fast search and find of density peaks , 2018, Inf. Sci..

[43]  Yong Qi,et al.  Markov Chains based route travel time estimation considering link spatio-temporal correlation , 2020 .

[44]  Rasim M. Alguliyev,et al.  Weighted consensus clustering and its application to Big data , 2020, Expert Syst. Appl..

[45]  Feiping Nie,et al.  Modified fuzzy clustering with segregated cluster centroids , 2019, Neurocomputing.

[46]  G. Currie,et al.  Spatial biases in residential mobility: Implications for travel behaviour research , 2020 .

[47]  Fagui Liu,et al.  Adaptive density trajectory cluster based on time and space distance , 2017 .