A Grid-Based Approach for Measuring Similarities of Taxi Trajectories

Similarity measurement is one of the key tasks in spatial data analysis. It has a great impact on applications i.e., position prediction, mining and analysis of social behavior pattern. Existing methods mainly focus on the exact matching of polylines which result in the trajectories. However, for the applications like travel/drive behavior analysis, even for objects passing by the same route the trajectories are not the same due to the accuracy of positioning and the fact that objects may move on different lanes of the road. Further, in most cases of spatial data mining, locations and sometimes sequences of locations on trajectories are most important, while how objects move from location to location (the exact geometries of trajectories) is of less interest. For the abovementioned situations, the existing approaches cannot work anymore. In this paper, we propose a grid aware approach to convert trajectories into sequences of codes, so that shape details of trajectories are neglected while emphasizing locations where trajectories pass through. Experiments with Shanghai Float Car Data (FCD) show that the proposed method can calculate trajectories with high similarity if these pass through the same locations. In addition, the proposed methods are very efficient since the data volume is considerably reduced when trajectories are converted into grid-codes.

[1]  Masashi Miyagawa SPACING OF INTERSECTIONS IN HIERARCHICAL ROAD NETWORKS , 2018 .

[2]  Fan Zhang,et al.  Growing the charging station network for electric vehicles with trajectory data analytics , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[3]  Bernard W. Silverman,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[4]  Guoliang Li,et al.  Signature-Based Trajectory Similarity Join , 2017, IEEE Transactions on Knowledge and Data Engineering.

[5]  Junjie Wu,et al.  Scaling up cosine interesting pattern discovery: A depth-first method , 2014, Inf. Sci..

[6]  M.V.L.R. Anjaneyulu,et al.  Interaction between Road Network Connectivity and Spatial Pattern , 2016 .

[7]  Yufei Tao,et al.  Query Processing in Spatial Network Databases , 2003, VLDB.

[8]  Simon Scheider,et al.  Where to go and what to do: Extracting leisure activity potentials from Web data on urban space , 2019, Comput. Environ. Urban Syst..

[9]  Yong Gao,et al.  Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality , 2013 .

[10]  Wai Khuen Cheng,et al.  Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment , 2020, Sensors.

[11]  Nicholas Jing Yuan,et al.  Sensing the Pulse of Urban Refueling Behavior , 2015, ACM Trans. Intell. Syst. Technol..

[12]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[13]  Yu Zheng,et al.  Trajectory Data Mining , 2015, ACM Trans. Intell. Syst. Technol..

[14]  Clu-istos Foutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[15]  Rabindra Bista,et al.  Spatio-temporal Similarity Measure Algorithm for Moving Objects on Spatial Networks , 2007, ICCSA.

[16]  Timothy Beatley,et al.  The Nature of (in) Cities , 2011 .

[17]  Eamonn J. Keogh,et al.  Experimental comparison of representation methods and distance measures for time series data , 2010, Data Mining and Knowledge Discovery.

[18]  Qingquan Li,et al.  Spatial variations in urban public ridership derived from GPS trajectories and smart card data , 2018 .

[19]  Song Gao,et al.  Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling , 2019, Comput. Environ. Urban Syst..

[20]  Ge Cui,et al.  A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories , 2020, Sensors.

[21]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[22]  Liqiu Meng,et al.  Understanding Taxi Driving Behaviors from Movement Data , 2015, AGILE Conf..

[23]  Christophe Claramunt,et al.  A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity , 2018, Entropy.

[24]  Liangxu Liu,et al.  Using Relative Distance and Hausdorff Distance to Mine Trajectory Clusters , 2013 .

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

[26]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[27]  Harvey J. Miller,et al.  Assessing public transit performance using real-time data: spatiotemporal patterns of bus operation delays in Columbus, Ohio, USA , 2019, Int. J. Geogr. Inf. Sci..

[28]  A. Stewart Fotheringham,et al.  Analysis of human mobility patterns from GPS trajectories and contextual information , 2016, Int. J. Geogr. Inf. Sci..

[29]  Xuan Song,et al.  Prediction of human emergency behavior and their mobility following large-scale disaster , 2014, KDD.

[30]  Hangbin Wu,et al.  Exploring Spatiotemporal Patterns of Long-Distance Taxi Rides in Shanghai , 2017, ISPRS Int. J. Geo Inf..

[31]  Ling Liu,et al.  Road-Network Aware Trajectory Clustering: Integrating Locality, Flow, and Density , 2015, IEEE Transactions on Mobile Computing.

[32]  Martin Raubal,et al.  Measuring similarity of mobile phone user trajectories– a Spatio-temporal Edit Distance method , 2014, Int. J. Geogr. Inf. Sci..

[33]  Xinyue Ye,et al.  A framework of comparative urban trajectory analysis , 2018 .

[34]  David M Levinson,et al.  Mutual Causality in Road Network Growth and Economic Development , 2016 .

[35]  Robert Weibel,et al.  Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects , 2009, Comput. Environ. Urban Syst..

[36]  Pascal Neis,et al.  Quality assessment for building footprints data on OpenStreetMap , 2014, Int. J. Geogr. Inf. Sci..

[37]  Guoyin Wang,et al.  Research of Spatio-temporal Similarity Measure on Network Constrained Trajectory Data , 2010, RSKT.

[38]  Yannis Manolopoulos,et al.  Searching for similar trajectories in spatial networks , 2009, J. Syst. Softw..

[39]  A. Tversky Features of Similarity , 1977 .

[40]  Tetsuya Watanabe,et al.  Tactile Map Automated Creation System Using OpenStreetMap , 2014, ICCHP.

[41]  P. Sojan Lal,et al.  Spatio-Temporal Similarity of Network-Constrained Moving Object Trajectories Using Sequence Alignment of Travel Locations , 2012 .

[42]  Jing Zhang,et al.  Investigating Public Facility Characteristics from a Spatial Interaction Perspective: A Case Study of Beijing Hospitals Using Taxi Data , 2017, ISPRS Int. J. Geo Inf..

[43]  Ismail Ahmedy,et al.  LCSS-Based Algorithm for Computing Multivariate Data Set Similarity: A Case Study of Real-Time WSN Data , 2019, Sensors.

[44]  C. Pettit,et al.  Assessing geographical representativeness of crowdsourced urban mobility data: An empirical investigation of Australian bicycling , 2019 .

[45]  Marta C. González,et al.  Origin-destination trips by purpose and time of day inferred from mobile phone data , 2015 .

[46]  Zhifeng Wu,et al.  Using kernel density estimation to assess the spatial pattern of road density and its impact on landscape fragmentation , 2013, Int. J. Geogr. Inf. Sci..

[47]  Chen Xu,et al.  Detecting tourism destinations using scalable geospatial analysis based on cloud computing platform , 2015, Comput. Environ. Urban Syst..

[48]  Xintao Liu,et al.  Discovering spatial and temporal patterns from taxi-based Floating Car Data: a case study from Nanjing , 2017 .

[49]  Nicholas Jing Yuan,et al.  T-Finder: A Recommender System for Finding Passengers and Vacant Taxis , 2013, IEEE Transactions on Knowledge and Data Engineering.

[50]  Philip Chan,et al.  Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..

[51]  Eamonn Keogh Exact Indexing of Dynamic Time Warping , 2002, VLDB.

[52]  Tijs Neutens,et al.  A GIS toolkit for measuring and mapping space–time accessibility from a place-based perspective , 2012, Int. J. Geogr. Inf. Sci..

[53]  Santiago Ontan'on,et al.  An overview of distance and similarity functions for structured data , 2020, Artificial Intelligence Review.

[54]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[55]  Nicholas Jing Yuan,et al.  Scalable Content-Aware Collaborative Filtering for Location Recommendation , 2018, IEEE Transactions on Knowledge and Data Engineering.

[56]  Mao Ye,et al.  Location recommendation for location-based social networks , 2010, GIS '10.

[57]  Christian S. Jensen,et al.  iPark: identifying parking spaces from trajectories , 2013, EDBT '13.

[58]  Itzhak Benenson,et al.  GIS-based method for assessing city parking patterns , 2015 .

[59]  Min Lu,et al.  Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories , 2020, Sensors.