Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (10): 91-96.doi: 10.11896/jsjkx.200100001

Special Issue: Big Data & Data Scinece

• Database & Big Data & Data Science • Previous Articles     Next Articles

Mobility Pattern Mining for People Flow Based on Spatio-Temporal Data

SUN Tian-xu1, ZHAO Yun-long1,2, LIAN Zuo-wei1, SUN Yi1, CAI Yue-xiao1   

  1. 1 College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
    2 Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 210023,China
  • Received:2020-01-02 Revised:2020-05-16 Online:2020-10-15 Published:2020-10-16
  • About author:SUN Tian-xu,born in 1995,M.S.candidate,is a member of China Computer Federation.His main research interests include urban computing and pervasive computing.
    ZHAO Yun-long,born in 1975,Ph.D,professor,is a member of China Computer Federation.His main research interests include wireless network,collective computing,pervasive computing,data mining,wearable computing,etc.
  • Supported by:
    National Defense Basic Scientific Research Program of China (JCKY2016605B006) and Six Talent Peaks Project in Jiangsu Province (XYDXXJS-031)

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Abstract: With the accelerating urbanization of many countries,managing people flow and mining mobility patterns become more and more important.Simultaneously,with the development of information technology,especially mobile crowd sensing,the concept of smart city is proposed by many scholars,sensing data in smart cities also provides the possibility for analysis of people flow.In smart city,spatio-temporal data is the most common data.Based on the spatio-temporal data,this paper proposes a modeling method to represent different kinds of spatio-temporal data as people flow model.Then,based on the thinking of clustering,this paper mines mobility pattern from people flow by an improved density-based clustering algorithm,designs a transportation application in smart city,and proposes a method for evaluating the effectiveness of mobility pattern.Finally,experimenting on a real dataset of a city in China and analyzing the results.The results show that the mobility pattern obtained in this paper can reduce costs by 25% in the transportation application of smart city,verifying the effectiveness of the mobility pattern.

Key words: Data mining, Mobility pattern, People flow, Smart city, Spatio-Temporal data

CLC Number: 

  • TP311.13
[1]ITOH M,YOKOYAMA D,TOYODA M,et al.Visual Exploration of Changes in Passenger Flows and Tweets on Mega-City Metro Network[J].IEEE Transactions on Big Data,2016,2(1):85-99.
[2]ALESSANDRINI A,GIOIA C,SERMI F,et al.WiFi positioning and Big Data to monitor flows of people on a wide scale[C]//2017 European Navigation Conference (ENC).IEEE,2017.
[3]JING Y,YU Z,XING X.Discovering regions of different functions in a city using human mobility and POIs[C]//18th SIGKDD conference on Knowledge Discovery and Data Mining.
[4]LYU Y,CHOW C Y,VICTOR C,S,et al.T2CBS:Mining Taxi Trajectories for Customized Bus Systems[C]//Computer Communications Workshops.IEEE,2016.
[5]ZHANG J,ZHENG Y,QI D.DeepSpatio-Temporal ResidualNetworks for Citywide Crowd Flows Prediction[C]//Procee-dings of the Thirty-First AAAI Conference on Artificial Intelligence.AAAI Press,2017:1655-1661.
[6]HASHEM I A T,CHANG V,ANUAR N B,et al.The role of big data in smart city[J].International Journal of Information Management,2016,36(5):748-758.
[7]STEENBRUGGEN J,TRANOS E,NIJKAMP P.Data frommobile phone operators[M].Oxford:Pergamon Press,2015,39(3):335-346.
[8]DEMISSIE M G,PHITHAKKITNUKOON S,KATTAN L.Trip Distribution Modeling Using Mobile Phone Data:Emphasis on Intra-Zonal Trips[J].IEEE Transactions on Intelligent Transportation Systems,2018,20(7):2605-2617.
[9]DEMISSIE M G,PHITHAKKITNUKOON S,SUKHVIBULT,et al.Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data:A Case
Study of Senegal[J].IEEE Transactions on Intelligent Transportation Systems,2016,17(9):2466-2478.
[10]FEI X,GKOUNTOUNA O.Spatio temporal clustering in urban transportation:a bus route case study in Washington D.C[J].Sigspatial Special,2018,10(2):26-33.
[11]ZHENG Y.Trajectory data mining:an overview[J].ACM Trans-actions on Intelligent Systems and Technology (TIST),2015,6(3):29.
[12]LIU Z C,YU J B,XIONG W T,et al.Using mobile phone data to explore spatial-temporal evolution of home-based daily mobi-lity patterns in Shanghai[C]//International Conference on Behavioral,Economic and Socio-Cultural Computing (BESC).IEEE,2016.
[13]YABE T,TSUBOUCHI K,SEKIMOTO Y.CityFlowFragility:Measuring the Fragility of People Flow in Cities to Disasters using GPS Data Collected from Smartphones[J].Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies,2017,1(3):1-17.
[14]DO C X,TSUKAI M.Exploring Potential Use of Mobile Phone Data Resource to Analyze Inter-regional Travel Patterns in Japan [M]//Data Mining and Big Data.2017:314-325.
[15]LI Y,BAO J,LI Y,et al.Mining the Most Influential k -Location Set from Massive Trajectories[J].IEEE Transactions on Big Data,2017,4(4):556-570.
[16]LIAN J,LI Y,GU W,et al.Joint Mobility Pattern Mining with Urban Region Partitions[C]//Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems:Computing,Networking and Services.ACM,2018:362-371.
[17]LIU T,CEDER A A.Analysis of a new public-transport-service concept:Customized bus in China[J].Transport Policy,2015,39:63-76.
[18]MA S,ZHENG Y,WOLFSON O.Real-Time City-Scale TaxiRidesharing[J].IEEE Transactions on Knowledge and Data Engineering,2015,27(7):1782-1795.
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