Data Mining II Mobility Data Mining
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1 Data Mining II Mobility Data Mining F. Giannotti& M. Nanni KDD Lab ISTI CNR Pisa, Italy
2 Outline Mobility Data Mining Introduction MDM methods MDM methods at work. Understanding Human Mobility Clustering Trajectory Pattern Mining Prediction Dimensions of mobility analytics Models of human mobility The Mobility Atlas Module 3 Case studies OD Matrix, D4D, Sociometer, Network& Mobility
3 Derived patterns and models Combination & refinement of basic patterns and models Individual Mobility Profile: routines consistently followed by a single moving object T-PTree: predictive tree built by combining T-Patterns
4 User s Mobility Profile Given the user history as an ordered sequence of spatiotemporal points, we want to extract a set of routines in order to create the his\her mobility profile. Where: A Routine is a typical local behavior of the user. A Mobility profile is the set of user s routines
5 Discovering individual systematic movements Work-Home Home-Work
6 Derived patterns and models: mobility profiles User history An ordered sequence of spatio-temporal points. Trips construction Cutting the user history when a stop is detected Stops Spatial Threshold Stops Temporal Threshold Grouping Pruning Performing a density based clustering equipped with a spatio temporal distance function Groups with a small Number of trips are Pruned Spatial Tollerance Temporal Tollerance Spatio temporal distance Support Threshold Profile extraction The medoid of each group becomes user s routines and the all set become the user s mobility profile Trasarti, Pinelli, Nanni, Giannotti. Mining mobility user profiles for car pooling. ACM SIGKDD 2011
7 Derived patterns and models: TPrediction Tree Rule-based prediction model Each T-Pattern is used as a case Tree = combination / simplification of a set of T-Patterns + + Monreale, Pinelli, Trasarti, Giannotti. Where Next: a predictor on Trajectory pattern mining. Proc. ACM SIGKDD 2009
8 Basic Idea: People move as the crowd moves How to realize this idea: Extract patterns from all the available movements in a certain area instead of on the individual history of an object; Using these Local movement patterns as predictive rules. Build a prediction tree as global model. Prediction Tree Trajectories dataset Local patterns
9 Predict by means of T-Pattern tree Given a new trajectory: Search for best match Candidate generation Make predictions Best Match Prediction How to compute the Best Match?
10 Computing the path score The path score is the aggregation of all punctual scores along a path. Punctual score: 1 Punctual Score:.58 8 min 10 min 10 min 15 min The Best Match is the path having: the maximum path score; at least one admissible prediction. Punctual Score:.8 11 min 16 min Path score.79
11 Derived patterns and models: T-PTree Example: Compare actual trajectory against the T-PTree Spatial and temporal similarity used to choose best rule A E B D C
12 M-Atlas system Download from:
13 The (GeoP)KDD process Mobile phone data, GPS tracks End user Mobility manager Mobility Patterns Mobility Data Raw data Privacy and anonymity protection Mobility Data Mining
14 M-Atlas input M-Atlas: An atlas for urban mobility behaviors. A framework to query, analyze and navigate the results on mobility data
15 M-Atlas platform A tool kit to extract, store, combine different kinds of models to build mobility knowledge discovery processes.
16 From DATA to KNOWLEDGE Data Geographic data Models T-Patterns Movement data T-Clustering Transport data Demographic data Validation Forecasts
17 outline Introduction MDM methods Clustering Trajectory Pattern Mining Prediction Semantic enrichment MDM methods at work. Understanding Human Mobility Dimensions of mobility analytics Models of human mobility The Mobility Atlas
18 Sensing the movement Several datasources avaiable
19 GSM data Mobile Cellular Networks handle information about the positioning of mobile terminals CDR Call Data Records: call logs (tower position, time, duration,..) Handover data: time of tower transition More sophisticated
20 GPS tracks Onboard navigation devices send GPS tracks to central servers Ide;Time;Lat;Lon;Height;Course;Speed;PDOP;State;NSat 8;22/03/07 8;22/03/07 8;22/03/07 8;22/03/07 8;22/03/07 8;22/03/07 8;22/03/07 8;22/03/07 8;22/03/07 8;22/03/07 8;22/03/07 08:51:52; ; ; 08:51:56; ; ; 08:51:59; ; ; 08:52:03; ; ; 08:52:06; ; ; 08:52:09; ; ; 08:52:12; ; ; 08:52:15; ; ; 08:52:18; ; ; 08:52:21; ; ; 08:52:24; ; ; 67.6;345.4;21.817;3.8;1808;4 68.4;35.6;14.223;3.8;1808;4 68.3;112.7;25.298;3.8;1808;4 68.8;119.8;32.447;3.8;1808;4 68.1;124.1;30.058;3.8;1808;4 67.9;117.7;34.003;3.8;1808;4 66.9;117.5;37.151;3.8;1808;4 67.0;99.2;39.188;3.8;1808;4 68.8;90.6;41.170;3.8;1808;4 71.1;82.0;35.058;3.8;1808;4 68.6;117.1;11.371;3.8;1808;4 Sampling rate 30 secs Spatial precision 10 m
21 Road side sensors Measure the flow of a specific road arc Laser-based sensors Inductive loops Traffic cameras
22 Other data sources Social web services Flickr Foursquare Gowalla Twitter Presence estimation Hotel statistics Airport departures and arrivals Bus and public transportation Park usage Weather conditions
23 Dimensions to explore Space Administrative borders Space E.g.: city Distance travelled How Dimensions Individual Individual Preferred locations EigenMobility much a person is travelling Time Time Hour of day Day of week Weekdays/weekends
24 A small city: Pisa Space Dimensions Individu al Time
25 First dimension: space Travel length distribution Space Dimensions Individu al Time
26 Travel length on the map
27 Exploring Origin and Destinations
28 The general process Browse DWH at high spatial level (provinces) Identify interesting flows and drill down to specific flow Navigate the cube at finer level (cities) From flow to trajectories: entails original data Do specific analysis on the real trips
29 Exploring Origins and Destinations
30 Exploring the origins of trips 0km 5km 5km 15Km > 150km
31 Exploring origins of trips > 150km 19 trips
32 Second dimension: time When people move to Pisa? Space Dimensions Individu al Time
33 Let s focus at city level 0km 5Km 5km 15Km
34 Trips segmented by similarity Space Dimensions Individu al Time
35 Explore clusters: Florence
36 Explore clusters: A1
37 Explore clusters: A12
38 Explore Clusters: Valdera
39 Explore clusters: Versilia
40 Trip segmentation by time Space Dimensions Individu al Time
41 Trips Segmented by Time: from 5 to 8
42 Discover traffic jams
43 Discovering access patterns to Pisa with GPS tracks data
44 Access patterns using T-clustering Lucca Marina di Pisa/Tirrenia A12 Sud Cascina
45 Characterizing the access patterns: origin & time 1,50 % Origin Distribuzione Origini distribution A12 Sud Pisa Marina/Tirrenia A12 (Nord) FiPiLi (Empoli) A12 (Sud) Lucca A11 (Pistoia) Collesalvetti Ponsacco SS12 (Nord Lucca) Montecatini Torre del Lago Calci Asciano Altre origini Rumore 2,90 % Marina di Pisa/Tirrenia
46 Studying the attractiveness/efficiency of a service with GPS tracks
47
48 Aggregate trips by common destinations
49 Seaside: Tirrenia and Marina di Pisa
50 Seaside: Tirrena and Marina di Pisa Tirrenia Marina di Pisa
51 Industry: Saint Gobain
52 Industry: Saint Gobain
53 Residential Area: I Passi
54 Residential Area: I Passi
55 Residential vs Industrial
56 Atlas of Urban Mobility
57 From Profiles to Systematicity Indicator Each routine of a profile is associated with a measure of frequency Routines are sorted according to their frequency: rank 1, rank 2, rank 3, A minimum frequency threshold allow to distinguish a
58 Rapporto Sistematici/Occasionali
59 Impact of systematic mobility on access patterns
60 Atlas of Urban Mobility
61 Pisa Traffico in Ingresso
62 Pisa Incoming Traffic
63 Trajectories by residence Ingressi per Residenza Uscite per Residenza
64 Cosa succede a San Giuliano Terme? Ingressi per Residenza Uscite per Residenza
65 Internal trajectories in Pisa Numero Viaggi Self per residenza Pisa San Giuliano Terme Cascina Livorno Pontedera Vecchiano Viareggio Collesalvetti Calci Lucca Fauglia Vicopisano
66 Trip distribution per day Pisa S. Giuliano Cascina
67 What-if scenarios
68 Studying proactive car pooling Matching Network Community = Suggestion
69 Discovering individual systematic movements Work-Home Home-Work
70 Mobility profile matching User A (as driver) Spatio Temporal Routing matches Mobility Profile A can serve most of the routines of B the match is suggested. User B (as passenger) Mobility Profile
71
72 Carpooling Network Th space = 1800s Th time = 1000m U ---w--> V If U could take lifts from V
73 Carpooling Network Pisa - Communities A B
74 Carpooling Network Pisa
75 Service: Montacchiello (Car Pooling?) Traj Blu DT: Traj Red DT: 11:52:06 Traj Green DT: 06:46:53 06:51:41 Blu can give a ride to Green
76 Application: Car pooling Pro-active suggestions of sharing rides opportunities without the need for the user to explicitly specify the trips of interest. Matching two routines: Mobility profile share-ability:
77 Communities of users
78 Car Pooling Carpoolin g Network Inclusion Synch Carpooling Communities Top 10 Drivers Passengers Authority Scores Hub Scores
79 OctoPisa Carpooling potential vehicles 1,449,258 trips % di systematic trips % di matching trips % highly successful matching trips Saved trips Saved Kms
80 Carpooling propensity of Pisa & Florence
81 Car pooling potential 67.2% routines match with a routine of other users 32.5% users share one or more routines with other users
82 Electrificability Joint work with UPM
83
84
85 Electrifiability index Pisa Yes No Livorno Yes No
86 Electrifiability index Firenze 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Yes No
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