Modeling of Human Movement Behavioral Knowledge fromGPS Traces for Categorizing Mobile Users

Shreya GhoshDepartment of Computer Science and

EngineeringIndian Institute of Technology Kharagpur, India

shreya.cst@gmail.com

Soumya K GhoshDepartment of Computer Science and

EngineeringIndian Institute of Technology Kharagpur, India

skg@iitkgp.ac.in

ABSTRACTHuman movement analysis and categorization of mobile usersbased on their movement semantics are challenging tasks.Further, due to security and privacy issues, insufficient la-belled or user-annotated data (or, ground-truth data) makesthe user-classification from GPS traces more complex. Inthis work, we present a framework which models user move-ment patterns containing both spatio-temporal and seman-tic information, generates semantic stay-point taxonomy byanalysing GPS traces of all users, summarizes individuals’GPS traces and clusters users based on the semantics oftheir movement patterns. To alleviate labelled data scarcityproblem while user categorization in a particular region ofinterest (ROI), we propose a method to transfer knowledgederived from a set of GPS traces of a geographically dis-tanced but similar type of ROI. An extensive set of experi-ments using real GPS trace dataset of Kharagpur, India andDartmouth, Hanover, USA have been carried out to demon-strate the effectiveness of our proposed framework.

KeywordsTrajectory, GPS Data, GeoCoding, Geo-tagging, Catego-rization, Clustering, Spatial Transfer Learning

1. INTRODUCTIONThe growing popularity of GPS embedded devices have mo-tivated extensive research on analysing the voluminous GPStraces and various location-aware applications. Further, mo-bile users are capable to accumulate their own GPS logs con-veniently (ex. Google Map Timeline) leading to generationof huge amount of location traces. It provides unprecedentedopportunities to analyse and derive valuable knowledge ofhuman mobility patterns, specifically, human interests andintentions which in turn facilitates varied location based ser-vices. The question is: “Can we map the knowledge of oneknown region to another unknown (target) region and usethis knowledge to categorize the users in the target region?”

c©2017 International World Wide Web Conference Committee (IW3C2),published under Creative Commons CC BY 4.0 License.WWW 2017 Companion, April 3–7, 2017, Perth, Australia.ACM 978-1-4503-4914-7/17/04.http://dx.doi.org/10.1145/3041021.3054150

With the huge volume of collected GPS data, another inter-esting and obvious question arises that whether all the GPSpoints have same information or some of the GPS pointscarry more contextual information than rest. To this end,we aim to model and efficiently store user-movement traceswithout losing any significant information. However, insteadof raw GPS log (time-stamped latitude, longitude data) hu-man movement patterns are better understood when somecontextual information, landmarks on the route, duration ofstay points or activities performed at stay points are consid-ered. Motivated by the potential merits, recently researchtrend is to extract semantic information or capturing in-herent meaning of these huge volume of human movementdata. This semantic enrichment of raw GPS log bridgesthe gap between collected GPS traces and various locationbased applications. We aim to capture behavioral differ-ences in the movement patterns of the individuals and uti-lize the knowledge to cluster users having similar movementpatterns. The problem becomes quite challenging for theunpredictable behavior of human mobility. For example,user X and user Y both are students, but may take differentroutes to university. To tackle this challenge, we proposeBayesian network for modelling user’s movement patternwhich captures probabilistic measures of the randomness ofmovement. Although advances in location-acquisition tech-niques have generated huge amount of GPS data, but unfor-tunately, scarcity of user-annotated or labelled data is stilla major challenge in categorization of users. Therefore, weaim to learn movement patterns of a known region (source)and map the knowledge of the source region to another re-gion (destination) of same domain (say, academic, commer-cial etc.). This problem of trajectory knowledge transfer hasnot been reported well in the existing literature.

To address the above mentioned challenges and issues, wepropose a framework which involves (i) generating user tra-jectory from raw GPS log (ii) creating User Trace Model torepresent individuals movement behavior, generating placeknowledge base of the region from the GPS traces (iii) spatio-temporal movement pattern mining and similarity measure-ment (iv) transfer the human movement behavior knowledgeto other geographical place.

The remainder of the paper is organized as follows: section 2provides state-of-the-art works, section 3 introduces the pro-posed framework and gives a brief description of it. Section4 depicts trajectory summarization, knowledge mining andclassification and section 5 describes the method to transfer

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the trajectory knowledge to different region. Finally, section6 provides description of dataset and some experimental re-sults and we conclude in section 7.

2. RELATED WORKSThe problem of human movement pattern analysis has beenstudied in recent years. In this section we brief some of therecent significant research works in three broad categoriesnamely, semantic trajectory processing, trajectory cluster-ing and classification and machine learning method transferlearning to map the knowledge from one region to anotherdomain of interest.

One of the major challenges in any supervised learning tech-nique is insufficient labelled or training dataset. Also, train-ing and future data may not be in same data distribution orsame feature space. To tackle this issue, researchers use anovel learning technique, Transfer Learning [14] to avoid theexpensive data-labelling efforts. [20] provides novel learningscenario, heterogeneous transfer learning, where no corre-spondence between data instances of source and target do-main is provided. Enabling knowledge transfer between do-mains with multimodal data has been carried out in [23],[13],[17]. [22] captures the multi-view nature of various real-life data set and transfers both model parameters and in-stances to target domain. Fang et. al [5] proposes a methodcalled DISMUTE, which leverages discriminative feature se-lection for multi-view cross-domain learning and used in theapplication of object identification and image classificationagainst. Most of the studies on transfer learning focuses ontext document classifications [3], [2], classification of web-pages, email-spam detection etc [23]. [13] introduces a novelproblem of future health prediction from multimodal obser-vation. To the best of our knowledge, there are only fewattempts to resolve urban issues and challenges using trans-fer learning. Wei et. al [19] proposes FLORAL methodto transfer knowledge from a city to another where datais insufficient using multimodal transfer learning. The pa-per learns semantically related dictionaries from multimodaldata and predicts air-quality in three different cities.

Ongoing research trend is devoted to overcome the semanticgap between raw GPS log collected from mobile devices andactual personal activity performed in that particular loca-tion. Most of the recent studies append contextual informa-tion along with the time-stamped latitude, longitude infor-mation for enhancing semantic richness of the trajectories.[15] provides a systematic overview of the recent trends ofcapturing semantics of the movement patterns rather thananalysing raw GPS traces. Semantic trajectory is definedbased on the application’s requirements, namely appendingtransportation mode, [24] street information, point of inter-ests [6], city map etc. [16] presents a novel idea to mapa syntactic trajectory to a semantic trajectory. Based onthe movement pattern discovery and human behavior infer-ence, it formalizes a semantic-enriched knowledge discoveryprocess. Trajectory segmentation is another pre-processingstep of trajectory data mining. In several work, trajec-tory is shown as sequence of stop and moves [27],[26]. Yuet. al [26] presents a modeling human location history andmining correlations between different locations. [9] presentsa framework to detect semantic places automatically fromGPS trajectories. The paper presents a Bayes classifier to

categorize trajectory stop-points into predefined category ofplaces. There are various applications (e.g, next locationprediction [21], travel route recommendation, hot-spot de-tection) and methods of trajectory clustering and patternmining. Recent studies [10], [27] propose various similaritymeasurements and novel methods to forecast next move ofan user and travel sequences. Various spatio-temporal clus-tering [27], [26], [6] based on the application have been intro-duced in the last decade. [27] introduces HITS-based modelto mine users’ interesting location and clustering the move-ment patterns. Ghosh et. al [6] proposes spatio-temporalclustering TempCS, variant of LCS to extract the commonmovement patterns of users.

To the best of our knowledge, only a very few studies [6],[25], [8] have focused on mobile-user categorization or find-ing similar users. [4] has presented an innovative problem tocatch pick pocket suspects from large scale transit records.We have proposed an end-to-end framework to model hu-man movement behavioral knowledge and categorize mobile-users. The novelty of the present work is to transfer theknowledge of human movement pattern to another regionand finding similar user movements.

3. PROPOSED FRAMEWORKIn this section, we present architecture of our proposed frame-work. We explain the terms Semantic-Trajectory, User-trajectory Segment, SSPTaxonomy and User-Trace Summary.

Basically, a GPS log is a collection of time stamped GPSpoints P= {p1, p2,.... , pn}. Each GPS point pi ∈ P containslatitude (pi, Lat), longitude (pi, Lngt) and timestamp (pi, ti)when the point is captured. [8]

1. User Trajectory SegmentUser Trajectory Segment is a triple of< S[],W [], T raj Win[] > We represent S[] as list ofstay-points, s= < lat, lon,Geotagg > where within d >Dthres distance user stays T > tthres time and Geotaggis present within r radius of the point.Here, Geotagg or Geotagged Stay Point is introducedwhere each GPS stay point is associated with the near-est land use information. Each GPS point pi contains(pi, place) along with latitude, longitude and times-tamp information

W[], list of waiting points, w =< lat, lon > is similar tostay points, but no land use is present within r radiusof the point.

Traj Win = {S1, (x1, y1), (x2, y2), ..., S2)} where, S1

and S2 are two consecutive stay-points of the trace.

2. Semantic Stay Point Taxonomy or (SSPTaxonomy)SSPTaxonomy =< N,Nc,W >; where, N representsplace type at different height of the Taxonomy and Nc

associated code of the node-place, W denotes aggre-gated footprints of the users in that particular node.SSPTaxonomy represents the type of stay points of users(ex, university, food-joints etc) in a hierarchical man-ner.

3. User-Trace Summary(UTS)We define User-Trace Summary as probabilistic di-rected graph G = (V,E), where each node represents

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Figure 1: Architecture of the proposed framework

Figure 2: Snapshot of User-Trajectory Segment

a random variable, consisting the visited place of theindividual along with the timestamp and time spentat the point.

We propose modelling of User-Trace Summary of indi-viduals in three different categories, namely WeekdayUTS, Weekend UTS and Anomaly UTS. Here, Week-day UTS and Weekend UTS represents user move-ment patterns in weekdays and weekends respectively.Anomaly UTS represents movement patterns whichare not in the defined ROI or deviates from the regularmovement pattern. Generation of UTS is explained insection 4.

4. Semantic-Trajectory TraceSemantic-Trajectory Trace (or STT) is defined as

i) Each of the staypoint of the trajectory segment isgeo-tagged or ii) For a particular ROI R1 and cor-responding SSPTaxonomy, STT is a User-Trace sum-mary which represents signature movement pattern ofan user-category in R1.

Figure 3: Snapshot of Semantic Stay Point Taxonomy

For geo-tagging of stay-points, we extracted geo-tagged lo-cation information from Google-Map using iterative reversegeocoding and refinement of the geo-tagged data. To use thisgeo-tagged data in trajectory analysis, a systematic modelto capture the semantics of the data is needed. We convertedthe geo-tagged data to a SSPTaxonomoy and weighted eachnode of the taxonomy with the visit-frequency all of theusers. Figure 1 shows the overall architecture of the frame-work. The ‘GPS traces Pre-processing’ module generatesuser-trajectory segments from input raw GPS log, ‘Trajec-tory Summarization’ module builds the place taxonomy ofthe region and creates UTS for each user and ‘TrajectoryKnowledge Mining’ module clusters users based on seman-tic analysis of movement patterns and categorizes when user-labelled data is available of the region. As mentioned earlier,

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the main challenge is to get labelled GPS trace of mobileusers for categorization. We present a method to transferknowledge from source, where labelled data available to tar-get region where labelled user trace is insufficient. By mea-suring the place taxonomy and analysing user trace feature,we can transfer human movement behavioral knowledge toanother spatial region.

4. GENERATING USER-TRACE SUMMARYAlthough human movement pattern is unpredictable, thereis always some intent behind how user moves. Bayesiannetwork model is deployed to capture the intents of humanmovement patterns and measure the similarity with othermovement patterns.

4.1 Modeling of User-Trace SummaryThe behavioral patterns of the human movement traces (col-lected through GPS footprints) can be effectively modelledusing Bayesian network. Given a set of user trajectory seg-ments of individuals or a set of users, how to constructBayesian network to capture the movement pattern fromindividual as well as aggregate level? Consider a finite set ofrandom variables, S = {S1, S2, ..., Sn}, where each variabledenotes a stay-point in the user trace. Each variable has twovalues, ‘visited’ (denoted as 1) and ‘not-visited’ (denoted as0). Let P be the joint probability distribution over the vari-ables in S. Here, we aim to build UTS in a form of a Bayesiannetwork, which encodes joint probability distribution over aset of stay-points in the region based on the movements usermakes. Given a set of source and destination points, pathsfollowed by an user or a group of users are not pre-definedand it is dependent on the users’ personal choices. For ex-ample, 80% user-trajectory segments (only stay points aredepicted) of user1 is - [< Hall, 8 >,< Academics, 9 >,<Hall, 12 >], and 20% user-trajectory segments of user1 is[< Hall, 8 >,< cafe, 9 >,< Academics, 10 >,< Hall, 12 >]. Now, to build the user trace summary, we need to re-flect both the trajectory segments along with the ratio (4:1)of following above two segments. Hence, the probability tofollow different segments can not be completely captured ingraph based approaches [6]. To tackle this challenge, wegenerate a probabilistic graphical model (UTS) which is ca-pable to represent all movement patterns of an user withoutany loss of information.

We define UTS, NB =< G, θ > , where G =< V,E >,vi ∈ V consists of (Nc, ti); where Nc is the code-place ofthe SSPTaxonomy and ti is the temporal value at the node.ei ∈ E represents dependencies between the vertices, i.estay-points of the trace. We assume that each variable inS is independent of its non-descendants in G given its par-ents. This independence assumption holds because two stay-points are independent when there is no direct movementfrom one to another in the given user trajectory segment. θcontains the set of parameters

θsi|Pasi= P (si|Pasi) (1)

, which quantifies the network, for each possible values of si(in our case ‘0’ or ‘1’) in Si and Pasi of PaSi (the parentset of Si in G). The joint probability distribution is given by

PN (S1, S2, ..., Sn) =

n∏i=1

PN (Si|PaSi) =

n∏i=1

θSi |PaSi (2)

Figure 4: Bayesian Network for User-Classification

Each variable (Si, stay-points in our problem) in the net-work has a associated conditional probability distribution.The conditional probability distribution of Si, given its par-ent PaSi is denoted by P (Si|PaSi). It is calculated from theGPS-log of the users by analysing the frequency of visitingSi after visiting its parent PaSi and visiting Si from otherstay-points which are not included its parent set. To clus-ter or grouping trajectory segments, location information,time duration in a stay-point, speed of movement, movementpatterns of the trajectories need to be considered. We haveused Temporal Common Sub-sequence, (TempCS) clusteringalgorithm [6], which captures common subsequences amongthe trajectories. In our problem, given a User-Trace Sum-mary NB1 =< G1, θ1 >; and a set of NB , similarity matrixamong the networks is required. To measure the similar-ity between two probability distribution, we have used theBhattacharyya distance [1] given as:

DB(Si, Sj) = −ln∑

x∈[0,1]

√Si(x)Sj(x) (3)

Using TempCS on complete UTSs (say, NB1 and NB2), wegenerate a list (Lc) of all common staypoints and the com-mon subsequences (say Ls). Using DB in equation 3, it maybe observed the similarity between two probability distri-bution of each two consequent staypoints in Ls. Hence, wecome up with the similarity measures between NB1 and NB2

as:

SimSequence(NB1 , NB2) =|Ls||Lc|

∑Si∈Ls

DB(Si, Si+1) (4)

and

SimTemporal(NB1 , NB2) =∑

Si∈Lc

Min(T 1Si, T 2

Sj) (5)

where T jSi

denotes time-duration spent in stay-point Si ofNBj .

4.2 User CategorizationWe aim to classify users into pre-defined categories based onthe features of the GPS traces. Bayes classifier is used as our

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goal is to get the probability of multiple user-categories ofthe target user. The classification task for a user u providesa output probability vector, PVu = {p1, p2, ..., pi} where iis the user-category and pi is the probability of the user uto be categorized in ith class. The user-classification is donebased on three observable features, i) visit in types of places(f1), ii) Speed of movement or transportation mode (f2), iii)User Movement patterns (f3). f2 can take values ‘Bi-Cycle’or ‘Four-wheeler’ or any other transportation mode (whentransportation mode is available) or speed can be computedand value can be discretized into ‘high’, ‘medium’ and ‘low’value. f1 and f3 can be deduced from User-Trace Summarygenerated from the GPS log and the corresponding similaritymeasures given in equation 4 and 5. For each user-trace, wefind out the weighted feature vector and create a Bayesiannetwork [as depicted in Figure 4] to classify the user to apre-defined user-category.

Considering our problem, say a new instance (GPS tracesof user u) u having three feature values f1, f2, f3; C be thetarget feature which takes value c. u is classified to the classwith maximum posterior probability as defined:

Ucategory = argmaxcP (c)P (f1, f2, f3|c) (6)

As all features are independent of each other,

P (f1, f2, f3|c) =

3∏i=1

(fi|c) (7)

In our problem-scenario, it is obvious that user classificationis more dependent on user movement patterns or stay-pointsrather than transportation mode, i.e. the assumption thatthe features having equal importance in classification doesnot hold. The Bayesian classification with feature weighting[7] is defined:

Uw−category(u,w(i)) = argmaxcP (c)

3∏i=1

(fi|c)w(i) (8)

where w(i) ∈ R+, i.e each feature i has its own weight w(i).We have used Kullback-Leibler measure [7] for weightingeach of the features in our Bayesian learning. In this sec-tion, we describe how user categorization can be done fromlabelled GPS traces using the GPS trace features.

5. MAPPING KNOWLEDGE FROM ONE RE-GION TO ANOTHER OF SIMILAR TYPEUSING TRANSDUCTIVE TRANSFERLEARNING

Our objective is to extract knowledge from the GPS tracesof source region (in our experiment IIT Kharagpur, India)and transfer the knowledge, more specifically labelled dataof Kharagpur region to target region (Dartmouth College,Hanover city, USA) for user-categorization at the target re-gion. Both the regions (source and target) are academicinstitutes/universities.According to [14], Transductive Transfer Learning is de-fined on a source domain DS and a corresponding learningtask TS , a target domain DT and a corresponding learningtask TT , where it aims to improve the learning of the tar-get predictive function fT (.) in DT using the knowledge inDS and TS , where DS 6= DT and TS = TT It is mapped

in our problem as analysing and learning categorical move-ment patterns from labelled GPS traces or ground truthdata of source region, and carry out the user categorizationtask in the target region, where we have only unlabelledGPS traces of users. The formulation of our problem isstated as Given a semantic source region of interest SROI

and user-classification task TS ; different but related seman-tic target region of interest TROI , how to learn the classifierfrom SROI and TS to perform the classification or learningtask in TT . We define semantic source region of interest asSROI = (SSPS

Taxnomy, P (wS)) and semantic target region of

interest as TROI = (SSPTTaxnomy, P (wT )). The former term

in the tuple represents the taxonomy of stay-points of theparticular region and the later term presents the probabilitydistribution of visiting frequencies in the nodes of the taxon-omy. Given two different region of interests, (here, Kharag-pur, India and Dartmouth, Hanover, USA), the problem oftransferring the knowledge of movement patterns becomesmore challenging due to different life-styles of people. Forexample, we have observed, wide variation in probability dis-tribution of footprints in the staypoints namely, Gym, Sta-dium, Restaurants. Further, there are differences in tempo-ral pattern of visiting in the two regions of our experiment.Basically, we aim to find proper feature-representation thatminimizes the divergence of two regions and the classifica-tion error. We aim to reduce the classification error in thetarget region of interest:

error(h) =1

|UT |×

∑<u,c>∈|UT |

Pr(h(u) 6= c) (9)

Given the training data set (or labelled data) of source re-gion and data (unlabelled) distribution of target region, weaim to estimate the correct label or category of users in thetarget region. To tackle the above issue, as depicted in Fig-ure 1, we carry out the trajectory summarization module onthe target region data and generate the SSPT

Taxnomy of theregion and UTS for each user. We extract geo-tagged infor-mation of the GPS points (latitude, longitude) from GoogleMap by reverse geocoding technique. For example, for aparticular POI, say Department of Mathematics, geo-taggedinformation is in the form: [point of interest, establishment,academic, university, department] or another POI, say GileHall has geo-tagged information array: [point of interest,establishment, lodging, Student Housing Complex, Hall ofResidence, Undergraduate Students’ Hall]. Clearly, it storesthe places in a hierarchical form, we convert the array in ataxonomy where each of the nodes is given an unique codeto store the semantic meaning of the places. In our pro-posed coding scheme, we extract the parent’s code (say, cp)of a node (say c), and check whether the particular nodehas siblings. Let a node (c) has n siblings then we appendn+ 1 along with the parent’s code. Hence, the node gets acode cpn+ 1. Before assigning new code, we check whetherthe same place-type appears in the SSPS

Taxnomy and assignthe same code if it is present. From the coding scheme, itis obvious that all the nodes in level l of the taxonomy haveunique codes of length l. Also, the common sequence of twosuch codes represents common hierarchy of two nodes, i.e,similar semantic types of places. While inserting each node(n), we recursively add the visit frequency of each node fromn to the root following the code of the node.After the generation of SSPT

Taxnomy (destination taxonomy),

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we compare it with the SSPSTaxnomy (source taxonomy). Be-

cause, transductive transfer learning method does not workwhen the target and source region of interests and distribu-tion of GPS traces are completely different. For example,we can’t simply compare an academic ROI and a commer-cial ROI as clearly the movement patterns are completelydifferent which will lead to poor classification result. Hence,we need to analysis where it is feasible to transfer knowledgebetween two domains. Otherwise it may lead to ‘negativetransfer’ [14]. Following the coding scheme of the taxonomy,we find out the maximum common sequence and comparethe GPS footprint distribution at each of the level of thetaxonomy.

We aim to find a good feature representation that will min-imize the differences between two regions. By comparingtwo such structures, we get the common taxonomy (saySSPC

Taxonomy) features of source and target region of inter-est. We generate the UTS of users in target region and train-ing data of source region using the SSPC

Taxonomy. The Bayesclassifier, as explained in section 4, computes the probabilityvector of a user u being categorized in all of the pre-definedclasses. It depends on the distribution of the training data.Here, along with the training data of source region, we usetest data distribution of target region for user-classification.We have implemented Transductive Bayes classifier [2] algo-rithm, where at each iteration weight of the classifier basedon the labelled test data is increased.

In this section, we propose how spatial trajectory knowledgecan be refined and transferred by capturing the commonplace taxonomy of two regions and probability distributionof GPS trace data.

5.1 Motivating Example Scenario: Extendingto other Regions of Interest

One of the promising application of the problem is to buildup various SSPTaxonomy from GPS traces collected from dif-ferent types of users and from varied locations. If we canbuild several such stay-point taxonomy (University Cam-pus/Commercial places/ Residential areas) and construct aknowledge base of visiting patterns, we can train Bayes clas-sifier using more categorical movement patterns. Further,we can extend our framework for better urban planning.

6. EXPERIMENTAL EVALUATIONSIn this section, we present brief description of the datasetused and some results representing the performance of theframework.

6.1 DatasetThe GPS dataset is collected voluntarily from 56 universitystudents/professors from their GPS-enabled mobile devicesand Google Map’s timeline. The dataset is mainly generatedin Kharagpur, India region and captures daily movementpatterns of the subjects from October 2015 to November2016. Also, the dataset is manually labelled (ex. categoryof users - student/professors, daily movement patterns de-scribed by them etc.) by the subjects. We also carry outour experiment with Dartmouth data [11],[18],[12] consist-ing GPS traces of students and professors, employees of the

university. Table 1 shows the final user-distribution of thedatasets.

User Category Kharagpur DartmouthRegion Region

Undergraduate Student 8 2Graduate Student 11 15Graduate Student 22 NA(Research Student)

Employees/Professors 8 3Non-Residential Students 7 NA

Table 1: GPS Data-set Description of Kharagpur and Dart-mouth region

6.2 Accuracy of User-ClassificationWe generate a confusion matrix and find out the recall andprecision for each of the class. Precision is the measure howaccurate the classifier is and recall is how well it can classifya positive class label; Precision = TruePositive

TruePositive+FalsePositive

and Recall = TruePositiveTruePositive+FalseNegative

Table 2, representsthe evaluation measure of the classifier. Here, in Kharag-pur region, we divide the dataset into training (70% ) andtest dataset and learn the Bayesian classifier for user catego-rization. In Dartmouth region, we did not use any labelleddata of the region to train the classifier, rather we used theknowledge gained from Kharagpur region to classify users inDartmouth region.

Kharagpur DartmouthUser Category Precision Recall Precision RecallUndergraduate 0.75 0.75 0.78 0.82

StudentGraduate Student 0.846 0.95 0.9 0.82Graduate Student 0.6315 0.545(Research Student) NA NA

Employees/Professors 0.555 0.625 0.62 0.56Non-Residential 0.714 0.714 NA NA

Students

Table 2: Precision/Recall values of experiment on Kharag-pur and Dartmouth region

We have developed a demonstration system to visualize theresults. Figure 5 depicts summarized movement patterns ofstudent and professor category of users in Kharagpur region.

7. CONCLUSION AND FUTURE WORKThe paper addresses the user categorization problem fromthe GPS traces of the users. User categorization or similaritymeasurement of users based on movement patterns providesinsights of common needs or interests of people and may helpin various application like car-pooling, business settlements,city planning etc. We propose a framework to model in-dividuals’ movement patterns, analyzing human movementpatterns both from semantic and spatio-temporal contextand extracting implicit information. Finally, we attempt topresent an insight on the transferring knowledge base fromone city domain to another unknown city where data is in-sufficient.

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(a) Summarized Undergraduate StudentUser-Trajectory Segment

(b) Summarized Research Student User-Trajectory Segment

(c) Summarized Professor User-Trajectory Segment

Figure 5: Summarized User Trajectory Segments of different categories of users in Kharagpur Region

In the future, we would like to extend features of our sys-tem to analyze indoor movements and scale up the system toincorporate different methods for activity learning or behav-ioral pattern mining. Further, we can extend the learningtask for different domains and build up a city hierarchy us-ing the transfer learning methods. We would like to improveexperimental section of our work using more number andvaried GPS traces. Moreover, we would like to explore theparallel computation and fast data access mechanisms whichis highly required for the towering growth rate of trajectorytraces.

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