DeepMob: Learning Deep Knowledge of Human Emergency ...users.cs.fiu.edu/~taoli/pub/a41-song.pdf · 41 DeepMob: Learning Deep Knowledge of Human Emergency Behavior and Mobility from

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DeepMob: Learning Deep Knowledge of Human Emergency Behaviorand Mobility from Big and Heterogeneous Data

XUAN SONG and RYOSUKE SHIBASAKI, The University of TokyoNICHOLOS JING YUAN and XING XIE, Microsoft ResearchTAO LI, Florida International University and Nanjing University of Posts and TelecommunicationsRYUTARO ADACHI, Zenrin DataCom Co’ Ltd

The frequency and intensity of natural disasters has increased significantly in recent decades, and this trendis expected to continue. Hence, understanding and predicting human evacuation behavior and mobility willplay a vital role in planning effective humanitarian relief, disaster management, and long-term societalreconstruction. However, existing models are shallow models, and it is difficult to apply them for under-standing the “deep knowledge” of human mobility. Therefore, in this study, we collect big and heterogeneousdata (e.g., GPS records of 1.6 million users over 3 years, data on earthquakes that have occurred in Japan over4 years, news report data, and transportation network data), and we build an intelligent system, namely,DeepMob, for understanding and predicting human evacuation behavior and mobility following differenttypes of natural disasters. The key component of DeepMob is based on a deep learning architecture thataims to understand the basic laws that govern human behavior and mobility following natural disasters,from big and heterogeneous data. Furthermore, based on the deep learning model, DeepMob can accuratelypredict or simulate a person’s future evacuation behaviors or evacuation routes under different disaster con-ditions. Experimental results and validations demonstrate the efficiency and superior performance of oursystem, and suggest that human mobility following disasters may be predicted and simulated more easilythan previously thought.

Categories and Subject Descriptors: H.2 [Database Management]: Data Mining; H.2 [DatabaseManagement]: Spatial Databases and GIS; H.4 [Information Systems Applications]: Decision Support(e.g., MIS)

General Terms: Information Systems

Additional Key Words and Phrases: Human mobility, disaster informatics, urban computing, spatiotemporaldata mining

This work was partially supported by JST, Strategic International Collaborative Research Program(SICORP); Grant in-Aid for Scientific Research B (17H01784) of Japan’s Ministry of Education, Culture,Sports, Science, and Technology (MEXT); Microsoft Research collaborative research (CORE) program; USNational Science Foundation Grant (CNS-1461926); Chinese National Natural Science Foundation Grant(91646116).Authors’ addresses: X. Song (corresponding author) and R. Shibasaki, Center for Spatial InformationScience, The University of Tokyo, 5-1-5, Kshiwanoha, Kashiwa-City, Chiba, Japan 2778568; emails:{songxuan, shiba}@csis.u-tokyo.ac.jp; N. J. Yuan and X. Xie, Microsoft Research, Danling Street, HaidianDistrict Beijing, P.R. China 100080; emails: {nicholas.yuan, Xing.Xie}@microsoft.com; T. Li, School ofComputing and Information Sciences, Florida International University, 11200 SW 8th Street, ECS 354,Miami, FL, 33199 and School of Computer Science, Nanjing University of Posts and Telecommunications,Nanjing, Jiangsu, P.R. China 210023; email: [email protected]; R. Adachi, Zenrin DataCom Co’Ltd, 2-15-3Konan Minato-ku, Tokyo, Japan 1086206; email: [email protected] to make digital or hard copies of part or all of this work for personal or classroom use is grantedwithout fee provided that copies are not made or distributed for profit or commercial advantage and thatcopies show this notice on the first page or initial screen of a display along with the full citation. Copyrights forcomponents of this work owned by others than ACM must be honored. Abstracting with credit is permitted.To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of thiswork in other works requires prior specific permission and/or a fee. Permissions may be requested fromPublications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212)869-0481, or [email protected]© 2017 ACM 1046-8188/2017/06-ART41 $15.00DOI: http://dx.doi.org/10.1145/3057280

ACM Transactions on Information Systems, Vol. 35, No. 4, Article 41, Publication date: June 2017.

http://dx.doi.org/10.1145/3057280

41:2 X. Song et al.

ACM Reference Format:Xuan Song, Ryosuke Shibasaki, Nicholos Jing Yuan, Xing Xie, Tao Li, and Ryutaro Adachi. 2017. DeepMob:Learning deep knowledge of human emergency behavior and mobility from big and heterogeneous data.ACM Trans. Inf. Syst. 35, 4, Article 41 (June 2017), 19 pages.DOI: http://dx.doi.org/10.1145/3057280

1. INTRODUCTION

Japan is one of the most severely affected countries by natural disasters. Two of the fivemost devastating natural disasters in recent history (1995 and 2011) have occurred inJapan, resulting in heavy economic losses and numerous deaths. According to the JapanMeteorological Agency (JMA), there were over 10,681 earthquakes having an intensityof more than one throughout Japan in 2011 alone. Such severe natural disasters usuallycause large population movements and evacuations. Thus, it is critical to understandand predict population evacuation behavior and mobility following natural disastersin order to plan effective humanitarian relief, disaster management, and long-termsocietal reconstruction.

However, human evacuation behavior and mobility patterns following natural dis-asters are usually unconstrained and highly variable, and they will be affected bymany factors, e.g., disaster intensity, damage level, news reports, transportation con-ditions, and people’s social relationships. Song et al. [2014a, 2015] collected data from1.6 million GPS users in Japan to mine and model human mobility following large-scaledisasters; accordingly, they proposed the use of hidden Markov models (HMMs) to un-derstand and predict human evacuation behaviors and movements. Even though theirmodels provide some fundamental hypotheses and characteristics of human emergencybehaviors and mobility, important information or “deep knowledge” on these mattersremains largely unknown (e.g., how different factors will influence people’s decisionsafter disasters and how important are these factors). This is because such models are“shallow models,” and it is difficult to apply them to a big and heterogeneous datasource. Recently, deep learning technology has been shown to be a highly effectivelearning approach and it has demonstrated superior performance in various domains(e.g., vision, speech, and text) [Hinton et al. 2006; Hinton and Salakhutdinov 2006;Krizhevsky et al. 2012; Lee et al. 2008; Larochelle et al. 2009]. Therefore, in this study,we aim to understand the basic laws that govern human behavior and mobility follow-ing natural disasters by using a deep learning approach, and we develop a deep modelfor human mobility prediction and simulation.

We collect big and heterogeneous data and build an intelligent system, namely, Deep-Mob, for predicting and simulating human emergency behavior and mobility followingdifferent types of natural disasters (as shown in Figure 1). The deep learning archi-tecture of DeepMob serves two main purposes: learning feature representation in anunsupervised manner and learning predictive models in a supervised manner. In thebottom layers, the deep belief network (DBN) is employed to learn the deep knowledgeand feature representation from the raw data. Then, the sigmoid regression layer isadded above the DBN layers to learn the predictive models through a large amount oflabeled data. Furthermore, we propose the use of a multimodal learning architecture tojointly learn human behavior and mobility models from a heterogeneous data source. Fi-nally, given any person’s location transition or observed mobility, disaster information,news report level, and transportation conditions, DeepMob can automatically predictor simulate that person’s future evacuation behavior and mobility. To the best of ourknowledge, DeepMob is the first system that applies deep learning approaches to hu-man mobility modeling, and it has the following key characteristics that make it unique:

—Big and heterogeneous data: DeepMob is based on a big and heterogeneous datasource. It stores and manages the GPS records of 1.6 million users collected over


http://dx.doi.org/10.1145/3057280

DeepMob: Learning Deep Knowledge of Human Emergency Behavior and Mobility 41:3

Fig. 1. Can we learn the deep knowledge of human emergency behavior and mobility from big and hetero-geneous data?1 Human evacuation behavior and mobility patterns following natural disasters are uncon-strained and highly variable, and they will be affected by many factors. How will these factors influencepeople’s decisions and mobility? Can we learn deep models for predicting or simulating people’s behaviorunder different disaster conditions?

3 years,1 data on earthquakes that have occurred in Japan over 4 years, news reportdata, transportation network data, and so on.

—Deep and general model: DeepMob can discover deep knowledge of human emer-gency behavior and mobility. Furthermore, its predictive model is a general modelthat can be applied to any person, place, or disaster.

The remainder of this article is structured as follows. Section 2 briefly reviewssome related studies. Section 3 provides an overview of the entire system. Section 4

1We used “Konzatsu-Tokei (R)” from ZENRIN DataCom CO., LTD. “Konzatsu-Tokei (R)” data refers topeople flows and it is collected from mobile phone with enabled AUTO-GPS function under users’ consentand agreement through the “docomo map navi” service provided by NTT DOCOMO, INC. The statistics datais anonymized and aggregated so that individual information can never be retrieved. The anonymization andaggregation were made by NTT Docomo based on the request of Zenrin DataCom Co., LTD. Original locationdata is GPS data (latitude, longitude) sent in about every 5 minutes (minimum) and does not include anypersonal information, such as gender or age. The processing of raw GPS data for this study was conductedby NTT Docomo.


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introduces our big and heterogeneous data source. Section 5 describes the deep learn-ing architecture of our system. Section 6 presents our experimental results and systemevaluations. Finally, Section 7 summarizes our findings and concludes the article.

2. RELATED WORK

A number of studies have been conducted on human mobility following disasters[M. Moussaid and Helbing 2009; Pan et al. 2007]. These studies mainly focus onsmall-scale or short-term emergencies (e.g., crowd panics and fires). However, researchon the dynamics of human mobility on a nation- or city-wide scale following large-scalenatural disasters (e.g., hurricanes, earthquakes, and tsunamis) is limited [Lu et al.2012] because of the “result of difficulties in collecting representative longitudinal datain places where infrastructure and social order have collapsed” [Bagrow et al. 2011;Bengtsson et al. 2011] and where study populations move across vast geographical ar-eas [Lu et al. 2012]. However, recent years have witnessed the proliferation of people’smobile phone data, GPS trajectory data, and location-based online social networkingdata, which have become readily available. Such rapidly growing human mobilesensing data have become today’s “Big Data,” providing a new way to circumvent themethodological problems faced by previous studies on human mobility modeling anddisaster management [Li et al. 2017; Song et al. 2010b; Gonzalez et al. 2008; Lu et al.2012; Bagrow et al. 2011; Song et al. 2010a; Eagle et al. 2009]. Furthermore, under-standing, modeling, and mining human mobility [Ge et al. 2014; Chen et al. 2010, 2011;Giannotti et al. 2011; Li et al. 2010c; Yuan et al. 2012; Backstrom et al. 2010; Giannottiet al. 2007; Li et al. 2010b; Scellato et al. 2011; Zheng et al. 2009; Li et al. 2010a;Xue et al. 2013; Su et al. 2013; Yuan et al. 2013; Zhu et al. 2014; Ying et al. 2013; Zhenget al. 2014; Cho et al. 2011; Ye et al. 2013; Guo et al. 2016; Yu et al. 2015] has becomethe main research focus for smart city development and sustainable urbanization.

Recently, Lu et al. [2012] collected the mobile phone data of 1.9 million users toanalyze the population displacement after the 2010 Haitian earthquake and foundthat population mobility following natural disasters may be more predictable thanpreviously thought. Song et al. [2013] collected the GPS data of 1.6 million GPS usersin Japan to model population evacuation behaviors following the Great East JapanEarthquake of 2011 and the Fukushima Nuclear Accident, and demonstrated that theprediction of large population movements following large-scale natural disasters waspossible. However, their model cannot accurately predict the behavior or mobility ofindividuals. Thus, they proposed an HMM-based behavior model [Song et al. 2014b]for accurately predicting individual evacuation behavior or mobility following large-scale disasters (e.g., the Great East Japan Earthquake of 2011). However, owing to theuniqueness of this event, it is difficult to apply their model to places that are not affectedby such disasters or places that experience some small-scale disasters. Furthermore,the models discussed above are “shallow models” that face difficulties in handling a bigand heterogeneous data source.

Recently, deep learning technology [Hinton et al. 2006; Hinton and Salakhutdinov2006; Krizhevsky et al. 2012; Lee et al. 2008; Larochelle et al. 2009; Ge et al. 2013;Huang et al. 2014] has been shown to be a highly effective learning approach, and ithas demonstrated superior performance in various domains (e.g., vision, speech, text,and transportation). Hence, this study constitutes the first attempt to apply the deeplearning approach to human mobility understanding and modeling.

3. SYSTEM OVERVIEW

The system architecture is shown in Figure 2. It consists of four main components:database server, pre-processing module, deep learning module, and visualization andevaluation module. The database server module stores and manages the heterogeneous



Fig. 2. System Architecture. DeepMob mainly contains four components: database server, pre-processingmodule, deep learning module, and visualization and evaluation module. Please see the texts for more details.

data source. It can provide indexing, retrieval, editing, and visualization services. Thepre-processing module can discover and recognize people’s locations, including keylocations (e.g., home, workplace), locations based on social relationships, and unfamiliarlocations (hotel, shelter, etc.). In addition, this module can generate a sequence oftransitions between people’s key locations as well as the input data vector for trainingand testing. For further details, readers may refer to Song et al. [2015]. The deeplearning module is the key component of DeepMob; it includes the DBN layers and asigmoid regression layer. Further details on this module will be provided in Section 4.Finally, the visualization and evaluation module can visualize the results and evaluatethe performance of the overall system.

4. HETEROGENEOUS DATA SOURCE

In this study, we employ a big and heterogeneous data source to understand humanemergency behavior and mobility following different types of natural disasters (asshown in Figure 3). The data can be summarized as follows:

Human mobility data: We collected GPS records of approximately 1.6 millionanonymized users1 throughout Japan from August 1, 2010 to July 31, 2013 (as shownin Figure 3(a) and (b)). To manage these data, we employed five computers (Intel Xeon2.6GHz CPU, 8GB RAM, and 2x2 TB HDD) to build a Hadoop cluster that consistsof 32 cores, 32GB memory, and 16TB storage, and is able to run 28 tasks simulta-neously. Furthermore, we installed Hive on top of Hadoop to make the entire systemsupport Structured Query Language (SQL)-like spatial queries. This set up can provideindexing, retrieval, editing, and visualization services.

Disaster information data (earthquake and tsunami): We collected data onearthquakes2 that have occurred throughout Japan from January 1, 2010 to Decem-ber 31, 2013 (as shown in Figure 3(c) and Figure 5). These data include occurrencetime, earthquake hypocentral location, earthquake magnitude, earthquake intensityat affected locations, and damage level (1–7) (e.g., number of destroyed buildings andnumber of deaths due to earthquakes or tsunamis). Based on this disaster informa-tion, we can easily retrieve a large number of human emergency GPS traces with thedisaster status from our human mobility database (e.g., retrieval by time period orcity).

Disaster reporting data: We collected government declarations and news reportsfrom Japanese outlets as well as global media outlets for large-scale disasters (e.g., theGreat East Japan Earthquake of 2011 and the Fukushima Nuclear Accident). Based onthis information, we empirically divided the reports and declarations into four levels

2The data and retrieval system are open to the public by the JMA. Please visit the website (http://www.data.jma.go.jp/svd/eqdb/data/shindo/index.php) for details.


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Fig. 3. Big and heterogeneous data source.1 This figure shows the heterogeneous data source of our system.(a) Human mobility in Tokyo during the Great East Japan Earthquake, and (b) human mobility in Fukushimaduring the Fukushima Nuclear Accident. (c) Information on earthquakes throughout Japan in March 2011.(d) Urban mobility graph of Fukuoka. The edge color indicates the edge parameters. Here, it shows thetravel frequency after the disasters; warmer colors indicate higher travel frequency, and these values arenormalized from 0 to 1.

to measure the severity of the disasters (level one implies “not serious” and level fourimplies “extremely serious’)’.

Transportation network data: We collected the transportation network data ofsome important cities of Japan. These data include road structure and Point of Interest(POI) information. Transportation networks might come to a standstill in the event of amajor earthquake. Therefore, we used a large number of human emergency movementsto train the urban mobility graph [Song et al. 2014a] that includes transportationinformation (e.g., road connections, travel time, and travel frequency of each road) foremergencies (as shown in Figure 3(d)).

5. DEEP LEARNING ARCHITECTURE

In this section, we investigate how a big and heterogeneous data source can be used tolearn deep knowledge of human emergency behavior and mobility, and we present thedetails of our deep learning architecture. Then, we describe how to predict or simulatehuman evacuation behavior and mobility with the learning framework.

5.1. Preliminaries

Human emergency behavior: Based on our previous study [Song et al. 2013, 2014b],we find that although human behavior and mobility patterns following natural disas-ters are unconstrained and highly variable, the majority of human mobility is basedon random movements between several locations, including key locations (e.g., home,



workplace), locations based on social relationships (e.g., friends’ house, hometown),and unfamiliar locations (e.g., shelters, hotels). Furthermore, these mobility patternswill be influenced by various factors. For instance, if a low-intensity earthquake occursat midnight, people may remain at home and go back to sleep. In contrast, if a high-intensity earthquake occurs at midnight and destroys some buildings, people may leavetheir homes and find a safe place to stay. In such cases, they would need to considerthe travel distance or travel time. However, if a very-high-intensity earthquake (suchas the Great East Japan Earthquake of 2011) occurs and becomes a composite disasteraccompanied by many negative news reports, people may leave their city of residencefind a safe place far from the disaster area (e.g., hometown). Hence, we aim to under-stand how these factors will influence people’s decisions following natural disasters inorder to learn deep knowledge from big and heterogeneous disaster data.

Consider a set of individual activities, Activity = {act1, act2, . . . , actn}, following nat-ural disasters; each activity, acti = l1 → l2 → · · · → lm, denotes a series of m locationtransfers based on the disaster information. Each location l is a tuple of the forml = <uid, time, label, latitude, longitude, distance, intensity, damage, reporting>, whereuid is the id of the person, time is the current time, and label specifies the person’s loca-tion (key location, location based on social relationship, or unfamiliar location). Here,we use the approach adopted in Song et al. [2014b] to discover and recognize people’slocations. Further, latitude and longitude specify the geographic location, distance is thedistance from the earthquake or event, intensity is the seismic or intensity scale of theearthquake at that location, damage is the damage level at that location, and reportingis the level of news reports or government declarations. Hence, our objective is to (1)learn a deep feature representation Fbeh = { f 1

beh, f 2beh, . . . , f p

beh} from Activity; and (2)learn a predictive model that can predict the place to which a person will evacuate inthe next time period, given a set of human activities and disaster information.

Human emergency mobility: On the other hand, we also need to understandand predict people’s mobility or evacuation routes following natural disasters, whichwill be very important for disaster management and humanitarian relief. Con-sider a set of individual GPS trajectories T ra = {tra1, tra2, . . . , tran} following nat-ural disasters; each trajectory trai = r1 → r2 → · · · → rp, consists of a seriesof p GPS records and road information. Each record r is a tuple of the form ofr = <uid, time, latitude, longitude, road, trav time, trav f req>, where uid is the id ofthe person, time is the current time of this record, and latitude and longitude specifythe geographic position of the record. Further, road is a vector that includes road struc-ture and information where the GPS records appear (mapping the GPS records to thetransportation network), e.g., road or node index of the transportation network, road ornode connection, traffic regulations, and highest or lowest speed. In addition, trav timeis the average travel time of the road, and trav f req is the travel frequency of the road.All this information can be obtained from our transportation network data and thepre-trained mobility graph model [Song et al. 2013, 2014a]. Therefore, our objectiveis to (1) learn a deep feature representation Fmob = { f 1

mob, f 2mob, . . . , f q

mob} from T ra; (2)learn a predictive model that can predict the future movements or evacuation routesof a person given a set of human GPS traces and transportation information followingdisasters.

5.2. Architecture Overview

Our learning architecture needs to learn knowledge from a heterogeneous data sourceto perform the following tasks. (1) Learning the deep representation and predictivemodel of human behavior from human location transition sequences and disasterinformation data (as shown in Figure 4(a)) and (2) learning the deep representationand predictive model of human mobility from GPS traces and transportation network


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Fig. 4. Deep learning architecture. (a) shows the deep learning architecture of the behavior task, and(b) shows the learning architecture of the mobility task. The overall deep learning architecture (multimodallearning) of DeepMob is shown in (c).

data (as shown in Figure 4(b)). Even though these two tasks and the data sourceare heterogeneous, they share important information and are highly correlated witheach other. For instance, if a person has decided to evacuate to his/her friend’s houseafter a large-scale disaster but finds that the road condition is extremely poor, thenhe/she might change his/her mind and choose to evacuate to a hotel. In contrast, if aperson listens to news reports and obtains disaster information on the way to his/herworkplace, he/she might choose to return home. Hence, in this study, we propose theuse of multimodal learning [Ngiam et al. 2011; Huang et al. 2014; Ge et al. 2013;Srivastava and Salakhutdinov 2012] to jointly learn deep knowledge of human be-havior and mobility following disasters (as shown in Figure 4(c)). The key concept ofmultimodal learning is to learn several tasks simultaneously with the aim of gainingmutual benefits; thus, learning performance can be improved through parallel learn-ing while using a shared representation. Therefore, it is reasonable to expect betterresults from our application through this learning framework. Another advantage ofthe multimodal learning architecture is that we can use a single data source and sharedrepresentation to train the predictive model for some real-world applications in whichwe need to predict people’s future behavior or mobility without observing their actualmovements or obtaining information on their locations.

The overall deep learning architecture is shown in Figure 4. Our deep model consistsof two main parts. In the bottom layers, we employ a DBN [Hinton et al. 2006] to performknowledge discovery and unsupervised feature learning. For each task, we first trainseveral hidden layers for each data source, and then, we learn the shared representationfor human behavior and mobility. In the top layer, we employ a sigmoid regression layerabove the DBN layers to perform supervised learning of human emergency behaviorand mobility prediction.

5.3. Learning Deep Representation

To understand human emergency behavior and mobility following natural disasters,we need to learn deep feature representation from our heterogeneous data source.Given Activity and T ra, we aim to learn the deep feature representation of humanbehavior and mobility as well as the shared feature representation. In this study, we



employ a DBN to learn the feature representation of the heterogeneous data. A DBNis a stack of restricted Boltzmann machines (RBMs) that has been shown to be highlyeffective in learning representative features from big data in an unsupervised manner[Krizhevsky et al. 2012; Lee et al. 2008; Larochelle et al. 2009]. Here, the Boltzmannmachines (BMs) are a specific form of log-linear Markov random fields (MRFs), and theRBMs further restrict the BMs without hidden-hidden and visible-visible connections.

In an RBM, the visible units v specify the observation data, and the hidden unitsh specify some latent reasons or knowledge from which the observation data can beobtained. The probability distribution of a binary RBM can be written as:

P(v, h) = 1Z

e−E(v,h;θ), (1)

where Z is a partition function that sums over all pairs of hidden and visible vectors,and E is the energy function that can be written as:

E(v, h; θ ) = −|V |∑i=1

|H|∑j=1

vihjwi j −|V |∑i=1

bivi −|H|∑j=1

ajhj, (2)

where θ = (w, b, a) is the parameter set, ai, bj are the biases of visible and hiddenunits, respectively, and wi j are the weights among them. The number of hidden andvisible units is represented as |H| and |V |, respectively. If v or h is fixed, we can obtain:

P(hj |v; θ ) = sigmoid

⎛⎝

|V |∑i=1

wi jvi + aj

⎞⎠ , (3)

P(vi|h; θ ) = sigmoid

⎛⎝

|H|∑j=1

wi jhj + bi

⎞⎠ , (4)

where sigmoid is the sigmoid function, and θ = (w, b, a) can be easily learned throughthe contrastive divergence (CD) algorithm [Hinton 2002].

The advantage of the RBM is that it can discover hidden structures and thus richerknowledge from raw input data. In our application, the RBM can enable us to un-derstand the latent reasons or factors that will influence human decisions or mobilityfollowing natural disasters. Considering Equation (3), the latent units hj can representone possible underlying reason for human behavior (e.g., he worried about his familymembers very much), and P(hj |v; θ ) can show how “important” this reason is given theobservation data. Thus, we can easily understand how different factors will influencehuman emergency behavior and mobility following disasters. Moreover, each type ofreason might involve several factors based on detailed aspects. For instance, a personchooses to go home after a major earthquake because he/she is extremely worried abouthis/her family members, and this “worry” is due to the intensity scale and damage levelat his/her home location. Thus, these reasons form a hierarchical structure, and mul-tiple RBM layers form a DBN that can enable us to understand the “deep knowledge”of human emergency behavior and mobility.

In our study, we use multimodal learning [Ngiam et al. 2011; Huang et al. 2014; Geet al. 2013; Srivastava and Salakhutdinov 2012] to train the DBN layers from two typesof data input (Activity and Tra). First, we separately train several hidden layers foreach data source. Then, we combine the two DBNs by adding an additional layer on topof them. The input space X of the DBNs is the pre-processing data generated by the pre-processing module (as discussed in Section 4.1). For the behavior task, the number of


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the input node is |l|×m, where |l| is the length of vector l, and mis the number of locationtransitions. The entire input vector for behavior is act (as discussed in Section 4.1). Onthe other hand, the input vector for mobility is tra, and the number of the input nodeis |r| × p, where |r| is the length of vector r, and p is the number of GPS records. Here,m and p are dependent on the number of hours considered for the training. The aimof multimodal learning is to tune the top hidden layer to model the two types of inputdata sources more effectively. Thus, this “deep model” tries to find the shared latentrepresentation of human emergency behavior and mobility in order to understand therelevant deep knowledge. Finally, we can obtain the shared feature representationFshared = { f 1

shared, f 2shared, . . . , f u

shared} for human emergency behavior and mobility.

5.4. Behavior and Mobility Prediction

To plan effective humanitarian relief and disaster management, we need to predict(1) a person’s future behavior, e.g., where he/she will go (home, workplace, friend’shouse, hotel, shelter, etc.) and (2) the person’s future mobility, e.g., his/her evacuationroutes. Thus, we add a sigmoid regression layer above the DBN layers, and we use thelabeled data to train the predictive models.

In the regression layer, the RBM is replaced by real-valued units with Gaussiannoise to model the observation data [Hinton and Salakhutdinov 2008], and the energyfunction is given as:

E(v, h; θ ) = −|V |∑i=1

|H|∑j=1

vi

σihjwi j −

|V |∑i=1

(vi − bi)2

2σ 2i

−|H|∑j=1

ajhj, (5)

where σ is the standard deviation vector of Gaussian visible units, and the conditionalprobability distributions can be re-written as:

P(hj |v; θ ) = sigmoid

⎛⎝

|V |∑i=1

wi jvi + aj

⎞⎠ , (6)

P(vi|h; θ ) = N

⎛⎝σi

|H|∑j=1

wi jhj + bi, σ2i

⎞⎠ , (7)

where N(μ, σ 2) is the Gaussian distribution with mean μ and variance σ .In the sigmoid regression layer, the output of the behavior prediction task is a real-

valued score for each type of evacuation behavior, e.g., go home, go to workplace, orevacuate to a friend’s house or unfamiliar location (hotel, shelter, etc.). To preparethe training and testing data, we use the approach adopted in Song et al. [2015] togenerate the labeled data. On the other hand, the output of the mobility predictiontask constitutes real GPS records in the next few time periods.

In conclusion, the objective of the DBN, which lies at the bottom of our deep learningarchitecture, is to learn the deep feature representation of human emergency behaviorand mobility in an unsupervised manner. Then, the learned representative featuresare used as the input vector for learning the predictive model in a supervised manner.Furthermore, the learned features from the DBN can be fine-tuned via error back-propagation to the entire learning structure by using a large amount of labeled data.Thus, our deep learning architecture can obtain better results in terms of both featurelearning and behavior/mobility prediction.



Fig. 5. Example of disaster data retrieval.2 The first figure shows the interface of retrieval software, andthe third one shows the examples of retrieval results. The second figure shows the visualization of theretrieved earthquakes, and the last one shows the intensity distribution of affected places for the numberone earthquake in the retrieval results.

Table I. Disaster Information

Number of EarthquakesCity & Prefecture Intensity 1 to 3 Intensity 3 to 4 Intensity 4 to 5 Intensity Above 5Tokyo 42 25 8 3Osaka 12 2 0 1Kyoto 8 1 1 0Miyagi 26 15 21 19Fukushima 15 24 16 29Fukuoka 5 0 0 0

6. EXPERIMENTAL RESULTS

Based on the disaster information data (Japan earthquakes from 2010 to 2013),e.g., earthquake location, earthquake time, and earthquake intensity (as shown inFigure 5), we retrieved human movements (GPS traces) over 24 hours following eachearthquake between August 1, 2010 and July 31, 2013 from our human mobilitydatabase;1 the selected geotropical regions were some important cities (e.g., Tokyo,Osaka, Kyoto, Fukuoka, Fukushima, and Miyagi) of Japan where the earthquake in-tensity was greater than one. Then, we converted the GPS trajectories into a sequenceof location transitions, as discussed in Song et al. [2015]. Further, we used the approachadopted in Song et al. [2014a] to train the urban mobility graph for these cities, and weextracted the transportation information. Finally, these GPS trajectories and locationtransition sequences with the related disaster information, disaster reporting infor-mation, and transportation information formed the training and testing dataset. Thedisaster information and entire training/testing dataset were summarized as Table Iand Table II. In the training step, we used the first 3 hours of mobility data after eachdisaster as the training input and the next 6 hours of data as the training output. In thissection, we present the experimental results and performance evaluation of DeepMob.



Table II. Training & Testing Dataset

Earthquake IntensityIntensity 1 to 3 Intensity 3 to 4 Intensity 4 to 5 Intensity Above 5

Number of Earthquakes 108 67 46 52Number of Traj ID 353K 338K 316K 253KNumber of GPS Records 3.85B 1.63B 0.36B 0.13BNumber of Transition Sequence 26.3M 11.8M 3.5M 1.6M

Fig. 6. Effect of different parameter settings and selection of the training/testing dataset. (a,c,e) show theeffect of the number of hidden layers on system performance (accuracy and time cost of training), and (b,d,f)show the effect of the number of hidden units on system performance under a different selection of thetraining/testing dataset.

6.1. Parameter Setting and Performance Evaluation

Evaluation Metric: For evaluation, we considered behavior prediction as a classifica-tion task, and we used the classification accuracy score to evaluate behavior predictionaccuracy accbeh. For the mobility prediction task, we used the mean absolute percent er-ror (MAPE) for error measurement; the mobility prediction accuracy can be obtained asaccmob = 1−MAPE. Thus, the overall accuracy of DeepMob is acc = w1accbeh+w2accmob,where w is the weight, which indicates the importance of a task. Here, we setw1 = w2.

Parameters Setting: The key parameters of our deep learning model are the num-ber of hidden layers and the number of hidden units in each layer. We chose the numberof hidden layers to be in the range of 3–7. For simplicity, the number of hidden unitsin each layer was set to be equal. In our experimental setting, it was chosen from{10, 20, 40, 80, 160, 320}. First, we randomly chose the parameters from each set, andthen, we chose the best configuration from 30 random training runs. The best param-eters for our learning architecture are as follows: number of hidden layers is three;number of hidden units is 80.

Performance Evaluation: First, we examined the effect of each parameter ofDeepMob while keeping the others fixed and how the selection of the training/testingdataset influenced the system performance. In this experiment, we randomly selected90%, 80%, and 70% of the entire data for model training, respectively, and used theremaining 10%, 20%, and 30% for testing and evaluation. Here, we normalized all thetime costs into [0, 1] instead of using real training hours, and showed overall accuracyand average time costs together in Figure 6. Figure 6(a), (c), and (e) show the system



Table III. System Performance Evaluation

Behavior Prediction Accuracy Mobility Prediction AccuracyTraining&Input Small Large All Small Large All

Data Source Earthquake Earthquake Data Earthquake Earthquake DataBehavior Data Training&Input Only 83.59% 76.79% 78.29% 62.58% 58.38% 59.76%Mobility Data Training&Input Only 71.26% 60.37% 67.33% 76.87% 63.76% 71.19%

Behavior and Mobility Data 92.32% 83.19% 87.83% 81.58% 75.27% 79.57%

performance under different training/testing conditions when the number of hiddenunits is fixed (80 here). From this figure, we can see that in the case of three hiddenlayers, the system performance becomes very stable, and more complicated structuresdo not offer any advantage; however, the time cost of training is higher. This is muchdifferent from the image analysis where more hidden layers can get “deeper” under-standing about the image. That is because the input vector of image data is usuallyhigh-dimensional, which needs to be processed by the multiple nonlinear hidden layers.In contrast, the dimension of input data for the spatio-temporal task is much lower, andmore complicated structures usually do not offer any advantage. In addition, we can seethat the system performance is insensitive to the training/testing data selection, andthe overall accuracy does not change much under different training/testing conditions.On the other hand, Figure 6(b), (d), and (f) show the performance under differenttraining/testing conditions when the number of hidden layers is fixed (three here). Wecan see that the performance of DeepMob can be improved by increasing the numberof hidden units with different training/testing selections. However, when the numberof units reaches 80, the system performance becomes stable. Furthermore, the spatio-temporal complexity increases exponentially, and additional hidden units will imposean unnecessary burden on model training. Finally, the overall accuracy of DeepMobcan reach 82.68%, 83.70%, and 83.57% with three hidden layers and 80 nodes in eachlayer under the training/testing settings of 90%/10%, 80%/20%, and 70%/30%. In thefollowing, we use the training/testing setting of 80%/20% for the following experiments.

For some real-world applications, we may need to predict people’s future behavioror mobility without observing their real movements or obtaining information on theirlocations. Therefore, we investigated how different types of data source inputs willinfluence the system accuracy for each task, e.g., using only location transitions to pre-dict people’s mobility (simulation) or using only GPS traces to predict where people willgo. In addition, due to the imbalance of training data for small-scale and large-scaledisasters, we also wanted to train the specific models and see the system performancefor them, respectively. Thus, we separated our training/testing dataset into two parts:small-scale disaster data and large-scale disaster data. Here, if the earthquake inten-sity of transition sequences was below five, we put them to the small-scale disasterdataset; otherwise, they were put to the large-scale disaster one. For each dataset, werandomly selected 80% of the data for model training and used the remaining 20% fortesting and evaluation. In the supervised training step, we used different types of datasources (e.g., only behavior data, only mobility data, and both behavior and mobilitydata) to train the regression layer (through the shared feature representation layer).Table III summarizes the performance of DeepMob with different types of data inputsat the different training/testing dataset (e.g., small-scale disaster data, large-scale dis-aster data, and entire data). From this table, we can see that our system can achieveacceptable performance in both tasks even though it has only a single training or inputdata source. In addition, we also found that our system obtained a better performanceon the small-scale disaster dataset than the large one because people’s movements dur-ing small-scale disasters were more like their daily movements. In contrast, people’smovements during large-scale disasters were much more unpredictable.



Table IV. The Influence of DBN Layers

Behavior Prediction Accuracy Mobility Prediction AccuracyLearning Small Large All Small Large All

Architecture Earthquake Earthquake Data Earthquake Earthquake DataDeep LearningArchitecture 92.32% 83.19% 87.83% 81.58% 75.27% 79.57%

Learning Architecturewithout DBN Layers 57.69% 53.28% 56.68% 52.22% 46.25% 47.33%

Lastly, we investigated how the multi-task unsupervised feature learning and DBNlayers improved the system performance. For each task, we removed the DBN layersand kept the regression layer, then trained the separated regression models, respec-tively, for behavior prediction and mobility prediction. Table IV summarizes the perfor-mance of the models on a small-scale disaster dataset, large-scale disaster dataset, andentire dataset. From this table, we can see that the multi-task learning architectureand DBN layers can significantly improve the system performance.

6.2. Visualization of Results

The visualization of the results is shown in Figure 7. As shown in Figure 7(a) and(b), given a person’s observed movements (white lines), location, disaster information,and transportation information, our system can predict his/her future destination andmovements (orange lines). Figure 7(a) shows the sample results for a large-scale dis-aster (e.g., the Great East Japan Earthquake of 2011 and the Fukushima NuclearAccident); this person travels long distances and tries to evacuate to a safe place thatis far from his/her home. In contrast, Figure 7(b) shows the sample results for a small-scale disaster; the person’s mobility is very similar to his/her daily movements undernormal circumstances. We can see that our prediction results are very similar to thereal scenarios (blue lines).

On the other hand, if we cannot observe a person’s mobility, we can use his/herlocation and disaster information to perform mobility simulation. Figure 7(c) showssample simulation results for a single person, and Figure 7(d) shows the results for alarge number of people.

6.3. Comparisons on Behavior Prediction

Evaluation metrics: In order to evaluate the performance of different predictivemodels, we used harsher metrics than the classification accuracy score discussed inSection 5.1 because people may go to some locations that are different from thoseconsidered above (key location, location based on social relationship, or unfamiliarlocation). Here, we followed the work of Cho et al. [2011] and used the following metricsfor evaluation. (1) Predictive accuracy: This metric can measure the average accuracyof different predictive models, i.e., how accurately each model can predict the correctplace that people want to go given the time period of GPS traces in the testing set. Forexample, an accuracy of 0.75 implies that the model can correctly predict the correctplaces or locations that people will go to 75% of the time. (2) Log-likelihood: This metriccan measure the average log-likelihood of the training data in the testing set, and itcan measure how well the testing data fits the learned model. (3) Expected distanceerror: Unlike the previous metrics, this metric does not insist on predicting the exactplaces or locations. Furthermore, it can take into account the spatial proximity of thepredictive results as compared to the real destination. Readers may refer to Cho et al.[2011] for the definition and details of this metric.

Baseline models: In this study, we considered four baseline models for comparison.(1) Gaussian model (GM): This model was proposed by Gonzalez et al. [2008]; it models



Fig. 7. Visualization of the results.1 The first row shows the prediction results of DeepMob. Given a person’scurrent observed movements (white lines in (a) and (b)), location (orange circles), disaster information, andtransportation information, the person’s possible movements are predicted using orange lines ((a) and (b)),and the actual movements are shown by blue lines. (a) Results of a person following a large-scale disaster(the Great East Japan Earthquake of 2011 and the Fukushima Nuclear Accident), and (b) results followinga small-scale disaster. The second row of this figure shows the simulation results of DeepMob (no observedmovements). Given the person’s locations (orange circles in (c)), disaster information, and transportationinformation, his/her possible movements are simulated using orange lines (c), and the actual movements areshown by green lines (c). (c) Results of a single person, and (d) results of a large number of people.

human mobility or movements as a stochastic process centered around a single loca-tion. (2) Most frequented location model (MF): For every hour of the day, this modelpredicts the most likely (most frequently visited) place or location for a specific per-son. Lu et al. [2012] adopted this model for predicting population displacement afterthe 2010 Haitian earthquake. (3) Periodic mobility model (PMM): This model is basedon the assumption that the majority of human mobility involves periodic movementsamong several places or locations. A state-of-the-art approach proposed by Cho et al.[2011] can predict the locations of future human movements. (4) HMM: This modelwas proposed by Song et al. [2014b]; it uses an HMM to model dependencies amongdifferent disaster behaviors. This is a strong baseline model for disaster behaviorprediction.

Performance evaluation: We compared the performances of our model and thebaseline models. Table V summarizes the performances of all the models. From thistable, we can see that our approach achieved better performance than the baselinemodels. A possible explanation is that all the competing models are shallow models,and they do not have sufficient capabilities to handle the complexity of human behaviorsunder various types of disaster situations.



Table V. Comparisons of Behavior Prediction

Algorithm Predictive Accuracy Log-likelihood Distance ErrorMF 0.431 −5.62 0.0625GM 0.239 −8.61 0.0713

PMM 0.527 −3.58 0.0427HMM 0.616 −2.97 0.0315

Our System 0.693 −2.29 0.0226

Table VI. Comparisons of Mobility Prediction

Algorithm Matching 90% Matching Log-ProbHMM [Song et al. 2014b] 71.21% 52.68% −7.26

Method [Song et al. 2014a] 63.72% 46.33% −8.13Our System 77.58% 59.17% −6.39

6.4. Comparisons on Mobility Prediction

In order to evaluate the accuracy of the predictive paths or routes of a specific person,we used the three harsh metrics discussed in Ziebart et al. [2008]. The first evaluatesthe route distance shared between the model’s most likely route estimate and theactual demonstrated route. The second measures the percentage of the testing routesthat matches at least 90% (distance) of the predicted one. The third evaluates theaverage log probability of routes in the training set under the given training model.Further, the approach developed by Song et al. [2014a] was chosen as the first baselinemodel. This model uses a pre-trained urban mobility graph to predict a large number ofpopulation movements after large-scale disasters, but it does not consider the locationsof the people or the disaster states. In addition, we chose an HMM prediction model[Song et al. 2014b] as another baseline model.

Table VI summarizes the performances of our system and the baseline models. Fromthis table, we can see that our system outperforms the baseline models by 6.37%-13.86%.

7. CONCLUSION

In this study, we collected big and heterogeneous data to understand and model hu-man emergency behavior and mobility following natural disasters, and we built anintelligent system called DeepMob. The deep learning architecture of DeepMob candiscover the deep knowledge of human behavior or mobility patterns, and the learnedpredictive model can accurately predict or simulate people’s emergency behavior ormobility under different disaster conditions. The experimental results and validationsdemonstrated the efficiency and superior performance of our system. To the best ofour knowledge, DeepMob is the first system that applies deep learning approaches tohuman mobility modeling.

In the future, our system can be extended and improved in the following aspects.(1) Sometimes, human evacuation behavior and mobility following natural disasterswill be affected by social networking messages (e.g., Facebook, Twitter, and Microblog).Thus, we will try to collect social networking data and analyze how social networkingmessages could influence human mobility following disasters in the future. Specifi-cally, these social networking data can be considered as another data source inputof our deep learning architecture. (2) Currently, our deep learning architecture doesnot consider the temporal information of human evacuation behavior. Hence, sometemporal deep learning approaches, such as recurrent neural networks (RNN), can becarefully considered and explored in the future.



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Received July 2016; revised October 2016; accepted December 2016


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