Predicting human mobility through the assimilation of social media traces into mobility models

Beiró, Mariano G.; Panisson, André; Tizzoni, Michele; Cattuto, Ciro

doi:10.1140/epjds/s13688-016-0092-2

Cited by 66 publications

(40 citation statements)

References 48 publications

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“…It generally requires the detection of frequent travel sequences to gain a deep insight into the tourists' travel behaviors. Recently, with the advance of complex network science, a tourist's location history can be represented as a directed graph where a node is a location and an edge denotes the traveling sequence [22,23]. Popular travel sequences in those graphs can be termed as motifs in analogy to motifs in complex network, which were originally defined as patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks [24].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Liu

et al. 2017

IJGI

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Abstract:With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platforms like Flickr have provided a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists and then extends the concept of motifs from topological spaces, to temporal spaces and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns.

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Section: Introductionmentioning

confidence: 99%

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Liu

et al. 2017

IJGI

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“…This is oftentimes used as data basis for movement analysis and activity detection. Examples of these data sources include sport and fitness apps, such as Endomondo or Strava [22]; photo-sharing applications, such as Flickr or Panoramio [23][24][25]; business apps, such as Foursquare/Swarm [26]; or social media platforms, such as Twitter [17,27]. Tweets contain besides the message itself a rich set of metadata about the tweet (e.g., creation date) and the user (e.g., set language).…”

Section: Related Workmentioning

confidence: 99%

Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

Hübl

Cvetojevic

Hochmair

et al. 2017

IJGI

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Abstract:Over the past few years, analysts have begun to materialize the "Citizen as Sensors" principle by analyzing human movements, trends and opinions, as well as the occurrence of events from tweets. This study aims to use geo-tagged tweets to identify and visualize refugee migration patterns from the Middle East and Northern Africa to Europe during the initial surge of refugees aiming for Europe in 2015, which was caused by war and political and economic instability in those regions. The focus of this study is on exploratory data analysis, which includes refugee trajectory extraction and aggregation as well as the detection of topical clusters along migration routes using the V-Analytics toolkit. Results suggest that only few refugees use Twitter, limiting the number of extracted travel trajectories to Europe. Iterative exploration of filter parameters, dynamic result mapping, and content analysis were essential for the refinement of trajectory extraction and cluster detection. Whereas trajectory extraction suffers from data scarcity, hashtag-based topical clustering draws a clearer picture about general refugee routes and is able to find geographic areas of high tweet activities on refugee related topics. Identified spatio-temporal clusters can complement migration flow data published by international authorities, which typically come at the aggregated (e.g., national) level. The paper concludes with suggestions to address the scarcity of geo-tagged tweets in order to obtain more detailed results on refugee migration patterns.

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“…(Barchiesi et al, 2015) designed a ML algorithm to infer the probability of finding people in geographical locations and the probability of movement between pairs of locations using data from Flickr photo-sharing website. (Beiró et al, 2016) proposed a predictive model of human flow mobility that integrates a Flickr dataset with the classical gravity model, under a stacked regression procedure. They validated the performance and generalizability of the model using two ground-truth datasets on air travel and daily commuting in the United States.…”

Section: * Corresponding Authormentioning

confidence: 99%

“…Understanding travel behavior is vital in travel demand management as well as in urban and transportation planning (Yue et al, 2014;Beiró et al, 2016). Among the travel characteristics, trip destination and activity pattern received significant attention in recent studies (Ermagun et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Modeling a Spatio-Temporal Individual Travel Behavior Using Geotagged Social Network Data: A Case Study of Greater Cincinnati

Saeedimoghaddam

Kim

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Understanding individual travel behavior is vital in travel demand management as well as in urban and transportation planning. New data sources including mobile phone data and location-based social media (LBSM) data allow us to understand mobility behavior on an unprecedented level of details. Recent studies of trip purpose prediction tend to use machine learning (ML) methods, since they generally produce high levels of predictive accuracy. Few studies used LSBM as a large data source to extend its potential in predicting individual travel destination using ML techniques. In the presented research, we created a spatio-temporal probabilistic model based on an ensemble ML framework named "Random Forests" utilizing the travel extracted from geotagged Tweets in 419 census tracts of Greater Cincinnati area for predicting the tract ID of an individual's travel destination at any time using the information of its origin. We evaluated the model accuracy using the travels extracted from the Tweets themselves as well as the travels from household travel survey. The Tweets and survey based travels that start from same tract in the south western parts of the study area is more likely to select same destination compare to the other parts. Also, both Tweets and survey based travels were affected by the attraction points in the downtown of Cincinnati and the tracts in the north eastern part of the area. Finally, both evaluations show that the model predictions are acceptable, but it cannot predict destination using inputs from other data sources as precise as the Tweets based data.

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Predicting human mobility through the assimilation of social media traces into mobility models

Cited by 66 publications

References 48 publications

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

Modeling a Spatio-Temporal Individual Travel Behavior Using Geotagged Social Network Data: A Case Study of Greater Cincinnati

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