Inferring Unusual Crowd Events From Mobile Phone Call Detail Records

The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behaviors in urban environments. Cities can leverage such knowledge in order to provide better services (e.g., public transport planning, optimized resource allocation) and safer cities. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we provide a methodology that is able to detect unusual events from CDR data that typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10 times higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.Comment: 18 pages, 6 figure

The Effect of Pok\'emon Go on The Pulse of the City: A Natural Experiment

Pok\'emon Go, a location-based game that uses augmented reality techniques, received unprecedented media coverage due to claims that it allowed for greater access to public spaces, increasing the number of people out on the streets, and generally improving health, social, and security indices. However, the true impact of Pok\'emon Go on people's mobility patterns in a city is still largely unknown. In this paper, we perform a natural experiment using data from mobile phone networks to evaluate the effect of Pok\'emon Go on the pulse of a big city: Santiago, capital of Chile. We found significant effects of the game on the floating population of Santiago compared to movement prior to the game's release in August 2016: in the following week, up to 13.8\% more people spent time outside at certain times of the day, even if they do not seem to go out of their usual way. These effects were found by performing regressions using count models over the states of the cellphone network during each day under study. The models used controlled for land use, daily patterns, and points of interest in the city. Our results indicate that, on business days, there are more people on the street at commuting times, meaning that people did not change their daily routines but slightly adapted them to play the game. Conversely, on Saturday and Sunday night, people indeed went out to play, but favored places close to where they live. Even if the statistical effects of the game do not reflect the massive change in mobility behavior portrayed by the media, at least in terms of expanse, they do show how "the street" may become a new place of leisure. This change should have an impact on long-term infrastructure investment by city officials, and on the drafting of public policies aimed at stimulating pedestrian traffic.Comment: 23 pages, 7 figures. Published at EPJ Data Scienc

Trajectory Reconstruction and Mobility Pattern Analysis Based on Call Detail Record Data

Tehnoloogiad, mis kasutavad geograafilisi andmeid, on muutunud meie igapäevaelu tähtsaks osaks. Tänu sellele on kasvanud asukoha andmetemassiliine salvestamine ja kaevandamine. Seni on GPS tehnoloogiad olnud põhiliseks geograafiliste andmete kogumismeetodiks. Sellega paralleelselt on populaarsust kogunud mobiiliandmete kasutamine positsiooni tuvastamiseks ja liikumismustrite analüüsimiseks. Mobiiliandmete (CDR) põhjal trajektooride taastamiseks on vajalik meetodite kohendamine selleks, et tulemused oleksid korrektsed. Tänu sellele, et telekommunikatsiooni ettevõtted on alustanud suuremat koostööd ja hakanud CDR-andmeid järjest rohkem avalikustama, on mobiiliandmete kasutamine mitmetel aladel suurenenud. Töödeldud mobiiliandmed aitavad anda ülevaadet rahvastiku liikumisest erinevates ulatustes. Samal ajal on trajektooride taastamine CDR-andmetest kohati raskendatud võrreldes GPS-andmetega. Suurimaks probleemiks on algus- ja lõpp-positsioonide asukoha määramine, mis on veelgi enam raskendatud juhul kui objekt liigub.Selle lõputöö eesmärgiks on trajektooride taastamine anonüümsete kasutajatepoolt genereeritud CDR-andmete põhjal. Tulemuste valideerimine GPS-andmetega, mis on loodud paralleelselt mobiiliandmetega ning on vajalik selleks, et määrata saadud trajektooride täpsust. Loodud trajektoore saab kasutada objektide, sealhulgas ka inimeste, liikumismustrite analüüsimiseks ja rahvastiku paiknemise tuvastamiseks, mis aitab linnade planeerimisel ja infrastruktuuride optimeerimisel. Lõputöö väljunditeks on trajektooride taastamine ja täpsuse analüüsimine, lisaks sellele inimese liikumismudelite tuvastamine ja tihedamini külastatavate asukohtade identifitseerimine nagu näiteks kodu, töökoht ja poed.Up until now, GPS data has been greatly used for collecting highlyprecise locational data from moving objects including humans. In contrast, mobile phone data is becoming more and more popular in the last few years. The usage of mobile phone data, that is also known as CDR data, has many benefits over the widely used GPS. This means that the methods used for example in GPS trajectory reconstruction, need to have modifications made be compatible with CDR data.The fact that telecommunication companies have started to cooperate moreand share the CDR data with the public is also a boost to the usage of CDRdata. The processed and analyzed CDR data can be used to get an overview ofcrowd movement in different scales, for example traveling inside a city as opposed to between countries. Extracting trajectories from CDR data has numerous complications.This is due to the fact that the data might not be continuous anddiscovering of the starting point of the object in motion is complicated.The goal of this thesis is to use CDR data in the reconstruction of trajectoriesmade by an anonymous user and to validate the results with GPS data generated in parallel to the CDR data. Reconstructed trajectories can be used for movement analysis and population displacement and would help city planning by optimizing the infrastructures.Outcomes of this thesis are the reconstructed trajectories based on CDR dataand the precisions of final paths. Also, the frequency of CDR events is analyzedin addition to distance distribution. After that the areas that the user visits most frequently are extracted, such as home and work locations

Advances in crowd analysis for urban applications through urban event detection

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined

Unveiling patterns of international communities in a global city using mobile phone data

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Urban Anomaly Analytics: Description, Detection, and Prediction

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.Peer reviewe

Identifying shifts in multi-modal travel patterns during special events using mobile data: Celebrating Vappu in Helsinki

Large urban special events significantly contribute to a city's vibrancy and economic growth but concurrently impose challenges on transportation systems due to alterations in mobility patterns. This study aims to shed light on mobility patterns by utilizing a unique, comprehensive dataset collected from the Helsinki public transport mobile application and Bluetooth beacons. Earlier methods, relying on mobile phone records or focusing on single traffic modes, do not fully grasp the intricacies of travel behavior during such events. We focus on the Vappu festivities (May 1st) in the Helsinki Metropolitan Area, a national holiday characterized by mass gatherings and outdoor activities. We examine and compare multi-modal mobility patterns during the event with those during typical non-working days in May 2022. Through this case study, we find that people tend to favor public transport over private cars and are prepared to walk longer distances to participate in the event. The study underscores the value of using comprehensive multi-modal data to better understand and manage transportation during large-scale events.Comment: 6 pages, 12 figures, Submitted to ITSC202

Temporal patterns behind the strength of persistent ties

Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction. While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution.EM acknowledges funding from Ministerio de Economía y Competividad (Spain) through projects FIS2013-47532-C3-3-P and FIS2016-78904-C3-3-P