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The growing mobile phone penetration rates have led to the emergence of large-scale call detail records (CDRs) that could serve as a low-cost data source for travel behaviour modelling. However, to the best of our knowledge, there is no previous study evaluating the potential of CDR data in the context of route choice behaviour modelling. Being event-driven, the data are discontinuous and only able to yield partial trajectories, thus presenting serious challenges for route identification. This paper proposes techniques for inferring the users' chosen routes or subsets of their likely routes from partial CDR trajectories, as well as data fusion with external sources of information such as route costs, and then adapts the broad choice framework to the current modelling scenario. The model results show that CDR data can capture the expected travel behaviour and the derived values of travel time are found to be realistic for the study area.

The interior of urban rail transit system has always been the blind area of traditional passenger flow monitoring methods. The call detail record (CDR) data is a kind of data containing passenger travel information with the advantages of wide coverage and no dead angle. It can well complement the shortcoming that traditional passenger flow monitoring method has blind areas. This paper proposes an approach of urban rail passenger flow monitoring based on CDR data which can directly monitor passenger flow in metro station and passenger flow on different routes. It makes up the defect that previous research can only estimate passenger flow by models. Experiment with actual data, the one-week passenger flow of Beijing Metro Line 2 is monitored. It shows that the characteristics of passenger flow are closely related to the nature of land use and passengers are more likely to be affected by transfer times when transferring. The experimental results match reality and verify the feasibility of the algorithm.

With the impressive development of wireless devices and growth of mobile users, telecommunication operators are thirsty for understanding the characteristics of mobile network behavior. Based on the big data generated in the telecommunication networks, telecommunication operators are able to obtain substantial insights using big data analysis and computing techniques. This paper introduces the important aspects in this topic, including data set information, data analysis techniques, and two case studies. We categorize the data set in the telecommunication networks into two types, user-oriented and network-oriented, and discuss the potential application. Then, several important data analysis techniques are summarized and reviewed, from temporal and spatial analysis to data mining and statistical test. Finally, we present two case studies, using Erlang measurement and call detail record, respectively, to understand the base station behavior. Interestingly, the night burst phenomenon of college students is revealed by comparing the base stations location and real-world map, and we conclude that it is not proper to model the voice call arrivals as Poisson process.

In the last century, Europe has seen a strong migration from rural to urban areas. Urban mobility is key to the economic and social development of cities, but at the same time it generates a significant number of negative effects such as congestion or air pollution. Understanding urban mobility patterns is essential to evaluate which are the most appropriate policies and measures to achieve sustainable urban development. Most of the empirical studies on urban mobility are based on surveys, since they provide detailed information about population mobility patterns and a large amount of socio-demographic information. However, surveys have several practical limitations (Ortúzar & Willumsen, 2011) such as their high costs and long lead times. The pervasive used of mobile devices opens the opportunity of gather large amounts of anonymised, passively-collected geolocation data overcoming some of the limitations of traditional surveys. Mobile phone data are probably one of the best data sources from which extract population mobility patterns at city scale because of their advantages (large samples, wide spatial coverage, low data collection costs, etc.). The main objective of this research is to contribute to the recent advances in the analysis of mobile phone data by developing and validating a new methodology to extract population activity and mobility patterns in urban areas. The methodology developed present several improvements with respect to previous studies, such as the identification of frequent locations different from home and work, better trip time estimations, sample selection and expansion procedures and improvements on population density estimations. The methodology developed has been tested in three different case studies: (1) estimation of mobility statistics and origin-destination matrices, (2) analysis of the relationship between social network and travel behaviour and (3) evaluation of population exposure to air pollution taking into account population activity and mobility patterns. The results show the potential of mobile phone data to extract information about mobility patterns in urban areas, to better understand the relationship between social network and travel behaviour and to improve population exposure assessment to air pollutants. Despite the potential of mobile phone data to provide rich information about activity and mobility patterns, a number of drawbacks and limitations shall be taken into account. Limitations are mainly related to the spatio-temporal resolution of the data and the limited socio-demographic information available. The results of this research are of great interest for transport planning studies, social network and transport modelling, and population exposure assessments., En el último siglo, Europa ha vivido una fuerte migración del ámbito rural al urbano. La movilidad urbana es fundamental para el desarrollo económico y social de las ciudades pero al mismo tiempo conlleva a una serie de importantes efectos negativos, tales como la congestión o la contaminación del aire. El entendimiento de los patrones de movilidad urbana de los ciudadanos es esencial para que los gestores puedan evaluar cuáles son las políticas y medidas más adecuadas para conseguir un desarrollo urbano sostenible. La mayoría de los estudios empíricos sobre movilidad urbana se apoyan en encuestas. Sin embargo, las encuestas presentan una serie de limitaciones prácticas importantes (Ortúzar & Willumsen, 2011) tales como sus elevados costes económicos o sus largos plazos de ejecución. El uso generalizado de dispositivos móviles por parte de la población proporciona la posibilidad de recoger de manera anónima y pasiva una gran cantidad de información espacio-temporal de una gran muestra de usuarios, superando algunas de las limitaciones de los actuales métodos de recogida de información. En concreto, los datos de la red de telefonía móvil presentan una serie de ventajas que los posicionan como una de las mejores fuentes de datos para el estudio de la movilidad general de grandes núcleos de población (bajos costes de extracción de los datos, gran tamaño de muestra, amplía cobertura espacial, etc.). El objetivo principal de esta investigación es contribuir a los recientes avances en el campo del análisis de los datos de telefonía móvil mediante el desarrollo y validación de una metodología que permita extraer información de patrones de actividad y movilidad de la población en ámbitos urbanos. La metodología desarrollada presenta una serie de mejoras relevantes con respecto a estudios previos, como la estimación de localizaciones frecuentes distintas de casa y trabajo, la mejora en la estimación de la hora del viaje, procedimientos para la selección y expansión de la muestra o la mejora en la estimación del número de personas en un área específica a partir de los patrones de actividad y movilidad de las mismas. Esta metodología ha sido aplicada en tres casos de uso para: (1) la obtención de estadísticas básicas de movilidad y matrices origen-destino en ámbitos urbanos, (2) el análisis de la influencia de la red social en la movilidad y (3) el estudio de la exposición de la población a la contaminación. Los resultados obtenidos demuestran el potencial de los datos de telefonía móvil para extraer información sobre patrones de movilidad en ámbitos urbanos, entender mejor la influencia de la red social en la movilidad y mejorar las estimaciones de exposición de la población a la contaminación. A pesar de las ventajas que proporcionan los datos de telefonía móvil, también se han observado limitaciones relevantes en los distintos estudios realizados, derivadas principalmente de la resolución espacio-temporal de los datos y de la limitada información socio-demográfica disponible. Los resultados de esta investigación son de gran relevancia para estudios de planificación y gestión del transporte, para el desarrollo de nuevos modelos de transporte que tengan en consideración la influencia de la red social en la movilidad y en estudios de evaluación de la exposición de la población a la contaminación., En l'últim segle, Europa ha viscut una forta migració de l'àmbit rural a l'urbà. La mobilitat urbana és fonamental per al desenvolupament econòmic i social de les ciutats però al mateix temps comporta a una sèrie d'importants efectes negatius, com ara la congestió o la contaminació de l'aire. L'entesa dels patrons de mobilitat urbana dels ciutadans és essencial perquè els gestors puguin avaluar quines són les polítiques i mesures més adequades per aconseguir un desenvolupament urbà sostenible. La majoria dels estudis empírics sobre mobilitat urbana es recolzen en enquestes. No obstant això, les enquestes presenten una sèrie de limitacions pràctiques importants (Ortúzar & Willumsen, 2011) com ara els seus elevats costos econòmics o els seus llargs terminis d'execució. L'ús generalitzat de dispositius mòbils per part de la població proporciona la possibilitat de recollir de manera anònima i passiva una gran quantitat d'informació espai-temporal d'una gran mostra d'usuaris, superant algunes de les limitacions dels actuals mètodes de recollida d'informació. En concret, les dades de la xarxa de telefonia mòbil presenten una sèrie d'avantatges que els posicionen com una de les millors fonts de dades per a l'estudi de la mobilitat general de grans nuclis de població (baixos costos d'extracció de les dades, grans dimensions de mostra, amplia cobertura espacial, etc.). L'objectiu principal d'aquesta investigació és contribuir als recents avenços en el camp de l'anàlisi de les dades de telefonia mòbil mitjançant el desenvolupament i validació d'una metodologia que permeti extreure informació de patrons d'activitat i mobilitat de la població en àmbits urbans. La metodologia desenvolupada presenta una sèrie de millores rellevants pel que fa a estudis previs, com l'estimació de localitzacions freqüents diferents de casa i treball, la millora en l'estimació de l'hora del viatge, procediments per a la selecció i expansió de la mostra o la millora en l'estimació del nombre de persones en una àrea específica a partir dels patrons d'activitat i mobilitat de les mateixes. Aquesta metodologia ha estat aplicada en tres casos d'ús per a: (1) l'obtenció d'estadístiques bàsiques de mobilitat i matrius origen-destinació en àmbits urbans, (2) l'anàlisi de la influència de la xarxa social en la mobilitat i (3) l'estudi de l'exposició de la població a la contaminació. Els resultats obtinguts demostren el potencial de les dades de telefonia mòbil per extreure informació sobre patrons de mobilitat en àmbits urbans, entendre millor la influència de la xarxa social en la mobilitat i millorar les estimacions d'exposició de la població a la contaminació. Tot i els avantatges que proporcionen les dades de telefonia mòbil, també s'han observat limitacions rellevants en els diferents estudis realitzats, derivades principalment de la resolució espai-temporal de les dades i de la limitada informació sociodemogràfica disponible. Els resultats d'aquesta investigació són de gran rellevància per a estudis de planificació i gestió del transport, per al desenvolupament de nous models de transport que tinguin en consideració la influència de la xarxa social en la mobilitat i en estudis d'avaluació de l'exposició de la població a la contaminació., Picornell Tronch, M. (2017). METODOLOGÍA PARA LA EXTRACCIÓN DE PATRONES DE MOVILIDAD URBANA MEDIANTE EL ANÁLISIS DE REGISTROS DE ACTIVIDAD TELEFÓNICA (CALL DETAIL RECORD) [Tesis doctoral no publicada]. Universitat Politècnica de València. doi:10.4995/Thesis/10251/88397., TESIS

Compared with conventional travel data such as GPS data, detector data and float car data, call detail record data from the cell phone communication not only cost low but also has a large scale which demonstrate it is the best way to collect travel information for studying macroscopic travel activities. This paper presents a complete method to discover hot path and travel feature in a traffic network by clustering trajectory data which was acquired from cell phone signaling data. It is valuable for traffic planning, public transportation management, travel network monitoring.

With the development of growing number of China's mobile phone users and mobile Internet, intelligent transportation has become an effective way to solve the traffic problems. Many studies based on GPS data analysis are available. However, high costs lead to insufficient number of samples, the small number of studies based on call detail record (CDR) data analysis only apply a small range of short data sample. The study covers 27604 base stations in Beijing's Sixth Ring Road, collecting millions of data for research. By analyzing the location update of mobile communication network and mechanism of data and targeting, 75 zones are defined based on land use categories. The case study demonstrates CDR data for traffic characteristics analysis were practical. Travel distance consistent with the law of power law of Traffic zone. For different traffic zones (working or residential) travel mode choice, and improve the traffic situation provides a theoretical basis.

Tourist identification with a lower effort can be highly demandable for the tourism department and related organizations of a country. Nowadays, most of the people including tourists use cell phones to keep communication resulting in corresponding data for every transaction (call, SMS, MMS, mobile data) named call detail record. This kind of data is usually collected and stored by telecom operators mainly for billing reasons. Call detail record (CDR) can be mined to get the approximate location of cellular mobile phones. This article describes a framework that identifies tourists among total population by analyzing cellular phone location data from call detail record. The framework also includes an efficient yet effective data scan method from huge CDR database of the total cell phone users.

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.

With the expanding number of vehicles on the road is creating substantial traffic that is hard to control and maintain safety, particularly in extensive urban areas. To estimate the traffic density several works were carried out in the past. However, they are inappropriate and expensive due to the dynamics of traffic flow. Here we intend to use CDR to distinguish the traffic density location and to track the location of the mobile user. In our proposed method to discover the density scope of the traffic, we are using two algorithms called k-means clustering and the k nearest neighbour classification algorithms. The proposed technique will be tested among five different locations during the weekdays and the weekends, which show the noteworthiness of the proposed algorithm and show that our technique has high accuracy.

The rapid growth of cell phone users in cities enable the cell phone towers spread all over urban area in past years. The user call logs, which refer to users movement trajectory in urban area, can provide an opportunity to understand urban spatial structure. As the extraction of more popular channel of human movement in urban area, the hot lines highlighted the spatial morphology of human flows in urban structure. In this paper, we propose popularity index that utilizes diversity and density index of channel to identify the hot lines based on cell phone call detail record dataset. The density of cell phone users that travel across one channel and the diversity of travel behaviors from different cell phone users refer to one channel has been combined to infer the level of popularity index for each channel. In the case study, a call detail record dataset that generated from the users of an anonymous telecom in Wuhan has been applied to identify the hot lines. The results showed the effectiveness of our approach and can be used as references for more explicitly representing urban dynamics to support urban plan applications.