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

Mobile Customer Clustering Analysis Based on Call Detail Records

Competition in the mobile telecommunications industry is becoming more and more fierce. In order to improve mobile operator’s competitiveness and customer value, several data mining technologies can be used. One of the most important data mining technologies is customer clustering analysis. This targeting practice has been proven manageable and effective for mobile telecommunications industry. Most telecommunications carriers cluster their mobile customers by billing system data. This paper discusses how to cluster mobile customers based on their call detail records and analyze their consumer behaviors. Finally, an application of a mobile customer clustering analysis is given in this paper

Crime investigation and criminal network analysis using archive call detail records

Mobile phones are widely used in our day-to-day life. It's not only used by a common man but also used by antisocial elements and that's why it's not a surprise that today in almost every case the first step towards solving a Crime is to analyze the Call Records of the Suspects. Today in almost all the criminal cases, analysis of Mobile Phone calls of suspect's plays an important role in investigation of crime. In order to track the suspects/criminal, investigative agency need to analyze Call Detail Record (CDR) of the suspect's received in varied formats from the service providers. It is generally found that the anti-social elements have their own network and association with other criminals and anti-social elements. Many time they are associated together for committing a crime or they may be knowing about each other activities and crimes. Many of their associates might have been convicted or their name might have been recorded in old cases. Archiving the CDR in centralize database benefit the investigative agency for identification of possible suspects in other cases. This research paper discuss the implementation model for investigating the case using achieve CDR. © 2017 IEEE

Utilizing Call Detail Records for Travel Mode Discovery in Urban Areas

Mobile network operators often bill their customers based on their network usage. For this purpose, operators collect information about billable events, such as calls, text messages, and data usage. In recent years, operators have realized that they can monetize these billing records by selling insights extracted from them. In this thesis, a multi-stage data analysis algorithm is presented that uses these billing records for travel mode classification. This algorithm identifies whether a mobile phone user has traveled using a public transportation bus or using another transportation mode. The billing records collected by a network operator contain the time at which a billable event happened, as well as the network cell from which the event originated. The coverage area of each network cell is known to the operator. Therefore, the billing records of a mobile phone user give an overview of that user’s approximate location at different times. This data can be used to discover the sequence of network cells that the user has traveled through during a trip. Travel mode classification algorithms in literature analyze long-distance or medium- distance trips. The data analysis algorithm presented in this thesis is novel for analyzing and classifying short-distance, intra-city trips. To classify mobility traces, it uses publicly available bus timetable data and road network infrastructure data. The accuracy of the classification algorithm is evaluated using a two-fold cross-validation analysis

Call Detail Records and their Statistical Processing

Import 03/08/2012Tématem této bakalářské práce je „Podrobné záznamy o volání a jejich statistické zpracování“. Tato práce se zabývá získáváním informací z telefonních záznamů, výpočtem parametrů Call centra a jeho dimenzováním. Zpracovány jsou ATECO a Asterisk CDR záznamy a z těchto záznamů jsou získávány důležité statistické údaje. Tyto informace mohou sloužit ke správnému rozvoji a rozhodování firmy. Mezi důležité údaje patří hlavní provozní hodina, hodnoty provozního zatížení během dne a průměrná délka hovoru. K výpočtu parametrů Call centra jsou použity Markovovy modely. Díky tomu je možné zjistit, jak moc je vypočítaný systém stabilní a jakou úroveň kvality služby poskytuje, provozní parametry jsou k této optimalizaci Call centra velice důležité. Dále je možné vypočítat potřebný počet agentů v Call centru, to přináší možnost přizpůsobit Call centrum konkrétnímu provoznímu zatížení. Na konec je zde demonstrováno porovnání jednostupňového Call centra s vícestupňovým, obsahujícím systém interaktivní hlasové odezvy (IVR), opět pomocí Markovových modelů.The topic of this bachelor thesis is „Call Detail Records and their Statistical Processing“. This work deals with obtaining information from the call detail records and calculation parameters of Call center and its dimensioning. ATECO and Asterisk CDR records are processed and the important statistic data are collected from them. These data can be used for proper development and decision-making businesses. Important information includes the main operating hours, the values of service load during the day and an average length of the call. In order to compute the parameters of the Call center, the Markov models were applied. It makes possible to determine how stable the calculated system is and what level of the quality of service is provided, the traffic parameters are important for the call center optimization. The required number of agents in the Call center can be computed as well and thereby it brings a possibility to adapt the call center to the particular traffic situation. Finally, the comparison of the single-stage Call center to the multi-stage with an Interactive Voice Response (IVR) system is demonstrated, the design is based again on the Markov models.460 - Katedra informatikyvelmi dobř