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

How much data is enough to track tourists? The tradeoff between data granularity and storage costs

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn the increasingly technology-dependent world, data is one of the key strategic resources for organizations. Often, the challenge that many decision-makers face is to determine which data and how much to collect, and what needs to be kept in their data storage. The challenge is to preserve enough information to inform decisions but doing so without overly high costs of storage and data processing cost. In this thesis, this challenge is studied in the context of a collection of mobile signaling data for studying tourists’ behavioral patterns. Given the number of mobile phones in use, and frequency of their interaction with network infrastructure and location reporting, mobile data sets represent a rich source of information for mobility studies. The objective of this research is to analyze to what extent can individual trajectories be reconstructed if only a fraction of the original location data is preserved, providing insights about the tradeoff between the volume of data available and the accuracy of reconstructed paths. To achieve this, a signaling data of 277,093 anonymized foreign travelers is sampled with different sampling rates, and the full trajectories are reconstructed, using the last seen, linear, and cubic interpolations completion methods. The results of the comparison are discussed from the perspective of data management and implications on the research, especially the results of research with lower time-density mobile phone data

Algorithm for efficient 3D reconstruction of outdoor environments using mobile robots

In this paper, an algorithm for the reconstruction of an outdoor environment using a mobile robot is presented. The focus of this algorithm is making the mapping process efficient by capturing the greatest amount of information on every scan, ensuring at the same time that the overall quality of the resulting 3D model of the environment complies with the specified standards. With respect to existing approaches, the proposed approach is an innovation since there are very few information based methods for outdoor reconstruction that use resulting model quality and trajectory cost estimation as criteria for view planning

The path inference filter: model-based low-latency map matching of probe vehicle data

We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduce a new class of algorithms, called altogether path inference filter (PIF), that maps GPS data in real time, for a variety of trade-offs and scenarios, and with a high throughput. Numerous prior approaches in map-matching can be shown to be special cases of the path inference filter presented in this article. We present an efficient procedure for automatically training the filter on new data, with or without ground truth observations. The framework is evaluated on a large San Francisco taxi dataset and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The path inference filter has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco, Sacramento, Stockholm and Porto.Comment: Preprint, 23 pages and 23 figure

A map-matching algorithm dealing with sparse cellular fingerprint observations

The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations

Bayesian framework for mobility pattern discovery using mobile network events

Understanding human mobility patterns is of great importance for planning urban and extra-urban spaces and communication infrastructures. The omnipresence of mobile telephony in today’s society opens new avenues of discovering the patterns of human mobility by means of analyzing cellular network data. Of particular interest is analyzing passively collected Network Events (NEs) due to their scalability. However, mobility pattern analysis based on network events is challenging because of the coarse granularity of NEs. In this paper, we propose network event-based Bayesian approaches for mobility pattern recognition and reconstruction, mode of transport recognition and modeling the frequent trajectories

A Comparison of Mobile Scanning to a Total Station Survey at the I-35 and IA 92 Interchange in Warren County, Iowa, August 15, 2012

The purpose of this project was to investigate the potential for collecting and using data from mobile terrestrial laser scanning (MTLS) technology that would reduce the need for traditional survey methods for the development of highway improvement projects at the Iowa Department of Transportation (Iowa DOT). The primary interest in investigating mobile scanning technology is to minimize the exposure of field surveyors to dangerous high volume traffic situations. Issues investigated were cost, timeframe, accuracy, contracting specifications, data capture extents, data extraction capabilities and data storage issues associated with mobile scanning. The project area selected for evaluation was the I-35/IA 92 interchange in Warren County, Iowa. This project covers approximately one mile of I-35, one mile of IA 92, 4 interchange ramps, and bridges within these limits. Delivered LAS and image files for this project totaled almost 31GB. There is nearly a 6-fold increase in the size of the scan data after post-processing. Camera data, when enabled, produced approximately 900MB of imagery data per mile using a 2- camera, 5 megapixel system. A comparison was done between 1823 points on the pavement that were surveyed by Iowa DOT staff using a total station and the same points generated through the MTLS process. The data acquired through the MTLS and data processing met the Iowa DOT specifications for engineering survey. A list of benefits and challenges is included in the detailed report. With the success of this project, it is anticipate[d] that additional projects will be scanned for the Iowa DOT for use in the development of highway improvement projects

The complexity of mesoporous silica nanomaterials unravelled by single molecule microscopy

Mesoporous silica nanomaterials are a novel class of materials that offer a highly complex porous network with nanometre-sized channels into which a wide amount of differently sized guests can be incorporated. This makes them an ideal host for various applications for example in catalysis, chromatography and nanomedicine. For these applications, analyzing the host properties and understanding the complicated host–guest interactions is of pivotal importance. In this perspective we review some of our recent work that demonstrates that single molecule microscopy techniques can be utilized to characterize the porous silica host with unprecedented detail. Furthermore, the single molecule studies reveal sample heterogeneities and are a highly efficient tool to gain direct mechanistic insights into the host–guest interactions. Single molecule microscopy thus contributes to a thorough understanding of these nanomaterials enabling the development of novel tailor-made materials and hence optimizing their applicability significantly