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

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

Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM

Dense SLAM based on monocular cameras does indeed have immense application value in the field of AR/VR, especially when it is performed on a mobile device. In this paper, we propose a novel method that integrates a light-weight depth completion network into a sparse SLAM system using a multi-basis depth representation, so that dense mapping can be performed online even on a mobile phone. Specifically, we present a specifically optimized multi-basis depth completion network, called BBC-Net, tailored to the characteristics of traditional sparse SLAM systems. BBC-Net can predict multiple balanced bases and a confidence map from a monocular image with sparse points generated by off-the-shelf keypoint-based SLAM systems. The final depth is a linear combination of predicted depth bases that can be optimized by tuning the corresponding weights. To seamlessly incorporate the weights into traditional SLAM optimization and ensure efficiency and robustness, we design a set of depth weight factors, which makes our network a versatile plug-in module, facilitating easy integration into various existing sparse SLAM systems and significantly enhancing global depth consistency through bundle adjustment. To verify the portability of our method, we integrate BBC-Net into two representative SLAM systems. The experimental results on various datasets show that the proposed method achieves better performance in monocular dense mapping than the state-of-the-art methods. We provide an online demo running on a mobile phone, which verifies the efficiency and mapping quality of the proposed method in real-world scenarios

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

A Mobility Model for Synthetic Travel Demand from Sparse Individual Traces

Knowing how much people travel is essential for lowering carbon emissions in the transport sector. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and social media data have been used widely to study mobility patterns. However, these traces suffer from sparsity, an issue that has largely been overlooked. In order to extend the use of these low-cost and easy-to-access data, this study proposes an individual-based mobility model to fill the gaps in sparse mobility traces. The proposed model applies the fundamental mechanisms of exploration and preferential return to synthesise mobility to generate trips, designed to accommodate the sparse individual traces of geolocated social media data. We validated our model and found good agreement on origin-destination matrices and trip distance distributions for Sweden, the Netherlands, and S\ue3o Paulo, Brazil. The proposed model can be used to synthesise mobility at any geographic scale, and the results can later be applied to modelling travel demand. We further apply the model to characterise domestic trip distances for a mixture of cities and countries globally. The trip distance distributions from the model-synthesised trips using sparse geolocations from 22 regions largely follow lognormal distributions and they reflect reasonable characteristics of regional heterogeneity. Further exploration is needed to understand the regional differences between the 22 cities and countries tested

Identifying Hidden Visits from Sparse Call Detail Record Data

Despite a large body of literature on trip inference using call detail record (CDR) data, a fundamental understanding of their limitations is lacking. In particular, because of the sparse nature of CDR data, users may travel to a location without being revealed in the data, which we refer to as a "hidden visit". The existence of hidden visits hinders our ability to extract reliable information about human mobility and travel behavior from CDR data. In this study, we propose a data fusion approach to obtain labeled data for statistical inference of hidden visits. In the absence of complementary data, this can be accomplished by extracting labeled observations from more granular cellular data access records, and extracting features from voice call and text messaging records. The proposed approach is demonstrated using a real-world CDR dataset of 3 million users from a large Chinese city. Logistic regression, support vector machine, random forest, and gradient boosting are used to infer whether a hidden visit exists during a displacement observed from CDR data. The test results show significant improvement over the naive no-hidden-visit rule, which is an implicit assumption adopted by most existing studies. Based on the proposed model, we estimate that over 10% of the displacements extracted from CDR data involve hidden visits. The proposed data fusion method offers a systematic statistical approach to inferring individual mobility patterns based on telecommunication records

Platform for Trajectory Reconstruction Using Sparsely Sampled GPS Data

GPS vastuvõtjat sisaldavate seadmete populariseerumisega kaasneb ka GPS-andmete kogumise ja analüüsimise suurenemine. Analüüsitud andmeid saab kasutada kasutajale läbitud teekonna kohta informatsiooni andmiseks. \n\rLäbitud teekonna täpne leidmine võib osutuda keerukaks, sest GPS-signaalid võivad mõnes piirkonnas olla hajusad või puudulikud. Algus- ja lõpp-punkti vahel kõige lühema võimaliku teekonna kasutamine ei vii alati õige tulemuseni. Üks viis parandada õige teekonna leimise täpsust on arvesse võtta ka kasutaja poolt teekonna läbimiseks kulunud aega.\n\rSelle bakalaurusetöö eesmärk on välja pakkuda platvorm teekonna rekonstrueerimiseks kasutades hajusalt esitatud GPS-andmeid. Lisaks pakutakse välja meetod, mis kasutab trajektoori rekonstrueerimisel GPS-andmete ajalist aspekti.\n\rSelle bakalaureusetöö tulemusena luuakse platvorm teekonna rekonstrueerimiseks ja pakutakse välja meetod teekonna rekonstrueerimiseks. Platvorm võimaldab rekonstrueerida kasutaja poolt läbitud teekonna. Selle asemel, et kasutada ainult lühima tee otsimist, võtab otsust tegev algoritm arvesse ka kasutaja poolt teekonna läbimiseks kulunud aega, et leida tõenäolisem tee.\n\rPlatvorm võimaldab analüüsida ja visualiseerida GPS-andmeid ja rekonstrueerida trajektoor täpsemini kui ainuüksi kõige lühema võimaliku teekonna kasutamisel ajalist aspekti arvesse võtmata.\n\rTäpsema meetodi kasutamine tee rekonstrueerimisel kasutades GPS-admeid muudab õige trajektoori leidmise täpsemaks. Tänu sellele paraneb kasutajale antava informatsiooni kvaliteet.As the devices having GPS receivers are becoming more common, collecting and analysing GPS data is increasing as well. The analysed data can be used to give the user information about travelled trajectories. \n\rFinding the exact travelled real trajectory can be difficult to achieve as the GPS signals may be sparse or missing in some areas. Using the shortest possible path between the start and destination does not always lead to a correct result. One way to improve the accuracy of determining the correct trajectory is to consider the travel time of the user as well. \n\rThe aim of this thesis is to propose a platform for trajectory reconstruction using sparsely sampled GPS data. Additionally, it aims to propose a method that uses the temporal aspect of GPS data for trajectory reconstruction.\n\rAs a result of this thesis work a platform for trajectory reconstruction is created and the method for trajectory reconstruction is proposed. The platform enables to reconstruct the real trajectory travelled by the user. Instead of using only shortest path search the decision making algorithm uses the real travel time of the user to find more probable trajectory travelled by the user.\n\rThe provided platform enables to analyse and visualise GPS data and reconstruct the trajectory more accurately than it would have been performed without including the temporal aspect of GPS data. \n\rUsing more accurate method to reconstruct trajectories using GPS data makes finding the real trajectory more precise. Hence, the quality of the information given to the user improves