京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19851号 / 情博第602号 / 新制||情||105(附属図書館) / 32887 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 美濃 導彦, 教授 石田 亨, 教授 岡部 寿男 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Automation of travel diary collection, an essential input for transport planning, has been a fruitful line of research for the last years; in particular, concerning the problem of automatic inference of transport modes. Taking advantage of technological advance, several solutions based on the collection of mobile devices data, such as GPS locations and variables related to movement (such as speed) and motion (e.g. measurements from accelerometer), have been investigated. The literature shows that many of them rely on explicit initial segmentation of GPS trajectories into trip legs, followed by a segment-based classification problem. In some cases, GIS-related features are included in the classification instance, but usually in terms of distance to transport networks or to specific points of interest (POIs). The aim of this MSc Thesis is to investigate a novel transport mode inference procedure based on the generation of topological features from a multimodal map matching instance. We define topological features as the topological context of each point of a GPS trajectory. Further utilization of these features as part of a sequence classification problem leads to mode prediction and to the implicit definition of the trip legs. In addition to not depending on an explicit segmentation step, the proposed routine also has less requirements in terms of the complexity of the required GIS features: there is no need to consider distance features, and the proposed map matching implementation does not require the usage of one unified multimodal network —as other multimodal map matching approaches do. The procedure was tested with a travel diary data set collected in Stockholm, containing 4246 trips from 368 different commuters. The transport modes considered were walk, subway, commuter train, bus and tram. In order to assess the impact of the topological context, different feature set compositions were investigated, including topological and conventional movement and motion features. Three different classifiers —decision tree, support vector machine and conditional random field— were evaluated as well. The results show that the proposed procedure reached high accuracy, with a performance that is similar to the one offered by current approaches; and that the most performant feature set composition was the one that included both topological and movement and motion features. The best evaluation measures were obtained with decision tree and conditional random field classifiers, but with some differences: while both of the them presented similar recall, the former yielded better precision and the latter achieved a higher segmentation quality.

Trajectory data is playing an increasingly important role in our daily lives, as well as in commercial applications and scientific research. With the rapid development andpopularity of GPS, people can locate themselves in real time. Therefore, the users’behavior information can be collected by analyzing their GPS trajectory data, so as topredict their new trajectories’ destinations, ways of travelling and even thetransportation mode they use, which forms a complete personal travel diary. The taskin this thesis is to implement travel diary semantics enrichment of user’s trajectoriesbased on the historical labeled data of the user and trajectory similarity measures.Specially, this dissertation studies the following tasks: Firstly, trip segmentationconcerns detecting the trips from trajectory which is an unbounded sequence oftimestamp locations of the user. This means that it is important to detect the stops,moves and trips of the user between two consecutive stops. In this thesis, a heuristicrule is used to identify the stops. Secondly, tripleg segmentation concerns identifyingthe location / time instances between two triplegs where / when a user changesbetween transport modes in the user's trajectory, also called makes transport modetransitions. Finally, mode inference concerns identifying travel mode for each tripleg.Specially, steps 2 and 3 are both based on the same trajectory similarity measure andproject the information from the matched similar trip trajectory onto the unlabeled triptrajectory. The empirical evaluation of these three tasks is based on real word data set(contains 4240 trips and 5451 triplegs with 14 travel modes for 206 users using oneweek study period) and the experiment performance (including trends, coverage andaccuracy) are evaluated and accuracy is around 25% for trip segmentation; accuracyvaries between 50% and 55% for tripleg segmentation; for mode inference, it isbetween 55% and 60%. Moreover, accuracy is higher for longer trips than shortertrips, probably because people have more mode choices in short distance trips (likemoped, bus and car), which makes the measure more confused and the accuracy canbe increased by nearly 10% with the help of reverse trip identifiable, because it makesa trip have more similar historical trips and increases the probability that a newunlabeled trip can be matched based on its historical trips.

Dans cette thèse, nous avons étudier une méthode de classification et d'évaluation des modalités de transport utilisées par les porteurs de mobile durant leurs trajets quotidiens. Les informations de mobilité sont collectées par un opérateur au travers des logs du réseau téléphonique mobile qui fournissent des informations sur les stations de base qui ont été utilisées par un mobile durant son trajet. Les signaux (appels/SMS/3G/4G) émis par les téléphones sont une source d'information pertinente pour l'analyse de la mobilité humaine, mais au-delà de ça, ces données représentent surtout un moyen de caractériser les habitudes et les comportements humains. Bien que l'analyse des metadata permette d'acquérir des informations spatio-temporelles à une échelle sans précédent, ces données présentent aussi de nombreuses problématiques à traiter afin d'en extraire une information pertinente. Notre objectif dans cette thèse est de proposer une solution au problème de déduire la trajectoire réelle sur des réseaux de transport à partir d'observations de position obtenues grâce à l'analyse de la signalisation sur les réseaux cellulaires. Nous proposons « CT-Mapper" pour projecter les données de signalisation cellulaires recueillies auprès de smartphone sur le réseau de transport multimodal. Notre algorithme utilise un modèle de Markov caché et les propriétés topologiques des différentes couches de transport. Ensuite, nous proposons « LCT-Mapper » un algorithme qui permet de déduire le mode de transport utilisé. Pour évaluer nos algorithmes, nous avons reconstruit les réseaux de transport de Paris et de la région (Ile-de-France). Puis nous avons collecté un jeu de données de trajectoires réelles recueillies auprès d'un groupe de volontaires pendant une période de 1 mois. Les données de signalisation cellulaire de l'utilisateur ont été fournies par un opérateur français pour évaluer les performances de nos algorithmes à l'aide de données GPS. Pour conclure, nous avons montré dans ce travail qu'il est possible d'en déduire la trajectoire multimodale des utilisateurs d'une manière non supervisée. Notre réalisation permet d'étudier le comportement de mobilité multimodale de personnes et d'explorer et de contrôler le flux de la population sur le réseau de transport multicouche / Around half of the world population is living in cities where different transportation networks are cooperating together to provide some efficient transportation facilities for individuals. To improve the performance of the multimodal transportation network it is crucial to monitor and analyze the multimodal trajectories, however obtaining the multimodal mobility data is not a trivial task. GPS data with fine accuracy, is extremely expensive to collect; Additionally, GPS is not available in tunnels and underground. Recently, thanks to telecommunication advancement cellular dataset such as Call Data Records (CDRs), is a great resource of mobility data, nevertheless, this kind of dataset is noisy and sparse in time. Our objective in this thesis is to propose a solution to this challenging issue of inferring real trajectory and transportation layer from wholly cellular observation. To achieve these objectives, we use Cellular signalization data which is more frequent than CDRs and despite their spatial inaccuracy, they provide a fair source of multimodal trajectory data. We propose 'CT-Mapper’ to map cellular signalization data collected from smart phones over the multimodal transportation network. Our proposed algorithm uses Hidden Markov Model property and topological properties of different transportation layers to model an unsupervised mapping algorithm which maps sparse cellular trajectories on multilayer transportation network. Later on, we propose ‘LCT-Mapper’ an algorithm to infer the main mode of trajectories. The area of study in this research work is Paris and region (Ile-de-France); we have modeled and built the multimodal transportation network database. To evaluate our proposed algorithm, we use real trajectories data sets collected from a group of volunteers for a period of 1 month. The user's cellular signalization data was provided by a french operator to assess the performance of our proposed algorithms using GPS data as ground truth. An extensive set of evaluation has been performed to validate the proposed algorithms. To summarize, we have shown in this work that it is feasible to infer the multimodal trajectory of users in an unsupervised manner. Our achievement makes it possible to investigate the multimodal mobility behavior of people and explore and monitor the population flow over multilayer transportation network