8,158 research outputs found
Parking guiding system with occupation prediction
Parking availability is an increasingly scarce and expensive resource within large
cities, and this problem is considered to be one of the most critical transportation
management system inside a big city. To approach this problem a proof of concept
is presented as a way to guide a driver to the possible free parking lot through a
prediction process using past data, correlated with traffic, weather conditions and
time period features (year, month, day, holidays, and so on).
A feature selection was performed by the study of data patterns, in order to
understand the parking lot affluence and how certain features influence them, as
well as to comprehend the sudden changes in the total occupation of the parking
lot and which features really matter and have an impact on the total occupation.
Those conclusions helped to create a robust and efficient predictive model in order
to predict the parking lot availability rate more accurately.
Three algorithms were used to build the predictive models as a way to test
the most efficient and accurate one, namely Gradient Boosting Machine, Decision
Random Forest and Neural Networks. Various types of models were tested with
the aim of improving the results obtained, as well as understanding the impact of
each of the processing of the data used.
To complement this, a decision algorithm was created to guide the driver to the
most optimal parking lot that presents better conditions, taking into account the
location and driver characteristics, like the park more likely to have an available
parking space, closer to the user’s current position or a more attractive price for
the driver. Finally, these developments are integrated into a mobile application in
order to work like an interface that the driver can interact.A disponibilidade de estacionamento Ă© um recurso cada vez mais escasso e caro
nas grandes cidades, e este problema Ă© considerado um dos mais crĂticos nos sistemas
de gestĂŁo de transportes dentro de uma grande cidade. Para abordar este
problema, uma prova de conceito Ă© apresentada como uma forma de guiar um
condutor para o parque de estacionamento com lugares disponĂveis atravĂ©s de
um processo de previsão usando dados passados, correlacionados com o tráfego,
condições climáticas e caracterĂsticas do perĂodo de tempo (ano, mĂŞs, dia, feriados,
e assim por diante).
Uma seleção de caracterĂsticas foi realizada pelo estudo de padrões de dados, a
fim de entender a afluĂŞncia do estacionamento e como certas caracterĂsticas os influenciam,
bem como para compreender as mudanças repentinas na ocupação total
do estacionamento e quais caracterĂsticas realmente importam e tĂŞm um impacto
sobre a ocupação total. Essas conclusões ajudaram a criar um modelo preditivo
robusto e eficiente a fim de prever a taxa de disponibilidade do estacionamento
com mais precisĂŁo.
TrĂŞs algoritmos foram usados para construir os modelos preditivos como forma
de testar o mais eficiente e preciso, a saber: Gradient Boosting Machine, Decision
Random Forest e Neural Networks. Foram também testados vários tipos de modelos
com o objetivo de melhorar os resultados obtidos, bem como compreender o
impacto de cada um dos processamentos de dados utilizados.
Para complementar, foi criado um algoritmo de decisĂŁo para orientar o condutor
para o parque de estacionamento mais indicado e que apresente melhores
condições, tendo em conta a localização e as caracterĂsticas do condutor, como
o mais provável de ter um lugar de estacionamento disponĂvel, mais prĂłximo da
posição atual do utilizador ou um preço mais atrativo para o condutor. Finalmente,
estes desenvolvimentos são integrados numa aplicação móvel de forma a
que o utilizador consiga aceder através de uma interface
Gestion des ressources dans les réseaux cellulaires sans fil
L’émergence de nouvelles applications et de nouveaux services (tels que les applications multimédias, la voix-sur-IP, la télévision-sur-IP, la vidéo-sur-demande, etc.) et le besoin croissant de mobilité des utilisateurs entrainent une demande de bande passante de plus en plus croissante et une difficulté dans sa gestion dans les réseaux cellulaires sans fil (WCNs), causant une dégradation de la qualité de service. Ainsi, dans cette thèse, nous nous intéressons à la gestion des ressources, plus précisément à la bande passante, dans les WCNs.
Dans une première partie de la thèse, nous nous concentrons sur la prédiction de la mobilité des utilisateurs des WCNs. Dans ce contexte, nous proposons un modèle de prédiction de la mobilité, relativement précis qui permet de prédire la destination finale ou intermédiaire et, par la suite, les chemins des utilisateurs mobiles vers leur destination prédite. Ce modèle se base sur : (a) les habitudes de l’utilisateur en terme de déplacements (filtrées selon le type de jour et le moment de la journée) ; (b) le déplacement courant de l’utilisateur ; (c) la connaissance de l’utilisateur ; (d) la direction vers une destination estimée ; et (e) la structure spatiale de la zone de déplacement. Les résultats de simulation montrent que ce modèle donne une précision largement meilleure aux approches existantes.
Dans la deuxième partie de cette thèse, nous nous intéressons au contrôle d’admission et à la gestion de la bande passante dans les WCNs. En effet, nous proposons une approche de gestion de la bande passante comprenant : (1) une approche d’estimation du temps de transfert intercellulaire prenant en compte la densité de la zone de déplacement en terme d’utilisateurs, les caractéristiques de mobilité des utilisateurs et les feux tricolores ; (2) une approche d’estimation de la bande passante disponible à l’avance dans les cellules prenant en compte les exigences en bande passante et la durée de vie des sessions en cours ; et (3) une approche de réservation passive de bande passante dans les cellules qui seront visitées pour les sessions en cours et de contrôle d’admission des demandes de nouvelles sessions prenant en compte la mobilité des utilisateurs et le comportement des cellules. Les résultats de simulation indiquent que cette approche réduit largement les ruptures abruptes de sessions en cours, offre un taux de refus de nouvelles demandes de connexion acceptable et un taux élevé d’utilisation de la bande passante.
Dans la troisième partie de la thèse, nous nous penchons sur la principale limite de la première et deuxième parties de la thèse, à savoir l’évolutivité (selon le nombre d’utilisateurs) et proposons une plateforme qui intègre des modèles de prédiction de mobilité avec des modèles de prédiction de la bande passante disponible. En effet, dans les deux parties précédentes de la thèse, les prédictions de la mobilité sont effectuées pour chaque utilisateur. Ainsi, pour rendre notre proposition de plateforme évolutive, nous proposons des modèles de prédiction de mobilité par groupe d’utilisateurs en nous basant sur : (a) les profils des utilisateurs (c’est-à -dire leur préférence en termes de caractéristiques de route) ; (b) l’état du trafic routier et le comportement des utilisateurs ; et (c) la structure spatiale de la zone de déplacement. Les résultats de simulation montrent que la plateforme proposée améliore la performance du réseau comparée aux plateformes existantes qui proposent des modèles de prédiction de la mobilité par groupe d’utilisateurs pour la réservation de bande passante.The emergence of new applications and services (e.g., multimedia applications, voice over IP and IPTV) and the growing need for mobility of users cause more and more growth of bandwidth demand and a difficulty of its management in Wireless Cellular Networks (WCNs). In this thesis, we are interested in resources management, specifically the bandwidth, in WCNs.
In the first part of the thesis, we study the user mobility prediction that is one of key to guarantee efficient management of available bandwidth. In this context, we propose a relatively accurate mobility prediction model that allows predicting final or intermediate destinations and subsequently mobility paths of mobile users to reach these predicted destinations. This model takes into account (a) user’s habits in terms of movements (filtered according to the type of day and the time of the day); (b) user's current movement; (c) user’s contextual knowledge; (d) direction from current location to estimated destination; and (e) spatial conceptual maps. Simulation results show that the proposed model provides good accuracy compared to existing models in the literature.
In the second part of the thesis, we focus on call admission control and bandwidth management in WCNs. Indeed, we propose an efficient bandwidth utilization scheme that consists of three schemes: (1) handoff time estimation scheme that considers navigation zone density in term of users, users’ mobility characteristics and traffic light scheduling; (2) available bandwidth estimation scheme that estimates bandwidth available in the cells that considers required bandwidth and lifetime of ongoing sessions; and (3) passive bandwidth reservation scheme that passively reserves bandwidth in cells expected to be visited by ongoing sessions and call admission control scheme for new call requests that considers the behavior of an individual user and the behavior of cells. Simulation results show that the proposed scheme reduces considerably the handoff call dropping rate while maintaining acceptable new call blocking rate and provides high bandwidth utilization rate.
In the third part of the thesis, we focus on the main limitation of the first and second part of the thesis which is the scalability (with the number of users) and propose a framework, together with schemes, that integrates mobility prediction models with bandwidth availability prediction models. Indeed, in the two first contributions of the thesis, mobility prediction schemes process individual user requests. Thus, to make the proposed framework scalable, we propose group-based mobility prediction schemes that predict mobility for a group of users (not only for a single user) based on users’ profiles (i.e., their preference in terms of road characteristics), state of road traffic and users behaviors on roads and spatial conceptual maps. Simulation results show that the proposed framework improves the network performance compared to existing schemes which propose aggregate mobility prediction bandwidth reservation models
A framework for the analytical and visual interpretation of complex spatiotemporal dynamics in soccer
Pla de Doctorat Industrial de la Generalitat de CatalunyaSports analytics is an emerging field focused on the application of advanced data analysis for assessing the performance of professional athletes and teams. In soccer, the integration of data analysis is in its initial steps, primarily due to the difficulty of making sense of soccer's complex spatiotemporal relationships and effectively translating findings to practitioners. Recently, the availability of spatiotemporal data has given rise to applying statistical approaches to address problems such as estimating passing and scoring probability, or the evaluation of players' mental pressure. However, most of these approaches focus on isolated aspects of the sport, while coaches tend to focus on the broader interplay of all 22 players on the pitch. To address the non-stop flow of questions that coaching staff deal with daily, we identify the need for a flexible analysis framework that allows us to answer these questions quickly, accurately, and in a visually-interpretable way while capturing the complex spatial and contextual factors that rule the game.
We propose developing such a comprehensive framework through the concept of the expected possession value (EPV). First introduced in basketball, EPV constitutes an instantaneous estimate of the expected points to be scored at the end of a possession. However, aside from a shared high-level goal, our focus on soccer necessitates a drastically different approach to account for the sport's nuances, such as looser notions of possession, the ability of passes to happen at any location, and space-time dependent turnover evaluation. Following this, we propose modeling EPV in soccer by addressing the question, "can we estimate the expectation of a team scoring or conceding the next goal at any time in the game?" From here, we address a series of derived interrogations, such as how should the EPV expression be structured so coaches can more easily interpret it? Can we produce calibrated and interpretable estimates for each of its components? Can we develop representative and soccer-specific features with the aid of coaches? Is it possible to learn complex features from raw level spatiotemporal data? Finally, and most importantly, can we produce compelling practical applications?
These questions are successfully addressed in this thesis, where we present a series of contributions for both the machine learning and soccer analytics fields related to the modeling and practical interpretation of complex spatiotemporal dynamics. We propose a decomposed modeling approach where a series of foundational soccer components can be estimated separately and then merged to provide a single EPV estimation, providing flexibility to this integrated model. From a practical standpoint, we leverage several function approximation approaches to exploit complex relationships in spatiotemporal tracking data. An essential contribution of this work is the proposal of SoccerMap, a flexible deep learning architecture capable of producing accurate and visually-interpretable probability surfaces in a broad range of problems. Based on a large set of spatial and contextual features developed, we model and provide accurate estimates for each of the components of the EPV components. The flexibility and interpretation capabilities of the proposed model allow us to produce a broad set of practical applications related to on-ball performance, off-ball performance, and match analysis in soccer, and open the door for its future adaption to other sports.
This thesis was developed under an Industrial Ph.D. program and carried out entirely at FĂştbol Club Barcelona, which promoted a close collaboration with professional coaches. As a result, a vast part of the ideas developed in this thesis is now part of the club's daily player and team performance analysis pipeline.Sports analytics es una área de investigaciĂłn de gran crecimiento y que se encuentra enfocada en la aplicaciĂłn de análisis avanzado de datos para la evaluaciĂłn del rendimiento de equipos y deportistas profesionales. En el fĂştbol, la integraciĂłn del análisis de datos se encuentra en una etapa incipiente, principalmente dado la dificultad de evaluar los complejos factores espacio-temporales del juego, y de traducir los hallazgos al lenguaje de los entrenadores. La reciente disponibilidad de datos espacio-temporales ha dado pie a la aplicaciĂłn de mĂ©todos estadĂsticos para explorar problemas tales como la estimaciĂłn de la probabilidad de pasar o rematar exitosamente, o la evaluaciĂłn de la presiĂłn mental durante el juego, entre muchos otros. Sin embargo, la mayorĂa de los estudios hasta la fecha se han enfocado en aspectos aislados del juego, mientras que el análisis de los entrenadores suele tomar una Ăłptica más integral en la que considera la interacciĂłn de los 22 jugadores en el campo. En base a todo esto, identificamos la necesidad de contar con un completo sistema (framework) de análisis que permite responder al contĂnuo flujo de preguntas de los cuerpos tĂ©cnicos de forma ágil y visualmente interpretable, y que al mismo tiempo permita capturar los complejos fenĂłmenos espaciales y contextuales que rigen al fĂştbol. Proponemos el desarrollo de este sistema a travĂ©s del concepto del valor esperado de la posesiĂłn (EPV, por sus siglas en inglĂ©s). El EPV, que fue introducido inicialmente en el baloncesto, constituye la estimaciĂłn segundo a segundo de los puntos que se esperan obtener al final de una posesiĂłn de balĂłn. Sin embargo, su adaptaciĂłn al fĂştbol requiere de un enfoque completamente diferente para poder captar conceptos esenciales tales como que los pases pueden ir a cualquier ubicaciĂłn en el campo, una definiciĂłn menos rĂgida de la posesiĂłn de balĂłn, y los efectos de perder el balĂłn de acuerdo al espacio y tiempo en que este ocurre. En base esto, proponemos modelar el EPV enfocándonos en responder la siguiente pregunta Âżpodemos estimar la esperanza de que un equipo marque o reciba el prĂłximo gol, en cualquier instante del partido? A partir de aquĂ, desarrollamos una serie de preguntas derivadas relacionadas con la capacidad de proveer flexibilidad e interpretabilidad a nuestro modelo, asĂ como desarrollar aplicaciones prácticas de forma ágil. Estas interrogantes son desarrolladas con Ă©xito en esta tesis, donde presentamos una serie de contribuciones tanto al área de machine learning como a la de sports analytics. Proponemos un novedoso enfoque en el que se descompone el EPV en una serie de componentes esenciales, que pueden ser estimados de forma separada y luego integrados para producir una estimaciĂłn Ăşnica del EPV, dotando de mayor flexibilidad a este modelo integrado. Desde un punto de vista práctico, nos apoyamos en una serie de mĂ©todos de aproximaciĂłn de funciones para sacar provecho de relaciones complejas en datos espacio-temporales de tracking. Derivado de esto, proponemos SoccerMap, una flexible arquitectura de deep learning capaz de producir superficies de probabilidad precisas y visualmente interpretables. Adicionalmente, nos apoyamos en una larga serie de variables espaciales y contextuales, desarrolladas en este trabajo, para modelar y proveer estimaciones acuradas de cada uno de los componentes del EPV. La flexibilidad de este modelo nos permite producir una vasta cantidad de aplicaciones prácticas relacionadas al rendimiento con y sin balĂłn, y al análisis de partidos en fĂştbol, y marca un camino para su integraciĂłn en otros deportes. Esta tesis fue desarrollada con el apoyo del Plan de Doctorados Industriales del Departamento de InvestigaciĂłn y Universidades de la Generalitat de Catalunya, y llevado a cabo en el FĂştbol Club Barcelona, contando con la colaboraciĂłn de entrenadores y profesionales del club.Postprint (published version
THOR: A Hybrid Recommender System for the Personalized Travel Experience
One of the travelers’ main challenges is that they have to spend a great effort to find and
choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized
items. Recommendation systems provide an effective way to solve the problem of information
overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid,
personalized recommender system for the transportation domain. THOR assigns every traveler a
unique contextual preference model built using solely their personal data, which makes the model
sensitive to the user’s choices. This model is used to rank travel offers presented to each user
according to their personal preferences. We reduce the recommendation problem to one of binary
classification that predicts the probability with which the traveler will buy each available travel
offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal
preference model. Moreover, to tackle the cold start problem for new users, we apply clustering
algorithms to identify groups of travelers with similar profiles and build a preference model for each
group. To test the system’s performance, we generate a dataset according to some carefully designed
rules. The results of the experiments show that the THOR tool is capable of learning the contextual
preferences of each traveler and ranks offers starting from those that have the higher probability of
being selected
Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data
Traffic forecasting has recently attracted increasing interest due to the
popularity of online navigation services, ridesharing and smart city projects.
Owing to the non-stationary nature of road traffic, forecasting accuracy is
fundamentally limited by the lack of contextual information. To address this
issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network
(H-STGCN), which is able to "deduce" future travel time by exploiting the data
of upcoming traffic volume. Specifically, we propose an algorithm to acquire
the upcoming traffic volume from an online navigation engine. Taking advantage
of the piecewise-linear flow-density relationship, a novel transformer
structure converts the upcoming volume into its equivalent in travel time. We
combine this signal with the commonly-utilized travel-time signal, and then
apply graph convolution to capture the spatial dependency. Particularly, we
construct a compound adjacency matrix which reflects the innate traffic
proximity. We conduct extensive experiments on real-world datasets. The results
show that H-STGCN remarkably outperforms state-of-the-art methods in various
metrics, especially for the prediction of non-recurring congestion
A Data-Driven Approach for Modeling Agents
Agents are commonly created on a set of simple rules driven by theories, hypotheses, and assumptions. Such modeling premise has limited use of real-world data and is challenged when modeling real-world systems due to the lack of empirical grounding. Simultaneously, the last decade has witnessed the production and availability of large-scale data from various sensors that carry behavioral signals. These data sources have the potential to change the way we create agent-based models; from simple rules to driven by data. Despite this opportunity, the literature has neglected to offer a modeling approach to generate granular agent behaviors from data, creating a gap in the literature.
This dissertation proposes a novel data-driven approach for modeling agents to bridge the research gap. The approach is composed of four detailed steps including data preparation, attribute model creation, behavior model creation, and integration. The connection between and within each step is established using data flow diagrams.
The practicality of the approach is demonstrated with a human mobility model that uses millions of location footprints collected from social media. In this model, the generation of movement behavior is tested with five machine learning/statistical modeling techniques covering a large number of model/data configurations. Results show that Random Forest-based learning is the most effective for the mobility use case. Furthermore, agent attribute values are obtained/generated with machine learning and translational assignment techniques.
The proposed approach is evaluated in two ways. First, the use case model is compared to another model which is developed using a state-of-the-art data-driven approach. The model’s prediction performance is comparable to the state-of-the-art model. The plausibility of behaviors and model structure in the use case model is found to be closer to real-world than the state-of-the-art model. This outcome indicates that the proposed approach produces realistic results. Second, a standard mobility dataset is used for driving the mobility model in place of social media data. Despite its small size, the data and model resembled the results gathered from the primary use case indicating the possibility of using different datasets with the proposed approach
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