A survey on Human Mobility and its applications

Human Mobility has attracted attentions from different fields of studies such as epidemic modeling, traffic engineering, traffic prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diffusion and flow in the network and makes it easier to estimate how much one part of the network influences another by using metrics like centrality measures. We aim to study population flow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of different data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges

AoI-based Multicast Routing over Voronoi Overlays with Minimal Overhead

The increasing pervasive and ubiquitous presence of devices at the edge of the Internet is creating new scenarios for the emergence of novel services and applications. This is particularly true for location- and context-aware services. These services call for new decentralized, self-organizing communication schemes that are able to face issues related to demanding resource consumption constraints, while ensuring efficient locality-based information dissemination and querying. Voronoi-based communication techniques are among the most widely used solutions in this field. However, when used for forwarding messages inside closed areas of the network (called Areas of Interest, AoIs), these solutions generally require a significant overhead in terms of redundant and/or unnecessary communications. This fact negatively impacts both the devices' resource consumption levels, as well as the network bandwidth usage. In order to eliminate all unnecessary communications, in this paper we present the MABRAVO (Multicast Algorithm for Broadcast and Routing over AoIs in Voronoi Overlays) protocol suite. MABRAVO allows to forward information within an AoI in a Voronoi network using only local information, reaching all the devices in the area, and using the lowest possible number of messages, i.e., just one message for each node included in the AoI. The paper presents the mathematical and algorithmic descriptions of MABRAVO, as well as experimental findings of its performance, showing its ability to reduce communication costs to the strictly minimum required.Comment: Submitted to: IEEE Access; CodeOcean: DOI:10.24433/CO.1722184.v1; code: https://github.com/michelealbano/mabrav

Efficient Traffic Management in Urban Environments

[ES] En la actualidad, uno de los principales desafíos a los que se enfrentan las grandes áreas metropolitanas es la congestión provocada por el tráfico, la cual se ha convertido en un problema importante al que se enfrentan las autoridades de cada ciudad. Para abordar este problema es necesario implementar una solución eficiente para controlar el tráfico que genere beneficios para los ciudadanos, como reducir los tiempos de viaje de los vehículos y, en consecuencia, el consumo de combustible, el ruido, y la contaminación ambiental. De hecho, al analizar adecuadamente la demanda de tráfico, es posible predecir las condiciones futuras del tráfico, y utilizar esa información para la optimización de las rutas tomadas por los vehículos. Este enfoque puede ser especialmente efectivo si se aplica en el contexto de los vehículos autónomos, que tienen un comportamiento más predecible, lo cual permite a los administradores de la ciudad mitigar los efectos de la congestión, como es la contaminación, al mejorar el flujo de tráfico de manera totalmente centralizada. La validación de este enfoque generalmente requiere el uso de simulaciones que deberían ser lo más realistas posible. Sin embargo, lograr altos grados de realismo puede ser complejo cuando los patrones de tráfico reales, definidos a través de una matriz de Origen/Destino (O-D) para los vehículos en una ciudad, son desconocidos, como ocurre la mayoría de las veces. Por lo tanto, la primera contribución de esta tesis es desarrollar una heurística iterativa para mejorar el modelado de la congestión de tráfico; a partir de las mediciones de bucle de inducción reales hechas por el Ayuntamiento de Valencia (España), pudimos generar una matriz O-D para la simulación de tráfico que se asemeja a la distribución de tráfico real. Si fuera posible caracterizar el estado del tráfico prediciendo las condiciones futuras del tráfico para optimizar la ruta de los vehículos automatizados, y si se pudieran tomar estas medidas para mitigar de manera preventiva los efectos de la congestión con sus problemas relacionados, se podría mejorar el flujo de tráfico en general. Por lo tanto, la segunda contribución de esta tesis es desarrollar una Ecuación de Predicción de Tráfico para caracterizar el comportamiento en las diferentes calles de la ciudad en términos de tiempo de viaje con respecto al volumen de tráfico, y aplicar una regresión logística a esos datos para predecir las condiciones futuras del tráfico. La tercera y última contribución de esta tesis apunta directamente al nuevo paradigma de gestión de tráfico previsto, tratándose de un servidor de rutas capaz de manejar todo el tráfico en una ciudad, y equilibrar los flujos de tráfico teniendo en cuenta las condiciones de congestión del tráfico presentes y futuras. Por lo tanto, realizamos un estudio de simulación con datos reales de congestión de tráfico en la ciudad de Valencia (España), para demostrar cómo se puede mejorar el flujo de tráfico en un día típico mediante la solución propuesta. Los resultados experimentales muestran que nuestra solución, combinada con una actualización frecuente de las condiciones del tráfico en el servidor de rutas, es capaz de lograr mejoras sustanciales en términos de velocidad promedio y tiempo de trayecto, ambos indicadores de un menor grado de congestión y de una mejor fluidez del tráfico.[CA] En l'actualitat, un dels principals desafiaments als quals s'enfronten les grans àrees metropolitanes és la congestió provocada pel trànsit, que s'ha convertit en un problema important al qual s'enfronten les autoritats de cada ciutat. Per a abordar aquest problema és necessari implementar una solució eficient per a controlar el trànsit que genere beneficis per als ciutadans, com reduir els temps de viatge dels vehicles i, en conseqüència, el consum de combustible, el soroll, i la contaminació ambiental. De fet, en analitzar adequadament la demanda de trànsit, és possible predir les condicions futures del trànsit, i utilitzar aqueixa informació per a l'optimització de les rutes preses pels vehicles. Aquest enfocament pot ser especialment efectiu si s'aplica en el context dels vehicles autònoms, que tenen un comportament més predictible, i això permet als administradors de la ciutat mitigar els efectes de la congestió, com és la contaminació, en millorar el flux de trànsit de manera totalment centralitzada. La validació d'aquest enfocament generalment requereix l'ús de simulacions que haurien de ser el més realistes possible. No obstant això, aconseguir alts graus de realisme pot ser complex quan els patrons de trànsit reals, definits a través d'una matriu d'Origen/Destinació (O-D) per als vehicles en una ciutat, són desconeguts, com ocorre la majoria de les vegades. Per tant, la primera contribució d'aquesta tesi és desenvolupar una heurística iterativa per a millorar el modelatge de la congestió de trànsit; a partir dels mesuraments de bucle d'inducció reals fetes per l'Ajuntament de València (Espanya), vam poder generar una matriu O-D per a la simulació de trànsit que s'assembla a la distribució de trànsit real. Si fóra possible caracteritzar l'estat del trànsit predient les condicions futures del trànsit per a optimitzar la ruta dels vehicles automatitzats, i si es pogueren prendre aquestes mesures per a mitigar de manera preventiva els efectes de la congestió amb els seus problemes relacionats, es podria millorar el flux de trànsit en general. Per tant, la segona contribució d'aquesta tesi és desenvolupar una Equació de Predicció de Trànsit per a caracteritzar el comportament en els diferents carrers de la ciutat en termes de temps de viatge respecte al volum de trànsit, i aplicar una regressió logística a aqueixes dades per a predir les condicions futures del trànsit. La tercera i última contribució d'aquesta tesi apunta directament al nou paradigma de gestió de trànsit previst. Es tracta d'un servidor de rutes capaç de manejar tot el trànsit en una ciutat, i equilibrar els fluxos de trànsit tenint en compte les condicions de congestió del trànsit presents i futures. Per tant, realitzem un estudi de simulació amb dades reals de congestió de trànsit a la ciutat de València (Espanya), per a demostrar com es pot millorar el flux de trànsit en un dia típic mitjançant la solució proposada. Els resultats experimentals mostren que la nostra solució, combinada amb una actualització freqüent de les condicions del trànsit en el servidor de rutes, és capaç d'aconseguir millores substancials en termes de velocitat faig una mitjana i de temps de trajecte, tots dos indicadors d'un grau menor de congestió i d'una fluïdesa millor del trànsit.[EN] Currently, one of the main challenges that large metropolitan areas have to face is traffic congestion, which has become an important problem faced by city authorities. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, use of fuel, noise and environmental pollution. In fact, by properly analyzing traffic demand, it becomes possible to predict future traffic conditions, and to use that information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution by improving the traffic flow in a city in a fully centralized manner. Validating this approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, are unknown, as occurs most of the times. Thus, the first contribution of this thesis is to develop an iterative heuristic for improving traffic congestion modeling; starting from real induction loop measurements made available by the City Hall of Valencia, Spain, we were able to generate an O-D matrix for traffic simulation that resembles the real traffic distribution. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. Thereby, the second contribution of this thesis was to develop a Traffic Prediction Equation to characterize the different streets of a city in terms of travel time with respect to the vehicle load, and applying logistic regression to those data to predict future traffic conditions. The third and last contribution of this thesis towards our envisioned traffic management paradigm was a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. Thus, we perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed solution, combined with frequent updating of traffic conditions on the route server, is able to achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity.Finally, I want to thank the Ecuatorian Republic through the "Secretaría de Educación Superior, Ciencia, Tecnología e Innovación" (SENESCYT), for granting me the scholarship to finance my studies.Zambrano Martínez, JL. (2019). Efficient Traffic Management in Urban Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/129865TESI

Uma arquitetura para planejamento de rotas veiculares em cidades inteligentes

The large number of vehicles and the disorderly growth of cities have contributed to the problem of traffic congestion. Large cities already have several problems arising from the large number of vehicles, from pollution to inefficiency in emergency care. These problems cause major damage to the quality of life of people who spend a lot of time in traffic, the health system that cannot meet emergency demands quickly enough, the public coffers that need to allocate large amounts of resources to mitigate the accidents consequences. To solve traffic related problems, one of the promising solutions is the implementation of an Intelligent Transport System - SIT. SITs aim to provide innovative services to establish smarter and more harmonious transportation systems where multiple users can travel safer and faster. In this work, an architecture was developed to evaluate methods of dynamic route planning between vehicles that allows obtaining optimal and suboptimal routes, computationally viable in terms of processing time and quality, considering quality measures as congestion levels, route length and travel time. The evaluated system also aimed to allow the routes planned by the vehicles between origin and destination points to be constructed taking into consideration the planning of other vehicles in the domain, allowing the sharing of information between vehicles and computational infrastructures. through vehicular networks (VANETs). The results showed that the existence of a dynamic route planning system improves traffic conditions, as well as the fact that the algorithm is developed with a parallel approach using GPU, which reduces the time required for route calculation.A grande quantidade de veículos e o crescimento desordenado das cidades contribuíram para o agravamento do problema dos congestionamentos de trânsito. Grandes cidades já apresentam vários problemas decorrentes da grande quantidade de veículos, desde a poluição até a ineficiên cia em atendimentos de emergência. Esses problemas causam grandes prejuízos à qualidade de vida das pessoas que perdem bastante tempo no trânsito, ao sistema de saúde que não consegue atender demandas emergenciais com a rapidez necessária, aos cofres públicos que precisam destinar grandes quantidades de recursos para mitigar as consequências de acidentes. Para re solver problemas relacionados ao tráfego, uma das soluções promissoras é a implementação de um Sistema Inteligente de Transporte - SIT. Os SITs objetivam prover serviços inovadores para estabelecer sistemas de transportes mais inteligentes e harmoniosos, onde os vários usuários podem viajar de forma mais segura e mais rápida. Nesse trabalho foi desenvolvido uma arquite tura para avaliação de métodos de planejamento dinâmico de rotas entre veículos que permita a obtenção de rotas ótimas e sub-ótimas, computacionalmente viáveis em termos de tempo de processamento e com qualidade, considerando medidas de qualidade como níveis de conges tionamento, comprimento de rotas e tempo de viagem. O sistema proposto também objetivou permitir que as rotas planejadas pelos veículos, entre um ponto de origem e um ponto de destino, sejam construídas levando em consideração o planejamento dos demais veículos do domínio de atuação, permitindo o compartilhamento de informações entre os veículos e infraestruturas computacionais através de redes veiculares (VANETs). Os resultados obtidos mostraram que a existência de um sistema de planejamento dinâmico de rotas melhora as condições do trânsito, assim como, o uso de algoritmos desenvolvidos utilizando paralelismo através de Graphics Processors Units - GPU, reduz o tempo de processamento necessário para cálculos de rotas.São Cristóvão, S

Constraint-based navigation for safe, shared control of ground vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 138-147).Human error in machine operation is common and costly. This thesis introduces, develops, and experimentally demonstrates a new paradigm for shared-adaptive control of human-machine systems that mitigates the effects of human error without removing humans from the control loop. Motivated by observed human proclivity toward navigation in fields of safe travel rather than along specific trajectories, the planning and control framework developed in this thesis is rooted in the design and enforcement of constraints rather than the more traditional use of reference paths. Two constraint-planning methods are introduced. The first uses a constrained Delaunay triangulation of the environment to identify, cumulatively evaluate, and succinctly circumscribe the paths belonging to a particular homotopy with a set of semi autonomously enforceable constraints on the vehicle's position. The second identifies a desired homotopy by planning - and then laterally expanding - the optimal path that traverses it. Simulated results show both of these constraint-planning methods capable of improving the performance of one or multiple agents traversing an environment with obstacles. A method for predicting the threat posed to the vehicle given the current driver action, present state of the environment, and modeled vehicle dynamics is also presented. This threat assessment method, and the shared control approach it facilitates, are shown in simulation to prevent constraint violation or vehicular loss of control with minimal control intervention. Visual and haptic driver feedback mechanisms facilitated by this constraint-based control and threat-based intervention are also introduced. Finally, a large-scale, repeated measures study is presented to evaluate this control framework's effect on the performance, confidence, and cognitive workload of 20 drivers teleoperating an unmanned ground vehicle through an outdoor obstacle course. In 1,200 trials, the constraint-based framework developed in this thesis is shown to increase vehicle velocity by 26% while reducing the occurrence of collisions by 78%, improving driver reaction time to a secondary task by 8.7%, and increasing overall user confidence and sense of control by 44% and 12%, respectively. These performance improvements were realized with the autonomous controller usurping less than 43% of available vehicle control authority, on average.by Sterling J. Anderson.Ph.D

Congestion-aware traffic routing for large-scale mobile agent systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 191-201).Traffic congestion is a serious world-wide problem. Drivers have little knowledge of historical and real-time traffic congestion for the paths they take and often tend to drive suboptimal routes. Congestion phenomena are sure to be influenced by the coming of autonomous cars. This thesis presents route planning algorithms and a system for either autonomous or human-driven cars in road networks dealing with travel time uncertainty and congestion. First, a stochastic route planning algorithm is presented that finds the best path for a group of multiple agents. Our algorithm provides mobile agents with optimized routes to achieve time-critical goals. Optimal selections of agent and visit locations are determined to guarantee the highest probability of task achievement while dealing with uncertainty of travel time. Furthermore, we present an efficient approximation algorithm for stochastic route planning based on pre-computed data for stochastic networks. Second, we develop a distributed congestion-aware multi-agent path planning algorithm that achieves the social optimum, minimizing aggregate travel time of all the agents in the system. As the number of agents grows, congestion created by agents' path choices should be considered. Using a data-driven congestion model that describes the travel time as a function of the number of agents on a road segment, we develop a practical method for determining the optimal paths for all the agents in the system to achieve the social optimum. Our algorithm uses localized information and computes the paths in a distributed manner. We implement the algorithm in multi-core computers and demonstrate that the algorithm has a good scalability. Third, a path planning system using traffic sensor data is then implemented. We predict the traffic speed and flow for each location from a large set of sensor data collected from roving taxis and inductive loop detectors. Our system uses a data-driven traffic model that captures important traffic patterns and conditions using the two sources of data. We evaluate the system using a rich set of GPS traces from 16,000 taxis in Singapore and show that the city-scale congestion can be mitigated by planning drivers' routes, while incorporating the congestion effects generated by their route choices.by Sejoon Lim.Ph.D

Cell Towers as Urban Sensors: Understanding the Strengths and Limitations of Mobile Phone Location Data

Understanding urban dynamics and human mobility patterns not only benefits a wide range of real-world applications (e.g., business site selection, public transit planning), but also helps address many urgent issues caused by the rapid urbanization processes (e.g., population explosion, congestion, pollution). In the past few years, given the pervasive usage of mobile devices, call detail records collected by mobile network operators has been widely used in urban dynamics and human mobility studies. However, the derived knowledge might be strongly biased due to the uneven distribution of people’s phone communication activities in space and time. This dissertation research applies different analytical methods to better understand human activity and urban environment, as well as their interactions, mainly based on a new type of data source: actively tracked mobile phone location data. In particular, this dissertation research achieves three main research objectives. First, this research develops visualization and analysis approaches to uncover hidden urban dynamics patterns from actively tracked mobile phone location data. Second, this research designs quantitative methods to evaluate the representativeness issue of call detail record data. Third, this research develops an appropriate approach to evaluate the performance of different types of tracking data in urban dynamics research. The major contributions of this dissertation research include: 1) uncovering the dynamics of stay/move activities and distance decay effects, and the changing human mobility patterns based on several mobility indicators derived from actively tracked mobile phone location data; 2) taking the first step to evaluate the representativeness and effectiveness of call detail record and revealing its bias in human mobility research; and 3) extracting and comparing urban-level population movement patterns derived from three different types of tracking data as well as their pros and cons in urban population movement analysis

Advanced Transport Protocols for Wireless and Mobile Ad Hoc Networks

This thesis comprises transport protocols in the following different areas of research: Fast Handover allows mobile IP end-devices to roam between wireless access routers without interruptions while communicating to devices in an infrastructure (e.g., in the Internet). This work optimizes the Fast Handover algorithm and evaluates the performance of the transport protocols UDP and TCP during fast handovers via measurements. The following part of the thesis focuses on vehicular ad hoc networks. The thesis designs and evaluates through simulations a point-to-point transport protocol for vehicular ad hoc networks and an algorithm to facilitate the reliable and efficient distribution of information in a geographically scoped target area. Finally, the thesis evaluates the impact of wireless radio fluctuations on the performance of an Ad Hoc Network. Measurements quantify the wireless radio fluctuations. Based on these results, the thesis develops a simple but realistic radio model that evaluates by means of simulations the impact on the performance of an ad hoc network. As a result, the work provides guidelines for future ad hoc protocol design