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Many astrophysical and terrestrial scenarios involving magnetic fields can be approached in axial geometry. Although the smoothed particle hydrodynamics (SPH) technique has been successfully extended to magneto-hydrodynamics (MHD), a well-verified, axisymmetric MHD scheme based on such technique does not exist yet. In this work we fill that gap in the scientific literature and propose and check a novel axisymmetric MHD hydrodynamic code, that can be applied to physical problems which display the adequate geometry. We show that the hydrodynamic code built following these axisymmetric hypothesis is able to produce similar results than standard 3D-SPMHD codes with equivalent resolution but with much lesser computational load., Peer Reviewed, Postprint (author's final draft)

Parcial, Resolved, 2023-2024, 1r quadrimestre

Final, Resolved, 2022-2023, 1r quadrimestre

Barcelona city (Spain) is applying a series of traffic restriction measures that aim at renewing and reducing the amount of circulating vehicles to improve air quality. The measures include changes in the built environment to reduce private vehicle space in specific areas through the so-called “superblocks” and tactical urban planning actions, along with the implementation of a city-wide Low Emission Zone (LEZ) that restricts the entry of the most polluting vehicles to the city. Our study quantifies the impact of these measures in the greater area of Barcelona combining a coupled macroscopic traffic and pollutant emission model with a multi-scale air quality model. Our modelling system allows estimating the effect of different traffic restrictions upon traffic and the associated emissions and air quality levels at a very high resolution (20 m). The measures were evaluated both individually and collectively to assess both their relative and overall impact upon emissions and air quality. We show that in the absence of traffic demand reductions, the application of isolated measures that reduce private vehicle space, either through superblocks or tactical urban planning, have no overall emission impacts; only localized street-level NOx positive and negative changes (±17%) are found due to traffic re-routing and the generation of new bottlenecks. It is only when these measures are combined with optimistic fleet renewal as a result of the LEZ implementation and demand reductions, that relevant global emission reductions in NOx are obtained (-13% and -30%, respectively) with estimated NO2 reductions of -36% and -23% at the two traffic air quality monitoring stations. Despite the potential improvements, our simulations suggest that current measures are insufficient to comply with EU air quality standards and that further traffic restriction policies to reduce traffic demand are needed., The authors acknowledge CARNET-The Future Mobility Research HUB to allow the usage and work on the BCN-VML network, as well as PTV VISUM for the traffic software license. The author thankfully acknowledges the computer resources at MareNostrum and the technical support pro- vided by Barcelona Supercomputing Center (PRACE, AECT-2020-1-0007, AECT-2021-1-0027). The authors also acknowledge the support from the Ministerio de Ciencia, Innovación y Universidades (MICINN) as part of the BROWNING project RTI2018-099894-B-I00 and NUTRIENT project CGL2017-88911-R and the support from the Agencia Estatal de Investigacion (AEI) as part of the VITALISE project (PID2019-108086RA- I00/AEI/10.13039/501100011033). Carlos Pérez García-Pando acknowl- edges the long-term support from the AXA Chair in Sand and Dust Storms (AXA Research Fund), as well as the support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of Economy and Competitiveness., Peer Reviewed, Postprint (published version)

Nowadays, one of the most important problems in urban areas concerns traffic congestion. This, in turn, has an impact on the economy, nature, human health, city architecture, and many other facets of life. Part of the vehicular traffic in cities is caused by parking space availability. The drivers of private vehicles usually want to leave their vehicles as close as possible to their destination. However, the parking slots are limited and may not be enough to sustain the demand, especially when the destination pertains to an attractive area. Thus, individuals looking for a place to park their vehicles contribute to increasing traffic flow density on roads where the parking demand cannot be satisfied. An Internet of Things (IoT) approach allows us to know the state of the parking system (availability of the parking slots) in real time through wireless networks of sensor devices. An intelligent treatment of this data could generate forecasted information that may be useful in improving management of on-street parking, thus having a notable effect on urban traffic. Smart parking systems first appeared in 2015, with IoT platforms in Santander, San Francisco and Melbourne. That is the year when those cities began to provide on-street real-time parking data in order to offer new services to their citizens. One of the most interesting services that these kinds of platforms can offer is parking availability forecasting, for which the first works in this field studied the temporal and spatial correlations of parking occupancy to support short-term forecasts (no more than 30 minutes). Those short-term forecasts are not useful at all to the end user of this service; thus, the necessary prediction intervals should be at the order of magnitude of hours. In this context, this thesis focuses on using parking and other sources of data to characterize and model different parking systems. The methodology used employs novel techniques for providing real-time forecasts of parking availa, En la actualidad uno de los mayores problemas de las zonas urbanas tiene origen en la congestión del tráfico con un alto impacto en la economía, el medio ambiente, la salud y otras facetas de la vida urbana. En muchas ocasiones parte de la congestión del trafico tiene origen en la disponibilidad de las plazas de aparcamiento debido a que los conductores de vehículos privados suelen querer aparcar sus vehículos lo más cerca posible de su destino pero las plazas de aparcamiento son limitadas y pueden no ser suficientes para mantener la demanda. Un enfoque basado en el Internet of Things (IoT) nos permite en tiempo real conocer la disponibilidad de plazas de estacionamiento a través de redes inalámbricas de sensores. Un tratamiento inteligente sobre estos datos puede generar información que ayude a predecir la futura demanda de estacionamiento en las zonas sensorizadas mejorando así la gestión del estacionamiento y teniendo un efecto en el tráfico urbano. Los primeros trabajos académicos en este área se centraron en estudiar las correlaciones temporales y espaciales de la ocupación del estacionamiento para proveer pronósticos a corto plazo (predicciones a tiempo máximo de 30 minutos) y que en muchas ocasiones no son de utilidad ya que para el usuario final es preferible tener estimaciones de la disponibilidad de estacionamiento en el order de magnitud de horas. En este contexto, esta tesis se centra en el uso de datos de aparcamientos y otras fuentes para caracterizar y modelizar diferentes sistemas de aparcamiento. La metodología utilizada emplea técnicas innovadoras para proporcionar predicciones en tiempo real sobre la disponibilidad de aparcamiento basadas en datos de sensores. Los modelos se desarrollan a partir de cuatro metodología: Autoregressive Integrated Moving Average (ARIMA), Multilayer Perceptron (MLP), Long-Short Term Memory (LSTM) y Gated Recurrent Unit (GRU). La primera ha sido el enfoque estándar de predicción en la literatura sobre Sistemas de Transp, Postprint (published version)

Nowadays, multiple passenger ridesharing and its variants look one of the more promising emerging mobility concepts. However, the real implementation of these systems accounting specifically requirements for users should raise certain challenges, even further for disabled people, which inherits requirements that need special consideration: short ride times, specific vehicle characteristics depending on the mobility handicaps, narrow time windows constraints, etc. This paper presents the real case study of the public transport service that Barcelona city offers to people with reduced mobility, which could program weekly taxi trips within the city at a specific time. The proposed routing algorithm integrated into this service management system is based on a tabu search heuristic approach used to minimize the dimension of the heterogeneous fleet and the total traveled time. Furthermore, this work exhaustively analyses the operational factors of this mobility service to analyze how they affect the service performance and car-sharing factors. The obtained results show that certain operational decisions could make better use of the resources allocated to the sharing services., This research was funded by Secretaria d’Universitats i Recerca de la Generalitat de Catalunya (2017-SGR-1749)., Peer Reviewed, Postprint (published version)

We present a coupled macroscopic traffic and emission modelling system tailored to the Barcelona metropolitan area that allows estimating hourly road transport emissions at road link level. We use the developed system to perform an emission sensitivity analysis of typically high uncertainty emission features and assess their impact. We also explore the uncertainties of our system compared to a microscopic approach in a representative area of Barcelona. The developed macroscopic system shows a high sensitivity to spatially-resolved vehicle fleet composition inputs, meteorological effects on diesel engines (19% in NO) and non-exhaust sources (80% of total PM emissions). The comparison with the microscopic system shows that discrepancies grow as a function of the congestion level, up to 65% in NO. The resulting coupled system will be used in further steps of the research to evaluate the impact of traffic management strategies upon urban emissions and air quality levels in Barcelona., Daniel Rodriguez-Rey’s PhD work is funded by grant BES-2016-078116 from the FPI program by the Spanish Ministry of Economyand Competitiveness. The authors acknowledge CARNET-The Future Mobility Research HUB to allow the usage and work onthe BCN-VML network, as well as PTV VISUM for the traffic software licence. The authors also acknowledge the support fromthe Ministerio de Ciencia, Innovación y Universidades (MICINN) as part of the BROWNING project RTI2018-099894-B-I00 andNUTRIENT project CGL2017-88911-R and the support from the Agencia Estatal de Investigacion (AEI) as part of the VITALISEproject (PID2019-108086RA-I00/AEI / 10.13039/501100011033). Carlos Pérez García-Pando acknowledges the long-term supportfrom the AXA Chair in Sand and Dust Storms (AXA Research Fund), as well as the support received through the Ramón y Cajalprogramme (grant RYC-2015-18690) of the Spanish Ministry of Economy and Competitiveness., Peer Reviewed, Postprint (published version)

Estimating and forecasting passenger demand is one of the major applications for Intelligent Transport Systems (ITS) data sources. Forecasting public bus transport occupations can be useful in order to improve user comfort levels, but also to help with operational decision making to better supply the demand. In the COVID-19 context, informing users with enough anticipation about future occupations can help to avoid crowds and recover the trust in public transportation services. Depending on the considered time horizon to predict, passenger flow estimation can be divided into long-term if predictions aim to forecast average passenger demand for the coming days, weeks, or even longer; and short-term if the predictive period is less than 30 minutes and recent or real-time information is available. This master thesis proposes long-term and short-term occupation forecasting models to estimate bus passenger flow at stop level. Both long- and short-term forecasting approaches are developed using deep learning models such as Deep Neural Networks (DNN) and Long-Short Term Memory (LSTM) networks, and trained with different transportation data sources. The implemented models are intended to be actively used in several real public transport networks from different bus operators. Thus, this work presents training and prediction pipelines that are in charge of pre-processing raw data, building and training the models, and performing predictions on demand. This pipeline system is designed to efficiently and quickly deploy the forecasting models to different cities based on bus operators standardized data sources. Finally, regarding the implantation of these models into real-life industrial settings, explainability techniques are explored to help identify which features are critical to solve occupation prediction and better understand the inner insights of the models.

Nowadays, connected cars are uncommon on our streets, but their percentage is expected to grow uninterruptedly. These devices would provide a lot of data in real-time that can be used for road operators to improve the traffic. This research is focused on one of these applications, which is shockwave damping on freeways. This work evaluates two shockwave detection methods that use probe vehicle data and fixed sensors data and one mitigation algorithm that uses variable speed limits to resolve shockwaves. This paper analyses through microscopic traffic simulation the performance of the selected algorithms and how their parameters affect them in several scenarios. Also, the effect of the penetration rate of probe vehicle data is evaluated. Finally, the best algorithm with the best parameters configuration is applied to a realistic model of the AP7 freeway in Girona (Spain). The obtained results show that the algorithms applied greatly reduce the total travel time in this network., This research was funded by Secretaria d’Universitats i Recerca de la Generalitat de Catalunya (2017-SGR-1749)., Peer Reviewed, Postprint (published version)

Nowadays, urban traffic affects the quality of life in cities as the problem becomes even more exacerbated by parking issues: congestion increases due to drivers searching slots to park. An Internet of Things approach permits drivers to know the parking availability in real time and provides data that can be used to develop predictive models. This can be useful in improving the management of parking areas while having an important effect on traffic. This work begins by describing the state-of-the-art parking predictive models and, then, introduces the recurrent neural network methods that were used Long Short-Term Memory and Gated Recurrent Unit, as well as the models developed according to real scenarios in Wattens and Los Angeles. To improve the quality of the models, exogenous variables related to weather and calendar are considered. Finally, the results are described, followed by suggestions for future research., This research was funded by Secretaria d’Universitats i Recerca de la Generalitat de Catalunya [2017-SGR-1749] and under the Industrial Doctorate Program [2016-DI-79]., Peer Reviewed, Postprint (author's final draft)

Hoy en día, la predicción de tráfico juega un papel importante en la optimización de la movilidad interurbana. Gracias al aumento del porcentaje de vehículos conectados, los servicios PVD (Probe Vehicle Data), que trabajan con datos de vehículo conectado, han ganado protagonismo en este campo. C-Roads es un proyecto europeo que tiene como objetivo implementar Sistemas Inteligentes de Transporte (ITS) para los usuarios de las vías de circulación europeas. El laboratorio de investigación inLab FIB colabora en el piloto del Corredor Mediterráneo, en cuyo marco se ha desarrollado este TFG. El problema con el que trabajamos es la predicción de tiempo de viaje en vías interurbanas con datos de vehículo conectado. Después de analizar la literatura existente, se decidió utilizar modelos de ARIMA, SARIMA y Redes Neuronales debido a su capacidad para trabajar con series temporales. Con el objetivo de analizar el comportamiento de estos modelos, se han desarrollado un conjunto de experimentos que permiten analizar su precisión utilizando datos de escenarios reales. Estos experimentos se han desarrollado teniendo en cuenta factores como: el tipo de sensor utilizado, la superficie de acción, el horizonte de predicción y la cantidad de vehículos conectados. A fin de representar escenarios reales de forma veraz, los datos para alimentar los modelos se han generado mediante simulación a partir de un modelo de red vial de la AP-7 y demandas de tráfico reales, utilizando el software AIMSUN Next. Los resultados han demostrado que la precisión de los modelos depende en gran medida de los experimentos, sobre todo en los modelos de SARIMA, que solo obtiene buenos resultados en experimentos que presentan un número suficiente de sensores., Nowadays, traffic forecasting plays a key role in interurban mobility optimization. Thanks to the raising percentage of connected vehicles, Prove Vehicle Data services, (that work with connected vehicle data) have gained importance in this field. C-Roads is a European project which aims to implement Intelligent Transport Systems (ITS) for the users of European circulation aisles. The inLab FIB, a research laboratory, collaborates in the Mediterranean Corridor pilot, and this final degree project has been developed within its framework. The problem we are facing is interurban travel time prediction with Probe Vehicle Data. After analyzing previous publications, we decided to work with ARIMA, SARIMA and Neural Networks models as they are able to work with time series. In order to analyze the performance of these models, we have developed a set of experiments to analyze their accuracy using a real-world setting. The development of the experiments has considered these elements: the type of sensor used, the total surface of activity, the forecast prediction and the number of connected vehicles. In order to represent real-world settings in a truthful way, the data used to feed the models has been generated by simulation based on an AP-7 road network model and real traffic demands, using AIMSUM Next software. We have seen that the precision of the models depends to a great extent on the experiment. This happens especially with SARIMA models, as they only get good results in experiments that present enough number of sensors.

Cities today must address the challenge of sustainable mobility, and traffic state forecasting plays a key role in mitigating traffic congestion in urban areas. For example, predicting path travel time is a crucial issue in navigation and route planning applications. Furthermore, the pervasive penetration of information and communication technologies makes floating car data an important source of real-time data for intelligent transportation system applications. This paper deals with the problem of forecasting urban traffic when floating car data is available. A comparison of four deep learning methods is presented to demonstrate the capabilities of the neural network approaches (recurrent and/or convolutional) in solving the traffic forecasting problem in an urban context. Different tests are proposed in order to not only evaluate the developed deep learning models, but also to analyze how the penetration rates of floating cars affect forecasting accuracy. The presented experiments were designed according to a microscopic traffic simulation approach in order to emulate floating car data fleets, which provide vehicle position and speed, and to validate the obtained results. Finally, some conclusions and further research are presented., Postprint (published version)

Urban traffic currently affects the quality of life in cities and metropolitan areas as the problem becomes ever more aggravated by parking issues: congestion increases due to individuals looking for slotsto park their vehicles. An Internet of Things approach allows drivers to know the state of the parkingsystem in real time through wireless networks of sensor devices. This work focuses on studying the data generated by parking systems in order to develop predictive models that generate forecasted information. This can be useful in improving the managementof parking areas, especially on-streetparking, while having an important effect on urban traffic. This research begins by looking at thestate of the art in predictive methods based on machine learning for time series. Similar studies and proposed solutions for parking predictionare described in terms of the technology and current state-of-the-art predictive models. This paper then introduces the recurrent neural network methodsthat were usedin this research,namely Long Short-Term Memory and Gated Recurrent Unit, as well as the models developed according to real scenarios in different cities. In order to improve the quality of the models, exogenous variables like hourly weather and calendar effects are taken into account,and the baseline models are compared to the models that usedthis information. Finally, the preliminary encouraging results are described, followed by suggestions for corresponding future work, Postprint (published version)

El Consorci d'Educació de Barcelona proveeix als Centres d'Educació Especial d'un servei de transport escolar per ajudar a les famílies dels alumnes d'aquests centres. Actualment, la gestió d'aquest servei està descentralitzada, de manera que els centres que se'n beneficien s'encarreguen de gestionar-lo. Això fa que els centres no comparteixin vehicles, cosa que, en cas de fer-ho, podria augmentar la qualitat del servei ofert i el nombre d'estudiants beneficiats. A més, les rutes que es duen a terme actualment es creen de forma manual, aprofitant les rutes de l'any anterior, eliminant els alumnes que ja no assisteixen a l'escola, i afegint els que la comencen. El problema al qual fem front és una versió del problema d'optimització combinatori Vehicle Routing Problem (VRP), que és una generalització del conegut Problema del Viatjant de Comerç (TSP). De fet, donat que el que es transporta són persones, sovint es parla del Dial-a-Ride Problem, que és una extensió del VRP que té en compte aspectes particulars del transport de persones. En aquest cas, a més, es tracten bastants extensions del problema, com per exemple el de la flota heterogènia o restriccions sobre la compartició de vehicles entre certs alumnes. Aquesta classe de problemes són NP-Hard en les versions d'optimització, de manera que l'ús de mètodes exactes no és factible a l'hora de tractar instàncies de dimensions realistes. Per aquest motiu, en aquest Treball de Fi de Grau, realitzat en el marc d'un conveni de col·laboració amb l'inLab FIB, es realitza un estudi sobre la viabilitat d'aplicar algorismes de routing amb l'objectiu de millorar el servei proporcionat pel Consorci. Per a fer-ho, es presenta una formalització del problema, amb un model de programació lineal entera, i una metaheurística basada en Deterministic Annealing per a poder tractar instàncies realistes. A més, s'exploren diversos escenaris variant alguns dels paràmetres, tot observant com això afecta el cost i a la qualitat de la solució, The Barcelona Education Consortium provides the Special Education Centers with a school transport service to help the families of the students at these centers. Currently, the management of this service is decentralized, meaning that the centers that benefit from it are in charge of managing it. This means that the centers do not share vehicles, which, if done, could increase both the quality of the service offered and the number of students who benefit from it. In addition, the routes that are currently being carried out are created manually, taking advantage of the routes of the previous year, removing the students who no longer attend school, and adding those who start to. The problem we face is a version of the Vehicle Routing Problem (VRP), which is a generalization of the well-known Travelling Salesman Problem (TSP). In fact, since what is transported are people, we are actually facing a version of the Dial-a-Ride Problem, which is an extension of the VRP that takes into account particular aspects of transporting people. In addition, in this case we have to deal with many extensions of the problem, such as the heterogeneous fleet or restrictions on the sharing of vehicles between certain students. This kind of problems are NP-Hard in the optimization versions, so the use of exact methods is not feasible when dealing with instances of realistic dimensions. For this reason, in this Final Degree Project, carried out within the framework of a collaboration agreement with the inLab FIB, a study is carried out on the feasibility of applying routing algorithms with the aim to improve the service provided by the Consortium. To do this, a formalization of the problem, with an integer linear programming model, and a metaheuristic based on Deterministic Annealing to be able to deal with realistic instances are presented. In addition, various scenarios are explored varying some of the parameters, observing how this affects the cost and quality of the solution obtained. To

Over the last century, cities have developed as a function of increased usage of automobiles as the standard transport mode. The number of cars increased along with the population as highways and parking spots became essential in city planning. Now, there is more focus on how the existing infrastructure could be used as efficiently as possible rather than increasing capacity by merely building new roads. An important part of traffic planning is a sustainable transport system, which thereby reduces congestion and emissions by using the available capacity in a more efficient way. Traffic simulation models are tools for assessing new mobility solutions and analysing changes in the infrastructure, such as rearranging intersections and building new roads. Transportation is undergoing a profound and significant transformation as it seeks to fulfil the promise of connected mobility for people and goods while limiting its carbon footprint. Physical changes to the road network mean large investments that must be comprehensively considered before acting. Modelling different scenarios of infrastructural changes allows making forecasts without any physical changes. Autonomous vehicles are potentially changing the economics of ownership as well as the use of the transportation networks, which will likely accelerate trends towards greater use of app-based ride hailing and/or sharing by private transportation network companies. American and European cities are seeing a rise in several potential business models with varying degrees of ride sharing and public vs. private involvement in delivering mobility services (MaaS). Implications for transit agencies and mobility service providers must be evaluated, and this can be done by traffic simulation models that provide a model-based framework for evaluating the mobility impact of new services., Peer Reviewed, Postprint (author's final draft)

This master thesis focuses on the problem of shockwave damping on freeways. To this purpose, we implement two algorithms for detecting shockwaves and a mitigation algorithm. They are tested using simulation in test networks and finally, they are evaluated in the model of a real freeway, the AP7.

This master thesis deals with the problem of traffic forecasting using probe vehicle data on freeways. The main goal of this master thesis is to perform travel forecasting using different models and evaluate these models with distinct conditions.

Traffic forecasting plays a key role in mitigating traffic congestion in urban areas. The main objective of this master thesis is to perform traffic forecasting in urban contexts by developing machine learning models trained with simulated Floating Car Data.

Es presenta una metaheurística basada en una Cerca Tabú per a resoldre el Pickup and Delivery Problem with Time Windows que s'utilitzarà en un sistema per a optimitzar les rutes d'un servei de taxis per a persones amb mobilitat reduïda del Servei Públic de transport especial de Barcelona., A metaheuristic based on a Tabu Search is presented to solve the Pickup and Delivery Problem with Time Windows This algorithm will be used in a system to optimize vehicle routes for a taxi service for persons with reduced mobility of the Public Service of Special Transport of Barcelona.

Probe vehicle (or connected car) data are becoming an important source of real-time travel information for a variety of intelligent transportation system applications. Since traditional sensors have significant installation and maintenance costs, technological companies are interested in traffic data from these alternative detection techniques for computing traffic-aware shortest routes. This paper analyzes and evaluates the use of data provided by probe vehicles in two reactive navigation strategies and how this affects a set of city and driver key performance indicators. The case study adopts a microscopic simulation approach to emulate real-size fleets of probe cars providing positions and speed data. The paper presents and discusses the modeling approach and the obtained results after conducting an experimental design for a Barcelona district scenario. Moreover, a simulation-based framework is introduced for simplifying the analysis of simulation results and easily visualizing origin-destination paths for the proposed driver segments (experts, regular and tourists)., This research was funded by TRA2016-76914-C3-1-P of the Spanish R+D National Programs and by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya under 2017 SGR 1749., Peer Reviewed, Postprint (author's final draft)

Today, internet of things (IoT) platforms offer proprietary interfaces and protocols. To enable interoperable interaction with those platforms we present the generic BIG IoT API that employs a novel approach for self-description and semantic annotation to fully adapt arbitrary IoT platforms. We have deployed this approach for multiple platforms from the mobility domain., Peer Reviewed, Postprint (author's final draft)

Los nuevos conceptos de movilidad sostenible están a la orden del día. Sin embargo, no hay una manera fácil de hacer realidad estas ideas, o de probar lo buenas que son como soluciones de movilidad (Montero et al., 2017, Linares et al., 2017). Virtual Mobility Lab ofrece la oportunidad de evaluar el impacto de los nuevos conceptos de movilidad antes de llevarlos al mundo real. En este trabajo, se desarrolla un modelo de simulación de tráfico macroscópico multimodal de la Primera Corona Metropolitana de Barcelona, que incluye tanto la red de transporte tanto público, como la red de transporte privado. El modelo desarrollado presenta una caracterización detallada de la jerarquía de redes y los intercambiadores multimodales en las redes de transporte público, permitiendo una visión sistémica de los impactos globales de los distintos escenarios que pueden desarrollarse. Los datos empleados de demanda origen-destino proceden de aplicaciones TIC. La propuesta actualmente en fase de calibración pretende aprovechar tanto fuentes de datos tradicionales, como aquellas procedentes de nuevas tecnologías., Peer Reviewed, Postprint (author's final draft)

Probe vehicle data is becoming an important source of real-time travel information for a variety of intelligent transportation system applications. Since traditional sensors have significant installation and maintenance costs, technological companies are interested in traffic data from these alternative detection techniques for computing traffic aware shortest routes. This paper analyzes and evaluates the use of data provided by probe vehicles in two reactive navigation strategies and how this affects a set of city and driver key performance indicators. The case study adopts a microscopic simulation approach to emulate real-size fleets of probe cars providing positions and speed data. The paper presents and discusses the modeling approach and the obtained results after conducting an experimental design for a Barcelona district scenario. Moreover, a simulation-based framework is introduced for simplifying simulation results analysis and easily visualizing origin-destination paths for the proposed driver segments (experts, regular and tourists)., Peer Reviewed, Postprint (published version)

Implementació del pilot de Barcelona del projecte europeu BIG IoT (Bridging the Interoperability Gap of the Internet of Things), Implementation of the BIG IoT Barcelona pilot.

New sustainable mobility concepts and smart resilient ideas are arising every day. However, there is not an easy way to bring these ideas into reality, or to test how good they are as mobility solutions. Virtual Mobility Lab offers the opportunity to evaluate the impact of new mobility concepts before taking them to the real world. In this work, a multimodal macroscopic traffic simulation model of the Barcelona Metropolitan Area is developed, including both public and private transport network. This paper explains the remarkable features developed for this model, such as the network hierarchy and the multimodal public network interchangers, allowing demand to exchange between public transportation modes along their origin-destination paths., Peer Reviewed, Postprint (author's final draft)

Probe-based technologies are proliferating as a means for inferring traffic states. Technological companies are interested in traffic data for computing the best routes in a traffic-aware manner and they also provide real-time traffic information with certain temporal accuracy. This paper analyses and evaluates how data provided by a fleet of probe cars can be used to develop a navigation service and how the penetration rate of this service affects a set of city-scale KPIs (Key Performance Indicators) and driver KPIs. The case study adopts a model-driven approach in which microscopic simulation emulates real-size fleets of probe vehicles that provide positions and speed data. What is noteworthy about the modelling behaviour is that drivers are segmented according to their knowledge of network conditions for selected trips: experts, regular drivers and tourists. The paper presents and discusses the modelling approach and the results obtained from an experimental Barcelona CBD model designed to evaluate the penetration rates of probe vehicles and route guidance. An analysis of the simulation experiments reveals remarkable links among city-scale KPIs, which – from a multivariate point of view – is a novelty. A simulation-based framework for results analysis and visualization is also introduced in order to simplify the simulation results analysis and easily visualize OD paths for driver segments., Peer Reviewed, Postprint (author's final draft)

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, There is a wide evidence that sustainable mobility is not only a technological question, automotive technology will be part of the solution as a necessary but not sufficient condition, sufficiency is emerging as a combination of a paradigm shift from car ownership to vehicle usage consequence of socio-economic changes, withthe application of Information and Communication Technologies (ICT) that make possible for a userto have access to a mobility service from anywhere to anywhere at any time. Among the many emergent mobility services Multiple Passenger Ridesharing and its variants look the more promising. However, implementations of these systems accounting specifically for time dependencies, and time windows reflecting users’ needs raise challenges in terms of real-time fleet dispatching and dynamic route calculation. On the other handthe feasibility and impacts analysis in terms of the many factors influencing the behavior of the system, as for example the service demand, the size of the service fleet, the capacity of the shared vehicles, the time windows requirements, soft or tight. This paper analyzes both aspects. The first is approached in terms of a Decision Support System whose solutions are computed in terms of ad hoc heuristics of variants of Pick Up and Delivery Problems with Time Windows and Feasibility and Profitability criteria rooted on Dynamic Insertion Heuristics. For the evaluation of the applications a Simulation Framework is proposed based on a microscopic simulation model thatemulates real-time traffic conditions and a real traffic information system, and interacts with the Decision Support System feeding it with the required data to make the decisions that are implemented in the simulation to emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The paper is completed with the discussion of the achieved results, Peer Reviewed, Postprint (published version)

En els últims anys ha crescut la preocupació per dur un estil de vida saludable i tenir una alimentació equilibrada. Alhora l'ús de les tecnologies mòbils s'ha disparat entre la població i cada vegada ofereixen més prestacions als usuaris. Dins d'aquest context, intentarem crear una eina que ajudi als usuaris a planificar una dieta equilibrada de manera còmode i eficient aprofitant els avantatges de les tecnologies mòbils. Per fer-ho, crearem un model d'optimització que generi un menú equilibrat per a una setmana i el resoldrem mitjançant un solver comercial. També desenvoluparem una aplicació nativa per a dispositius mòbils iOS que, com a funcionalitat principal, indiqui als usuaris un menú equilibrat adaptat a les seves necessitats. Per tal de mostrar el menú als usuaris, plantejarem i implementarem un mètode per resoldre el model d'optimització dins l'aplicació.

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The CitScale tool is a software platform for visualizing, analyzing and comparing the impacts of smart city policies based on innovative mobility concepts in urban areas. It places emphasis on new automotive vehicles aimed at reducing traffic or environmental impacts. This paper introduces this traffic simulation-based tool, and two case studies developed for different scenarios in Barcelona City are briefly presented to demonstrate the capabilities of the tool when it is combined with microscopic traffic simulation software. The first case presents an extensive evaluation of new innovative vehicles (electric vehicles, bikes and three-wheeled scooters) and mobility concepts (trip-sharing). In the second one, data provided by connected cars is analyzed in order to compare different developed navigation strategies and how they affect the city. Finally, some of the obtained results from both cases are concisely presented in order to show the potential of the proposed tool., Peer Reviewed, Postprint (author's final draft)

Sustainable mobility is not merely a technological question. While automotive technology will be part of the solution, it will also be combined with a paradigm shift from car ownership to vehicle usage, which itself will be facilitated by the application of Information and Communication Technologies that make it possible for a user to have access to a mobility service from anywhere to anywhere at any time. Multiple Passenger Ridesharing and its variants appear to be one of the promising mobility concepts that are emerging. However, in implementing these systems while accounting specifically for time dependencies and time windows that reflect user needs, challenges are raised in terms of real-time fleet dispatching and dynamic route calculation. This paper analyzes and evaluates both aspects through microscopic simulation emulating real-time traffic information while also interacting with a Decision Support System. The paper presents and discusses the obtained results for a Barcelona’s Central Business District model. The obtained results prove the potential feasibility of the mobility concept., Peer Reviewed, Postprint (published version)

The CitScale tool is a software platform for visualizing, analyzing and comparing the impacts of new mobility concepts in urban models with special emphasis on new automotive vehicles or those based on innovative mobility concepts aimed at reducing environmental impacts. CitScale is simulation software agnostic. Using traffic simulation techniques requires executing multiple independent replications for each scenario in an experimental design, and each replication generates a huge amount of data that is not easy to analyze or compare. The analysis and comparison of experiments and scenarios is based on global, link-based and path-based key performance indicators from the perspective of users and mobility authorities while covering the scope of driver-type and transport mode., Peer Reviewed, Postprint (published version)

There is a wide body of evidence that suggests sustainable mobility is not only a technological question, but that automotive technology will be a part of the solution in becoming a necessary albeit insufficient condition. Sufficiency is emerging as a paradigm shift from car ownership to vehicle usage, which is a consequence of socio-economic changes. Information and Communication Technologies (ICT) now make it possible for a user to access a mobility service to go anywhere at any time. Among the many emerging mobility services, Multiple Passenger Ridesharing and its variants look the most promising. However, challenges arise in implementing these systems while accounting specifically for time dependencies and time windows that reflect users’ needs, specifically in terms of real-time fleet dispatching and dynamic route calculation. On the other hand, we must consider the feasibility and impact analysis of the many factors influencing the behavior of the system – as, for example, service demand, the size of the service fleet, the capacity of the shared vehicles and whether the time window requirements are soft or tight. This paper analyzes - a Decision Support System that computes solutions with ad hoc heuristics applied to variants of Pick Up and Delivery Problems with Time Windows, as well as to Feasibility and Profitability criteria rooted in Dynamic Insertion Heuristics. To evaluate the applications, a Simulation Framework is proposed. It is based on a microscopic simulation model that emulates real-time traffic conditions and a real traffic information system. It also interacts with the Decision Support System by feeding it with the required data for making decisions in the simulation that emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The obtained results prove the potential feasibility of the mobility concept., Postprint (published version)

Sustainable mobility is not only a technological question, automotive technology will be part of the solution combined with a paradigm shift from car ownership to vehicle usage, and the application of Information and Communication Technologies (ICT) making possible for a user to have access to a mobility service from anywhere to anywhere at any time. Multiple Passenger Ridesharing and its variants look one of the more promising emerging mobility concepts. However, implementations of these systems accounting specifically for time dependencies, and time windows reflecting users’ needs raise challenges in terms of real-time fleet dispatching and dynamic route calculation. This paper analyzes and evaluates both aspects by microscopic traffic simulation emulating real-time traffic conditions and a real traffic information system, and interacting with a Decision Support. The simulation framework has been implemented in a model of Barcelona’s Central Business District. The paper presents and discusses the achieved results., Peer Reviewed, Postprint (published version)

We present a detailed simulation model with the purpose of analyzing the congestion and interaction between bus lines and passengers at stops. Our main goal is to perform a complete validation of a simulation model formalized in a standard language in order to use it as a basis to perform more complex experiments. The basis of the model is a queuing model that leads us to perform an operational validation. Since the model is completely represented using a formal language, the specialist can perform a formal validation of the model previously to any implementation. Thanks to the modular structure of the formal language used to define the model, the model can be easily expanded to represent more complex systems. Due to a formal representation, the implementation process can be done automatically implying that analysts should only be concerned about the correct definition of the diagrams that represent the model behavior, Peer Reviewed, Postprint (published version)

The Information and Communications Technologies penetration in the transport sector has led to the emergence of new mechanisms for attracting trac data. The importance of nding the best location for these mechanisms into the transport network leads to the use of mathematical optimization approaches to this problem. Now the challenge is to solve these models without using commercial solvers. Thus, the development of heuristic methods that use the structure of the problem to obtain near optimal solutions using fewer resources is one of the highest priorities.

SEAT

The dynamic network loading problem is crucial for performing dynamic traffic assignment. It must reproduce the network flow propagation, while taking into account the time and a variable traffic demand on each path of the network. In this paper, we consider a simulation-based approach for dynamic network loading as the best-suited option. We present a multiclass multilane dynamic network loading model based on a mesoscopic scheme that uses a continuous-time link-based approach with a complete demand discretization. In order to demonstrate the correctness of the model, we computationally validate the proposed simulation model using a variety of laboratory tests. The obtained results look promising, showing the model's ability to reproduce multilane multiclass traffic behaviors for medium-size urban networks., Peer Reviewed, Postprint (author's final draft)

XII Premio Abertis en gestión de infraestructuras de Transporte en la modalidad de tesis doctoral, 2014, In terms of sustainability, traffic is currently a significant challenge for urban areas, where the pollution, congestion and accidents are negative externalities which have strongly impacted the health and economy of cities. The increasing use of private vehicles has further exacerbated these problems. In this context, the development of new strategies and policies for sustainable urban transport has made transport planning more relevant than ever before. Mathematical models have helped greatly in identifying solutions, as well as in enriching the process of making decisions and planning. In particular, dynamic network models provide a means for representing dynamic traffic behavior; in other words, they provide a temporally coherent means for measuring the interactions between travel decisions, traffic flows, travel time and travel cost. This thesis focuses on dynamic traffic assignment (DTA) models. DTA has been studied extensively for decades, but much more so in the last twenty years since the emergence of Intelligent Transport Systems (ITS). The objective of this research is to study and analyze the prospects for improving this problem. In an operational context, the objective of DTA models is to represent the evolution of traffic on a road network as conditions change. They seek to describe the assignment of the demand on different paths which connect every OD pair in a state of equilibrium. The behaviour following each individual decision during a trip is a time-dependent generalization of Wardrop's First Principle, the Dynamic User Equilibrium (DUE). This hypothesis is based on the following idea: When current travel times are equal and minimal for vehicles that depart within the same time interval , the dynamic traffic flow through the network is in a DUE state based on travel times for each OD pair at each instant of time ([ran-1996]). This work begins with the time-continuous variational inequalities model proposed by [friesz-1993] for solving the DUE pr, En los últimos tiempos, el problema del tráfi co urbano ha situado a las áreas metropolitanas en una difícil situación en cuanto a sostenibilidad se refi ere (en términos de la congestión, los accidentes y la contaminación). Este problema se ha visto acentuado por la creciente movilidad promovida por el aumento del uso del vehículo privado. Además, debido a que la mayor parte del trá fico es canalizada a través de los modos de carretera, el tiempo perdido por los usuarios al realizar sus viajes tiene un importante efecto económico sobre las ciudades. En este contexto, la plani cación de transporte se vuelve relevante a través del desarrollo de nuevas estrategias y políticas para conseguir un transporte urbano sostenible. Los modelos matemáticos son de gran ayuda ya que enriquecen las decisiones de los gestores de trá fico en el proceso de plani ficación. En particular podemos considerar los modelos de trá fico para la predicción, como por ejemplo los modelos de asignación dinámica de tráfi co (ADT), los cuales proveen de una representación temporal coherente de las interacciones entre elecciones de trá fico, fl ujos de trá fico y medidas de tiempo y coste. Esta tesis se centra en los modelos ADT. Durante las últimas décadas, los modelos ADT han sido intensamente estudiados. Este proceso se ha acelerado particularmente en los últimos veinte años debido a la aparición de los Sistemas Inteligentes de Transporte. El objetivo de esta investigación es estudiar y analizar diferentes posibilidades de mejorar la resolución del problema. En un contexto operacional, el objetivo de los modelos ADT es representar la evolución de la red urbana cuando las condiciones de trá fico cambian. Estos modelos tratan de describir la asignación de la demanda en los diferentes caminos que conectan los pares OD siguiendo un estado de equilibrio. En este caso se ha considerado que el comportamiento de los conductores en cada una de sus decisiones individuales tomadas durante el viaje es una ge, Award-winning, Postprint (published version)

Dealing with traffic demand trip matrices to feed models to support decisions in traffic management is still a problem that deserves research efforts, aimed to find practical solutions applicable in the short term horizon, before future developments become available. This paper analyzes the role of OD matrices in the framework proposed for Analysis, Modeling and Simulation (AMS), namely when moving from static OD matrices to the time-sliced OD required by (AMS) applications. The paper reviews some of the time-slicing used in practice, proposes an improvement in terms of a bilevel approach, solving the lower level problem as a Dynamic User Equilibrium based on a mesoscopic approach, and shows how this off-line procedure can generate efficient initialization of recent Kalman filter approaches, that exploit measurements from Information and Communication Technologies (ICT), whose computational performance make them able for real-time applications., Peer Reviewed, Postprint (author’s final draft)

Origin–destination (O-D) trip matrices that describe the patterns of traffic behavior across a network are the primary data input used in principal traffic models and, therefore, a critical requirement in all advanced systems supported by dynamic traffic assignment models. However, because O-D matrices are not directly observable, the current practice consists of adjusting an initial or seed matrix from link flow counts that are provided by an existing layout of traffic-counting stations. The availability of new traffic measurements provided by information and communication technologies (ICT) allows more efficient algorithms, namely for real-time estimation of O-D matrices that are based on modified Kalman filtering approaches to exploit the new data. The quality of the estimations depends on various factors such as the penetration of the ICT devices, the detection layout, and the quality of the initial information. The feasibility of real-time applications depends on the computational performance of the proposed algorithms for urban networks of sensitive size. This paper presents the results of a set of computational experiments with a microscopic simulation of the network of Barcelona’s central business district that explore the sensitivity of the Kalman filter estimates in relation to design factor values., Peer Reviewed, Postprint (author draft version)

The main traffic models, either for planning or operational purposes, use as major data input Origin-Destination (OD) trip matrices describing the patterns of traffic behavior across the network. OD matrices become in this way a critical requirement of Advanced Traffic Management or Information Systems supported by Dynamic Traffic Assignment models. However, as far as OD matrices are not directly observable, the current practice consist of adjusting an initial or seed matrix from link flow counts provided by an existing layout of traffic counting stations. The adequacy of the detection layout strongly determines the quality of the adjusted OD. Usual approaches to the Detection Layout problem assume that detectors are located at network links but some of the Information and Communication Technologies specially those based on the detection of the electronic signature of on board devices, as for example Bluetooth devices, allow the location of sensor at intersections. This paper proposes a reformulation of the link detection layout problem adapting the classical set covering approaches with side constraints and solving it efficiently by a tabu search metaheuristic. For the intersection layout covering problem a reformulation is proposed in terms of a node covering problem with side constraints that for practical purposes can be efficiently solved with standard professional solvers., Peer Reviewed, Postprint (published version)