Dynamic behaviour of underspanned suspension road bridges under traffic loads

Underspanned suspension bridges are structures with important economical and aesthetic advantages, due to their high structural efficiency. However, road bridges of this typology are still uncommon because of limited knowledge about this structural system. In particular, there remains some uncertainty over the dynamic behaviour of these bridges, due to their extreme lightness. The vibrations produced by vehicles crossing the viaduct are one of the main concerns. In this work, traffic-induced dynamic effects on this kind of viaduct are addressed by means of vehicle-bridge dynamic interaction models. A finite element method is used for the structure, and multibody dynamic models for the vehicles, while interaction is represented by means of the penalty method. Road roughness is included in this model in such a way that the fact that profiles under left and right tyres are different, but not independent, is taken into account. In addition, free software {PRPgenerator) to generate these profiles is presented in this paper. The structural dynamic sensitivity of underspanned suspension bridges was found to be considerable, as well as the dynamic amplification factors and deck accelerations. It was also found that vehicle speed has a relevant influence on the results. In addition, the impact of bridge deformation on vehicle vibration was addressed, and the effect on the comfort of vehicle users was shown to be negligible.

Using aircraft as wind sensors for estimating accurate wind fields for air traffic management applications

A study which examines the use of aircraft as wind sensors in a terminal area for real-time wind estimation in order to improve aircraft trajectory prediction is presented in this paper. We describe not only different sources in the aircraft systems that provide the variables needed to derivate the wind velocity but the capabilities which allow us to present this information for ATM Applications. Based on wind speed samples from aircraft landing at Madrid-Barajas airport, a real-time wind field will be estimated using a data processing approach through a minimum variance method. Finally the accuracy of this procedure will be evaluated for this information to be useful to Air Traffic Control.

Development of the infrastructure for a multi-agent traffic management system simulation

This document contains detailed description of the design and the implementation of a multi-agent application controlling traffic lights in a city together with a system for simulating traffic and testing. The goal of this thesis is to design and build a simplified intelligent and distributed solution to the problem with the traffic in the big cities following different good practices in order to allow future refining of the model of the real world. The problem of the traffic in the big cities is still a problem that cannot be solved. Not only is the increasing number of cars a reason for the traffic jams, but also the way the traffic is organized. Usually, the intersections with traffic lights are replaced by roundabouts or interchanges to increase the number of cars that can cross the intersection in certain time. But still there are places where the infrastructure cannot be changed and the traffic light semaphores are the only way to control the car flows. In real life, the traffic lights have a predefined plan for change or they receive information from a centralized system when and how they have to change. But what if the traffic lights can cooperate and decide on their own when and how to change? Using this problem, the purpose of the thesis is to explore different agent-based software engineering approaches to design and build a non-conventional distributed system. From the software engineering point of view, the goal of the thesis is to apply the knowledge and use the skills, acquired during the various courses of the master program in Software Engineering, while solving a practical and complex problem such as the traffic in the cities.

Event-based time series data preprocessing: application to traffic flow time series

Traffic flow time series data are usually high dimensional and very complex. Also they are sometimes imprecise and distorted due to data collection sensor malfunction. Additionally, events like congestion caused by traffic accidents add more uncertainty to real-time traffic conditions, making traffic flow forecasting a complicated task. This article presents a new data preprocessing method targeting multidimensional time series with a very high number of dimensions and shows its application to real traffic flow time series from the California Department of Transportation (PEMS web site). The proposed method consists of three main steps. First, based on a language for defining events in multidimensional time series, mTESL, we identify a number of types of events in time series that corresponding to either incorrect data or data with interference. Second, each event type is restored utilizing an original method that combines real observations, local forecasted values and historical data. Third, an exponential smoothing procedure is applied globally to eliminate noise interference and other random errors so as to provide good quality source data for future work.

Development and assessment of traffic-related emission abatement measures for the Madrid city (Spain) through the WRF-SMOKE-CMAQ modelling system

The achievement of the limit values established in the European legislation pose an important handicap for large urban areas with intense road traffic, such as Madrid (Spain). Despite permanent measures included in air quality plans it is important to assess additional measures that may be temporally applied under unfavourable conditions. This paper reports on the simulation of different traffic restriction strategies in Madrid for high-pollution episodes.

Predicción de tráfico de contenedores a corto plazo mediante técnicas de minería de datos : redes neuronales artificiales y redes bayesianas

El correcto pronóstico en el ámbito de la logística de transportes es de vital importancia para una adecuada planificación de medios y recursos, así como de su optimización. Hasta la fecha los estudios sobre planificación portuaria se basan principalmente en modelos empíricos; que se han utilizado para planificar nuevas terminales y desarrollar planes directores cuando no se dispone de datos iniciales, analíticos; más relacionados con la teoría de colas y tiempos de espera con formulaciones matemáticas complejas y necesitando simplificaciones de las mismas para hacer manejable y práctico el modelo o de simulación; que requieren de una inversión significativa como para poder obtener resultados aceptables invirtiendo en programas y desarrollos complejos. La Minería de Datos (MD) es un área moderna interdisciplinaria que engloba a aquellas técnicas que operan de forma automática (requieren de la mínima intervención humana) y, además, son eficientes para trabajar con las grandes cantidades de información disponible en las bases de datos de numerosos problemas prácticos. La aplicación práctica de estas disciplinas se extiende a numerosos ámbitos comerciales y de investigación en problemas de predicción, clasificación o diagnosis. Entre las diferentes técnicas disponibles en minería de datos las redes neuronales artificiales (RNA) y las redes probabilísticas o redes bayesianas (RB) permiten modelizar de forma conjunta toda la información relevante para un problema dado. En el presente trabajo se han analizado dos aplicaciones de estos casos al ámbito portuario y en concreto a contenedores. En la Tesis Doctoral se desarrollan las RNA como herramienta para obtener previsiones de tráfico y de recursos a futuro de diferentes puertos, a partir de variables de explotación, obteniéndose valores continuos. Para el caso de las redes bayesianas (RB), se realiza un trabajo similar que para el caso de las RNA, obteniéndose valores discretos (un intervalo). El principal resultado que se obtiene es la posibilidad de utilizar tanto las RNA como las RB para la estimación a futuro de parámetros físicos, así como la relación entre los mismos en una terminal para una correcta asignación de los medios a utilizar y por tanto aumentar la eficiencia productiva de la terminal. Como paso final se realiza un estudio de complementariedad de ambos modelos a corto plazo, donde se puede comprobar la buena aceptación de los resultados obtenidos. Por tanto, se puede concluir que estos métodos de predicción pueden ser de gran ayuda a la planificación portuaria. The correct assets’ forecast in the field of transportation logistics is a matter of vital importance for a suitable planning and optimization of the necessary means and resources. Up to this date, ports planning studies were basically using empirical models to deal with new terminals planning or master plans development when no initial data are available; analytical models, more connected to the queuing theory and the waiting times, and very complicated mathematical formulations requiring significant simplifications to acquire a practical and easy to handle model; or simulation models, that require a significant investment in computer codes and complex developments to produce acceptable results. The Data Mining (DM) is a modern interdisciplinary field that include those techniques that operate automatically (almost no human intervention is required) and are highly efficient when dealing with practical problems characterized by huge data bases containing significant amount of information. These disciplines’ practical application extends to many commercial or research fields, dealing with forecast, classification or diagnosis problems. Among the different techniques of the Data Mining, the Artificial Neuronal Networks (ANN) and the probabilistic – or Bayesian – networks (BN) allow the joint modeling of all the relevant information for a given problem. This PhD work analyses their application to two practical cases in the ports field, concretely to container terminals. This PhD work details how the ANN have been developed as a tool to produce traffic and resources forecasts for several ports, based on exploitation variables to obtain continuous values. For the Bayesian networks case (BN), a similar development has been carried out, obtaining discreet values (an interval). The main finding is the possibility to use ANN and BN to estimate future needs of the port’s or terminal’s physical parameters, as well as the relationship between them within a specific terminal, that allow a correct assignment of the necessary means and, thus, to increase the terminal’s productive efficiency. The final step is a short term complementarily study of both models, carried out in order to verify the obtained results. It can thus be stated that these prediction methods can be a very useful tool in ports’ planning.

Aeroelastic effects in a traffic sign panel induced by a passing vehicle

Here, a simple theoretical model of the vehicle induced flow and its effects on traffic sign panels is presented. The model is a continuation of a previous one by Sanz-Andrés and coworkers, now including the flexibility of the panel (and, therefore, the flow effects associated to the motion of the panel). Through the paper an aeroelastic one-degree-of-freedom model is developed and the flow effects are computed from unsteady potential theory. The influence of panel's mechanical properties (mass, damping ratio, and stiffness) in the motion induced forces are numerically analyzed.

Vehicle-induced loads on traffic sign panels

The determination of the loads on traffic sign panels in the current standards does not, in general, take into account the vehicle-induced loads, as explained by Quinn, Baker and Wright (QBW in what follows) (J. Wind Eng. Ind. Aerodyn. 89 (2001) 831). On the other hand, a report from Cali and Covert (CC) (J. Wind Eng. Ind. Aerodyn. 84 (2000) 87) indicates that in highway sign support structures, vehicle-induced loads have led to premature failures in some cases. The aim of this paper is to present a mathematical model for the vehicle-induced load on a flat sign panel, simple enough to give analytical results, but able to explain the main characteristics of the phenomenon. The results of the theoretical model help to explain the behaviour observed in the experiments performed in previous studies.