Ultrasonic sensors in urban traffic driving-aid systems

Currently, vehicles are often equipped with active safety systems to reduce the risk of accidents, most of which occur in urban environments. The most prominent include Antilock Braking Systems (ABS), Traction Control and Stability Control. All these systems use different kinds of sensors to constantly monitor the conditions of the vehicle, and act in an emergency. In this paper the use of ultrasonic sensors in active safety systems for urban traffic is proposed, and the advantages and disadvantages when compared to other sensors are discussed. Adaptive Cruise Control (ACC) for urban traffic based on ultrasounds is presented as an application example. The proposed system has been implemented in a fully-automated prototype vehicle and has been tested under real traffic conditions. The results confirm the good performance of ultrasonic sensors in these systems. ©2011 by the authors.

Comparasion of road traffic emission models in Madrid (Spain)

Many cities in Europe have difficulties to meet the air quality standards set by the European legislation, most particularly the annual mean Limit Value for NO2. Road transport is often the main source of air pollution in urban areas and therefore, there is an increasing need to estimate current and future traffic emissions as accurately as possible. As a consequence, a number of specific emission models and emission factors databases have been developed recently. They present important methodological differences and may result in largely diverging emission figures and thus may lead to alternative policy recommendations. This study compares two approaches to estimate road traffic emissions in Madrid (Spain): the COmputer Programme to calculate Emissions from Road Transport (COPERT4 v.8.1) and the Handbook Emission Factors for Road Transport (HBEFA v.3.1), representative of the ‘average-speed’ and ‘traffic situation’ model types respectively. The input information (e.g. fleet composition, vehicle kilometres travelled, traffic intensity, road type, etc.) was provided by the traffic model developed by the Madrid City Council along with observations from field campaigns. Hourly emissions were computed for nearly 15 000 road segments distributed in 9 management areas covering the Madrid city and surroundings. Total annual NOX emissions predicted by HBEFA were a 21% higher than those of COPERT. The discrepancies for NO2 were lower (13%) since resulting average NO2/NOX ratios are lower for HBEFA. The larger differences are related to diesel vehicle emissions under “stop & go” traffic conditions, very common in distributor/secondary roads of the Madrid metropolitan area. In order to understand the representativeness of these results, the resulting emissions were integrated in an urban scale inventory used to drive mesoscale air quality simulations with the Community Multiscale Air Quality (CMAQ) modelling system (1 km2 resolution). Modelled NO2 concentrations were compared with observations through a series of statistics. Although there are no remarkable differences between both model runs, the results suggest that HBEFA may overestimate traffic emissions. However, the results are strongly influenced by methodological issues and limitations of the traffic model. This study was useful to provide a first alternative estimate to the official emission inventory in Madrid and to identify the main features of the traffic model that should be improved to support the application of an emission system based on “real world” emission factors.

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.

PEPSI: Privacy-Enhanced Participatory Sensing Infrastructure.

Participatory Sensing combines the ubiquity of mobile phones with sensing capabilities of Wireless Sensor Networks. It targets pervasive collection of information, e.g., temperature, traffic conditions, or health-related data. As users produce measurements from their mobile devices, voluntary participation becomes essential. However, a number of privacy concerns -- due to the personal information conveyed by data reports -- hinder large-scale deployment of participatory sensing applications. Prior work on privacy protection, for participatory sensing, has often relayed on unrealistic assumptions and with no provably-secure guarantees. The goal of this project is to introduce PEPSI: a Privacy-Enhanced Participatory Sensing Infrastructure. We explore realistic architectural assumptions and a minimal set of (formal) privacy requirements, aiming at protecting privacy of both data producers and consumers. We design a solution that attains privacy guarantees with provable security at very low additional computational cost and almost no extra communication overhead.

Determinación de la distribución de tamaño y número de partículas provenientes de motores diesel operando en trafico real con equipos embarcados

El presente trabajo se propone determinar la distribución de tamaño y número de partículas nanométricas provenientes de motores diésel con equipos embarcados en tráfico extraurbano. Para ello, se utilizaron equipos de medición de última generación en condiciones promedio de conducción en tráfico extraurbano por más de 800 km a lo largo del trayecto Madrid-Badajoz-Madrid mediante un vehículo característico del parque automotor español y se implementaron métodos novedosos y pioneros en el registro de este tipo de emisiones. Todo ello abre el camino para líneas de investigación y desarrollo que contribuirán a entender, dimensionar y cualificar el comportamiento de las partículas, así como su impacto en la calidad de vida de la población. El estudio hace dos grandes aportes al campo. Primero, permite registrar las emisiones en condiciones transitorias propias del tráfico real. Segundo, permite mantener controladas las condiciones de medición y evita la formación aleatoria de partículas provenientes de material volátil, gracias al sistema de adecuación de la muestra de gases de escape incorporado. Como resultado, se obtuvo una muestra abundante y confiable que permitió construir modelos matemáticos para calcular la emisión de partículas nanométricas, ultrafinas, finas y totales sobre las bases volumétrica, espacial y temporal en función de la pendiente del perfil orográfico de la carretera, siempre y cuando esté dentro del intervalo ±5.0%. Estos modelos de cálculo de emisiones reducen tanto los costos de experimentación como la complejidad de los equipos necesarios, y fundamentaron el desarrollo de la primera versión de una aplicación informática que calcula las partículas emitidas por un motor diésel en condiciones de tráfico extraurbano ("Partículas Emitidas por Motores Diésel, PEMDI). ABSTRACT The purpose of this research is to determine the distribution of size and number of nanometric particles that come from diesel engines by means of on-board equipment in extra-urban traffic. In order to do this, cutting-edge measuring equipment was used under average driving conditions in extra-urban traffic for more than 800 km along the Madrid-Badajoz-Madrid route using a typical vehicle from Spain's automotive population and innovative, groundbreaking registering methods for this type of emissions were used. All this paves the way for lines of research and development which should help understand, measure and characterize the behavior of such particles, as well as their impact in the quality of life of the general population. The study makes two important contributions to the field. First, it makes it possible to register emissions under transient conditions, which are characteristic to real traffic. Secondly, it provides a means to keep the measuring conditions under control and prevents the random formation of particles of volatile origin through the built-in adjustment system of the exhaust gas sample. As a result, an abundant and reliable sample was gathered, which enabled the building of mathematical models to estimate the emission of nanometric, ultrafine, fine and total particles on volumetric, spatial and temporal bases as a function of the orographic outline of the road within a ±5.0% range. These emission estimating models lower both the experimentation costs and the required equipment's complexity, and they provided the basis for the development of a first software application version that estimates the particles emitted from diesel engines under extra-urban traffic conditions (Partículas Emitidas por Motores Diésel, PEMDI).

Reduced carbon and energy footprint in highway operations: the Highway Energy Assessment (HERA) methodology

Global demand for mobility is increasing and the environmental impact of transport has become an important issue in transportation network planning and decision-making, as well as in the operational management phase. Suitable methods are required to assess emissions and fuel consumption reduction strategies that seek to improve energy efficiency and furthering decarbonization. This study describes the development and application of an improved modeling framework – the HERA (Highway EneRgy Assessment) methodology – that enables to assess the energy and carbon footprint of different highways and traffic flow scenarios and their comparison. HERA incorporates an average speed consumption model adjusted with a correction factor which takes into account the road gradient. It provides a more comprehensive method for estimating the footprint of particular highway segments under specific traffic conditions. It includes the application of the methodology to the Spanish highway network to validate it. Finally, a case study shows the benefits from using this methodology and how to integrate the objective of carbon footprint reductions into highway design, operation and scenario comparison.

Modelización del efecto de la pendiente de la vía sobre el consumo de vehículos turismo

El presente trabajo tiene por objetivo generar una metodología validada que permita predecir el consumo de vehículos turismo circulando en cualquier tramo de vía a partir del perfil orográfico y del diagrama velocidad-tiempo. Para la generación de la metodología, se ha realizado un modelo de simulación con el programa ADVISOR que permite calcular el consumo de combustible para un determinado recorrido en el que se tiene en cuenta el perfil orográfico. Este modelo fue validado con datos reales medidos con equipos on-board y se usó para calcular el consumo de combustible diferencial debido al efecto de la pendiente de la vía, al poderse simular con y sin pendiente. Se realizaron múltiples simulaciones de recorridos con velocidad máxima variable con el fin de obtener un número significativo de datos. Con los resultados de las diferentes simulaciones, se realizó un estudio estadístico, para determinar las variables influyentes y se generó una función estadística (Ecuación de Consumo Estimado – ECE) que permite calcular el consumo de combustible debido a la pendiente de la vía, conociendo el consumo del vehículo en carretera llana (sin pendiente). Esta función estadística generada (ECE), se validó con datos reales medidos en tráfico real. Con el fin de darle generalidad y aplicabilidad a la función generada, y teniendo en cuenta que el consumo de combustible en carretera llana no está siempre disponible, se ha calculado el consumo de combustible sin pendiente utilizando la metodología Copert 4, metodología oficial desarrollada por la Agencia de Medio Ambiente de Europa (EEA) para la estimación de emisiones y consumo de combustible que está basada en datos experimentales pero que no tiene en cuenta la pendiente de la vía. La Ecuación de Consumo Estimado (ECE) aplicada a los consumos calculados por la metodología Copert 4, se valida también usando datos reales medidos en tráfico real y se comprueba que esta función se ajusta considerablemente bien a la realidad, con un error en el consumo acumulado frente al del ensayo real de un 1% y una correlación con el consumo instantáneo del ensayo real de 0,93. Se concluye, que la Función de Consumo Estimado (ECE), permite predecir el efecto de la pendiente sobre el consumo de combustible de un vehículo turismo en tráfico real con un error menor del 1%. Abstract This projects aims to develop a validated methodology that enables to predict cars consumption while circulating at any kind of road section based on its orographic outline and the speed-time diagram. In order to develop this methodology, a simulation model has been performed with the programme ADVISOR, that allows fuel consumption calculation for an specific route in which the orographic outline is considered. This model was validated by real data measured with an on-board equipment and it was used to calculate the differential fuel consumption caused by the effect of the slope on the road, as it was possible to simulate with or without slope. Many simulations were run with routes with variable maximum speed, aiming to obtain a significant amount of data. An statistical study was carried out with the results of those simulations with the purpose to determine the influential variables and an statistical function ( Estimated Consumption Equation – ECE) that enables fuel consumption calculation due to the road’s slope when the consumption of a vehicle on horizontal road (without any slope) is known. This statistical function (ECE) was validated by real data measured in real traffic conditions. With the purpose to generalise the function and increase its applicability, considering that the consumption of a vehicle on horizontal road is not always available, the nonslope fuel consumption has been calculated through Copert 4 methodology, which is the official methodology developed by the European Environmental Agency (EEA) for emissions and fuel consumption calculation based on experimental data, but without taking into consideration the road’s slope. The Estimated Consumption Equation (ECE) applied to the consumption calculated through Copert 4 methodology is also validated using real data measured in real traffic conditions. It was verified that this function considerably adjusts to reality, with an error on the accumulated consumption compared to the real test of 1% and a correlation with the real test immediate fuel consumption of 0,93. It is concluded that the Estimated Consumption Equation (ECE) enables to predict the effect of the slope on the fuel consumption of a car in real traffic conditions with an error less than 1%.

Optimización del consumo de combustible de un vehículo dependiendo de las condiciones de la carretera

El consumo de combustible en un automóvil es una característica que se intenta mejorar continuamente debido a los precios del carburante y a la creciente conciencia medioambiental. Esta tesis doctoral plantea un algoritmo de optimización del consumo que tiene en cuenta las especificaciones técnicas del vehículo, el perfil de orografía de la carretera y el tráfico presente en ella. El algoritmo de optimización calcula el perfil de velocidad óptima que debe seguir el vehículo para completar un recorrido empleando un tiempo de viaje especificado. El cálculo del perfil de velocidad óptima considera los valores de pendiente de la carretera así como también las condiciones de tráfico vehicular de la franja horaria en que se realiza el recorrido. El algoritmo de optimización reacciona ante condiciones de tráfico cambiantes y adapta continuamente el perfil óptimo de velocidad para que el vehículo llegue al destino cumpliendo el horario de llegada establecido. La optimización de consumo es aplicada en vehículos convencionales de motor de combustión interna y en vehículos híbridos tipo serie. Los datos de consumo utilizados por el algoritmo de optimización se obtienen mediante la simulación de modelos cuasi-estáticos de los vehículos. La técnica de minimización empleada por el algoritmo es la Programación Dinámica. El algoritmo divide la optimización del consumo en dos partes claramente diferenciadas y aplica la Programación Dinámica sobre cada una de ellas. La primera parte corresponde a la optimización del consumo del vehículo en función de las condiciones de tráfico. Esta optimización calcula un perfil de velocidad promedio que evita, cuando es posible, las retenciones de tráfico. El tiempo de viaje perdido durante una retención de tráfico debe recuperarse a través de un aumento posterior de la velocidad promedio que incrementaría el consumo del vehículo. La segunda parte de la optimización es la encargada del cálculo de la velocidad óptima en función de la orografía y del tiempo de viaje disponible. Dado que el consumo de combustible del vehículo se incrementa cuando disminuye el tiempo disponible para finalizar un recorrido, esta optimización utiliza factores de ponderación para modular la influencia que tiene cada una de estas dos variables en el proceso de minimización. Aunque los factores de ponderación y la orografía de la carretera condicionan el nivel de ahorro de la optimización, los perfiles de velocidad óptima calculados logran ahorros de consumo respecto de un perfil de velocidad constante que obtiene el mismo tiempo de recorrido. Las simulaciones indican que el ahorro de combustible del vehículo convencional puede lograr hasta un 8.9% mientras que el ahorro de energía eléctrica del vehículo híbrido serie un 2.8%. El algoritmo fusiona la optimización en función de las condiciones del tráfico y la optimización en función de la orografía durante el cálculo en tiempo real del perfil óptimo de velocidad. La optimización conjunta se logra cuando el perfil de velocidad promedio resultante de la optimización en función de las condiciones de tráfico define los valores de los factores de ponderación de la optimización en función de la orografía. Aunque el nivel de ahorro de la optimización conjunta depende de las condiciones de tráfico, de la orografía, del tiempo de recorrido y de las características propias del vehículo, las simulaciones indican ahorros de consumo superiores al 6% en ambas clases de vehículo respecto a optimizaciones que no logran evitar retenciones de tráfico en la carretera. ABSTRACT Fuel consumption of cars is a feature that is continuously being improved due to the fuel price and an increasing environmental awareness. This doctoral dissertation describes an optimization algorithm to decrease the fuel consumption taking into account the technical specifications of the vehicle, the terrain profile of the road and the traffic conditions of the trip. The algorithm calculates the optimal speed profile that completes a trip having a specified travel time. This calculation considers the road slope and the expected traffic conditions during the trip. The optimization algorithm is also able to react to changing traffic conditions and tunes the optimal speed profile to reach the destination within the specified arrival time. The optimization is applied on a conventional vehicle and also on a Series Hybrid Electric vehicle (SHEV). The fuel consumption optimization algorithm uses data obtained from quasi-static simulations. The algorithm is based on Dynamic Programming and divides the fuel consumption optimization problem into two parts. The first part of the optimization process reduces the fuel consumption according to foreseeable traffic conditions. It calculates an average speed profile that tries to avoid, if possible, the traffic jams on the road. Traffic jams that delay drivers result in higher vehicle speed to make up for lost time. A higher speed of the vehicle within an already defined time scheme increases fuel consumption. The second part of the optimization process is in charge of calculating the optimal speed profile according to the road slope and the remaining travel time. The optimization tunes the fuel consumption and travel time relevancies by using two penalty factors. Although the optimization results depend on the road slope and the travel time, the optimal speed profile produces improvements of 8.9% on the fuel consumption of the conventional car and of 2.8% on the spent energy of the hybrid vehicle when compared with a constant speed profile. The two parts of the optimization process are combined during the Real-Time execution of the algorithm. The average speed profile calculated by the optimization according to the traffic conditions provides values for the two penalty factors utilized by the second part of the optimization process. Although the savings depend on the road slope, traffic conditions, vehicle features, and the remaining travel time, simulations show that this joint optimization process can improve the energy consumption of the two vehicles types by more than 6%.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Experimental analysis of energy savings and hygrothermal conditions improvement by means of air curtaines in stores with intensive pedestrian traffic

Current worldwide building legislation requirements aim to the design and construction of technical services that reduce energy consumption and improve indoor hygrothermal conditions. The retail sector in Spain, with a lot of outdated technical systems, demands energy conservation measures in order to reduce the increasingly electrical consumption for cooling. Climatic separation with modern air curtains and advanced hygrothermal control systems enables energy savings and can keep suitable indoor air temperature and humidity of stores with intense pedestrian traffic, especially when located in hot humid climates. As stated in the article, the energy savings in commercial buildings with these systems exceeds 30%

On the efficiency of a dedicated LMA for multicast traffic distribution in PMIPv6 domains

IP multicast allows the efficient support of group communication services by reducing the number of IP flows needed for such communication. The increasing generalization in the use of multicast has also triggered the need for supporting IP multicast in mobile environments. Proxy Mobile IPv6 (PMIPv6) is a network-based mobility management solution, where the functionality to support the terminal movement resides in the network. Recently, a baseline solution has been adopted for multicast support in PMIPv6. Such base solution has inefficiencies in multicast routing because it may require multiple copies of a single stream to be received by the same access gateway. Nevertheless, there is an alternative solution to support multicast in PMIPv6 that avoids this issue. This paper evaluates by simulation the scalability of both solutions under realistic conditions, and provides an analysis of the sensitivity of the two proposals against a number of parameters.

Implementation and management of private traffic limitation in urban areas: experiences and methodologies.

This paper shows the results of a research aimed to formulate a general model for supporting the implementation and management of an urban road pricing scheme. After a preliminary work, to define the state of the art in the field of sustainable urban mobility strategies, the problem has been theoretically set up in terms of transport economy, introducing the external costs’ concept duly translated into the principle of pricing for the use of public infrastructures. The research is based on the definition of a set of direct and indirect indicators to qualify the urban areas by land use, mobility, environmental and economic conditions. These indicators have been calculated for a selected set of typical urban areas in Europe on the basis of the results of a survey carried out by means of a specific questionnaire. Once identified the most typical and interesting applications of the road pricing concept in cities such as London (Congestion Charging), Milan (Ecopass), Stockholm (Congestion Tax) and Rome (ZTL), a large benchmarking exercise and the cross analysis of direct and indirect indicators, has allowed to define a simple general model, guidelines and key requirements for the implementation of a pricing scheme based traffic restriction in a generic urban area. The model has been finally applied to the design of a road pricing scheme for a particular area in Madrid, and to the quantification of the expected results of its implementation from a land use, mobility, environmental and economic perspective.

Coupled models for the dynamics of bridges under high-speed rail traffic

The dynamic effects of high-speed trains on viaducts are important issues for the design of the structures, as well as for determining safe running conditions of trains. In this work we start by reviewing the relevance of some basic moving load models for the dynamic action of vertical traffic loads. The study of lateral dynamics of running trains on bridges is of importance mainly for the safety of the traffic, and may be relevant for laterally compliant bridges. These studies require 3D coupled vehicle-bridge models and consideration of wheel to rail contact. We describe here a fully nonlinear coupled model, formulated in absolute coordinates and incorporated into a commercial finite element framework. An application example is presented for a vehicle subject to a strong wind gust traversing a bridge, showing the relevance of the nonlinear wheel-rail contact model as well as the interaction between bridge and vehicle.

Self-Tuning PID controller for autonomous car tracking in urban traffic

In this paper an on line self-tuned PID controller is proposed for the control of a car whose goal is to follow another one, at distances and speeds typical in urban traffic. The bestknown tuning mechanism is perhaps the MIT rule, due to its ease of implementation. However, as it is well known, this method does not guarantee the stability of the system, providing good results only for constant or slowly varying reference signals and in the absence of noise, which are unrealistic conditions. When the reference input varies with an appreciable rate or in presence of noise, eventually it could result in system instability. In this paper an alternative method is proposed that significantly improves the robustness of the system for varying inputs or in the presence of noise, as demonstrated by simulation.

Efficient hybrid monocular-stereo approach to on-board, video-based traffic sign detection and tracking

In this paper we propose an innovative method for the automatic detection and tracking of road traffic signs using an onboard stereo camera. It involves a combination of monocular and stereo analysis strategies to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. Firstly, an adaptive color and appearance based detection is applied at single camera level to generate a set of traffic sign hypotheses. In turn, stereo information allows for sparse 3D reconstruction of potential traffic signs through a SURF-based matching strategy. Namely, the plane that best fits the cloud of 3D points traced back from feature matches is estimated using a RANSAC based approach to improve robustness to outliers. Temporal consistency of the 3D information is ensured through a Kalman-based tracking stage. This also allows for the generation of a predicted 3D traffic sign model, which is in turn used to enhance the previously mentioned color-based detector through a feedback loop, thus improving detection accuracy. The proposed solution has been tested with real sequences under several illumination conditions and in both urban areas and highways, achieving very high detection rates in challenging environments, including rapid motion and significant perspective distortion