Abstract

Considering the expected air traffic growth, the innovation and development of new tools able to allow a more efficient and safer way of managing aircraft operations is necessary to achieve future expectations. In this context, it is important to be capable of accurately predict aircraft trajectories to ensure efficient aircraft operations (e.g., planning and dispatching of flights, in-flight trajectory prediction, etc.) as well as to make more robust the Air Traffic Management (ATM) system (which includes ground based systems for ATC, predicting demand in ATC sectors, etc). The way of predicting them is based on aircraft performance models (APM), i.e., sets of equations that allow to model the aircraft performance according to some specific coefficients that depend on the aircraft carrying out the flight. Therefore, the predicted trajectories accuracy will depend directly on the aircraft performance model used. If the APM does not reflect the reality, the predicted trajectories will not be accurate enough. Moreover, since these trajectories will not be longer optimal according to the real performance model, the performance in cost-efficiency and the environmental impact of aircraft operations will be decreased. Then, it is required the use of aircraft performance models as realistic as possible. The objective of this Master Thesis is the design of an algorithm capable to estimate the coefficients of the functions describing the aircraft performance model considered, which will be the Base of Aircraft Data (BADA), from Quick Access Recorder (QAR) flight data. The idea is to obtain the real coefficients values of each specific tail number aircraft to accurately model their performance for trajectory prediction purposes. To perform the coefficients' estimation, real flight data has been used, consisting in a set of trajectories carried out by more than 30 different tail number aircrafts and obtained from their respective QAR. Among these aircrafts, only one (i.e, its trajectories) will be selected to design the estimation algorithm, which consist in a nonlinear least squares optimization problem. The output will be the coefficients value that better fits to the QAR data used, i.e, that minimize the difference between the real trajectories and the generated predicted trajectories as a result of applying the estimated coefficient's value to the BADA model equations. The results show a good estimation accuracy for the four aircrafts analysed, obtaining a very low prediction error in all cases, which proves the reliability of the algorithm and the necessity of tailoring the existing performance models (in this case BADA).

Abstract

Day by day the number of vehicles on roads is growing, increasing also the number of accidents, traffic jams and carbon dioxide emissions. For this reason, the European Union has adopted an action plan for improving urban mobility where the public transport is the main key. To present day, solutions as special bus lanes, electrical and hydrogen buses and rail-guided buses have been tested and implemented in several cities as London and Madrid. In this article an automated stopping system for electrical buses is presented. Stopping system have been implemented by the AUTOPIA program on an electric minibus and tested on a private circuit with satisfactory results. Peer Reviewed

Abstract

Train dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so-called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off-peak hours separately. The peak-hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off-peak-hour dwell times are estimated using a non-parametric regression model, in particular, the k-nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%-88.5% during peak hours and 80.1% during off-peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible.

Abstract

:This paper presents an autocalibration method to determine the pose of a stereo vision system based on knowing the geometry of the ground in front of the cameras. This pose changes considerably while the vehicle is driven, therefore it is good to know constantly the pose of the camera for several applications based on computer vision, such as advanced driver assistance systems, autonomous vehicles or robotics. These constant changes of the pose make interesting to be able to detect constantly the variations in its extrinsic parameters (height, pitch, roll). The validation of the autocalibration method is accomplished by a visual odometry implementation. A study of the improvement of the results of the visual odometry estimation taking into account the changes of the camera pose is presented, demonstrating the advantages of the autocalibration method. This work was also supported by Spanish Government through the CICYT projects FEDORA (Grant TRA2010-20255-C03-01) and Driver Distraction Detector System (Grant TRA2011-29454-C03-02).

Abstract

"jats:sec" "jats:title"Purpose"/jats:title" "jats:p"With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the state-of-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research."/jats:p" "/jats:sec" "jats:sec" "jats:title"Methods"/jats:title" "jats:p"Available articles regarding response-based methods to measure road surface condition were collected mainly from “Scopus” database and partially from “Google Scholar”. The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation."/jats:p" "/jats:sec" "jats:sec" "jats:title"Results"/jats:title" "jats:p"The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking."/jats:p" "/jats:sec Document type: Article

Abstract

Long haulage road transport consumes considerable amounts of energy in today's world. Predictive control strategies based on digital maps can significantly lower the portion being wasted in traditional cruise control operated highway driving. Such control strategies rely on high quality stored road grade information. This paper describes a newly developed method to estimate the road grade using sensors commonly found on standard heavy duty vehicles. The method utilizes a piecewise linear road model derived from highway design methodologies. The estimation method has been implemented and evaluated experimentally, and is shown to give better results compared to an existing method. © 2010 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. QC 20120213

Abstract

The introduction of light emitting diodes (LED) in automotive exterior lighting systems provides opportunities to develop viable alternatives to conventional communication and sensing technologies. Most of the advanced driver-assist and autonomous vehicle technologies are based on Radio Detection and Ranging (RADAR) or Light Detection and Ranging (LiDAR) systems that use radio frequency or laser signals, respectively. While reliable and real-time information on vehicle speeds is critical for traffic operations management and autonomous vehicles safety, RADAR or LiDAR systems have some deficiencies especially in curved road scenarios where the incidence angle is rapidly varying. In this paper, we propose a novel speed estimation system so-called the Visible Light Detection and Ranging (ViLDAR) that builds upon sensing visible light variation of the vehicle's headlamp. We determine the accuracy of the proposed speed estimator in straight and curved road scenarios. We further present how the algorithm design parameters and the channel noise level affect the speed estimation accuracy. For wide incidence angles, the simulation results show that the ViLDAR outperforms RADAR/LiDAR systems in both straight and curved road scenarios. Publisher's Version

Abstract

Data analysis plays a key role in supporting the development of sustainable transportation. Using the large-scale household mobility survey data collected in Milan, Italy during 2005–2006, we study whether the large-scale data contribute to improving accuracy in estimating household travel modes. This paper presents three machine learning methods including multinomial logit (MNL) model, random forest (RF) and support vector machine (SVM) to estimate the household travel mode. Their model accuracies are 70.41%, 71.89%, 72.74% respectively under the full sample size. It is found that the accuracies of these three methods fluctuate fiercely when the sample size is less than 20,000 and then stabilize gradually with continuous increasing it. After stabilization occurs, accuracies with these three methods do not significantly increase as the sample size continues to increase. We also study the travel characteristics derived from the large-scale survey data, which is fundamental for developing a sustainable transportation system. The collected data items include five explanatory variables, i.e., household size (HS), vehicle ownership, household income (HI), travel distance, travel time and one response variable (i.e., household travel mode), which includes public transport (PT), private car, usage of PT and private car simultaneously and the others travel modes (e.g., walk). We further investigate the importance of explanatory variables in terms of estimating household travel mode choice with the MNL model. It is found that vehicle ownership is the most critical factor influencing household travel mode choice, followed by travel distance, travel time, HS and HI. The ranking result is consistent with the RF approach.

Abstract

This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system. Document type: Article