Hybrid choice model for propensity to travel and tour complexity

During the last years cities around the world have invested important quantities of money in measures for reducing congestion and car-trips. Investments which are nothing but potential solutions for the well-known urban sprawl phenomenon, also called the “development trap” that leads to further congestion and a higher proportion of our time spent in slow moving cars. Over the path of this searching for solutions, the complex relationship between urban environment and travel behaviour has been studied in a number of cases. The main question on discussion is, how to encourage multi-stop tours? Thus, the objective of this paper is to verify whether unobserved factors influence tour complexity. For this purpose, we use a data-base from a survey conducted in 2006-2007 in Madrid, a suitable case study for analyzing urban sprawl due to new urban developments and substantial changes in mobility patterns in the last years. A total of 943 individuals were interviewed from 3 selected neighbourhoods (CBD, urban and suburban). We study the effect of unobserved factors on trip frequency. This paper present the estimation of an hybrid model where the latent variable is called propensity to travel and the discrete choice model is composed by 5 alternatives of tour type. The results show that characteristics of the neighbourhoods in Madrid are important to explain trip frequency. The influence of land use variables on trip generation is clear and in particular the presence of commercial retails. Through estimation of elasticities and forecasting we determine to what extent land-use policy measures modify travel demand. Comparing aggregate elasticities with percentage variations, it can be seen that percentage variations could lead to inconsistent results. The result shows that hybrid models better explain travel behavior than traditional discrete choice models.

Desarrollo de un sistema automático de detección de disgrafías en niños de 4 a 5 años

La escritura es una actividad psicomotora muy importante en el desarrollo infantil. Tanto es así que su correcto aprendizaje condicionará el futuro de una persona, pues estará presente en todo tipo de situaciones cotidianas. La disgrafía es el término utilizado para referirse a los problemas relacionados con la escritura, y se manifiesta cuando la escritura de un determinado sujeto es ilegible o lenta como resultado de un aprendizaje fallido de los procesos motores de la escritura. Estos problemas intentan resolverse durante el desarrollo infantil mediante diferentes pruebas que miden las capacidades visomotoras de los niños basándose en criterios de forma (número y posición correcta de trazos). Sin embargo, a lo largo de los años estos criterios han demostrado no ser totalmente precisos en la detección prematura de posibles casos de disgrafía. Por ello, en este proyecto se ha desarrollado una aplicación que ayuda a ampliar la fiabilidad de los test actuales, utilizando un criterio cinemático. Esta aplicación, desarrollada para una tableta Android, muestra al niño una serie de figuras que él debe copiar en la tableta haciendo uso de un lápiz óptico. Los trazos registrados por la aplicación son analizados para valorar aspectos como la fluidez, continuidad y regularidad, ampliando así la fiabilidad de los test actuales, lo que permite desechar falsos positivos y detectar irregularidades que antes no podían ser detectadas. La aplicación desarrollada ha sido validada con un total de ocho niños de cuatro años y siete meses de media de edad, confirmando que cumple con las expectativas planteadas. ABSTRACT. Writing is a very important psychomotor activity in child development because it will be present in all kinds of everyday situations; therefore its proper learning will determine the future of the individual. Dysgraphia is the term used to refer to the problems related to writing, and it takes place when a particular person’s writing is unreadable or slow-moving as a result of a failed learning of writing motor processes. These problems are usually detected by different tests that measure children’s visual motor abilities based on shape criteria (correct number and position of strikes). However, over the years these criteria haven’t proved to be completely accurate in the early detection of possible cases of dysgraphia. Therefore, in this project is presented an application that extends the reliability of current test, using a kinematic approach. This application, developed for an Android tablet, displays a series of figures that the child must copy to the tablet by using a stylus. Strokes recorded by the application are then analyzed to assess aspects such as fluidity, continuity and regularity, expanding the reliability of the current test, discarding false positives created by the conventional criteria and detecting irregularities that previously could not be detected. The developed application has been validated with a total of eight children about four years and seven months in average age, confirming that the application fulfills the initial expectations.

Bridge damage identification from moving load induced deflection based on wavelet transform and Lipschitz exponent

The wavelet transform and Lipschitz exponent perform well in detecting signal singularity.With the bridge crack damage modeled as rotational springs based on fracture mechanics, the deflection time history of the beam under the moving load is determined with a numerical method. The continuous wavelet transformation (CWT) is applied to the deflection of the beam to identify the location of the damage, and the Lipschitz exponent is used to evaluate the damage degree. The influence of different damage degrees,multiple damage, different sensor locations, load velocity and load magnitude are studied.Besides, the feasibility of this method is verified by a model experiment.

Mapping wide row crops with video sequences acquired from a tractor moving at treatment speed

This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for image sequence stabilization was needed and consequently designed and developed. The proposed stabilization method uses the centers of some crop rows in the image sequence as features to be tracked, which compensates for the lateral movement (sway) of the camera and leaves the pitch unchanged. A region of interest is selected using the tracked features, and an inverse perspective technique transforms the selected region into a bird’s-eye view that is centered on the image and that enables map generation. The algorithm developed has been tested on several video sequences of different fields recorded at different times and under different lighting conditions, with good initial results. Indeed, lateral displacements of up to 66% of the inter-row spacing were suppressed through the stabilization process, and crop rows in the resulting maps appear straight

Speed-independent gait identification for mobile devices

Due to the intensive use of mobile phones for diferent purposes, these devices usually contain condential information which must not be accessed by another person apart from the owner of the device. Furthermore, the new generation phones commonly incorporate an accelerometer which may be used to capture the acceleration signals produced as a result of owner s gait. Nowadays, gait identication in basis of acceleration signals is being considered as a new biometric technique which allows blocking the device when another person is carrying it. Although distance based approaches as Euclidean distance or dynamic time warping have been applied to solve this identication problem, they show di±culties when dealing with gaits at diferent speeds. For this reason, in this paper, a method to extract an average template from instances of the gait at diferent velocities is presented. This method has been tested with the gait signals of 34 subjects while walking at diferent motion speeds (slow, normal and fast) and it has shown to improve the performance of Euclidean distance and classical dynamic time warping.

On a two species chemotaxis model with slow chemical diffucion.

In this paper we consider a system of three parabolic equations modeling the behavior of two biological species moving attracted by a chemical factor. The chemical substance verifies a parabolic equation with slow diffusion. The system contains second order terms in the first two equations modeling the chemotactic effects. We apply an iterative method to obtain the global existence of solutions using that the total mass of the biological species is conserved. The stability of the homogeneous steady states is studied by using an energy method. A final example is presented to illustrate the theoretical results.

Low-Cost Radar-Based Target Identification Prototype using an Expert System

Smart and green cities are hot topics in current research because people are becoming more conscious about their impact on the environment and the sustainability of their cities as the population increases. Many researchers are searching for mechanisms that can reduce power consumption and pollution in the city environment. This paper addresses the issue of public lighting and how it can be improved in order to achieve a more energy efficient city. This work is focused on making the process of turning the streetlights on and off more intelligent so that they consume less power and cause less light pollution. The proposed solution is comprised of a radar device and an expert system implemented on a low-cost platform based on a DSP. By analyzing the radar echo in both the frequency and time domains, the system is able to detect and identify objects moving in front of it. This information is used to decide whether or not the streetlight should be turned on. Experimental results show that the proposed system can provide hit rates over 80%, promising a good performance. In addition, the proposed solution could be useful in kind of other applications such as intelligent security and surveillance systems and home automation.

Optimized HOG for on-road video based vehicle verification

Vision-based object detection from a moving platform becomes particularly challenging in the field of advanced driver assistance systems (ADAS). In this context, onboard vision-based vehicle verification strategies become critical, facing challenges derived from the variability of vehicles appearance, illumination, and vehicle speed. In this paper, an optimized HOG configuration for onboard vehicle verification is proposed which not only considers its spatial and orientation resolution, but descriptor processing strategies and classification. An in-depth analysis of the optimal settings for HOG for onboard vehicle verification is presented, in the context of SVM classification with different kernels. In contrast to many existing approaches, the evaluation is realized in a public and heterogeneous database of vehicle and non-vehicle images in different areas of the road, rendering excellent verification rates that outperform other similar approaches in the literature.

Vehicle induced loads on pedestrian barriers

This paper is a continuation of a previous one, Sanz-Andrés, Santiago-Prowald, Baker and Quinn (J. Wind Eng. Ind. Aerodyn. 91 (2003) 925) concerning the loads generated on a structural panel (traffic sign) by vehicle running along the road, although obviously, the results are also applicable to the effects of other moving vehicles such as trains. The structural panel was modelized as a large plate whose largest dimension is perpendicular to the vehicle motion direction. In this paper a similar approach is used to develop a mathematical model for the vehicle-induced load on pedestrian barriers, modelized as a large plate whose largest dimension is parallel to the vehicle motion direction. The purpose of the work is to develop a model simple enough to give analytical results, although with the physical phenomena correctly accounted for, such as to be able to explain, at least qualitatively, the main characteristics of the phenomenon, as observed in the experiments performed by Quinn et al. (J. Wind Eng. Ind. Aerodyn. 89 (2001) 831). Actually, in spite of the model simplicity, results of the theoretical model show a reasonable good quantitative agreement with the experimental results. The aim of this and previous publications is to provide to the transport infrastructure community with some simple tools that can help to explain, and in some cases also to compute, the unsteady loading produced by moving vehicles on persons and installations placed close to the roads or tracks.

Vehicle-induced force on pedestrians

The main object of this study is to contribute to the study of the train-induced force on pedestrians with a theoretical model based on unsteady potential flow. The same method can be applied to other bodies and other kind of moving vehicles. The outcome of this theoretical model is that the force coefficient (referred to the vehicle speed and the pedestrian cross-section diameter) acting on the pedestrian are proportional to a single parameter which involves the pedestrian cross-section diameter, the vehicle cross-section area and the distance between the pedestrian and the vehicle. The results of the present model concerning the change in modulus and orientation experienced by the pedestrian, as the vehicles pass by, has a similar appearance to that considered in the European standards. The results obtained are mainly qualitative because of the simplifying assumptions needed to obtain a simple formulation leading to analytical results, except in the case of a vehicle with streamlined front shapes, where quantitative results can be expected.

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.