Towards Comfortable Cycling: A Practical Approach to Monitor the Conditions in Cycling Paths

This is a no brainer. Using bicycles to commute is the most sustainable form of transport, is the least expensive to use and are pollution-free. Towns and cities have to be made bicycle-friendly to encourage their wide usage. Therefore, cycling paths should be more convenient, comfortable, and safe to ride. This paper investigates a smartphone application, which passively monitors the road conditions during cyclists ride. To overcome the problems of monitoring roads, we present novel algorithms that sense the rough cycling paths and locate road bumps. Each event is detected in real time to improve the user friendliness of the application. Cyclists may keep their smartphones at any random orientation and placement. Moreover, different smartphones sense the same incident dissimilarly and hence report discrepant sensor values. We further address the aforementioned difficulties that limit such crowd-sourcing application. We evaluate our sensing application on cycling paths in Singapore, and show that it can successfully detect such bad road conditions.Comment: 6 pages, 5 figures, Accepted by IEEE 4th World Forum on Internet of Things (WF-IoT) 201

“Uso de algoritmos para la identificación de imperfecciones en la calzada: Un mapeo sistemático”

La gran mayoría de los accidentes de tránsito son provocados por las imperfecciones en la calzada, por este motivo se ha ido adaptando diferentes algoritmos de inteligencia artificial para su detección. El propósito de este trabajo se centra en el desarrollo de un análisis de la literatura del periodo comprendido entre las dos últimas décadas que incluye temas relacionados con el uso de algoritmos de inteligencia artificial para la identificación de imperfecciones en la calzada. La metodología empleada en este trabajo se basa en técnicas de Mapeo Sistemático, un proceso que consta de tres etapas: Definiciones de Protocolo, Ejecuciones de Búsqueda y Discusión de Resultados. Como resultado de este análisis, se obtuvieron 74 artículos relevantes de acuerdo a los criterios de inclusión donde se proponen 41 algoritmos y tres enfoques de identificación de imperfecciones en la calzada, con porcentajes de exactitud desde el 95.45% hasta el 99.8%. Mismos que fueron obtenidos de repositorios como SciencieDirect, IEEE y Scopus.The vast majority of traffic accidents are caused by imperfections in the road, for this reason different artificial intelligence algorithms have been adapted for their detection. The purpose of this work is focused on the development of an analysis of the literature of the period between the last two decades that includes topics related to the use of artificial intelligence algorithms for the identification of imperfections in the road. The methodology used in this work is based on Systematic Mapping techniques, a process that consists of three stages: Protocol Definitions, Search Executions and Results Discussion. As a result of this analysis, 74 relevant articles were obtained according to the inclusion criteria where 41 algorithms and three approaches to identify imperfections in the road are proposed, with percentages of accuracy from 95.45% to 99.8%. The same ones that were obtained from repositories such as SciencieDirect, IEEE and Scopus

Using Sensor Redundancy in Vehicles and Smartphones for Driving Security and Safety

The average American spends around at least one hour driving every day. During that time the driver utilizes various sensors to enhance their commute. Approximately 77% of smartphone users rely on navigation apps daily. Consumer grade OBD dongles that collect vehicle sensor data to monitor safe driving habits are common. Existing sensing applications pertaining to our drive are often separate from each other and fail to learn from and utilize the information gained by other sensing streams and other drivers. In order to best leverage the widespread use of sensing capabilities, we have to unify and coordinate the different sensing streams in a meaningful way. This dissertation explores and validates the following thesis: Sensing the same phenomenon from multiple perspectives can enhance vehicle safety, security and transportation. First, it presents findings from an exploratory study on unifying vehicular sensor streams. We explored combining sensory data from within one vehicle through pairwise correlation and across multiple vehicles through normal models built with principal component analysis and cluster analysis. Our findings from this exploratory study motivated the rest of this thesis work on using sensor redundancy for CAN-bus injection detection and driving hazard detection. Second, we unify the phone sensors with vehicle sensors to detect CAN bus injection attacks that compromise vehicular sensor values. Specifically, we answer the question: Are phone sensors accurate enough to detect typical CAN bus injection attacks found in literature? Through extensive tests we found that phone sensors are sufficiently accurate to detect many CAN-bus injection attacks. Third, we construct GPS trajectories from multiple vehicles nearby to find stationary and mobile driving hazards such as a bicyclist on the side of the road. Such a tool will effectively extend the repertoire of current navigation assistant applications such as Google Maps which detect and warn drivers about upcoming stationary hazards. Finally, we present an easy-to-use tool to help developers and researchers quickly build and prototype data-collection apps that naturally exploit sensing redundancy. Overall, this thesis provides a unified basis for exploiting sensing redundancy existing inside a single vehicle as well as between different vehicles to enhance driving safety and security.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155154/1/arungan_1.pd

Creating intelligible metrics road traffic analysis

Dissertação de mestrado em Computer ScienceThe increasing pervasiveness and lower cost of electronic devices equipped with sensors is leading to a greater and cheaper availability of localized information. The advent of the internet has brought phenomena such as crowd-sourced maps and related data. The combination of the availability of mobile information, community built maps, with the added convenience of retrieving information over the internet creates the opportunity to contextualize data in new ways. This work takes that opportunity and attempts to generalize the detection of driving events which are deemed problematic as a function of contextual factors, such as neighbouring buildings, areas, amenities, the weather, and the time of day, week or month. In order to research the problem at hand, the issue is first contextualized properly, providing an overview of important factors, namely Smart Cities, Data Fusion, and Machine Learning. That is followed by a chapter concerning the state of the art, that showcases related projects and how the various facets of road traffic expression are being approached. The focus is then turned to creating a solution. At first this consists in aggregating data so as to create a richer context than would be present otherwise, this includes the retrieval from different services, as well as the composition of a unique view of the same driving situation with new dimensions added to it. And then Models were created using different Machine Learning methods, and a comparison of results according to selected and justified evaluation metrics was made. The compared Methods are Decision Tree, Naive Bayes, and Support Vector Machine. The different types of information were evaluated on their own as potential classifiers and then were evaluated together, leading to the conclusion that the various types combined allow for the creation of better models capable of finding problems with more confidence in such results. According to the tests performed the chosen approach can improve the performance over a baseline approach and point out problematic situations with a precision of over 90%. As expected by not using factors concerning the driver state or acceleration the scope of problems which are detected is limited in domain.A expansão e menor custo de dispositivos eletrónicos equipados com sensores está a levar a uma maior e mais barata disponibilidade de informação localizada. O advento da internet criou fenómenos como a criação de mapas e dados relacionados gerados por comunidades. A combinação da disponibilidade de informação móvel e mapas construídos pela comunidade, em conjunto com uma obtenção de informação através da internet mais conveniente, criou a oportunidade de contextualizar os dados de novas maneiras. Este trabalho faz uso dessa oportunidade e tenta generalizar eventos de condução que são considerados problemáticos em função de factores contextuais, tais como a presença de edifícios, áreas, e comodidades na vizinhança, o clima, e a hora do dia, a semana, ou o mês. De modo a investigar esta questão, o problema é contextualizado como emergente no tópico de Cidades Inteligentes, e explorado com recurso a Fusão de Dados e a Aprendizagem Máquina. O estado da arte é exposto, através de projectos relacionados à expressão do tráfego rodoviário, dando relevo às várias facetas até então investigadas por outros autores de modo a enquadrar o trabalho presente. Dado o enquadramento e concretização do problema, é proposta uma solução. Esta solução passa por inicialmente agregar dados de modo a enriquecer o contexto, incluindo a recolha destes de vários serviços, e uma composição dos dados recolhidos numa perspectiva única referente a uma situação de condução. Após este enriquecimento dos dados, são criados modelos com base em diferentes técnicas de Aprendizagem Máquina. Os métodos utilizados são Decision Tree, Naive Bayes, e Support Vector Machine. Os resultados conseguidos com estes modelos são depois comparados de acordo com as métricas de avaliação seleccionadas. Uma comparação foi feita também com diferentes tipos de informação separadamente e também em conjunto, levando à conclusão de que os vários tipos combinados permitem a criação de melhores modelos capazes de encontrar problemas com mais confiança nos resultados produzidos. De acordo com os testes executados a abordagem escolhida consegue melhorar resultados de um modelo base e descobrir situações problemáticas de condução com uma precisão acima dos 90%. No entanto, como seria de esperar, o âmbito dos problemas detectados tem um domínio limitado aos aspectos seleccionados

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

A Corpus-driven Approach toward Teaching Vocabulary and Reading to English Language Learners in U.S.-based K-12 Context through a Mobile App

In order to decrease teachers’ decisions of which vocabulary the focus of the instruction should be upon, a recent line of research argues that pedagogically-prepared word lists may offer the most efficient order of learning vocabulary with an optimized context for instruction in each of four K-12 content areas (math, science, social studies, and language arts) through providing English Language Learners (ELLs) with the most frequent words in each area. Educators and school experts have acknowledged the need for developing new materials, including computerized enhanced texts and effective strategies aimed at improving ELLs’ mastery of academic and STEM-related lexicon. Not all words in a language are equal in their role in comprehending the language and expressing ideas or thoughts. For this study, I used a corpus-driven approach which is operationalized by applying a text analysis method. For the purpose of this research study, I made two corpora, Teacher’s U.S. Corpus (TUSC) and Science and Math Academic Corpus for Kids (SMACK) with a focus on word lemma rather than inflectional and derivational variants of word families. To create the corpora, I collected and analyzed a total of 122 textbooks used commonly in the states of Florida and California. Recruiting, scanning and converting of textbooks had been carried out over a period of more than two years from October 2014 to March 2017. In total, this school corpus contains 10,519,639 running words and 16,344 lemmas saved in 16,315 word document pages. From the corpora, I developed six word lists, namely three frequency-based word lists (high-, mid-, and low-frequency), academic and STEM-related word lists, and essential word list (EWL). I then applied the word lists as the database and developed a mobile app, Vocabulary in Reading Study – VIRS, (available on App Store, Android and Google Play) alongside a website (www.myvirs.com). Also, I developed a new K-12 dictionary which targets the vocabulary needs of ELLs in K-12 context. This is a frequency-based dictionary which categorizes words into three groups of high, medium and low frequency words as well as two separate sections for academic and STEM words. The dictionary has 16,500 lemmas with derivational and inflectional forms

The Playful Citizen

This edited volume collects current research by academics and practitioners on playful citizen participation through digital media technologies

The Playful Citizen

This edited volume collects current research by academics and practitioners on playful citizen participation through digital media technologies