Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices

Exploiting road traffic data for very short term load forecasting in smart grids

If accurate short term prediction of electricity consumption is available, the Smart Grid infrastructure can rapidly and reliably react to changing conditions. The economic importance of accurate predictions justifies research for more complex forecasting algorithms. This paper proposes road traffic data as a new input dimension that can help improve very short term load forecasting. We explore the dependencies between power demand and road traffic data and evaluate the predictive power of the added dimension compared with other common features, such as historical load and temperature profiles

The europeanization of the common road safety policy: an econometric analysis

The 2001 White Paper and its development in the 3rd European Road Safety Action Program, represent a turning point in the history of the European Road Safety Policy. The possible determinants of the road mortality in the EU over (2000-2009) are examined using a panel data. Our main finding is the negative effect and statistical significance of the Europeanization variable (the number of years that a country has been in the EU). By this variable, we test the effectiveness of EU programs to save lives in road accidents according to the years that each country has been in the EU

Nuevo marco para utilizar la minería de datos y reglas de asociación para la clasificación de la gravedad de accidentes de tráfico

Introduction: Traffic accidents are an undesirable burden on society. Every year around one million deaths and more than ten million injuries are reported due to traffic accidents. Hence, traffic accidents prevention measures must be taken to overcome the accident rate. Different countries have different geographical and environmental conditions and hence the accident factors diverge in each country. Traffic accident data analysis is very useful in revealing the factors that affect the accidents in different countries. This article was written in the year 2016 in the Institute of Technology & Science, Mohan Nagar, Ghaziabad, up, India. Methology: We propose a framework to utilize association rule mining (arm) for the severity classification of traffic accidents data obtained from police records in Mujjafarnagar district, Uttarpradesh, India. Results: The results certainly reveal some hidden factors which can be applied to understand the factors behind road accidentality in this region. Conclusions: The framework enables us to find three clusters from the data set. Each cluster represents a type of accident severity, i.e. fatal, major injury and minor/no injury. The association rules exposed different factors that are associated with road accidents in each category. The information extracted provides important information which can be employed to adapt preventive measures to overcome the accident severity in Muzzafarnagar district.Introducción: los accidentes de tránsito son una carga indeseable para la sociedad. Cada año se reportan alrededor de un millón de muertes y más de diez millones de lesiones debido a accidentes de tráfico. Por lo tanto, se deben implementar medidas de prevención de accidentes de tráfico para superar la tasa de accidentalidad. Los países tienen diferentes condiciones geográficas y ambientales y, por ello, las variables que inciden varían en cada país. El análisis de los datos de accidentes de tráfico es muy útil para revelar los factores o variables que inciden en la accidentalidad en diferentes países. Este artículo fue escrito en el 2016 en el Instituto de Tecnología y Ciencia, Mohan Nagar, Ghaziabad, UP, India. Metodología: proponemos un marco para utilizar la minería de datos y reglas de asociación (arm) para la clasificación de severidad de los datos de accidentes de tráfico obtenidos de registros policiales en eldistrito de Mujjafarnagar, Uttarpradesh, India Resultados: los resultados revelan ciertamente algunos factores ocultos que se pueden aplicar para entender las variables detrás de la accidentalidad de tráfico en esta región. Conclusiones: el marco permite establecer tres categorías en el conjunto de datos que representan el tipo de gravedad del accidente: fatal, lesiones graves, y lesiones menores o inexistentes. Las reglas de asociación expusieron diferentes factores relacionados con los accidentes de tráfico en cada categoría. Los datos extraídos proporcionan información importante que se puede emplear para adaptar las medidas preventivas para superar la gravedad de los accidentes de tráfico en el distrito de Muzzafarnagar