Isolating key features in urban traffic dynamics and noise emission: a study on a signalized intersection and a roundabout

Urban planning and transport network are considered as major urban systems with great impact on the sound environment. Most of the work done in transport management and traffic design to improve the quality of both outdoor and indoor sound environment relies on conventional noise mapping software outcomes. This type of tool is based on macroscopic traffic modelling, considering traffic flow as a steady noise source. A commonly implemented practice intended to reduce noise in urban areas is the transformation of a signalised crossing into a roundabout. However, the individual vehicle behaviour becomes relevant in these decisions, where high time-pattern fluctuations are responsible for changes in the quality of the urban sound environment and of human activity. The present paper studies a set of indicators from isolated key features in these two road traffic configurations and their possible variations (acceleration, heavy vehicles, etc.). A VISSIM microscopic traffic simulation model combined with the CNOSSOS-EU noise emission model is used to test cases based on real situations, now in development stage. The approach presented aims to provide stronger basis in the reasoning behind why different road traffic configurations adopted in the urban planning practice give certain effects in relation to the urban sound environment

Background traffic noise synthesis

When planning the development of urban areas, it is important to assess the future acoustic environment. Currently, this evaluation is achieved with the help of acoustic indicators, but they do not suffice for a holistic description of the perceived sound environment. New indicators can be extracted through listening tests and analysis of different acoustic scenarios. However, generating such scenarios using auralisation models for outdoors sound propagation is often computationally highly demanding. Here, a simplified auralisation model is described, focusing on background traffic noise simulation on flat city scenarios. For computational efficiency, the proposed method partly relies on physical models for air attenuation, ground effects and spherical spreading. The doppler effect and the contribution of individual vehicle pass-bys are achieved with the help of modulation transfer functions, and spatial imagery is realised by both non-corellated phase spectra and modulation transfer functions. Power profiles from measurements are used to model rolling noise. The proposed model is assessed through listening tests against the LISTEN demonstrator on its perceived speed and distance from the listener. The perceived speed is matching better to the LISTEN between 70 kmph and 90 kmph, while above 300 m and up to 900 m from the source, the distance is more correctly guessed from the subjects

Novelty detection for risk-based user authentication on mobile devices

User authentication acts as the first line of defense verifying the identity of a mobile user, often as a prerequisite to allow access to resources in a mobile device. For several decades, user authentication was based on the “something the user knows”, known also as knowledge-based user authentication. Recent studies state that although knowledge-based user authentication has been the most popular for authenticating an individual, nowadays it is no more considered secure and convenient for the mobile user as it is imposing several limitations. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Toward this direction, user authentication based on the “something the user is” has caught the attention. This category includes authentication methods which make use of human physical characteristics (also referred to as physiological biometrics), or involuntary actions (also referred to as behavioral biometrics). In particular, risk-based user authentication based on behavioral biometrics appears to have the potential to increase mobile authentication security without sacrificing usability. In this context, we, firstly, present an overview of user authentication on mobile devices and discuss risk-based user authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Afterwards, a set of novelty detection algorithms for risk estimation is tested and evaluated to identify the most appropriate ones for risk-based user authentication on mobile devices