Improvements in the acoustical modelling of traffic noise prediction: theoretical and experimental results

2009 - 2010Traffic acoustical noise is one of the most important component of the urban environmental pollution in densely populated areas all over the world. A very recent ACI-Censis study [1] on Italian urban areas shows that car is the favorite mean of transportation for 90% of population. In particular, this study shows that during years ranging from 2000 to 2007, the number of circulating vehicles is grown of 14.5%. To this growth did not always correspond an improvement of national street network. This problem can be evidenced by the high growth of the traffic charge on urban, sub-urban and extra-urban roads, with a clear impact on costs, security and environment, even in term of acoustical noise. A similar tendency can be observed in the framework of many european countries. Traffic noise affects areas surrounding roads especially when high traffic load and high speed conditions occur and can lead to a degradation of the quality of life in residential areas. The impact of noise on mental and physical health and on daily activities has been widely documented in the scientific literature [2, 3, 4]. In particular a continuous exposure to acoustical noise may affect sleep and/or conversation, may lead to perception of annoyance, may cause hearing loss, cardiovascular problems etc. As a consequence, during last years, a large number of anti-noise laws, ordinances and regulations were decreed by many national governments and international institutions. Looking to Italy, it is the D.P.C.M. 01.03.1991 [5] which regulates noise pollution matters, giving the main acoustical elements definitions such as maximum limit of noise exposure in inner and external environment, acoustic zoning criteria, etc. Then the Framework Law n. 447/1995 has defined a general policy on the noise pollution that has been implemented in different decrees and regulations. Among these, one of the most interesting is the D.M.A. 16.03.1998 ”Noise pollution detection and measurement method” (Tecniche di rilevamento e di misurazione dell’inquinamento acustico) which deals with the vehicular and railway noise detection procedure. Moreover the D.Lgs 194/2005 (Attuazione della direttiva 2002/49/CE relativa alla determinazione e alla gestione del rumore ambientale) establishes the method to set the acoustic indicator for the different kind of noise sources such as vehicular traffic. In this Ph.D. thesis our aim is to improve the current prediction tools for traffic noise prediction in non trivial situations such as traffic lights, traffic jam, intersections etc., accounting some aspects of traffic dynamics by the use of traffic models (TM), i.e. following the leader model and Cellular Automata. This thesis is organized as follows. In the first chapter we briefly discuss the main features of sound and noise propagation. In the second chapter we focus our attention on vehicle noise emission and existing traffic noise models (TNM) while in the third we present a new noise prediction procedure: GERIAN2009. In chapter four some general features of physics of road traffic and transportation are discussed. In the last three chapters we propose an integration of traffic noise model and traffic dynamic model in the ”following the leader” and Cellular Automata (CA) framework, with a particular attention on road’s intersection issue. [edited by author]IX n.s

Advanced Information Processing Methods and Their Applications

This Special Issue has collected and presented breakthrough research on information processing methods and their applications. Particular attention is paid to the study of the mathematical foundations of information processing methods, quantum computing, artificial intelligence, digital image processing, and the use of information technologies in medicine

Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events

Optimal traffic control under incident-driven congestion is crucial for road safety and maintaining network performance. Over the last decade, prediction and simulation of road traffic play important roles in network operation. This dissertation focuses on development of a machine learning-based prediction model, a stochastic cell transmission model (CTM), and an optimisation model. Numerical studies were performed to evaluate the proposed models. The results indicate that proposed models are helpful for road management during road incidents

A Human Operator Model for Medical Device Interaction Using Behavior-Based Hybrid Automata

This paper describes the design and implementation of a control-theoretic model that can be used to model both the discrete and continuous behavior of a human operator. The human operator model can be used to compare different device user interfaces in terms of human performance. The implemented human operator model combines an ON–OFF control model and a behavior-based hybrid automaton with three controllers. The controllers, defined as continuous, discrete, and fine-tuning behavior, simulate the user’s conceptual model of the user interface. The device model used is that of a commercial syringe pump with chevron keys, described as a formal specification. Results of the human operator model simulation were generated for 20 different numbers obtained from syringe pump log files. The simulation results werecompared over 33 trials to a lab study employing a device based on the formal specification. The result of the simulation shows a significant similarity to the result of the lab study for all the numbers used

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

PREDICTIVE ENERGY MANAGEMENT IN SMART VEHICLES: EXPLOITING TRAFFIC AND TRAFFIC SIGNAL PREVIEW FOR FUEL SAVING

This master thesis proposes methods for improving fuel economy and emissions of vehicles via use of future information of state of traffic lights, traffic flow, and deterministic traffic flow models. The first part of this thesis proposes use of upcoming traffic signal information within the vehicle\u27s adaptive cruise control system to reduce idle time at stop lights and lower fuel use. To achieve this goal an optimization-based control algorithm is formulated for each equipped vehicle that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle. The objectives are timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed. Three example simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time. The second part of this thesis addresses the use of traffic flow information to derive the fuel- or time-optimal velocity trajectory. A vehicle\u27s untimely arrival at a local traffic wave with lots of stops and goes increases its fuel use. This paper proposes predictive planning of the vehicle velocity for reducing the velocity transients in upcoming traffic waves. In this part of the thesis macroscopic evolution of traffic pattern along the vehicle route is first estimated by combining a traffic flow model and real-time traffic data streams. The fuel optimal velocity trajectory is calculated by solving an optimal control problem with the spatiotemporally varying constraint imposed by the traffic. Simulation results indicatethe potential for considerable improvements in fuel economy with a little compromise on travel time