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Wednesday, November 18, 2020 | History

2 edition of Road traffic noise:a computer prediction method. found in the catalog.

Road traffic noise:a computer prediction method.

Abdulrahman Akil M. Al-Janahi

Road traffic noise:a computer prediction method.

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  • 14 Currently reading

Published in Bradford .
Written in English


Edition Notes

M.Sc.dissertation.Typescript.

SeriesDissertations
ID Numbers
Open LibraryOL13979549M

History. Attempts to produce a mathematical theory of traffic flow date back to the s, when Frank Knight first produced an analysis of traffic equilibrium, which was refined into Wardrop's first and second principles of equilibrium in Nonetheless, even with the advent of significant computer processing power, to date there has been no satisfactory general theory that can be. Structural health monitoring (SHM) refers to the process of implementing a damage detection and characterization strategy for engineering structures such as bridges and buildings. Here damage is defined as changes to the material and/or geometric properties of a structural system, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance.


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Road traffic noise:a computer prediction method. by Abdulrahman Akil M. Al-Janahi Download PDF EPUB FB2

It details assessment methods for perceived noise, and outlines noise prediction methods that can be integrated with noise mapping software. It also explores the economic benefits and positive effects on urban air quality and CO2 levels.

The material is this book: Includes up-to-date results on noise mitigation using vegetation and ground. Samuels () developed a method for the prediction of traffic noise at relatively uncomplicated signalized intersections.

Measured and predicted traffic noise levels were compared at selected intersections in Australia and New by: 7. Traffic noise prediction models are required as aids in the design of highways and other roads and sometimes in the assessment of existing or envisaged changes in traffic noise conditions.

They are commonly needed to predict sound pressure levels, specified in terms of L eq L10, etc., set by government by:   When assessing the effects of road traffic noise on sleep in a laboratory context, the noise offered consists of road traffic noise recorded in the field, which is then reproduced, calibrated and/or manipulated (noise levels, number of passages, etc.) so as to offer the desired noise by:   The methodology is implemented using data for accident counts on the Swiss national roads from to It has been found that ANNs can be used as a viable method to predict the frequency of road accidents.

As accident occurrences are relatively rare events, the data are characterised by a large portion of zero by: 5. Smart Cities: Big Data Prediction Methods and Applications is the first reference to provide a comprehensive overview of smart cities with the latest big data predicting techniques.

This timely book discusses big data forecasting for smart cities. It introduces big data forecasting techniques for the key aspects (e.g., traffic, environment, building energy, green grid, etc.) of smart cities. The data was collected from road traffic accident from the real-world data collected at accident time in Addis Ababa City/Ethiopia.

Totally datasets with 20 attributes were used. " Noise of passenger cars - - The paradox of a 2 dB (A) increase in traffic noise and a 8 dB (A) decrease in type approval limits " Executive summary of interim results of VROM project-M + P Raadgevende ingenieurs b.

The real-world dataset is collected in Xuancheng, Anhui Province, China. As shown in Fig. 2(a), the partial road network in the downtown area with fully covered surveillance cameras is selected. Fig. 2(b) shows the topology of the network, which contains 9 intersections and 24 road 5-min traffic volume is obtained from the records of surveillance cameras, which.

The main purpose of this research is to develop a dynamic travel time prediction model for road networks. In this paper we propose a new method to predict travel times using Naïve Bayesian Classification (NBC) model because Naïve Bayesian Classification has exhibited high accuracy and speed when applied to large databases.

Traffic prediction, as an important part of intelligent transportation systems, plays a critical role in traffic state monitoring. While many studies accomplished traffic forecasting task with deep learning models, there is still an open issue of exploiting spatial-temporal traffic state features for better prediction performance, and the model interpretability has not been taken serious.

The Calculation of Road Traffic Noise (CRTN) prediction model was employed to predict noise levels at the locations chosen for the study.

Data required for the model include traffic volume, speed, percentage of heavy vehicles, road surface, gradient, obstructions, distance, noise path, intervening ground, effect of shielding, and angle of view.

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the.

Local traffic volume was the most important predictor of noise; road features, land use, and meteorology including humidity, temperature, and wind speed also contributed. We show that a novel, on-foot mobile noise measurement method coupled with machine learning approaches enables highly accurate prediction of small-scale spatial patterns in.

Abstract. The aim of this paper is to provide a comprehensive overview of the state of the art on railway-induced ground vibration. The governing physical mechanisms, prediction methods, and mitigation measures of ground-borne vibration are discussed, with focus on low frequency feelable vibration and the case of railway traffic at grade.

HOG feature extraction was based on 9 orientations, 8 pixels per cell and 2 cells per block. Increasing orientations and pixel per cell parameters did improve prediction time but the accuracy rate of the model went down.

Model training. In order to detect the car based on our feature set, we would need a prediction model. The complexity of the 3D buildings and road networks gives the simulation of urban noise difficulty and significance. To solve the problem of computing complexity, a systematic methodology for computing urban traffic noise maps under 3D complex building environments is presented on a supercomputer.

A parallel algorithm focused on controlling the compute nodes of the supercomputer is designed. Book Search tips Selecting this option will search all publications across the Scitation and F.

Besnard, “ The revision of the French method for road traffic noise prediction,” J. Acoust. Soc. (5), (). https (PSOLA),” in Proceedings of the International Computer Music Conference, Berlin, Germany.

Short-term traffic speed forecasting is an important issue for developing Intelligent Transportation Systems applications. So far, a number of short-term speed prediction approaches have been developed. Recently, some multivariate approaches have been proposed to consider the spatial and temporal correlation of traffic data.

This study uses classification algorithms to establish models to predict the severity of crash injuries when motorcycle crashes occur. In this study, the power of multi-layer perceptron (MLP), rule induction (PART) and classification and regression trees (SimpleCart) models for predicting the severity of motorcycle crash was evaluated by comparing their results.

According to the World Health Organization, high levels of exposure to road traffic noise are associated with adverse health effects. Earlier studies suggest that cyclists are exposed to higher noise levels than motorists. Other studies have demonstrated that cyclists’ exposure to noise could vary significantly according to their routes.

The aim of this study is to compare cyclists. The collected data are highly needed to make serious traffic decisions such as rerouting, safe-driving decision, etc. With this rich volume and velocity of data, it is challenging to build reliable prediction models based on traditional relational database and machine learning methods.

Tire road noise is the major contributor to traffic noise, which leads to general annoyance, speech interference, and sleep disturbances. Standardized methods to measure tire road noise are expensive, sophisticated to use, and they cannot be applied comprehensively.

This paper presents a method to automatically classify different types of pavement and the wear condition to identify noisy road.

Advanced neural network training methods for low false alarm stock trend prediction E.W. Saad, D.V. Prokhorov and D.C. Wunsch A hybrid neural networks based machine condition forecaster and classifier by using multiple vibration parameters. Owing to the non-stationary nature of road traffic, forecasting accuracy is fundamentally limited by the lack of contextual information.

To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume. Road transportation is the backbone of modern economies, albeit it annually costs million deaths and trillions of dollars to the global economy, and damages public health and the environment.

Deep learning is among the leading-edge methods used for transportation-related predictions, however, the existing works are in their infancy, and fall short in multiple respects, including the use.

The GPS data we used in this paper are Floating Car Data (FCD). FCD is a method to determine the traffic speed on the road network. It is based on the collection of localization data, speed, and direction of travel and time information from mobile phones or GPS devices in vehicles being driven.

The FCD are the essential source for traffic. Hobeika and C. Kim. Traffic-flow-prediction systems based on upstream traffic. In Vehicle Navigation and Information Systems Conference, Proceedings.,pagesAug Google Scholar Cross Ref; C.

Holt. Forecasting seasonals and trends by exponentially weighted moving averages. An accurate road surface friction forecasting algorithm can allow travelers and managers to schedule trips and maintenance activities based on the road weather condition to enhance traffic safety and efficiency in advance.

Previously, scholars developed multiple forecasting models to predict road surface conditions using historical data. Traffic has been growing in major cities around the world given the increase in densities of cars on roads and the slow development of road infrastructure.

With research starting inresearch scientist and developer teams at Microsoft Research pioneered the use of machine learning methods to build predictive models for traffic. The noise levels were calculated by the City of Oslo according to the Nordic Prediction Method for Road Traffic and Railway Noise, respectively [29,30,31,32].

Geographic information system (GIS) methodology was applied in the software package CadnaA. The grids for the noise calculations were 5 × 5 m and calculation height was 4 m above terrain. A new machine learning algorithm is poised to help urban transportation analysts relieve bottlenecks and chokepoints that routinely snarl city traffic.

The tool, called TranSEC, was developed at. Noise, vibration, and harshness (NVH), also known as noise and vibration (N&V), is the study and modification of the noise and vibration characteristics of vehicles, particularly cars and noise and vibration can be readily measured, harshness is a subjective quality, and is measured either via "jury" evaluations, or with analytical tools that can provide results reflecting human.

Traffic flow prediction based on a time series method is a widely used traffic flow prediction technology. Levin and Tsao applied Box-Jenkins time series analysis to predict highway traffic flow and found that the ARIMA (0, 1, 1) model was useful in the prediction of the most statistically significant [ 17 ].

This paper presents a case study of real-time traffic state estimation. The adopted general approach to the design of universal traffic state estimators for freeway stretches is based on stochastic macroscopic traffic flow modeling and extended Kalman.

Attempts at the practical on-road driving test and the hazard perception test and the risk of traffic crashes in young drivers. Traffic Injury Prevention, 12, – doi: / CiteScore: ℹ CiteScore: CiteScore measures the average citations received per peer-reviewed document published in this title.

CiteScore values are based on citation counts in a range of four years (e.g. ) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of.

Numerical Weather Prediction (NWP) data are the form of weather model data we are most familiar with on a day-to-day basis. NWP focuses on taking current observations of weather and processing these data with computer models to forecast the future state of weather. The experiments show that the proposed method can reach as high as % accuracy at the average level for traffic flow prediction based on only – relevant sensors selected through sparse representation as the input, which outperforms remarkably the least squared fitting method and the methods by confining the spatial context into just.

The prediction of path loss from the two-ray ground model gradually starts to “exaggerate” as distances between the communicating vehicles increase, until it coincides with the free-space model and CONER. Therefore, all three models (i.e. free-space model, two-ray ground model and CORNER) are not suitable to predict path loss in a road tunnel.

In Part 4 and Part 5 of the blog series, we discussed lane detection and navig a tion. A true autonomous vehicle would also need to be aware of its surroundings at all times. In this article, we will discuss another important perception feature, namely, detecting traffic signs and this feature is not available in any vehicles, except maybe Tesla.

A dB higher level of exposure to road traffic noise at the current residence and during the previous 5 years was associated with statistically significant 8% (95% CI:) and 11% (95% CI:) higher risk of incident diabetes, respectively, based on fully adjusted models (model 3, Table 2).The analysis of road traffic noise as a categorical variable was generally consistent.IDC examines consumer markets by devices, applications, networks, and services to provide complete solutions for succeeding in these expanding markets.