A traffic noise model for road intersections in the city of Cartagena de Indias,Colombia

Road traffic noise models are fundamental tools for designing and implementing appropriate prevention plans to minimize and control noise levels in urban areas. The objective of this study is to develop a traffic noise model to simulate the average equivalent sound pressure level at road intersections based on traffic flow and site characteristics, in the city of Cartagena de Indias (Cartagena), Colombia. Motorcycles are included as an additional vehicle category since they represent more than 30% of the total traffic flow and a distinctive source of noise that needs to be characterized. Noise measurements are collected using a sound level meter Type II. The data analysis leads to the development of noise maps and a general mathematical model for the city of Cartagena, Colombia, which correlates the sound levels as a function of vehicle flow within road intersections. The highest noise levels were 79.7 dB(A) for the road intersection María Auxiliadora during the week (business days) and 77.7 dB(A) for the road intersection India Catalina during weekends (non-business days). Although traffic and noise are naturally related, the intersections with higher vehicle flow did not have the highest noise levels. The roadway noise for these intersections in the city of Cartagena exceeds current limit standards. The roadway noise model is able to satisfactorily predict noise emissions for road intersections in the city of Cartagena, Colombia.     

A new probability statistical model for traffic noise prediction on free flow roads and control flow roads

A new traffic noise prediction approach based on a probability distribution model of vehicle noise emissions and achieved by Monte Carlo simulation is proposed in this paper. The probability distributions of the noise emissions of three types of vehicles are obtained using an experimental method. On this basis, a new probability statistical model for traffic noise prediction on free flow roads and control flow roads is established. The accuracy of the probability statistical model is verified by means of a comparison with the measured data, which has shown that the calculated results of L_eq, L₁₀, L₅₀, L₉₀, and the probability distribution of noise level occurrence agree well with the measurements. The results demonstrate that the new method can avoid the complicated process of traffic flow simulation but still maintain high accuracy for the traffic noise prediction.     

Noise prediction for highways in Thailand

A model of highway traffic noise is formulated based on vehicle types. The data were collected from local highways in Thailand with free-flow traffic conditions. First, data on vehicle noise was collected from individual vehicles using sound level meters placed at a reference distance. Simultaneously, measurements were made of vehicles’ spot speeds. Secondly, are data for building the highway traffic noise model. This consists of traffic noise levels, traffic volumes by vehicle classification, average spot speeds by vehicle type, and the geometric dimension of highway sections. The free-flow traffic noise model is generated from this database. A reference energy mean emission level (the basic noise) level for each type of vehicles is developed based on direct measurement of Leq (10 s) from the real running condition of each type of vehicles. Modification of terms and parameters are used to make the model fit highway traffic characteristics and different types of vehicle.     

Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles

In this paper, a forward power-train plug-in hybrid electric vehicle model with an energy management system and a cycle optimization algorithm is evaluated for energy efficiency. Using wirelessly communicated predictive traffic data for vehicles in a roadway network, as envisioned in intelligent transportation systems, traffic prediction cycles are optimized using a cycle optimization strategy. This resulted in a 56-86% fuel efficiency improvements for conventional vehicles. When combined with the plug-in hybrid electric vehicle power management system, about 115% energy efficiency improvements were achieved. Further improvements in the overall energy efficiency of the network were achieved with increased penetration rates of the intelligent transportation assisted enabled plug-in hybrid electric vehicles.     

Modelling of the pavement acoustic longevity in Hong Kong through machine learning techniques

Traffic noise emission has long been a pervasive environmental and ecological problem, especially in the metropolitan cities with large-scale traffic network and high population density. Low noise road surface (LNRS) has been actively developed and applied as an effective measure to maintain the quieter environment of mobility service system. However, when LNRS is applied for noise abatement, the relationship between the acoustic performance and degradation of pavement has not been fully understood yet. To this end, this study aims to model the acoustic longevity of asphalt pavement as a function of the thickness, binder content, maximum aggregate size, and air void content of the pavement surface, as well as vehicle speed based on the long-term tyre-road noise data collected from 270 asphalt pavement sections in Hong Kong. Two machine learning techniques, namely artificial neural networks (ANN) and support vector machines (SVM), were employed and compared. It was found that both ANN and SVM could successfully model the pavement acoustic performance with acceptable model performance metrics. A case study showed that the ANN model was more aligned with the aging mechanisms of porous road surface, but the SVM model showed better training performance. The predicted acoustic deterioration rates of the porous surface case varied from −0.1 to 0.28 dB(A)/month rather than keeping a constant linear increasing trend, depending on pavement ageing periods and vehicle speed levels. The two-dimension sensitivity analysis (2D-SA) revealed the relative importance of pavement age and vehicle speed in controlling the acoustic performance.     

Options for reducing noise from roads and railway lines

The fundamental noise generation mechanisms of road and rail vehicles are discussed with attention to noise abatement measures. Based on an evaluation of publicly available tire noise data and the European road traffic noise emission model CNOSSOS, it is shown that on the road side there is a significant noise reduction potential in the usage of low-noise tires. From a three months measurement campaign a noise model was derived to predict the maximal sound pressure level of heavy duty vehicles during a pass-by in 7.5 m distance with the parameters vehicle speed and number of axles. With help of recently published information about external costs caused by heavy duty vehicles and the noise prediction tool, a model was developed to derive a money equivalent that can be used as a bonus/malus in a heavy duty vehicle fee. As a measure at the infrastructure, the installation of low-noise pavements is an effective, durable and economically attractive measure. Recent experiences with different technologies from all over the world are compiled and evaluated. On the rail side, an overview of the possible noise reduction strategies is given, followed by a discussion of the current policy and legislation in the EU and on the national level of different European countries.     

广西高速公路噪声源强预测模式修正研究

文章根据广西高速公路营运的实际情况,对现有高速公路各类型车辆的噪声辐射源强进行了监测,并对监测数据进行了回归分析。通过与《环境影响评价技术导则-声环境》（HJ2.4-2009）中推荐的公路交通单车辐射噪声级公式的理论计算结果进行对比分析,得出了车辆噪声源强的修正模型,为今后广西交通噪声预测工作提供参考。     

Variability in sound power levels: Implications for static and dynamic traffic models

Classically, one mean vehicle representative of each category is used by both static and dynamic traffic noise prediction models. The spectrum associated with this mean vehicle is determined from a linear statistical regression analysis based on measurement campaigns on a track or in situ. However, the variability of individual vehicle emissions can influence predictions and hinder comparison between static and dynamic models. In order to estimate the induced bias, statistical analysis of the distributions of sound power levels emitted by the individual passage of vehicles during 82 measurement campaigns was carried out. The results show that 92% of the residual regression distributions are Gaussian and that standard deviations can reach 3.6 dBA. The value of the proposed correction term for this case study could reach 1.4 dBA for light vehicles and 1.2 dBA for heavy vehicles. This analysis also shows that the variability in sound power levels and thus the corresponding corrections are higher at the lowest speeds that correspond to urban driving conditions.     

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Thus, accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is very important. Mainly, this is an essential component of many advanced traveller information systems coming under the intelligent transportation systems umbrella. In India, the automated traffic data collection is in the beginning stage, with many of the cities still struggling with database generation and processing, and hence, a less‐data‐demanding approach will be attractive for such applications, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data‐driven technique, namely, artificial neural networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data‐driven approach. Grey model, GM(1,1), which is also reported as a good prediction tool, is selected as the less‐data‐demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction. The results showed comparable performance by both the methods. However, ANN required around seven times data compared with GM for comparable performance. Thus, considering the comparatively lesser input requirement of GM, it can be considered over ANN in situations where the historic database is limited. Copyright © 2016 John Wiley & Sons, Ltd.     

A stochastic model of traffic flow: Theoretical foundations

In a variety of applications of traffic flow, including traffic simulation, real-time estimation and prediction, one requires a probabilistic model of traffic flow. The usual approach to constructing such models involves the addition of random noise terms to deterministic equations, which could lead to negative traffic densities and mean dynamics that are inconsistent with the original deterministic dynamics. This paper offers a new stochastic model of traffic flow that addresses these issues. The source of randomness in the proposed model is the uncertainty inherent in driver gap choice, which is represented by random state dependent vehicle time headways. A wide range of time headway distributions is allowed. From the random time headways, counting processes are defined, which represent cumulative flows across cell boundaries in a discrete space and continuous time conservation framework. We show that our construction implicitly ensures non-negativity of traffic densities and that the fluid limit of the stochastic model is consistent with cell transmission model (CTM) based deterministic dynamics.     

Comparing prediction methods for maritime accidents

ABSTRACT

The purpose of maritime accident prediction is to reasonably forecast an accident occurring in the future. In determining the level of maritime traffic management safety, it is important to analyze development trends of existing traffic conditions. Common prediction methods for maritime accidents include regression analysis, grey system models (GM) and exponential smoothing. In this study, a brief introduction is provided that discusses the aforementioned prediction models, including the associated methods and characteristics of each analysis, which form the basis for an attempt to apply a residual error correction model designed to optimize the grey system model. Based on the results, in which the model is verified using two different types of maritime accident data (linear smooth type and random-fluctuation type, respectively), the prediction accuracy and the applicability were validated. A discussion is then presented on how to apply the Markov model as a way to optimize the grey system model. This method, which proved to be correct in terms of prediction accuracy and applicability, is explored through empirical analysis. Although the accuracy of the residual error correction model is usually higher than the accuracy of the original GM (1,1), the effect of the Markov correction model is not always superior to the original GM (1,1). In addition, the accuracy of the former model depends on the characteristics of the original data, the status partition and the determination method for the status transition matrix.     

Geoinformatic prediction of motorway noise on buildings in 3D GIS

This study predicts and displays the impacts of motorway traffic noise on a nearby building, utilizing a motorway traffic noise model in combination with geoinformatic technique. Two- and three-dimensional geographical information systems are used to visualize noise impacts in the form of three-dimensional noise contour on the building and ground surface. The results offer a clear, visual display of impact levels from roadway noise in the form of noise contour overlay on vicinity area and building blocks along the roadway in three-dimensional form. The area can be turned and investigated at different angles.     

Roadway and traffic characteristics for bicycling

The promotion of bicycle transportation includes the provision of suitable infrastructure for cyclists. In order to determine if a road is suitable for bicycling or not, and what improvements need to be made to increase the level of service for bicycles on specific situations, it is important to know how cyclists perceive the characteristics that define the roadway environment. The present paper describes research developed to define which roadway and traffic characteristics are prioritized by users and potential users in the evaluation of quality of roads for bicycling in urban areas of Brazilian medium-sized cities. A focus group discussion identified 14 attributes representing characteristics that describe the quality of roads for bicycling in Brazilian cities. In addition, an attitude survey was applied with individuals to assess their perception on the attributes, along with the importance given to each one of them. The results were analyzed through the Method of Successive Intervals Analysis, which allows the transformation of categorical data into an interval scale. The analysis suggests that both the roadway and traffic characteristics related to segments and those related to intersections are important to the survey respondents. The five most important attributes, in their opinion, are: (1) lane width; (2) motor vehicle speed; (3) visibility at intersections; (4) presence of intersections; and (5) street trees (shading). Therefore, the research suggests that to promote bicycle use in Brazilian medium-sized cities, these attributes must be prioritized.     

A network wide simulation strategy of alternative fuel vehicles

This paper presents an integrated simulator “CUIntegration” to evaluate routing strategies based on energy and/or traffic measures of effectiveness for any Alternative Fuel Vehicles (AFVs). The CUIntegration can integrate vehicle models of conventional vehicles as well as AFVs developed with MATLAB-Simulink, and a roadway network model developed with traffic microscopic simulation software VISSIM. The architecture of this simulator is discussed in this paper along with a case study in which the simulator was utilized for evaluating a routing strategy for Plug-in Hybrid Electric Vehicles (PHEVs) and Electric Vehicles (EVs). The authors developed a route optimization algorithm to guide an AFV based on that AFV driver’s choice, which included; finding a route with minimum (1) travel time, (2) energy consumption or (3) a combination of both. The Application Programming Interface (API) was developed using Visual Basic to simulate the vehicle models/algorithms developed in MATLAB and direct vehicles in a roadway network model developed in VISSIM accordingly. The case study included a section of Interstate 83 in Baltimore, Maryland, which was modeled, calibrated and validated. The authors considered a worst-case scenario with an incident on the main route blocking all lanes for 30 min. The PHEVs and EVs were represented by integrating the MATLAB-Simulink vehicle models with the traffic simulator. The CUIntegration successfully combined vehicle models with a roadway traffic network model to support a routing strategy for PHEVs and EVs. Simulation experiments with CUIntegration revealed that routing of PHEVs resulted in cost savings of about 29% when optimized for the energy consumption, and for the same optimization objective, routing of EVs resulted in about 64% savings.     

广西高等级公路交通噪声预测值的衰减规律的研究

文章根据广西高速公路目前车流量、车型比以及车速情况,结合交通环境噪声的实测数据,对高速公路交通噪声的变化规律进行分析研究。     

城市桥梁声屏障设计及交通噪声控制研究

城市桥梁交通噪声污染随着城市道路的发展和车辆增多日益严重,引起了人们的普遍关注。文章以北大桥声屏障设计为研究对象,结合北大桥交通噪声污染调查分析,阐述了声屏障高度、声屏障结构形式等声屏障设计治理方案,并从规划阶段、运营阶段等方面探讨了城市桥梁交通噪声的控制对策。     

Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10

The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM₁₀) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM₁₀ concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM₁₀ at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM₁₀ concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM₁₀, gaseous pollutants and meteorological variables respectively.     

Urban road traffic noise spatiotemporal distribution mapping using multisource data

Many residents are disturbed by road traffic noise which needs to be controlled and managed. The noise map is a helpful and important tool for noise management and acoustical planning in urban areas. However, the static noise map is not sufficient for evaluating noise annoyance at different temporal periods. It is necessary to develop the dynamic noise map or the noise spatiotemporal distribution. In this study, a method about urban road traffic noise spatiotemporal distribution mapping is proposed to obtain the representative road traffic noise maps of different periods. This method relies on the proposed noise spatiotemporal distribution model with two time-dependent variables - traffic density and traffic speed, and the spatiotemporal characteristics derived from multisource data. There are three steps in the method. First, the urban road traffic noise spatiotemporal distribution model is derived from the law of sound propagation. Then, the temporal characteristics are extracted from traffic flow detecting data and E-map road segment speed data by the outlier detection analysis. Finally, the noise distributions corresponding to different periods are calculated by an efficient algorithm which can save 90% above of the computing time. Moreover, a validation experiment was conducted to evaluate the accuracy of the proposed method. There is only 2.26-dB[A] mean absolute error that is within an acceptable range, which shows that the method is effective.     

International experience with speed limits during and prior to the energy crisis of 1973–4

This paper presents an artificial neural network (ANN) based method for estimating route travel times between individual locations in an urban traffic network. Fast and accurate estimation of route travel times is required by the vehicle routing and scheduling process involved in many fleet vehicle operation systems such as dial‐a‐ride paratransit, school bus, and private delivery services. The methodology developed in this paper assumes that route travel times are time‐dependent and stochastic and their means and standard deviations need to be estimated. Three feed‐forward neural networks are developed to model the travel time behaviour during different time periods of the day‐the AM peak, the PM peak, and the off‐peak. These models are subsequently trained and tested using data simulated on the road network for the City of Edmonton, Alberta. A comparison of the ANN model with a traditional distance‐based model and a shortest path algorithm is then presented. The practical implication of the ANN method is subsequently demonstrated within a dial‐a‐ride paratransit vehicle routing and scheduling problem. The computational results show that the ANN‐based route travel time estimation model is appropriate, with respect to accuracy and speed, for use in real applications.