Parameter Estimation for Traffic Noise Models Using a Harmony Search Algorithm

The sidewalks we use in our daily lives are made of various surface materials, such as asphalt pavement and concrete pavement. In terms of barrier-free access, Braille blocks are provided on the sidewalks. Barrier-free maps are also available to assist wheelchair users in their daily lives. The Barrier-Free Map shows the location of wheelchair-accessible restrooms and the status of barrier-free facilities at stations and stores. However, it does not indicate the type of road surface, deterioration conditions and other information necessary for wheelchair users to travel. Wheelchair users may experience problems in riding and driving due to uneven road surfaces, bumps, and joints between blocks. However, it is difficult to select a comfortable travel route in advance because the road surface condition cannot be determined without visiting the road. In addition, wheelchair users have the problem of not knowing whether they can drive on their own or need assistance without directly checking the road surface conditions. If the road conditions can be provided to wheelchair users in advance, they can take the most appropriate route. In this study, we report on a road surface estimation method that uses the vibrations generated during wheelchair driving. The following six types of road surfaces were used in the experiment. These road surfaces were wood flooring, two types of asphalt pavement, two types of interlocking block pavement, and a wood deck floor. The wheelchair used was equipped with four vibration sensors for data collection. The vibration sensors were mounted on a frame with casters and tires on its axis. The vibration data collected in the experiment was the result of a 20 [m] straight run on each of the road surfaces. For road surface estimation, a learning model was built for deep learning using spectrum images for the vibration data. As a result, five types of road surfaces could be discriminated with an accuracy of more than 80%.

A road surface recognition sensor has been realized by using an optical spatial filter. The sensor transforms a road surface image into spatial frequency spectra. Frequency distribution depends on the road surface type. Ground velocity is shown by a filtered moving image. Experimental results show that five road surface types were recognized in cold districts. Both road surface condition and vehicle velocity can be detected by a noncontact sensor; therefore, the sensor is useful for active safety systems.

고속도로 이용차량의 증가와 함께 차량의 대형화와 고속화로 인해 고속도로 교통 소음레벨이 높아지고 있으며 저소음 포장노면 및 방음시설 설치 요청도 급격하게 증가하고 있다. 따라서 고속도로 교통소음으로 인한 민원예방과 함께 효율적이고 경제적인 소음저감 대책을 수립하기 위해서는 정확한 소음 예측 기술 마련이 필요하다. 본 연구에서는 시험도로에 포설된 다양한 포장노면에 대해서 CPX(Close Proximity Test) 및 Pass-by 소음 계측 방법을 혼용한 소음 계측 데이터를 이용하였고 차종별 단독 주행 시험을 실시하여 차량 및 노면별 음향파워레벨 산정식이 마련된 데이터를 이용하였다. 아울러, 상기 산정식의 정확성을 검증하기 위하여 고속도로 12개 지점에 대한 총 38회의 소음 계측한 데이터를 이용하여 해당 지점에 대한 소음 예측 모델을 구성하여 측정값과 예측값을 비교 평가하였다. 최종적으로 3차원 GUI 기능을 지원하는 도로교통 소음 예측 프로그램 KRON(Korea Road Noise)을 개발하였다. 이와 더불어 각 포장형태별 및 차종별에 따른 소음특성을 분석하였다. Domestic economic development, industrialization, and urbanization have brought along not only increased highway traffic but also elevated traffic noise levels. Thus, it is necessary to accurately predict the traffic noise levels in order to address the public demand of alleviating the noise levels in urban areas. In this study, the method of evaluating the sound power level of road traffic was investigated in terms of considering the types of road surface and vehicle, based on previous researches. Regarding CPX (Close Proximity Test) and Pass-by test, the measured noise data of Test Road of Korea Highway Corporation were utilized in order to construct the database of sound power levels of various vehicles. Specifically, the 38 noise measurement and analysis in 1/1-octave band frequencies at 12 pre-selected sites were carried out, considering topography and road surface. Finally, the comparison study was conducted between predicted and measured data in terms of traffic noise. The traffic noise prediction was based on the KRON (Korea Road Noise) program, which was developed being equipped wit 3-dimensional GUI. In addition, the traffic noise characteristics were evaluated in terms of vehicle types and pavement surface conditions.

Background Due to urbanisation, the number of residents exposed to high traffic noise levels is increasing. Traffic noise has been associated with an increased risk of ischaemic heart disease and several cardio-metabolic outcomes, with stress and sleep disturbance as potential effect pathways. It is, thus, plausible that traffic noise may have adverse effects on pregnancy outcomes, but the evidence is scarce and inconclusive. The present study aimed to address this gap by investigating whether residential exposure to road and rail traffic noise is associated with maternal pregnancy complications. Methods The study population included all singleton pregnancies from five Nordic metropolitan areas: Copenhagen, Denmark (years 2005-2016); Helsinki, Finland (2004-2014); Oslo, Norway (2005-2014); and Gothenburg and Stockholm, Sweden (2007-2012), total n = 540 439. Data were retrieved from national registers. Road and rail traffic noise levels were modelled at residential addresses. We ran logistic regression analyses separately in each country and combined the results in random-effect meta-analyses. Covariates included individual (age, parity, birth year, marital status, education), area-level (% low-income households in neighbourhood) and address-level (green and blue space, aircraft noise, air pollution) factors. Results In the meta-analyses, a 10 dB higher exposure to road traffic noise during pregnancy was associated with higher odds of gestational diabetes (OR 1.08; 95% CI 1.04-1.12) but not with gestational hypertension (0.95; 0.78-1.16) or mild (0.99; 0.91-1.07) or severe pre-eclampsia (1.00, 0.90-1.11). Rail traffic noise was associated with pregnancy complications in some countries but not in the meta-analyses. Conclusions Exposure to road traffic noise seems to be associated with higher risk of gestational diabetes in Nordic metropolitan areas. For maternal health, road traffic noise abatement is, thus, increasingly important in urban areas. Key messages • Residential road traffic noise was consistently associated with increased odds of gestational diabetes, but not gestational hypertension or pre-eclampsia, in urban Nordic areas. • Road traffic noise, an urban environmental stressor, seems adversely associated with gestational diabetes, which is known to increase the risk of severe pregnancy complications.

Road information services (RIS) is a major component of the information and communication technologies with the main purpose of RIS-based systems is to monitor road health conditions, weather information and traffic congestion. Considering the road conditions, there are various kinds of road surface types and anomalies with lack of efficient analysis of their behavior on the vehicle sensor measurements. Consequently, there are difficulties in detecting and categorizing the different road types and anomalies. This paper demonstrates road test results for the measurements of inertial sensors mounted in land vehicles while monitoring various road surface types and anomalies. In addition, a wavelet-based feature extraction together with statistical approach for the road types and anomalies are explored in this study. Two road test experiments on two different vehicles performed in Kingston, ON, Canada together with in-depth analysis are discussed in this paper.

Objective: There is little research simultaneously examining both teen drivers’ characteristics and the driving environment. This study aimed to investigate how traffic engineering factors (road designs, traffic operations, and surface conditions) contribute to risks of teen driver’s fatality. Understanding the role of traffic engineering factors can further improve efforts to reduce teen diving-related fatal crashes.Methods: This study used the Fatality Analysis Reporting System and General Estimate System for years 2014 to 2015. We explored numerous engineering variables: horizontal/vertical alignment, intersections types, interchange areas, traffic ways types, posted speed limit, work zone areas, types of traffic control devices and road surface conditions. Traditional variables considered in this study included sex, age, presence of passengers in relation to age group, time of day, lighting condition, season, vehicle model year, vehicle type, seatbelt use, crash type and movement prior to a crash. We ran cross-tabulations and unadjusted analyses to report unadjusted odds ratios of teen driver’s fatality. A multiple logistic regression model was used to identify which variables related to road design and traffic operations remained significant after being adjusted by other variables.Results: For traditional factors, results corroborated prior findings. All the engineering factors, except work zone area, were significant in the unadjusted analyses; four variables – horizontal alignments, posted speed limit, traffic control device type, and traffic way type – remained statistically significant in the final model. The adjusted odds ratio (AOR) of curved high-speed segment was nearly seven times (6.94; 95% CI = 3.58, 13.46) that of a straight segment with a low posted speed limit (reference group). The AOR of no traffic control was four times (4.17; 95% CI = 2.95, 5.88) that of roads with traffic controls. The AOR of a two-way undivided road was almost three times (2.75, 95% CI = 1.43, 5.29) that of a two-way median barrier road (reference group).Conclusions: This study suggests teen driver education programs consider addressing road design characteristics. For example, incorporating focused lessons on traffic signs and traffic control types may better prepare young people for their driving experiences. Additionally, more supervised driving time is needed on various road types, especially those that present greater risks (e.g., roads with grades/curves, intersections with no traffic signal, undivided roads, and unpaved roads).

Long-term exposure to traffic noise has been suggested to increase the risk of cardiovascular diseases (CVD). However, few studies have been performed in the general population and on railway noise. This study aimed to investigate the cardiovascular effects of living near noisy roads and railways. This cross-sectional study comprised 25,851 men and women, aged 18-80 years, who had resided in Sweden for at least 5 years. All subjects participated in a National Environmental Health Survey, performed in 2007, in which they reported on health, annoyance reactions and environmental factors. Questionnaire data on self-reported doctor's diagnosis of hypertension and/or CVD were used as outcomes. Exposure was assessed as Traffic Load (millions of vehicle kilometres per year) within 500 m around each participant's residential address. For a sub-population (n = 2498), we also assessed road traffic and railway noise in L(den) at the dwelling facade. Multiple logistic regression models were used to assess Prevalence Odds Ratios (POR) and 95% Confidence Intervals (CI). No statistically significant associations were found between Traffic Load and self-reported hypertension or CVD. In the sub-population, there was no association between road traffic noise and the outcomes; however, an increased risk of CVD was suggested among subjects exposed to railway noise ≥50 dB(A); POR 1.55 (95% CI 1.00-2.40). Neither Traffic Load nor road traffic noise was, in this study, associated with self-reported cardiovascular outcomes. However, there was a borderline-significant association between railway noise and CVD. The lack of association for road traffic may be due to methodological limitations.

ObjectivesTransportation noise has been associated with markers of obesity. We aimed to investigate whether road traffic and railway noise were associated with weight gain during and after pregnancy.MethodsAmong the women...

Introduction: Traffic noise is commonly associated with annoyance as well as sleep disturbances and increasing evidence also suggests harmful effects on the cardiovascular system. However, national quantifications of the health burden related to traffic noise are still rare. This study aims to assess the burden of disease from road traffic and railway noise for the complete transportation net in Sweden in terms of Disability Adjusted Life-Years (DALY). Methods: The number of DALYs was assessed for annoyance, sleep disturbance and cardiovascular disease, including hypertension, myocardial infarction (MI) and stroke, using a method previously implemented by the World Health Organization (WHO). Information on population exposure to road traffic and railway noise was obtained from the Swedish Environmental Protection Agency and the Swedish Road Administration. Data on age and sex-specific disease occurrence was gathered from registers held by the Swedish Board of Health and Welfare and Statistics Sweden. Disability weights and duration were based on WHO definitions. Finally, we used research-based exposure-response functions or relative risks to estimate disease attributable to noise in each exposure category. Results: Preliminary findings show that the number of DALYs attributed to road traffic and railway noise in Sweden is approximately 36.600 and 4.000, respectively. The most important contributor to the disease burden was sleep disturbances, which accounted for a total of 22.000 DALYs, followed by annoyance, accounting for 12.100 DALYs, and cardiovascular diseases, accounting for 6.500 DALYs (stroke 2.800, MI 2.100 and hypertension 1.600 DALYs). Conclusions: Road traffic and railway noise contribute significantly to the burden of disease in Sweden each year, in particular with regard to sleep disturbance. The total number of DALYs should, however, be interpreted with caution since the underlying data has several limitations.

Road surface adhesion condition identification has major impact on playing the various active safety automotive electronic control devices’ performance as much as possible, the vehicle model with 7 degrees of freedom is established with the application of magic formula tire model and engine model in this paper. By comparing the real-time vehicle dynamic parameters with 6 kinds of typical road characteristics when a vehicle accelerates or brakes, the current road surface type is determined. A road surface identification simulation is carried out when an automobile accelerates on uniform road and brakes on split road by using MATLAB/Simulink software. Finally, a bench test is done to prove the validity of this approach. It is showed by the simulation results that the actual road surface type and the identified road surface type are in good agreement. It can be used for active safety automotive electronic control devices’ road surface identification.

ObjectiveTo study the association between exposure to transportation noise and blood pressure (BP) reduction during nighttime sleep.Methods24-h ambulatory BP measurements at 15-min intervals were carried out on 149 persons living...

BackgroundRoad traffic noise is a prevalent and known health hazard. However, little is known yet about its effect on children’s cognition. We aimed to study the association between exposure to road traffic noise and the development of working memory and attention in primary school children, considering school-outdoor and school-indoor annual average noise levels and noise fluctuation characteristics, as well as home-outdoor noise exposure.Methods and findingsWe followed up a population-based sample of 2,680 children aged 7 to 10 years from 38 schools in Barcelona (Catalonia, Spain) between January 2012 to March 2013. Children underwent computerised cognitive tests 4 times (n = 10,112), for working memory (2-back task, detectability), complex working memory (3-back task, detectability), and inattentiveness (Attention Network Task, hit reaction time standard error, in milliseconds). Road traffic noise was measured indoors and outdoors at schools, at the start of the school year, using standard protocols to obtain A-weighted equivalent sound pressure levels, i.e., annual average levels scaled to human hearing, for the daytime (daytime LAeq, in dB). We also derived fluctuation indicators out of the measurements (noise intermittency ratio, %; and number of noise events) and obtained individual estimated indoor noise levels (LAeq) correcting for classroom orientation and classroom change between years. Home-outdoor noise exposure at home (Lden, i.e., EU indicator for the 24-hour annual average levels) was estimated using Barcelona’s noise map for year 2012, according to the European Noise Directive (2002). We used linear mixed models to evaluate the association between exposure to noise and cognitive development adjusting for age, sex, maternal education, socioeconomical vulnerability index at home, indoor or outdoor traffic-related air pollution (TRAP) for corresponding school models or outdoor nitrogen dioxide (NO2) for home models. Child and school were included as nested random effects.The median age (percentile 25, percentile 75) of children in visit 1 was 8.5 (7.8; 9.3) years, 49.9% were girls, and 50% of the schools were public. School-outdoor exposure to road traffic noise was associated with a slower development in working memory (2-back and 3-back) and greater inattentiveness over 1 year in children, both for the average noise level (e.g., ‒4.83 points [95% CI: ‒7.21, ‒2.45], p-value < 0.001, in 2-back detectability per 5 dB in street levels) and noise fluctuation (e.g., ‒4.38 [‒7.08, ‒1.67], p-value = 0.002, per 50 noise events at street level). Individual exposure to the road traffic average noise level in classrooms was only associated with inattentiveness (2.49 ms [0, 4.81], p-value = 0.050, per 5 dB), whereas indoor noise fluctuation was consistently associated with all outcomes. Home-outdoor noise exposure was not associated with the outcomes. Study limitations include a potential lack of generalizability (58% of mothers with university degree in our study versus 50% in the region) and the lack of past noise exposure assessment.ConclusionsWe observed that exposure to road traffic noise at school, but not at home, was associated with slower development of working memory, complex working memory, and attention in schoolchildren over 1 year. Associations with noise fluctuation indicators were more evident than with average noise levels in classrooms.

A road traffic noise prediction model (ASJ MODEL-1998) has been integrated with a road traffic simulator (AVENUE) to produce the Dynamic areawide Road traffic NoisE simulator-DRONE. This traffic-noise-GIS based integrated tool is upgraded to predict noise levels in built-up areas. The integration of traffic simulation with a noise model provides dynamic access to traffic flow characteristics and hence automated and detailed predictions of traffic noise. The prediction is not only on the spatial scale but also on temporal scale. The linkage with GIS gives a visual representation to noise pollution in the form of dynamic areawide traffic noise contour maps. The application of DRONE on a real world built-up area is also presented.

The current trend worldwide is less focused on reducing road traffic noise. This is in strong contrast to the severe impact of traffic noise to the general health and quality of life. A more holistic and combined strategy is needed. Current international rules and regulations regarding vehicles and tyres are not sufficient to reduce traffic noise levels in an effective way. Calculations show that these regulations will only yield a reduction of approximately 1.5 dB in Lden levels for urban traffic. Additional measures need to be implemented. By combining optimized tyres and road surfaces, a noise reduction of 4â–“6 dB can be achieved. Such tyres are currently being developed for electric and hybrid-electric vehicles. In addition to noise reduction, these tyres have less rolling resistance that can reduce vehicle energy consumption up to 15% on normal road surfaces. However, there are several obstacles still to be removed, such as the effectiveness of the EU tyre labeling system, and the implementation and durability of low-noise road surfaces. These challenges are discussed in the article.

A cost-effective approach to the evaluation of traffic noise exposure in the city of Quito, Ecuador