High Traffic Noise Research Articles

Effects of traffic noise on the psychophysiological responses of college students: An EEG study

Traffic noise harms public health, but the specific mechanisms are not fully understood. To understand how traffic noise at different sound pressure levels (SPLs) affects psychophysiological responses, this study examined five SPLs of traffic noise: 40, 45, 50, 55, and 60 dB. 38 young college students were recruited for Profile of Mood States (POMS) evaluations and measurements of electrocardiogram and electroencephalogram. The study found that traffic noise exposure led to a 0.95–1.64-fold increase in mood disturbance compared to no sound, with 60 dB traffic noise causing a significantly greater increase compared to other SPLs. As SPL increased, ΔLF/HF increased by 0.01–0.83, indicating heightened stress levels. Traffic noise also induced stress responses in the brain, with α power reduced by 13.08–24.15 % compared to no sound. High SPL traffic noise significantly harmed brain comfort. Compared to 40–45 dB, exposure to 50–60 dB traffic noise significantly increased α power by 28.66–85.80 % (p < 0.05). Additionally, 60 dB traffic noise increased the likelihood of supercritical brain activity by 32.49 % compared to no sound. Psychological response changes were closely related to physiological response changes. Δα power, particularly in the frontal and parietal lobes, was the strongest predictor of the increase in total mood disturbance, suggesting these lobes should be the focus of future traffic noise-brain studies. The study emphasizes the need for a comprehensive evaluation of the negative effects of traffic noise from a psychophysiological perspective, highlighting the harm of high SPLs. The findings provide valuable insights for developing indoor acoustic environment standards.

Estimation of population exposed to road traffic noise in Kosovo highway using GIS modelling and roadside measurements

New highways have been introduced into Kosovo landscape in recent years. The trajectory of the highways crosses through the fields and in the middle of some previously remote rural settlements. This has changed the environment for these areas and the environmental noise is a major concern. This study aims to estimate the population exposed to high traffic noise levels around the highway routes in Kosovo. GIS has been used to model the highway lines, the terrain and the buildings on site. Roadside traffic noise measurements as well as traffic flow measurements have been conducted to approximate the noise sources. An approximate noise map has been generated for the studied area. The population exposed to daytime and nighttime road traffic noise were estimated. The results show the need for road traffic noise reduction measures and areas where they are most needed.

Sound insulation of partially open windows - alternative laboratory measurement methods

Sound insulation with partially open windows has had growing attention in recent years. In Denmark, this is primarily caused by the Danish Environmental Protection Agency's guideline from 2007 "Noise from roads", which introduces noise limits with open windows (opening area of 0.35 m2) for situations with a high traffic noise level. The sound insulation of traditional windows in open position have been measured in both the laboratory and external environment, and for certain setups too different results have been observed. To investigate the reasons behind the discrepancies, the traditionally reverberant source room of laboratory measurements was modified to a semi-anechoic room to remove reflections from unwanted reflections from room surfaces and to imitate the external environment during field measurements. Four different window types were tested as part of the research projects "Optimized measurement method for sound reduction of partially open windows part 1 and 2" (MetÅV and MetÅV2) in collaboration with four Danish window manufacturers. The primary focus of the project is to explore alternative laboratory methods to the traditional diffuse field method by studying reference cases from field measurements and taking into consideration different sound source positions and window geometry. This paper describes the results so far.

Influence of uncomfortable environmental conditions on odor detection thresholds for n-butanol determined with a dynamic dilution olfactometer and in room air

Odor guide values (OGV) are used when a person is exposed to an odor in a room and complains about odor annoyance. OGV are derived from odor detection thresholds (ODT) measured under standard environmental conditions. The study question was whether uncomfortable environmental conditions alter odor perception and should therefore be considered when applying OGV. ODT for n-butanol were determined with an olfactometer and in room air. Twenty healthy, non-smoking volunteers (10 women/10 men, 19–50 years) were selected and trained compliant with the European Standard for Olfactometry EN 13725:2022. Standard conditions were an average temperature between 22 °C and 23 °C, warm light (2800 Kelvin), quiet fan noise (45 dB), 415 ppm carbon dioxide (CO2), and relative humidity between 34 % and 42 %. On each test day, one of five conditions was examined: high temperature (26°C), cold light (6500 Kelvin), traffic noise (70 dB with peaks up to 85 dB), 1000 ppm CO2 and 4000 ppm CO2. Comparability of log-transformed ODT values was assessed by Bland-Altman plot analysis. None of the five conditions systematically affected ODT, either when measured with an olfactometer or in room air. Compared to standard conditions (Limits of Agreement (± LoA) 1.03), the variability of ODT was higher at high temperature (± LoA 1.70) and traffic noise (± LoA 1.45), but not at cold light (± LoA 1.02), 1000 ppm CO2 (± LoA 0.87) or 4000 ppm CO2 (± LoA 0.93). The results show that OGV can be used in uncomfortable environmental conditions. However, because ODT were lower or higher in a few subjects at high temperatures and traffic noise, individual occupant’s perception of temperature and noise should always be considered when applying the OGV concept. Furthermore, the results confirm that the experimental determination of ODT should be performed under controlled and standardized environmental conditions.

Cultivating a harmonious soundscape in urban public space: An empirical study of sound and activities

This study examines the relationship between sound and activities in urban public spaces in an attempt to create a pleasant acoustic environment that improves the quality of activities conducted in such areas. The study collected data from park visitors in Bandung using field measurements and questionnaires. The findings indicate that sound sources have a more significant effect on visitors' participation in activities than the overall soundscape quality. Visitors would continue to engage in activities despite the high traffic noise levels as long as the appropriate sound sources are present. Additionally, the study identified that the Social-Nature Connection activities require a soundscape that is lower in background noise, has some of the natural sound components, and with the addition of music to provide some dynamic vibes. On the other hand, the Restorative Retreats activities show no relation with either sound sources or noise level, and Exploring Natural Landscapes activities are influenced positively by man-made sound, specifically recreational sound. This study provides valuable insights that may be used to improve the design of soundscapes in urban public spaces, enabling designers to create a more balanced acoustic environment that supports various activities and enhances the quality of visitors' experiences.

Statistical Analysis of Traffic Noise Status at Selected Junctions in Port Harcourt Metropolis

Traffic noise is one of the dominant forms of environmental pollution in most urban cities that adversely affect human health globally. The traffic noise emanating from motor vehicles has become a common health problem in many metropolitan cities in developing countries of the world. This study evaluated traffic noise at selected major junctions (Rumuola, Garisson, Boro Pack, and Lagos Bus stop) in Port Harcourt Metropolis. Traffic noise levels at four congested junctions and a control junction in the Metropolis were monitored using a digital sound level meter. Noise indices, L10, L50, L90 equivalent noise levels (Leq), noise climate (NC), noise pollution index (NPI); traffic noise index (TNI); noise pollution level (Lnp); day-evening-night equivalent metric (LDEN) and percentage highly annoyed (%HA) were statistically determined. Traffic noise at Rumuola junction showed traffic noise ranging from 51.9 to 107.9dBA with a mean deviation of 91.3±8.4dBA; traffic noise at Garrison junction ranged from 52.8 to 107.6dBA with a mean deviation of 88.5±9.7dBA; Boro Park junction showed traffic noise ranging from 48.9 to 106.4dBA with a mean deviation of 83.7±10.1dBA; Lagos Bus Stop junction ranged from 45.4 to 94.9dBA with a mean deviation of 81.5±10.8dBA; while the Control junction (Ibadan Street junction)showed a traffic noise ranging from 48.4 to 61.3dBA with a mean deviation of 51.6±5.3dBA. Noise levels at the studied junctions were significantly high and exceeded permissible limit compared to the control junction (p-value = 0.00485; 95%CI). L10 (94.8dBA), L50 (86.8dBA), L90 (76.1dBA) and Leq (92.4dBA) were highest at Rumuola junction and lowest at Control junction (with 57.3dBA, 52.8dBA, 48.4dBA and 54.1dBA, respectively). Computed NPI values (1.25-1.34) indicated high traffic noise levels at the four junctions and low (0.77) at the control junction; Lnp values (105.0-116.6dBA) indicated high traffic noise pollution at the four junctions and low (63.0dBA) at the control junction; TNI values (120.9-138.5) indicated high traffic noise impacts at the four junctions and low (54.0) at the control junction. Computed LDEN and %HA indicated high level of traffic noise annoyance to the four junctions. The study revealed high traffic noise pollution at the four junctions that may significantly affect public health and therefore requires further investigation by concerned authorities to assess the health effects of traffic noise on the surrounding residents and traders at the studied junction.

Evaluation of Daily Vehicular Traffic Flows and Noise Levels at Major Junctions in Port Harcourt Metropolis, Nigeria

This study evaluated vehicular traffic flows and associated traffic noise levels at major junctions in Port Harcourt metropolis. Traffic volume was determined using visual traffic count method. Noise level was determined using an Extech digital sound level meter. Data were statistically analyzed and noise descriptors determined. Average daily traffic flows ranged from 149 to 4224. Average daily total traffic volume was minimum at Eliozu junction and maximum Eleme junction. Traffic noise varied proportionally with traffic flow. The result showed that Average noise levels varied from 70.5dB(A) to 95.9dB(A). The result showed that L10 mean values were 89.0, 90.2 91.7, 94.7 and 97.2dB(A) for Eliozu, Choba, Runuokuta, Runuokoro and Eleme junctions respectively; L50 mean values were 80.5, 81.2, 83.8, 85.2 and 89.3dB(A) for Eliozu, Choba, Runuokuta, Runuokoro and Eleme junctions respectively; L90 mean values were 69.5, 70.1, 70.4, 72.0 and 78.2dB(A) for Choba, Eliozu, Runuokuta, Runuokoro and Eleme junctions respectively. Noise climate (NC) aggregated mean values were 18.9, 19.0, 20.7, 21.3 and 22.7 for Eliozu, Eleme, Choba, Runuokuta and Runuokoro junctions respectively; Leq aggregated mean values were 86.5, 88.3, 91.4, 93.8 and 95.3dB(A) for Eliozu, Choba, Runuokuta, Runuokoro and Eleme junctions respectively; noise pollution index (Lnp) aggregated mean values were 105.4, 109.0, 113.0, 114.3 and 116.6dB(A) for Eliozu, Choba, Runuokuta, Eleme and Runuokoro junctions respectively; traffic noise index (TNI) aggregated mean values were 115.7, 122.3, 124.2, 125.6 and 132.8dB(A) for Eliozu, Choba, Runuokuta, Eleme and Runuokoro junctions respectively. The study showed evidences of high traffic noise pollution at major junctions in Port Harcourt metropolis, which may adversely affect public health and therefore requires intervention by all stakeholgers, including decision makers.

Traffic noise in the bedroom in association with markers of obesity: a cross-sectional study and mediation analysis of the respiratory health in Northern Europe cohort

BackgroundPrevious research suggests an association between road traffic noise and obesity, but current evidence is inconclusive. The aim of this study was to assess the association between nocturnal noise exposure and markers of obesity and to assess whether sleep disturbance might be a mediator in this association.MethodsWe applied data from the Respiratory Health in Northern Europe (RHINE) cohort. We used self-measured waist circumference (WC) and body mass index (BMI) as outcome values. Noise exposure was assessed as perceived traffic noise in the bedroom and/or the bedroom window’s location towards the street. We applied adjusted linear, and logistic regression models, evaluated effect modifications and conducted mediation analysis.ResultsBased on fully adjusted models we found that women, who reported very high traffic noise levels in bedroom, had 1.30 (95% CI 0.24–2.37) kg/m2 higher BMI and 3.30 (95% CI 0.39–6.20) cm higher WC compared to women, who reported no traffic noise in the bedroom. Women who reported higher exposure to road traffic noise had statistically significant higher odds of being overweight and have abdominal obesity with OR varying from 1.15 to 1.26 compared to women, who reported no traffic noise in the bedroom. For men, the associations were rather opposite, although mostly statistically insignificant. Furthermore, men, who reported much or very much traffic noise in the bedroom, had a statistically significantly lower risk of abdominal obesity. Sleep disturbance fully or partially mediated the association between noise in bedroom and obesity markers among women.ConclusionOur results suggest that self-reported traffic noise in the bedroom may be associated to being overweight or obese trough sleep disturbance among women, but associations were inconclusive among men.

ASSESSMENT OF ROAD TRAFFIC NOISE IN SELECTED URBAN RESIDENTIAL AND CONSTRUCTION AREAS

The work presented in this paper includes a field investigation of road traffic noise between residential and construction areas in a selected urban area. The aim of this study was to assess the level of noise pollution from road traffic noise in Seri Kembangan, Selangor. Therefore, this study included measurements of traffic noise level, evaluation of the percentage traffic volume between residential and construction areas; and identification of the correlation between traffic volume and traffic noise level. An EXTECH 407764 sound level meter was used to take noise level measurements for 1 hour and 30 minutes in one-minute intervals at both study sites. The parameter collected in the noise level monitoring was average noise level (LAeq). The traffic volume study was conducted using manual counts for 30 minutes. Four types of vehicles were counted, namely cars, vans, motorcycles, and heavy vehicles. Based on statistical analysis, it can be concluded that both sites had high traffic noise levels which exceeded the maximum permissible limit of 60 dBA during daytime, according to Malaysian guidelines, due to several factors. The findings show that road infrastructure such as noise barriers need to be further implemented, especially in residential areas. Mitigation of noise pollution must be further carried out by contractors. Additionally, further investigations need to be conducted by the relevant authorities to reduce the level of noise which may affect the quality of life for the communities in the area.

Deployment of an IoT System for Adaptive In-Situ Soundscape Augmentation

Soundscape augmentation is an emerging approach for noise mitigation by introducing additionalsounds known as "maskers" to increase indices like acoustic comfort. Traditionally, the choice of maskers is oftenpredicated on expert guidance or post-hoc analysis which can be time-consuming and sometimesarbitrary. Moreover, this often results in a static set of maskers that are inflexible to the dynamicnature of real-world acoustic environments. Overcoming the inflexibility of traditional soundscapeaugmentation is two-fold. First, given a snapshot of a soundscape, the system must be able to select anoptimal masker without human supervision. Second, the system must also be able to react to changesin the acoustic environment with near real-time latency. In this work, we harness the combinedprowess of cloud computing and internet-of-things (IoT) to allow in-situ listening and playback usingmicrocontrollers while delegating the computationally expensive inference tasks to the cloud. Inparticular, a serverless cloud architecture was used for inference, ensuring near real-time latencyand scalability without the need to provision computing resources. A working prototype of the systemis currently being deployed in a public area experiencing high traffic noise, as well as undergoingpublic evaluation for future improvements.

MODELLING THE PM2.5 CONCENTRATION WITH ARTIFICIAL INTELLIGENCE-BASED ENSEMBLE APPROACH

Fine particulate matter (PM2.5) has been linked to a number of adverse health effects, hence its prediction for epidemiological studies has become very crucial. In this study, a novel ensemble technique was proposed for the prediction of PM2.5 concentration in cities with high traffic noise using traffic noise as an input parameter. Air pollutants concentration (P), meteorological parameters (M) and traffic data (T) simultaneously collected from seven sampling points in North Cyprus were used for conducting the study. The modelling was done in 2 scenarios. In scenario I, PM2.5 was modelled using 4 different input combination without traffic noise as input parameter while in scenario II, traffic noise was added as an input variable for 4 input combinations. The models were evaluated using 4 performance criteria including Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Correlation Coefficient (CC) and Bias (BIAS). Modelling PM2.5 with combined relevant input parameters of P, M and T could improve the performance of the model developed with only one set of the parameters by up to 12, 17 and 29% for models containing only P, M and T respectively. All the models in scenario II have demonstrated high prediction accuracy than the corresponding model in scenario I by up to 12% in the verification stage. The Support Vector Regression-based Ensemble model (SVR-E) could improve the performance accuracy of single models by up to 17% in the verification stage.

An Optimized Deep Neural Network Approach for Vehicular Traffic Noise Trend Modeling

Vehicular traffic plays a significant role in terms of economic development; however, it is also a major source of noise pollution. Therefore, it is highly imperative to model traffic noise, especially for expressways due to their high traffic volume and speed, which produce very-high level of traffic noise. Previous traffic prediction models are mostly based on the regression approach and the artificial neural network (ANN), which often fail to describe the trends of noise. In this paper, a deep neural network-based optimization approach is implemented in two ways: i) using different algorithms for training and activation, and ii) integrating with feature selection methods such as correlation-based feature selection (CFS) and wrapper for feature-subset selection (WFS) methods. These methods are integrated to produce traffic noise maps for different time of the day on weekdays, including morning, afternoon, evening, and night. The novelty of this study is the integration of the feature selection method with the deep neural network for vehicular traffic noise modelling. New Klang Valley Expressway (NKVE) in Malaysia was used as a case study due to its increasing heavy and light vehicles, and the motorbike during peak hours, which result in high traffic noise. The results from the models indicate that the WFS-DNN model has the least mean-absolute-deviation (MAD) of 2.28, and the least root-mean-square-error (RMSE) of 3.97. Also, this model shows the best result compared to the other models such as DNN without integration with feature selection methods, CFS-DNN and the ANN networks (MLP and RBF). MAD improvement of 27% - 47% and RMSE improvement of 25% - 38% was achieved compared to other methods. The study provides a generic approach to key parameter selection and dimension reduction with novel trend descriptor which could be useful for future such modelling applications.

Impact of Residential Green Space on Sleep Quality and Sufficiency in Children and Adolescents Residing in Australia and Germany.

Increasing evidence suggests adults living in greener areas tend to have more favourable sleep-related outcomes, but children and adolescents are under-researched. We hypothesised that children and adolescents living in greener areas would have better quality and more sufficient levels of sleep on average, especially within the context of high traffic noise exposure. These hypotheses were tested using multilevel logistic regressions fitted on samples from the nationally representative Longitudinal Study of Australian Children (10–11 years old, n = 3469, and 14–15 years old, n = 2814) and the GINIplus and LISA cohorts (10 years old, n = 1461, and 15 years old, n = 4172) from the Munich, Wesel, and Leipzig areas of Germany. Questionnaire-based binary indicators of sleep sufficiency and sleep quality in each cohort were assessed with respect to objectively measured green space exposures adjusting for age, sex, and maternal education. Models were augmented with proxy measures of traffic noise and two-way interaction terms to test for effect modification. Cross-tabulations illustrated little convincing evidence of association between green space and insufficient sleep or poor sleep quality in either sample, except for insufficient sleep among 10 year old participants in Germany. These null findings were replicated in adjusted models. The proxy for traffic noise was associated with poor quality sleep in 15 year old participants in Germany, but no convincing evidence of modified association with green space was observed.

Analysis and Evaluation of Traffic Noise in Different Zones of Delhi, India

Noise pollution due to traffic has been recognized as one of the chief sources that negatively affects the quality of life in urban areas across the globe. It is important to monitor and analyze the noise level before purposing remedial measures. So, the main aim of this study is the analysis and evaluation of traffic noise in different zones of Delhi, India. Seven residential, three commercials, four industrial and two silent areas of city Delhi has been selected for this study. The Leq mentoring has been carried out for all selected locations in day and night time and the monitoring results are compared with the prescribed standard of Central Pollution Control Board. Furthermore, the average Leq, Lmin, and Lmax has also been registered for all locations. The variation of noise level clearly indicates that residential area is exposed to the highest noise level i.e. 76.62 dB followed by commercial area (59.36dB), silent area (58.58dB) and Industrial area (44.45dB). This is because of the fact that the residential area is located at the intersection of major roads having high traffic volume most of the time. The Commercial area which has been selected here is the central business district of Delhi and is of historic importance. It’s a famous tourist spot as well as having retail shops, offices and historic structures. All these contribute to high traffic noise. However, the Silent area selected for this study also has similar traffic noise as a commercial area because it has densely populated residential areas nearby. The lowest noise level is observed in the Industrial area because the area has small scale industries that are fully enclosed with high boundary walls which act as a buffer. Also, there is less traffic movement in this area as there is no residential or commercial area nearby and the only traffic is because of the factory workers and loading & unloading.

Zebra finches show spatial avoidance of near but not far distance traffic noise

Abstract Traffic noise is on the rise worldwide. Birds have been reported to decrease in number and diversity near highways. This could be indirectly caused by traffic if birds avoid overall poorer habitat quality near highways or directly if birds actively avoid noisy conditions. To test whether traffic noise directly affects birds’ spatial preferences, we designed a preference test where zebra finches (Taeniopygia guttata) could move freely between noisy and quiet aviaries that only differed in the type of noise playbacks. During playback of high amplitude traffic noise recorded near from highways (5–15 m), birds spent significantly more time in the quieter aviary. Such spatial preferences were not observed during playbacks of moderate amplitude traffic noise recorded further away (200–400 m). Our result provides experimental support for the growing notion that traffic noise itself rather than the presence of vehicles may suffice to deter birds from busy roads.

High Road Traffic Noise in Residential Buildings, High Annoyance and Self-perceived Health Problems in a Sample from Santiago, Chile

The purpose of this study was to examine the association between exposure to high traffic noise levels in housing and the proportion of high annoyance and self-perceived health problems, through a survey applied to comparable groups of individuals with and without exposure, selected in residential buildings from urban Santiago, Chile. It was an observational, cross-sectional study. Buildings with sides exposed and unexposed to high traffic noise levels were considered. In each building, independent samples of dwellings from each side were selected. A questionnaire consisting of three parts was applied: socio-demographic and related characteristics, self-perceived health state, and annoyance caused by different sources including traffic noise. To assess association between exposure to traffic noise and each self-perceived health response (including high annoyance), multiple logistic and log-binomial regression analyses were applied, adjusting by sociodemographic and related variables. Four residential buildings were included, achieving a total of 425 respondents, 200 living in exposed dwellings and 225 living in unexposed dwellings. Adjusted significant association was found between exposure and high annoyance, high degree of problems with concentration, sleeping and feeling rested, presence of head or neck ache, and feelings of depression, anguish or neurosis.

Spatial Data Driven Evaluation of City Locations

Moving to a new home or setting a new bureau in a new city is always difficult. One does not have knowledge about suitable locations; therefore, people are frequently unpleasantly surprised. High traffic noise, long distance to shops or high criminal activity are just few of many possible disturbing aspects. Certainly, there are many data sources that can help to see some particular aspect of the city life. Nonetheless, it is extremely complex and time‑consuming task to browse through large data sets and com‑pare provided information. Therefore, we developed a solution that comprises many different data sets that describe the city environment and created set of straightforward indices such as environment, safety, shopping etc. The users just provide the application his/her preferences and the application finds locations that are most suitable for particular cause. The application is presented on the example of the Brno city area.

Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III

Few studies have investigated associations between self-reported and modelled exposure to traffic pollution. The objective of this study was to examine correlations between self-reported traffic exposure and modelled (a) NOX and (b) traffic proximity in seven different northern European cities; Aarhus (Denmark), Bergen (Norway), Gothenburg, Umeå, and Uppsala (Sweden), Reykjavik (Iceland), and Tartu (Estonia). We analysed data from the RHINE III (Respiratory Health in Northern Europe, www.rhine.nu) cohorts of the seven study cities. Traffic proximity (distance to the nearest road with >10,000 vehicles per day) was calculated and vehicle exhaust (NOX) was modelled using dispersion models and land-use regression (LUR) data from 2011. Participants were asked a question about self-reported traffic intensity near bedroom window and another about traffic noise exposure at the residence. The data were analysed using rank correlation (Kendall's tau) and inter-rater agreement (Cohen's Kappa) between tertiles of modelled NOX and traffic proximity tertile and traffic proximity categories (0–150 metres (m), 150–200 m, >300 m) in each centre. Data on variables of interest were available for 50–99% of study participants per each cohort. Mean modelled NOX levels were between 6.5 and 16.0 μg/m3; median traffic intensity was between 303 and 10,750 m in each centre. In each centre, 7.7–18.7% of respondents reported exposure to high traffic intensity and 3.6–16.3% of respondents reported high exposure to traffic noise. Self-reported residential traffic exposure had low or no correlation with modelled exposure and traffic proximity in all centres, although results were statistically significant (tau = 0.057–0.305). Self-reported residential traffic noise correlated weakly (tau = 0.090–0.255), with modelled exposure in all centres except Reykjavik. Modelled NOX had the highest correlations between self-reported and modelled traffic exposure in five of seven centres, traffic noise exposure had the highest correlation with traffic proximity in tertiles in three centres. Self-reported exposure to high traffic intensity and traffic noise at each participant's residence had low or weak although statistically significant correlations with modelled vehicle exhaust pollution levels and traffic proximity.

Using triangulation to assess a suite of tools to measure community severance

There is a lack of tools to identify and measure community severance caused by large roads and motorised traffic, despite the evidence of its negative impacts on local communities. This paper reports the development of a suite of tools to measure and value community severance, undertaken as a part of the Street Mobility and Network Accessibility research project. The tools include participatory mapping, spatial analysis, a video survey, street audits, a health and neighbourhood mobility survey, and a valuation tool based on a stated preference survey. They were tested in the area around Finchley Road, a busy arterial road in North London. The study found that Finchley Road is an unpleasant place for pedestrians due to high traffic levels, air and noise pollution, and the lack and poor quality of pedestrian crossing facilities. This has a negative impact on the mobility and accessibility of local residents and, to some extent, on their health and wellbeing. The analysis showed coherence between the findings from the different measurement tools applied individually, but also revealed interconnections between factors which contribute to severance, demonstrating that overall the suite is reliable for assessing community severance in urban areas. Overall, the paper provides a multidisciplinary approach to developing standardised methods to measure a negative impact of transport that is still relatively unknown.

Association between aircraft, road and railway traffic noise and depression in a large case-control study based on secondary data

BackgroundFew studies have examined the relationship between traffic noise and depression providing inconclusive results. This large case-control study is the first to assess and directly compare depression risks by aircraft, road traffic and railway noise. MethodsThe study population included individuals aged ≥40 years that were insured by three large statutory health insurance funds and were living in the region of Frankfurt international airport. Address-specific exposure to aircraft, road and railway traffic noise in 2005 was estimated. Based on insurance claims and prescription data, 77,295 cases with a new clinical depression diagnosis between 2006 and 2010 were compared with 578,246 control subjects. ResultsFor road traffic noise, a linear exposure-risk relationship was found with an odds ratio (OR) of 1.17 (95% CI=1.10–1.25) for 24-h continuous sound levels ≥70dB. For aircraft noise, the risk estimates reached a maximum OR of 1.23 (95% CI=1.19–1.28) at 50–55dB and decreased at higher exposure categories. For railway noise, risk estimates peaked at 60–65dB (OR=1.15, 95% CI=1.08–1.22). The highest OR of 1.42 (95% CI=1.33–1.52) was found for a combined exposure to noise above 50dB from all three sources. ConclusionsThis study indicates that traffic noise exposure might lead to depression. As a potential explanation for the decreasing risks at high traffic noise levels, vulnerable people might actively cope with noise (e.g. insulate or move away).