Home Understanding perceived tranquillity in urban Woonerf streets: case studies in two Dutch cities
Article Open Access

Understanding perceived tranquillity in urban Woonerf streets: case studies in two Dutch cities

  • Theun Leereveld , Laura Estévez-Mauriz and Efstathios Margaritis EMAIL logo
Published/Copyright: July 30, 2024
Download Article (PDF)
Become an author with De Gruyter Brill
Author Information
Noise Mapping
From the journal Noise Mapping Volume 11 Issue 1

Abstract

Within the current urbanised society, the call for calm and quiet areas seems more pressing than ever. Such tranquil environments like the Woonerf streets in the Netherlands allow a more human-centred design, where traffic has a restricted speed limit of 15 km/h, while pedestrians and cars share the street without segregation. In the past, predictive models have been developed to assess the tranquillity levels based on indices related to noise exposure and the amount of greenery measured through the Green View Index. However, the urban environment encompasses multiple sound sources with people having different reactions towards the auditory stimuli. Because of this complexity, objective sound measurements are examined in combination with the subjective perception of noise through eight perceptual attributes. This is done by collecting audio and visual data in 61 Woonerf streets in the cities of Groningen and Leeuwarden, supported by additional questionnaire data gathered from the corresponding residents of the above-mentioned areas. Within the context of Woonerf streets, results indicate that sound levels are perceived as relatively pleasant and uneventful. Furthermore, a difference is observed between the predicted and subjective tranquillity.

Keywords: Woonerf concept; soundscape; tranquillity; green view index; pedestrian-oriented design

1 Introduction

Nearly 4.5 billion people, which is about 57% of the world’s population, are now living in urban areas. Within the EU, this percentage reaches 75 of the total population [1]. A minimum of 20% of Europeans are living in areas with noise levels which may potentially lead to adverse health effects [2]. In residential areas, a day-evening-night equivalent sound level (Lden) of 50–59 dB(A) results in a 10% increase in terms of the severe annoyance caused by road traffic compared to an Lden below 55 dB(A) [3]. On top of that, the Lden class of 60–64 dB(A) has the highest Disability-Adjusted Life Years (DALYs) lost in urban populations among all the noise exposure categories with 140,341 years (disability weight = 0.02) according to WHO [3]. In this highly urbanised continent, road traffic noise is considered the main source of noise, being a health threat to its population. Noise is also associated with a range of detrimental effects, including adverse impacts on cardiovascular and metabolic health, compromised cognitive performance, and decreased mental well-being [4]. Additionally, environmental noise has been closely linked to the experience of pronounced annoyance and disturbances in sleep patterns with approximately 437,000 DALYs (high sleep disturbance) lost in the EEA [2].

Previous works have shown the positive health benefits of access to quiet urban areas [5,6,7,8]. This became apparent through the broadly executed EU policy, where the goal has been to alleviate the effects of road traffic noise as highlighted in the Council Directive 2002/49/EC [9]. The EU estimated the social cost associated with road traffic noise higher than 55 dBA, at 40 billion Euros per year already since 2011 [10]; therefore, it is expected that this number has already increased by now. Furthermore, noise pollution has been linked to several negative health effects associated with psychological issues such as stress, anxiety, annoyance and sleep disturbances as well as cognitive problems regarding memory and concentration combined with high cardiovascular risks [11,12]. On a broader scale, the latest World Cities Report from the United Nations [13] highlighted in Chapter 2 the importance of a strong institutional framework that can tackle gentrification, noise pollution, and poor air quality.

While the EU published the Noise Directive in 2002, in the Netherlands, there were already design solutions on how to restrict urban traffic and protect the quality of life on a neighbourhood scale. Originating in 1975, the Dutch Association of Local Authorities (VNG) introduced the Woonerf concept [14]. Woonerf streets incorporate multiple features such as narrow passages, hedges, trees, and traffic calming measures that encourage safer driving behaviour and make the drivers feel like guests. Whereas other forms of traffic are allowed, the Woonerf central concept is the transformation of these urban streets into places suitable for social uses, where vehicles and pedestrians can co-exist in a liveable and attractive environment [15]. The unique combination of their components puts significant restrictions on speed limits and traffic volumes, which constitute the most influencing factors on urban noise levels according to Sorrentino et al. [16].

As stated also by Nalmpantis et al. [17], “[…] the Woonerf zone provides space for cars while fully accommodates the need of the residents.” As a measure of comparison, it is worthwhile mentioning that Cekrezi [18] found in his work that the average car traffic flow in high-traffic residential streets in Groningen was nearly eleven times higher compared to Woonerf residential streets in the city. The definition and standards by which a Woonerf must comply are set out in a list of 14 points according to the Dutch Ministry of Transport in 1976 [14]. A translated list of the Woonerf criteria is provided in Table 1.

Table 1

List of Woonerf criteria [14]

Criteria according to the Dutch Ministry of Transport in 1976
1. A Woonerf must be a primarily residential area
2. Roads or road networks within a Woonerf must only carry vehicular traffic, with an origin or destination within that particular Woonerf: through traffic should be excluded
3. No road within a Woonerf should carry a flow of traffic
4. The impression the highway is divided into separate roadway for motor vehicles and a footpath must be avoided. There should be no continuous difference in cross-sectional elements along the length of the road
5. Vertical elements such as plant tubs and shrubs must not restrict visibility
6. The entrances and exits of woonerven (plural) must be so designed that they can be clearly recognized, and it must be obvious to drivers of motor vehicles that these roads are access roads
7. The boundaries of parts of the highway intended for parking should be clearly marked and as a minimum the corners of the parking space should be marked
8. There must be adequate parking facilities for residents of a Woonerf, although if there is surplus car parking capacity available in the immediate vicinity of the Woonerf, the supply of parking spaces may be lower than demand
9. On those parts of the highway intended for use by motor vehicles, features must be introduced which will reduce the speed of all types of vehicles. These features should not be more than 50 meters apart
10. The features referred to in article 9, should not be located to cause vehicles to pass close to housing which fronts directly on to the street
11. In accordance with the regulations, the features referred to in article 9 should create no danger to traffic passing over them
12. Adequate street lighting must be provided to ensure that all features, especially those referred to in article 9, are fully visible at night
13. Areas specially designed as play areas must be clearly identified so, they can be readily distinguished from those areas that can be used by vehicles. Where possible play areas should be physically separated from those parts of the highway used by vehicles
14. The word Woonerf must be displayed along with the blue Woonerf sign

Environmental research into the tranquillity constructs, initially considered tranquillity within the concept of Attention Restoration Theory as a restorative environment that provides this feeling of “soft fascination” [19]. It has been quantified based on the perceived level of quietness or peacefulness of a place that helps people to get away from the demands of everyday life [20]. However, in 2014, the Good Practice Guide on Quiet Areas published by the European Environmental Agency [21] clarified that the aspect of quietness was shown to be less relevant as it was associated more with the presence of silence in public spaces and consequently the feeling of fear. Therefore, when describing the feeling of rated tranquillity in relation to the experience of sound in natural scenes, it is often regarded as a construct of two components: pleasantness and calmness [22].

Regarding the categorization and presence of sounds, various studies have divided sound sources into similar classifications. Some studies make a distinction between natural sounds and sounds generated by human activity [23], while industrial and construction sounds can also be incorporated in the “other” category. Overall, the publication of International Organization for Standardization (ISO) 12913 – Part 2 (Method A), helped a lot towards the formalization of the sound sources’ classification [24]. Depending on the scope of the study, the sound source taxonomy can vary with further diversification of the sound sources. For example, an extended categorization was provided by Watts et al. [25] by splitting the human sounds into people sounds and children playing, while other studies such as that by Mitchell et al. [26], preferred to break down the anthropic sources in more subcategories such as (“speech,” “laughter,” “shouting,” and “children”). High levels of natural sounds (e.g. bird songs) are positively associated with the feeling of tranquillity, while anthropogenic noise (e.g. motorised vehicles) are negatively associated with the tranquillity construct [27,28]. Natural sounds are generally associated with pleasantness, whereas mechanical sounds are usually perceived as unfavourable, especially when related to increasing age or educational level, and linked with annoyance [29]. On the contrary, sounds from people and children playing are overall regarded as more pleasant or neutral compared to technological sounds [30].

1.1 Emerging perceptual components: the tranquillity concept in outdoor sound evaluation

The perceived urban sound environment is characterised by more than just sound pressure levels. In this sense, perceptual models have included noise annoyance in the first instance and more recently, positive health aspects that the listeners may experience in the urban context [31,32]. For the past decade, the ISO has worked to standardise the definition and operationalization of soundscape, which resulted in the development of the ISO 12913 soundscape series [33,34,35]. According to ISO 12913-1, [33] soundscape was defined as the acoustic environment as perceived or experienced and/or understood by a person or people, in context. Following the guidelines on ISO 12913-2 [34], there are three data collection strategies depending on the nature of the research. These would include the analysis of in-situ questionnaires (Method A [34] or Method B [34]), or ex-situ narrative interview protocols (Method C [34]). The corresponding data analysis is based on the two-dimensional circumplex model introduced by Axelsson et al. [36] and further exemplified in Annex C of ISO 12913-2 [34]. It is based on the eight “perceptual attributes”: “Pleasant,” “Vibrant,” “Eventful,” “Chaotic,” “Annoying,” “Monotonous,” “Uneventful,” and “Calm” as shown in Figure 1. Tranquillity is associated with pleasantness and calmness [36]; therefore, a tranquil environment is expected to be found within the bottom right quadrant of the circumplex model [36] and be both pleasant and uneventful.

Figure 1 The circumplex soundscape model based on the eight perceptual attributes.
Figure 1

The circumplex soundscape model based on the eight perceptual attributes.

Tranquillity has consistently been explored over the last 16 years mainly through the Tranquillity Rating Prediction Tool (TRAPT), updated and validated since 2008 [19,25,37,38,39]. It combines audio and visual stimuli objectively using A-weighted sound pressure levels (LAeq) and the percentage of Natural and Contextual Features (NCF) [37] in the visual scene using a rating scale from 0 to 10 [40]. Natural elements within an urban setting or “streetscape greenery” “include all kinds of vegetation that give the street a green appearance.” [41]. Although the presence of greenery might not be the most acoustically effective measure, its attractiveness and acceptance rely more on its visual aesthetics and the attraction of natural sounds [42]. It is important to highlight that the revised measure accounted also for manmade features of cultural value that were considered equally important compared to the natural elements [37].

The tool has been used to explore the aspect of tranquillity within the British landscape and open spaces located in the urban setting [40]. The auditory input consisted of 32-s sound recordings during daytime in different sampling points. Its validation and update since 2013 enhanced its performance leading to a comparative analysis between open areas in the UK and Hong Kong [39]. Therefore, it is a robust tool to explore the perceived tranquillity in a micro-scale environment such as the Woonerf streets, and on the other hand, it offers the potential to explore the preservation of environmental noise quality – as mentioned in the END – in locations that cannot be filtered as “quiet” from the official noise mapping perspective.

1.2 Citizen science and affective sound quality

During the last 10 years, the fields of citizen science and affective sound quality have emerged as critical areas in soundscape and noise mapping research. In particular, empowering the active participation of local residents has been proven very valuable in large-scale data collection [43,44], co-design processes, and emotional mapping [45,46]. Although aspects related to the visualization of affective qualities of sound have been investigated mainly in public squares, parks, or educational settings [47,48], the acoustic characterization of Woonerf streets still remains widely unexplored [49]. They have been at the centre of attention for aspects related to the replication of their design principles as living streets [50] or children’s well-being [18], and there seems to be a need to further investigate their acoustic environment or morphological features, since such indicators can be used to validate their success story and paradigm shift in urban design.

A niche for investigation to include the acoustic quality of such spaces through the concept of tranquillity is detected. The aim of this article can be summarised in the following objectives for the selected Woonerf streets in Groningen and Leewarden: a) evaluate the perceived tranquillity on a street and neighbourhood level, b) identify potential non-auditory factors (e.g. demographic, environmental) affecting perceived tranquillity, c) investigate the sound source dominance and the soundscape perceptual attributes, d) explore the spatial variation of noise levels and natural–cultural factors, and e) explore the possible correlation between observed and predicted tranquillity.

2 Materials and methods

2.1 Study area

From a macroscopic perspective, this study focuses on the cities Groningen and Leeuwarden, which are the most populated ones in the North with approximately 125,000 and 235,000 citizens respectively. Both cities have a sufficient number of Woonerf streets to investigate accounting for 39 km of length for Groningen and 29 km for Leeuwarden. Also, both cities follow a monocentric model with a main business centre and are relatively densely populated as shown in Figure 2.

Figure 2 Population density (residents/sq km) of Leeuwarden (left) and Groningen (right) located in the Netherlands.
Figure 2

Population density (residents/sq km) of Leeuwarden (left) and Groningen (right) located in the Netherlands.

The approach for the case selection combines already known knowledge of characteristics specifically related to Woonerf streets and the use of Overpass turbo [51], a web-based tool for the OpenStreetMap database [52]. With Overpass turbo it was possible to run an Application Programming Interface query to select an overview of nodes and ways according to predefined criteria. First, a query was formulated according to the following criteria in the search for potential Woonerf streets: “highway = living street” and “max speed = 15.” Consequently, to verify if a street is indeed a Woonerf street, Google Street View is used to analyse if the characteristic blue Woonerf sign is present at the entrance and if a blue Woonerf sign with a red line across, or the start of a 30 km/h zone sign is present. The final selection ended up in eight neighbourhoods in the city of Groningen consisting of 31 Woonerf streets together with eight neighbourhoods in the city of Leeuwarden divided into 30 Woonerf streets. A visual representation of the sampling sites within the cities of Groningen and Leeuwarden is displayed in Figures 3 and 4 including the corresponding sampling points.

Figure 3 Sampling spots among the eight neighbourhoods (buurten) in the city of Groningen.
Figure 3

Sampling spots among the eight neighbourhoods (buurten) in the city of Groningen.

Figure 4 Sampling spots among the eight neighbourhoods (buurten) in the city of Leeuwarden.
Figure 4

Sampling spots among the eight neighbourhoods (buurten) in the city of Leeuwarden.

For the TRAPT tool, a combination of sound pressure level (SPL) measurements and manual photographic imagery is used. This method allows for an objective representation of the physical environment to determine the predicted tranquillity level in each context. However, to accommodate for the complexity of the subjective nature of soundscape as perceived by people, an additional questionnaire method was used to examine if the predicted tranquillity levels correspond to the actual perceived tranquillity levels.

For the soundscape assessment, residents of Woonerf Streets were asked to fill in a questionnaire based on Method A of the ISO/TS 12913-2-2018 document. It is important to highlight that qualitative data were asynchronically collected with respect to the quantitative ones, however, this fact is also one of the factors to explore in the present study. This will contribute to gaining knowledge on the possibilities that practitioners and stakeholders must perform studies about the tranquillity concept and take action on the quality of the visual and sound environment. As demographic factors may also influence the assessment of a soundscape [8], the questionnaire is composed of additional questions to account for demographic data on the residents living in the Woonerf streets.

2.2 Data collection

In this study, multiple data collection methods are used. For predicting the tranquillity rate (TR) according to the TRAPT tool, spot measurements of A-weighted SPL and panorama photos in the same locations were taken. Both input sources were used to determine the equivalent constant A-weighted level (LAeq) and the percentage of NCF in view. For the locations of the sampling locations, the following three criteria were used:

  1. Locations should be around five meters from the beginning and ending of the Woonerf, as the line of sight within the Woonerf street should be maximised for the observer allowing only forward visibility.

  2. For the same reason, spots where the main Woonerf street intersects with an adjacent street are excluded as well.

  3. When Conditions 1 and 2 are satisfied, a new audio and visual measurement can be taken.

Additionally, field research was performed by the large-scale distribution of questionnaires focused on quantitative data collection represented based on the Likert scale, in combination with several open questions. Approximately 3,000 questionnaire leaflets were distributed to all the residents living at 61 Woonerf streets during the same period as when the sound and visual data collection took place. Open-ended questions were included to collect additional information and contextual depth to the Woonerf streets and to allow for possible design/policy recommendations after data analysis.

Within the time period of 8:00 and 11:00 a.m., a total of 150 sound measurements were taken and 600 pictures as visual data were collected between 1 and 30 November 2022. The days selected for data collection were not planned, but rather on the day itself. This is due to the fact that precipitation or high winds would greatly influence the sound measurements, resulting in potential errors. Therefore, only with a Beaufort wind force of 2 or less and no precipitation of any kind (e.g. rain, mist, and snow), data were collected. This is also in line with Annex D3 of ISO 12913-2, p.25 [34].

2.2.1 Spot-based sound measurements

For the sound measurements, a mobile device (SM-G973) was positioned on a universal tripod (Studio ME) at a 1.5 m height (Figure 5). This height is comparable to the average ear height of an adult in an upright standing position [53], as cited in the study by Watts [28]. The tripod was placed in such a way that traffic or other forms of activities are not hindered in the process of sound data collection. To measure environmental noise, the NoiseCapture mobile application was used (Version 1.2.22.2) following previous researchers who have also used smartphone-based data collection [54,55,56]. Each sound measurement lasted one minute taking into consideration practical time constraints and the previous study of [39], where the sound recordings had a 30-s duration. Before the data collection started, manual calibration was performed with a reference device in the NoiseCapture app. The mobile device was manually calibrated in accordance with a calibrated reference device (Voltcraft SL-451) at 95 dB. Noise measurements were attached as spatial attributes to a basemap layer. For this step, the LocusGIS application was used. LocusGIS allows for data to be attached to specific coordinates and is stored in a shapefile format. This shapefile was later used for further analysis in a Geographical Information System (QGIS).

Figure 5 Equipment set-up ready for data collection in one of the Woonerf streets.
Figure 5

Equipment set-up ready for data collection in one of the Woonerf streets.

For the visual measurements, the same mobile device (SM-G973) was positioned on a universal tripod (Studio ME) at the same height as the average ear height perpendicular to the street’s surface. At the same location of each sound measurement, a visual measurement is also performed. The only difference is that the tripod is placed in the middle of the road to perform visual measurements. At each location, four pictures were taken at 0°, 90°, 180°, and 270°. Each time going clockwise, starting with the house side of the street. The mobile device is capable of a field of view (FOV) in the horizontal direction of 77°, allowing for a panoramic 360° view without any overlap of imagery. The vertical FOV of a standard camera lens is ±20° and compatible with studies related to a person’s eye FOV [53]. Coordinates of the visual measurement locations are automatically stored and allocated to corresponding pictures.

2.2.2 Questionnaires

For the development of the questionnaire, the map-based survey tool Maptionnaire was used. After a short introduction to the research topic, residents were able to access the questionnaire using a QR code or by filling in a URL link. Based on information derived from responses to the administered questionnaires, it is observed that the submission period was extended from 19 November to 15 December 2022. This assessment duration closely corresponds to the time frame during which the audio and visual measurements were executed. A translated English version was also added, allowing non-native Dutch speakers to participate in the questionnaire as well. It was not possible for the participants to proceed to the next page of the questionnaire if a question was not answered. Following the exclusion of respondents who did not complete the entire questionnaire, an adjusted response rate of 13.4% (N = 403) was calculated. Eight perceptual attributes were evaluated based on a 5-point Likert scale ranging from 1, “strongly disagree” to 5, “strongly agree.” Two questions regarding the tranquillity and importance of tranquillity were based on a 10-point Likert scale with “0” always being the lowest value and “10” representing the highest. The questionnaire also had categorical questions related to the dominance of sound sources (motorised traffic sounds, bicycle sounds, natural sounds, people sound, and children playing).

2.3 Analysis of auditory factors

Questions covering the eight affective attributes regarding noise perception were analysed using the corresponding Likert scales. These values were plotted into X,Y coordinates on a scatterplot ranging between −1 and +1 using Equations (1) and (2) provided by ISO 12913:3-2019.

(1) ISO Pleasant = ( ( pleasant − annoying ) + cos ( 45 ° ) × ( calm − chaotic ) + cos ( 45 ° ) × ( vibrant − monotonous ) ) × 1 / ( 4 + √ 32 ) ,

(2) ISO Eventful = ( ( eventful − uneventful ) + cos ( 45 ° ) × ( chaotic − calm ) + cos ( 45 ° ) × ( vibrant − monotonous ) ) × 1 / ( 4 + √ 32 ) .

2.4 Analysis of visual factors

In the 61 Woonerf streets, a total of 600 pictures were taken. The Green View Index (GVI) [57] was calculated based on the equation of Yang et al. [58]. This formula uses the total green pixel area of four pictures divided by the total amount of pixels (Equation (3)). Area g_i is the total amount of green pixels in the ith direction (north, east, south, and west). Area t_i is the total amount of pixels of the same four pictures.

(3) Green v iew = ∑ i = 1 4 Area g _ i ∑ i = 1 4 Area t _ i × 100 % .

The percentage of NCF value uses a similar formula; however, it also includes the contextual visual elements and excludes the visible sky area. This formula was used to calculate NCF values by the following equation:

(4) NCF = ∑ θ 6 An θ × 100 ( At θ ) 6 ,

where An θ represents the amount of green and contextual pixels, and Atθ is the total amount of pixels minus the sky. Six images cover the 360° horizontal surroundings pedestrians can see. Instead of six pictures, four pictures were taken in this study in the 0°, 90°, 180°, 270° direction. For the GVI and the NCF values, visible skies were excluded. Contextual pixels of visible cultural heritage buildings are also relevant and should be added to the number of green pixels. To investigate whether cultural heritage buildings are present in a particular Woonerf street, the Cultural Heritage Agency of the Netherlands (RCE) was consulted. The modified calculation formulas for GVI and NCF used in this study are as follows:

(5) Green v iew = ∑ i = 1 4 Area g _ i ∑ i = 1 4 Area t i − Area s i × 100 % ,

(6) NCF = ∑ i = 1 4 Area g _ i + Area c _ i ∑ i = 1 4 Area t i − Area s i × 100 % .

In these formulas, Area c_i is the number of contextual pixels and Area s i the number of sky pixels. ImageJ version 1.53t 24 and Adobe Photoshop version 24.1 were both used on a pilot case to examine if both programs provided the same NCF value. The four pictures taken at each point are laid in a square for efficiency purposes. Both programs showed similar results (16 and 17%, respectively). Consequently, it was decided to analyse the complete imagery dataset by using Adobe Photoshop, as this was a more convenient program for larger samples. Through Adobe Photoshop image analysis tools, manual extraction of pixel areas required for Equations (5) and (6) was performed as shown in Figure 6.

Figure 6 Example of the pixel analysis for a randomly taken picture: (a) four pictures laid together form the total panoramic view, (b) exclusion of sky pixels, and (c) the streetscape greenery.
Figure 6

Example of the pixel analysis for a randomly taken picture: (a) four pictures laid together form the total panoramic view, (b) exclusion of sky pixels, and (c) the streetscape greenery.

2.5 Spatial and linear models for noise and tranquillity prediction

Inverse Distance Weighted (IDW) interpolation was used in QGIS as the main tool for the visualization of noise data. Interpolation techniques have been used in the past to represent sound levels, and studies have emphasised the added value of interpolation techniques for spatial representation purposes [48,59]. As the data points are spread around the cities of Groningen and Leeuwarden, a 25-m buffer was set around the Woonerf streets and used via the clip raster to mask the layer tool. This distance best reflects the area where the influence of the streets on the interpolated values is most pronounced and strikes a balance between effectively masking the background and maintaining a clear distinction between the interpolated values and the base layer. For the calculation of the predicted tranquillity, the adjusted TRAPT equation for urban green spaces given by [39] was used. As mentioned, this is an alteration of the previous TRAPT tool, which was used in the context of urban green spaces [37]. This is Equation (7) is as follows:

(7) TR = 10.55 + 0.041 × NCF − 0.146 × LAeq + MF .

In this equation, the TR stands for the Predicted Tranquillity Rate ranging from 0 to 10. The number 10.55 is an adjusted constant from the original TRAPT equation, derived from insights based on the adaptation-level theory [60]. This theory implies that people living in densely urban populated areas have likely grown accustomed to higher noise levels and lower levels of greenery. NCF is the ratio of all the natural and historical visual elements compared to the total visual perception of a human; LAeq is the equivalent continuous A-weighted sound pressure level; MF serves as a moderating factor to take into account any negative factors (e.g. graffiti, litter) and positive factors (e.g. the sound of water) which might be present on site [61]. Since these moderating factors are found to be of limited effect with a maximum of one point difference [26], they are not considered in this study.

3 Results

3.1 Evaluation of perceived tranquillity on a street and neighbourhood level

The analysis explores two scales of interest with the first one concerning the street level (macro-scale) and the second one the neighbourhood level (meso-scale) investigated in a top-down approach. Since the criterion of normality was not always satisfied, it was decided to stick to non-parametric tests for inferential statistics. In the mesoscale analysis, concerning the two cities, respondents were asked to rate the street tranquillity with a number ranging from 0 and 10, where 0 denotes “least tranquil” and 10 “most tranquil.” In total, 150 noise measurements (LAeq) were collected, ranging from 39.8 to 63.1 dBA.

On a macro-scale level, the tranquillity rating along all Woonerf streets as shown in the histogram in Figure 7 revealed an expected asymmetry with a moderately right-skewed distribution (0.13). This is important to correctly interpret the results, as there was a high mean value of 7.98 (SD = 1.52). The latter shows that overall, the level of perceived tranquillity was high with small fluctuations, which was a good starting point to move deeper in the analysis. For the spatial aggregation of the results from street level to the neighbourhood level (buurt), only neighbourhoods with a minimum of 20 responses (N ≥ 20) were considered to minimize the risk of ecological bias. This equals 13 neighbourhoods with a total of 371 questionnaires (267 + 104) fully answered in both cities.

Figure 7 Grouped frequency distribution table of perceived tranquillity in Groningen and Leeuwarden within their Woonerf streets (N ≥ 20).
Figure 7

Grouped frequency distribution table of perceived tranquillity in Groningen and Leeuwarden within their Woonerf streets (N ≥ 20).

As shown in Figure 8, the lowest average perceived TR was measured in De Hoogte (5.6) and the highest in the Transvaalwijk (7.80). Between Groningen and Leeuwarden minimal differences were observed in the mean perceived TR with scores of 6.5 and 6.8, respectively. However, the results in Groningen presented a higher variance (3.7) compared to Leeuwarden (3.0).

Figure 8 Average perceived tranquillity rating among the 13 neighbourhoods in Groningen (orange) and Leeuwarden (blue) with N ≥ 20.
Figure 8

Average perceived tranquillity rating among the 13 neighbourhoods in Groningen (orange) and Leeuwarden (blue) with N ≥ 20.

To determine whether there are any statistically significant differences among the mean values of the perceived tranquillity in both cities, a one-way Welch ANOVA was conducted on a neighbourhood level. The assumptions of normality and homogeneity of variances were tested prior to the test confirming unequal variances F(12, 358) = 2.543, p < 0.01 among the groups.

The test (F(12, 112.405) = 3.55, p < 0.001) indicated a significant difference in perceived tranquillity among the 13 Woonerf neighbourhoods in Groningen and Leeuwarden. Since the variances across the groups were not assumed equal and sample sizes among the neighbourhoods were also unequal, a Games-Howell post-hoc test was performed to determine which specific groups differ significantly from each other. A significant difference in perceived tranquillity (p < 0.05) was observed at neighbourhood 13 (Transvaalwijk) compared to neighbourhoods 1, 3, 10, and 11 (Hortusbuurt-Ebbingekwartier, De Hoogte, Huizem-Bornia, and Achter de Hoven).

3.2 Relationship between age and perceived tranquillity

An additional statistical test was performed to account for potential differences between perceived tranquillity and the demographic factor of age among the 403 respondents as shown in Figure 9. Levene’s test indicated that there was not enough evidence to reject the null hypothesis of equal variances (F(5, 397) = 1.741, p > 0.05). Similarly, to the previous section, a one-way Welch ANOVA test was initially conducted to compare the means across the different age groups. Results (F(5, 168.3) = 6.39, p < 0.001) indicated that perceived tranquillity is not equal across the different age groups. Therefore, the Games–Howell post-hoc test revealed that there was a significant difference in the perceived tranquillity assessment among the following groups: a) age group 56–65 and <25 years (p < 0.01), 56–65 and 25–35 years (p < 0.001), 56–65 and 36–45 years (p < 0.01) with additional difference between the age group >65 years and 25–35 years (p < 0.05).

Figure 9 Differences in the age groups among the respondents between the neighbourhoods of Groningen and Leeuwarden (N ≥ 20).
Figure 9

Differences in the age groups among the respondents between the neighbourhoods of Groningen and Leeuwarden (N ≥ 20).

Frequency bar chart of the age groups among the neighbourhoods is depicted in Figure 10. Almost all the neighbourhoods have a relatively young demographic with most residents being under 35 years old. It is worthwhile highlighting the over-representation of Transvaalwijk respondents aged 56+ (60%) compared to the relatively young demographics of the rest of the neighbourhoods surveyed. This characteristic can most likely be attributed to the fact that this is in an affluent neighbourhood, with the highest percentage of homeowners among all neighbourhoods in Leeuwarden (60%). We keep this as evidence of a possible link between tranquillity and home ownership, however since we do not have additional data to explain this association, a possible causation needs to be further investigated.

Figure 10 Differences in age groups and perceived tranquillity ratings between neighbourhoods in Groningen and Leeuwarden (N = 403).
Figure 10

Differences in age groups and perceived tranquillity ratings between neighbourhoods in Groningen and Leeuwarden (N = 403).

3.3 Investigation of the sound sources’ dominance and soundscape perceptual attributes

Five sound source categories (Natural, Bicycle, Motorized traffic, People, and Children playing) were chosen as the most relevant sounds within the context of the Woonerf streets. Figure 11 shows the frequency of the reported most dominant sound sources within the Woonerf streets as a percentage of the 13 neighbourhoods in Groningen and Leeuwarden.

Figure 11 Most dominant sound sources perceived by the residents within the 13 neighbourhoods in Groningen and Leeuwarden (N ≥ 20).
Figure 11

Most dominant sound sources perceived by the residents within the 13 neighbourhoods in Groningen and Leeuwarden (N ≥ 20).

Primarily, it can be concluded that residents perceived motorised traffic and people’s sounds as the most dominant, while natural, bicycle, and children playing sounds were perceived as less dominant. This was true for all neighbourhoods except for Ulgersmaborg. In this neighbourhood the sound of children playing was perceived as most dominant compared to the other sound sources. Also, with regards to natural sounds, 20% of the respondents perceive natural sound as the most dominant sound source in two neighbourhoods (i.e. Schilderbuurt and Ulgersmaborg). In contrast, no residents in De Hoogte and Achter de Hoven neighbourhoods perceived natural sounds as the most dominant sound source in their streets. Finally, significant differences between perceived tranquillity and dominant sound sources in the 13 neighbourhoods were detected using the Games–Howell Post-hoc test between motorized and natural sound sources (p < 0.05).

The results of the soundscape analysis are depicted in Figure 12 using the ISO-based bidimensional circumplex model that displays the eight perceptual attributes in opposing directions. X and Y coordinates’ formulas (Equations (2) and (3)) were calculated for the ISO Pleasantness and ISO Eventfulness axes, respectively. These coordinates determine the locations of the 13 neighbourhoods in the Soundscape Scatter Plot.

Figure 12 Bidimensional circumplex soundscape model based on ISO 12913-3 with the investigated neighbourhoods in Groningen (orange) and Leeuwarden (blue).
Figure 12

Bidimensional circumplex soundscape model based on ISO 12913-3 with the investigated neighbourhoods in Groningen (orange) and Leeuwarden (blue).

Given these coordinate points, the following noticeable aspects can be distinguished. At first, concerning the Pleasantness axis, almost all neighbourhoods (12 out of 13) are located within the range of 0.2 and 0.6. The Transvaalwijk shows the highest score of 0.59 whereas De Hoogte is just outside the range (0.17) and ranks the lowest of all neighbourhoods with respect to Pleasantness. The fact that the lowest end is still on the side of the positive axis shows that residents’ perception of sound shows little resemblance with the feeling of annoyance and is rated as relatively pleasant.

In terms of Eventfulness, all neighbourhoods have neutral to low scores. More specifically, the vast majority of neighbourhoods (11 out of 13) are positioned between 0 and −0.2 on the Eventfulness axis. Only Oosterpoort and Transvaalwijk are just outside this range, with scores of 0.04 and −0.25, respectively.

When considering the two axes altogether, the Transvaalwijk scores relatively high on the Pleasantness axis and low on the Eventfulness axis. Hence, this neighbourhood shows the highest resemblance with associated feelings of calmness. Given these results, a clustered area can also be observed in Figure 12.

3.4 Spatial variability of noise levels, green space and cultural parameters

To understand the temporal–spatial characteristics of noise in the Woonerf streets, an IDW interpolation of the equivalent A-weighted sound pressure level (LAeq) was used. Results of the two focal noise maps in Groningen and Leeuwarden Woonerf streets are shown in Figure 13(a) and (b) with noise levels ranging between 39 and 62 dBA. In Groningen, street number 28 is located close to a public school which might explain the relatively high sound levels. The other red coloured spots are located near major roads and/or the railway station. In Leeuwarden, streets appear to be in general closer to the lower end of the spectrum. Streets 34 and 35 are located close to the railway station. However, streets 33 and 32 are positioned within the same radius. While these differences cannot be explained within the broader urban context, other influences are discussed in the next section.

Figure 13 Noise levels (LAeq) simulated for the 31 Woonerf streets in the city of Groningen (a) and in 30 streets in Leeuwarden (b).
Figure 13

Noise levels (LAeq) simulated for the 31 Woonerf streets in the city of Groningen (a) and in 30 streets in Leeuwarden (b).

GVI and Cultural percentages form the two constituent parts of the NCF percentage of Woonerf streets in Groningen and Leeuwarden. Interpolated visual maps showing these results are presented in Figure 14(a) and (b). The numbers represent the 61 street names of the Woonerf streets selected in Groningen and Leeuwarden. Figure 15 provides an illustration of how GVI levels differ between Woonerf streets. Each square consists of four pictures taken in the 0°, 90°, 180°, and 270° direction and represents the visual perception of residents on a street level at a random data measurement point. This figure compares the three highest and lowest-scoring streets to illustrate how variations in GVI levels can affect the overall appearance of a Woonerf street.

Figure 14 NCF Percentage simulated within the Woonerf streets in (a) Groningen and (b) Leeuwarden.
Figure 14

NCF Percentage simulated within the Woonerf streets in (a) Groningen and (b) Leeuwarden.

Figure 15 Visual representation of GVI levels on a pedestrian view. Upper row: 23. Davidstraat (left), 20. Eelderstraat (centre), 15. Bedumerstraat (right). Bottom row: 60. Cronjéstraat (left), 26. Lodewijkstraat (centre), and 58. Schalk Burgerstraat (right).
Figure 15

Visual representation of GVI levels on a pedestrian view. Upper row: 23. Davidstraat (left), 20. Eelderstraat (centre), 15. Bedumerstraat (right). Bottom row: 60. Cronjéstraat (left), 26. Lodewijkstraat (centre), and 58. Schalk Burgerstraat (right).

For the NCF percentage, cultural buildings should be included as well. In Groningen, street 10 scored the highest NCF score (38%) and street 23 the lowest (2%). In Leeuwarden, street 32 scored the highest (67%) and street 39 had the lowest score (6%). Both highest scoring streets share a relatively high percentage of culture. This high percentage of cultural visuality is due to the fact of the location of the street within the larger urban area. Street 32 and street 33 are both located in a complete block of residential houses, which are considered to be part of the cultural heritage.

There are six streets (11, 19, 26, 28, 29, and 31) in Groningen that score above 30% NCF. In Leeuwarden nine streets (32, 33, 48, 52, 56, 57, 58, 59, and 60) score above this value. When comparing Groningen to Leeuwarden this equals roughly 19 and 29% of the total streets being above 30% NCF, respectively. When comparing the city of Groningen to Leeuwarden regarding the total average visual levels of greenery/cultural levels, a 4% difference can be observed (19–23%). The analysis of the NCF percentage on a neighbourhood scale is displayed in Figure 16.

Figure 16 Average NCF and GVI percentage of neighbourhoods in Groningen and Leeuwarden.
Figure 16

Average NCF and GVI percentage of neighbourhoods in Groningen and Leeuwarden.

It can be observed that neighbourhood 3 (De Hoogte) is valued the lowest (3%), and neighbourhood 9 (Hollanderwijk) scores the highest NCF percentage (34%). When aggregation of streets occurs, cultural visual levels are relatively less influential considering street 10, which previously ranked highest in Groningen, is now ranked average of around 18% on a neighbourhood level. A difference between the visual levels of greenery/cultural levels of Woonerf streets can be observed. Woonerf streets NCF visual levels range from 2% at the lowest end to 67% at the highest. Apart from the presence of greenery in a Woonerf street, cultural buildings seem to have a determining role as well considering the NCF percentage of Woonerf areas. When only regarding GVI levels, streets considered to be average at first are ranked at the top end when cultural buildings are also taken into consideration. So, when present, cultural buildings have a considerable impact on street NCF levels.

3.5 Correlation of the perceived and predicted tranquillity on the street level

A Wilcoxon signed-ranks test was conducted to compare the average predicted tranquillity with the average perceived tranquillity levels within all Woonerf areas for the cities of Groningen and Leeuwarden. This entails only the Woonerf areas specifically and not the city. The Wilcoxon signed-Ranks Test was chosen over the paired t-test because the Shapiro-Wilk test for Average Perceived Tranquillity showed the following values (W = 0.962, p = 0.053, N = 61), (W = 0.949, p = 0.029, N = 51), (W = 0.956, p = 0.238, N = 30) & (W = 0.927, p = 0.037, N = 31). While there is some evidence in favour of normality, caution is warranted due to the small sample size (e.g. N = 30). Therefore, opting for a non-parametric test ensured a conservative approach, minimizing the risk of drawing erroneous conclusions due to potential violations of normality assumptions. The results of the first Wilcoxon signed-ranks test indicate a statistically significant difference in the median (Z = 6.66, p < 0.001). A Hodges–Lehmann test estimated perceived tranquillity levels to be 2.47 points higher than the predicted tranquillity levels (95% CI [2.09, 2.81]). Then, a linear regression analysis was performed to illustrate the strength of the correlation between the average predicted and perceived tranquillity of 61 Woonerf streets in Groningen and Leeuwarden as shown in Figure 17(a). The Spearman’s rank correlation test also indicated a non-significant, weak, and positive correlation (r s = 0.137, p > 0.05) on a street level between the perceived tranquillity levels and the predicted ones according to the TRAPT tool. A large variation was also shown between the perceived and predicted values (R 2 = 0.065) among the Woonerf streets of Groningen and Leeuwarden combined.

Figure 17 Average predicted and perceived TR of (a) 61 Woonerf streets in Groningen and Leeuwarden, (b) 51 Woonerf streets in Groningen and Leeuwarden (N ≥ 20), (c) 30 Woonerf streets in Leeuwarden (N ≥ 20), and (d) 31 Woonerf streets in Groningen (N ≥ 20).
Figure 17

Average predicted and perceived TR of (a) 61 Woonerf streets in Groningen and Leeuwarden, (b) 51 Woonerf streets in Groningen and Leeuwarden (N ≥ 20), (c) 30 Woonerf streets in Leeuwarden (N ≥ 20), and (d) 31 Woonerf streets in Groningen (N ≥ 20).

A second Wilcoxon signed-ranks test was conducted, including only streets located in neighbourhoods within Groningen and Leeuwarden with at least 20 respondents (N = 51). Additionally, a linear regression analysis of the two variables was performed to illustrate the strength of the relationship (Figure 17b). Again, a significant difference in median values (Z = 6.18, p < 0.001) can be observed. A Hodges–Lehman test estimated 2.69 points higher average between the two variables (95% CI [2.31, 3.06]). Perceived and predicted tranquillity scores were slightly higher but still weakly and positively correlated according to Spearman’s rank test (r s = 0.217, p > 0.05).

In the third instance, a distinct Wilcoxon signed-ranks test was performed, exclusively focusing on the 30 streets in Leeuwarden (Figure 17c). Like previous findings, a significant difference in median values persisted (Z = 4.47, p < 0.001). The Hodges–Lehman test estimated a 1.94-point rise in the average between the two variables, showcasing a 95% CI [1.38, 2.53]. Surprisingly, Spearman’s rank correlation test reveals a weak and positive correlation (r s = 0.092) between perceived and predicted tranquillity scores, but it falls short of statistical significance at p > 0.05. The linear regression graphically illustrates that an increase in perceived tranquillity has minimal impact on predicted tranquillity, maintaining a relatively stable relationship.

For the fourth analysis, a Wilcoxon signed-ranks test was executed, focusing exclusively on the 31 streets in Groningen. Once again, a distinction in medians emerged (Z = 4.86, p < 0.001). Additionally, a linear regression analysis unfolds a convergence of the two variables, as the starting point of the line approaches the origin (0, 1.84) and the gradient increases from 0.12, 0.20, and 0.03 to 0.28, as shown in Figure 17d. The Hodges–Lehmann test indicated a 2.92-point elevation in the average between the two variables, with a 95% CI [2.47, 3.35]. Contrary to the three previous analyses, a robust correlation surfaces through Spearman’s rank correlation test, denoting a value of r s = 0.285, which is statistically not significant at p > 0.05.

4 Discussion

High levels of perceived tranquillity (M = 7.98) were reported in both cities based on the street-level analysis. Such results confirm the fact that overall Woonerf areas receive a much lower traffic load compared to normal high-traffic streets combined with a boosted feeling of safety from the local residents [18], also exemplified by the presence of higher urban tree cover [62]. Although the European Noise Directive 2002/49, known as END [9] and the Good Practice Guide on Quiet Areas both push for the delineation and protection of quiet areas, in this case, it would be more useful to expand this legal obligation from the END, and the recommendations from the technical report on Good Practice Guide to Quite Areas, to the protection of areas of high acoustic quality. The latter refers mainly to the appropriateness of the dominant sound sources with the corresponding human activities, since Woonerf areas have the advantage and the infrastructure to be potentially both calm and vibrant when it comes to socializing or children playing.

Concerning our findings on the effect of age on tranquillity assessment, it is worthwhile noting that social–contextual factors including demographic or cultural parameters have a major influence on soundscape descriptors since they influence the behavioural expectations of the participants. Acoustic comfort has also been rated differently across age groups. This is in accordance with previous findings from Yang and Kang [63], as they state that personal characteristics may be of influence on the assessment of a person’s soundscape experience. On top of that, the SSID project has included a wider category of social–contextual factors, where demographics belong to the final soundscape index formula [64].

The data also show that the presence, or rather absence, of greenery has a negative influence on the perceived levels of tranquillity, which is in accordance with Watts’ research [28]. However, not all tranquillity differences can be explained by greenery levels alone, as not all areas with lower greenery levels automatically show a lower perceived level of tranquillity. Noise levels could be a determining factor, as well as other moderating factors (e.g. environmental factors such as lighting intensity and air quality, among others [64], which are not taken into consideration in this study).

A high level of annoyance caused by environmental noise should be considered as one of the environmental health burdens [3]. In this study, it was found that at least for residential Woonerf streets in Groningen and Leeuwarden, no severe annoyance was perceived by residents on average. However, as the circumplex model of soundscape is average-based, it does not necessarily mean that severe annoyance never occurs. Less noise does not automatically imply higher pleasantness levels [7].

This research has also shown that other sound sources can be perceived as most dominant (e.g. human sounds) in the Woonerf environment. Perceived tranquillity and pleasantness are positively associated with natural sounds, while motorised sounds are often regarded as a negative influence and associated with annoyance [25,26,27]. Human sounds and children playing are in general perceived as pleasant or neutral [28]. Given these statements, it is interesting to note that while in the context of Woonerf streets, motorised sounds are perceived as one of the most dominant sound sources in general, a neutral feeling between pleasantness and annoyance is reported by the residents.

The TRAPT tool developed by Pheasant and colleagues [19,25,37,38,39] may be an appropriate tool to provide an indication for relative tranquillity levels in other contexts instead of predicting actual tranquillity levels of Woonerf streets. One could argue that, when certain sound sources (e.g. natural sounds) are excluded, sound levels will be lower, as well as predicted tranquillity levels. However, in this research, no sound sources were excluded from the sound collection process.

The main reasons for the undervaluation of tranquillity levels may be due to the time of year this study was conducted. During the study period, a decrease in greenery is observed due to the naturally occurring seasonal change from summer to fall. However, even after the modification of the TRAPT tool by multiplying the GVI levels and sound levels through different constants, there was still no significant increase in correlation between the average predicted and perceived tranquillity levels found. This gives an indication that even when GVI levels were higher and sound levels were lower, correlation is still found to be weak between the predicted and actual tranquillity levels in the context of Woonerf streets.

Another aspect worth considering is the fact that residents are inside their homes when filling in the questions related to sound perception. These responses will form the basis of the two-dimensional measurement system for the affective soundscape quality [32]. However, Method A of ISO/TS 12913-2:2018 entails questionnaires to be filled in situ. It is unclear whether filling in the questionnaire indoors instead of outdoors affected the participants’ perception of tranquillity since with this method a bigger sample size was achieved. In the end, further investigation of the TRAP tool is needed in such urban settings since multiple parameters need to be moderated. However, TRAPT might still be useful in providing additional information regarding the two-dimensional representation system for the affective soundscape quality. A combination of the TRAPT approach including visual measurements with soundscape maps on a street level might provide a more holistic method to assess tranquillity in a micro-urban environment.

Short-term noise measurements on the other hand can be justified based on several reasons: a) the TRAPT tool relies on 30-s recordings [25,39] and other studies such as [65] have used the same time window to assess the development of soundscape indices, b) Woonerf streets are considered to be tranquil without intrusive noise events. Linked to this, there are considerable advantages to the use of smartphone applications in the quantification of the ambient sound environment. This approach can also leverage the active role of citizens in co-design and participatory planning rather than addressing exclusively the environmental noise management approach as explained by Brown [66]. Over the last 10 years, various sophisticated apps have been introduced in this direction [43,67,68,69] using geolocated noise measurements and providing an alternative approach to constructing focal noise and soundscape maps. These tools can provide a good approximation of the actual noise levels with an accuracy within 3 dB(A) from the reference values [70,71] and can further boost the missing user-centred design approach in urban design.

Finally, Woonerf streets constitute an ideal environment both from a morphological and ecological perspective, since they combine a low maximum speed limit and a cauliflower urban structure as mentioned in the “New Space for Living” report [72].

It is therefore important to explore two aspects in the near future: a) how policies related to nature-based solutions, urban resilience and the Sustainable Development Goals can incorporate Woonerf neighbourhoods and further promote the sense of community combined with ecological connectivity [73,74] and b) how we can create design support soundscape tools [75] that will enhance synergies between planners and residents linking the aforementioned crowdsourcing tools with advanced technology such as Virtual Reality. The first step in this direction has already been done with the City Ditty tool [76], which facilitates rapid audio-visual prototyping of urban soundscapes.

5 Conclusion

The current research investigated four aspects related to a) the evaluation of the perceived tranquillity on a street and neighbourhood level, b) the identification of non-auditory factors (e.g. demographic, environmental) affecting perceived tranquillity, c) the investigation of the sound source dominance and lastly d) the correlation between the perceived and calculated tranquillity. The research was conducted in 13 neighbourhoods around the Dutch cities of Groningen and Leeuwarden particularly designed to take place in streets that satisfy the Woonerf criteria. Through a comprehensive audio-visual analysis supported by questionnaires responses from the residents, the following conclusions can be extracted:

  • High levels of perceived tranquillity (M = 7.98) were reported in both cities based on the street-level analysis. Between Groningen and Leeuwarden minimal differences were observed in the mean perceived TR with the results in Groningen presenting a higher variance (3.7) compared to Leeuwarden (3.0). A significant difference in perceived tranquillity was also observed in neighbourhood 13 (Transvaalwijk) compared to neighbourhoods Hortusbuurt-Ebbingekwartier, De Hoogte, Huizem-Bornia, and Achter de Hoven.

  • With respect to the correlation of the non-auditory factor of age with perceived tranquillity, it was found that: there was a significant difference in the perceived tranquillity assessment among the following groups: a) age group 56–65 and <25 years (p < 0.01), 56–65 and 25–35 years (p < 0.001), 56–65 and 36–45 years (p < 0.01). There was also evidence that there might be a relationship between homeownership and perceived tranquillity; however, the small sample does not allow for the establishment of a causal relationship at this stage.

  • The assessment of the sound sources and the associated soundscape perceptual attributes clearly displayed that local residents perceived motorised traffic and people’s sounds as the most dominant, while natural, bicycle and children sounds were perceived as less dominant apart from the neighbourhood of Ulgersmaborg. Significant differences between perceived tranquillity and dominant sound sources in the 13 neighbourhoods were also detected between motorized and natural sound sources. Finally, the perception of the eight soundscape attributes showed a cluster of neighbourhoods slightly above neutral regarding “Pleasantness” and slightly more towards “Uneventfulness.” On top of this, the comparison of noise levels and the bidimensional circumplex model indicated that lower noise levels do not automatically result in a higher level of pleasantness.

  • Visual measurements and pixel analyses show that visual NCF levels vary between Woonerf streets. However, statistical test results indicated non-significant differences between Groningen and Leeuwarden on a city level as well as no significant difference on a neighbourhood level. Cultural buildings were found in less than 10% of the streets. However, if such buildings are indeed present, a relatively high increase in NCF levels may occur on an average neighbourhood level.

  • Among Woonerf streets, average perceived tranquillity scores were found to be consistently higher ranging from 1.94 to 2.92 points compared to the average levels of predicted tranquillity. The highest correlation was found between the average predicted and perceived tranquillity levels when comparing the Woonerf streets in Groningen. To conclude, auditory or visual levels alone cannot be used separately to predict the tranquillity of Woonerf areas. Tranquillity may be predicted to some extent by the noise and green/cultural visual levels. However, significant differences were observed between the average predicted and perceived tranquillity levels.

To conclude, this study demonstrates that the TRAPT tool may not be a sufficient measure to predict tranquillity levels in Woonerf streets. However, generalizing this conclusion and defining the degree of its efficiency can only be achieved after the completion of further studies within more cities in the Netherlands or abroad.

Acknowledgments

The authors wish to thank the numerous residents who filled in the questionnaires in the cities of Groningen and Leeuwarden and Prof. Greg Watts for his useful guidelines at the early stage of this work.

  1. Funding information: Authors state no funding involved.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results, and approved the final version of the manuscript. EM conceived the presented idea, revised the manuscript, and acted as the main supervisor of TL and the corresponding author of this document. TL developed the theoretical formalism, collected all the primary data, and performed the analytical and statistical calculations. LE-M verified the analytical methods and encouraged TL to investigate the calculation of the Tranquillity index based on an alternative formula. LE-M revised the manuscript and added valuable comments on the conceptual model and the statistical calculations.

  3. Conflict of interest: Author E.M., who is the co-author of this article, is a current Editorial Board member of Noise Mapping. This fact did not affect the peer-review process. The authors declare no other conflict of interest.

  4. Data availability statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

[1] Urban population: World Bank staff estimates based on the United Nations Population Division’s World Urbanization Prospects: 2018 Revision. The World Bank; 2018. https://data.worldbank.org/indicator/SP.URB.TOTL?end=2021&start=1960&view=chart (accessed 2024 12-01).Search in Google Scholar

[2] EEA. Environmental Noise in Europe-2020. Luxembourg: European Environmental Agency; 2020 [updated 13 Feb 2023; cited 20 Feb. 2024]. https://www.eea.europa.eu/publications/environmental-noise-in-europe.Search in Google Scholar

[3] Burden of disease from environmental noise, quantification of healthy life years lost in Europe. World Health Organization; 2011. https://www.who.int/publications-detail-redirect/9789289002295.Search in Google Scholar

[4] Li A, Martino E, Mansour A, Bentley R. Environmental noise exposure and mental health: evidence from a population-based longitudinal study. Am J Preventive Med. 2022;63(2):e39–48.10.1016/j.amepre.2022.02.020Search in Google Scholar PubMed

[5] Raimbault M, Dubois D. Urban soundscapes: Experiences and knowledge. Cities. 2005;22(5):339–50.10.1016/j.cities.2005.05.003Search in Google Scholar

[6] Payne SR, Bruce N. Exploring the relationship between urban quiet areas and perceived restorative benefits. Int J Environ Res Public Health. 2019;16(9):1611.10.3390/ijerph16091611Search in Google Scholar PubMed PubMed Central

[7] Radicchi A, Cevikayak Yelmi P, Chung A, Jordan P, Stewart S, Tsaligopoulos A, et al. Sound and the healthy city. Cities Health. 2021;5(1–2):1–13.10.1080/23748834.2020.1821980Search in Google Scholar

[8] Aletta F, Oberman T, Kang J. Associations between positive health-related effects and soundscapes perceptual constructs: a systematic review. Int J Environ Res Public Health. 2018;15:2392.10.3390/ijerph15112392Search in Google Scholar PubMed PubMed Central

[9] Directive (EC). 2002/49 of the European Parliament and the Council of 25 June 2002 relating to the assessment and management of environmental noise; 2002. p. L189/112–125.Search in Google Scholar

[10] EEA. Noise in Europe 2014 Luxembourg. European Environment Agency; 2014 [updated 26 Sept. 2023; cited 10 Febr. 2024]. https://www.eea.europa.eu/publications/noise-in-europe-2014/download.Search in Google Scholar

[11] Waye KP, Van Kempen E. Non-auditory effects of noise: an overview of the state of the science of the 2017-2020 period: Conference proceedings; 2021 14–17 June. In: 13th ICBEN Congress on Noise as a Public Health Problem [Internet]. Stockholm, Sweden: p. 1–35. https://www.icben.org/2021/ICBEN%202021%20Papers/full_paper_29017.pdf.Search in Google Scholar

[12] Mark B. Parameters of well-being and subjective health and their relationship with residential traffic noise exposure – a representative evaluation in Switzerland. Environ Int. 2011;37(4):723–33.10.1016/j.envint.2011.02.011Search in Google Scholar PubMed

[13] World Cities Report 2022. Envisage the future of cities [Internet]. UN HABITAT; 2022 [updated June 2022; cited 12 Jan. 2024]. https://unhabitat.org/sites/default/files/2022/06/wcr_2022.pdf.Search in Google Scholar

[14] Kraay JH. Woonerven and other experiments in the Netherlands. Built Environ (1978). 1986;12:20–9.Search in Google Scholar

[15] Collarte N. The woonerf concept: Rethinking a residential street in Somerville [Master of Arts]. Cambridge, MA: Tufts University; 2012.Search in Google Scholar

[16] Sorrentino F, Aletta F, Maffei L, Kang J, Masullo M. Effects of different urban traffic mitigation actions on cities’ noise levels. Riv Ital di Acust. 2015;39(4):23–41.Search in Google Scholar

[17] Nalmpantis D, Lampou S-C, Naniopoulos A. The concept of woonerf zone applied in university campuses: the case of the campus of the Aristotle University of Thessaloniki. Transp Res Procedia. 2017;24:450–8.10.1016/j.trpro.2017.05.071Search in Google Scholar

[18] Cekrezi E. Parental perception of residential street safety and the effects on children’s autonomy and wellbeing: Comparative research between Woonerf and High-traffic residential streets [dissertation on the Internet]. Groningen: University of Groningen; 2022 [cited 15 June 2024]. https://frw.studenttheses.ub.rug.nl/4102/1/Master%20Thesis%20Final%20Form%20Eriko%20Cekrezi%20(3).pdf.Search in Google Scholar

[19] Pheasant R, Horoshenkov K, Watts G, Barrett B. The acoustic and visual factors influencing the construction of tranquil space in urban and rural environments tranquil spaces-quiet places? J Acoust Soc Am. 2008;123(3):1446–57.10.1121/1.2831735Search in Google Scholar PubMed

[20] Herzog TR, Barnes GJ. Tranquility and preference revisited. J Environ Psychol. 1999;19(2):171–81.10.1006/jevp.1998.0109Search in Google Scholar

[21] EEA. Good practice guide on quiet areas Luxembourg. European Environmental Agency; 2014 [updated 25 Aug. 2016; cited 10 Febr. 2024]. https://www.eea.europa.eu/publications/good-practice-guide-on-quiet-areas.Search in Google Scholar

[22] Watts G, Pheasant RJ. Tranquillity in the Scottish Highlands and Dartmoor National Park – The importance of soundscapes and emotional factors. Appl Acoust. 2015;89:297–305.10.1016/j.apacoust.2014.10.006Search in Google Scholar

[23] Brown AL, Kang J, Gjestland T. Towards standardization in soundscape preference assessment. Appl Acoust. 2011;72:387–92.10.1016/j.apacoust.2011.01.001Search in Google Scholar

[24] ISO 9613-B. Acoustics-Attenuation of sound during propagation outdoors – Part 2: General method of calculation. Geneva, Switzerland: International Standard Organization; 1996.Search in Google Scholar

[25] Watts G, Miah A, Pheasant R. Tranquillity and soundscapes in urban green spaces- predicted and actual assessments from a questionnaire survey. Environ Plan B: Plan Des. 2013;40(1):170–81.10.1068/b38061Search in Google Scholar

[26] Mitchell A, Erfanian M, Soelistyo C, Oberman T, Kang J, Aldridge R, et al. Effects of soundscape complexity on urban noise annoyance ratings: a large-scale online listening experiment. Int J Environ Res Public Health. 2022;19(22):12.10.3390/ijerph192214872Search in Google Scholar PubMed PubMed Central

[27] Alvarsson JJ, Wiens S, Nilsson ME. Stress recovery during exposure to nature sound and environmental noise. Int J Environ Res Public Health. 2010;7(3):1036–46.10.3390/ijerph7031036Search in Google Scholar PubMed PubMed Central

[28] Watts G. The effects of “greening” urban areas on the perceptions of tranquillity. Urban Forestry Urban Green. 2017;26:11–7.10.1016/j.ufug.2017.05.010Search in Google Scholar

[29] Yu L, Kang J. Factors influencing the sound preference in urban open spaces. Appl Acoust. 2010;71(7):622–33.10.1016/j.apacoust.2010.02.005Search in Google Scholar

[30] Ratcliffe E. Toward a better understanding of pleasant sounds and soundscapes in urban settings. Cities Health. 2019;5(1–2):82–5.10.1080/23748834.2019.1693776Search in Google Scholar

[31] Aletta F, Oberman T, Kang J. Positive health-related effects of perceiving urban soundscapes: a systematic review. Lancet. 2018;392. 10.1016/S0140-6736(18)32044-0.Search in Google Scholar

[32] Lionello M, Aletta F, Kang J. A systematic review of prediction models for the experience of urban soundscapes. Appl Acoust. 2020;170:107479.10.1016/j.apacoust.2020.107479Search in Google Scholar

[33] ISO 12913-1. Definition and conceptual framework. International Organization for Standardization; 2014.Search in Google Scholar

[34] ISO 12913-2. Data collection and reporting requirements. International Organization for Standardization; 2018.Search in Google Scholar

[35] ISO 12913-3. Data analysis. International Organization for Standardization; 2019.Search in Google Scholar

[36] Axelsson Ö, Nilsson ME, Burglund B. A principal components model of soundscape perception. J Acoust Soc Am. 2010;128(5):2836–46.10.1121/1.3493436Search in Google Scholar PubMed

[37] Pheasant RJ, Horoshenkov KV, Watts G. Tranquillity rating prediction tool (TRAPT). Acoust Bull. 2010;35(6):18–24.10.3813/AAA.918234Search in Google Scholar

[38] Watts G, Pheasant RJ, Horoshenkov KV. Predicting perceived tranquillity in urban parks and open spaces. Environ Plan B: Plan Des. 2011;38(4):585–94.10.1068/b36131Search in Google Scholar

[39] Watts G, Marafa L. Validation of the tranquillity rating prediction tool (TRAPT): comparative studies in UK and Hong Kong. Noise Mapp. 2017;4(1):67–74.10.1515/noise-2017-0005Search in Google Scholar

[40] Marafa L, Tsang F, Watts G, Yuan X. Perceived tranquility in green urban open spaces. World Leisure J. 2018;60(3):221–34.10.1080/16078055.2018.1496529Search in Google Scholar

[41] de Vries S, van Dillen SME, Groenewegen PP, Spreeuwenberg P. Streetscape greenery and health: Stress, social cohesion and physical activity as mediators. Soc Sci Med. 2013;94:26–33.10.1016/j.socscimed.2013.06.030Search in Google Scholar PubMed

[42] Taghipour A, Gisladottir A, Aletta F, Bürgin M, Rezaei M, Sturm U. Improved acoustics for semi-enclosed spaces in the proximity of residential buildings. Glasgow, UK: InterNoise; 2022.10.3397/IN_2022_0158Search in Google Scholar

[43] Arnaud C, Auduber P, Aumond P, Geisler E, Guiu C, Lorino T, et al. Framework for urban sound assessment at the city scale based on citizen action with the smartphone application NoiseCapture as a lever for participation. Noise Mapp. 2023;10(1):20220166.10.1515/noise-2022-0166Search in Google Scholar

[44] Christiansen LB, Klein-Wengel TT, Koch S, Hoyer-Kruse J, Schipperijn J. Recreational walking and perceived environmental qualities: a national map-based survey in Denmark. Int J Health Geographics. 2023;22(1):21.10.1186/s12942-023-00339-2Search in Google Scholar PubMed PubMed Central

[45] Schröter B, Gottwald S, Castro-Arce K, Hartkopf E, Aguilar-González B, Albert C. Virtual participatory mapping of nature-based solutions in the Grande de Tárcoles River basin, Costa Rica: Connecting diverse knowledge systems in a context of physical immobility. Sci Total Environ. 2023;872:162195.10.1016/j.scitotenv.2023.162195Search in Google Scholar PubMed

[46] Pánek J. Emotional maps: Participatory crowdsourcing of citizens’ perceptions of their urban environment. Cartographic. Perspectives. 2019;2018(91):17–29.10.14714/CP91.1419Search in Google Scholar

[47] Kang J, Schulte-Fortkamp B, eds. Mapping of soundscape. In: Soundscape and the built environment. 1st edn. Boca Raton: CRS Press, Taylor and Francis; 2016. p. 324.10.1201/b19145-8Search in Google Scholar

[48] Margaritis E, Van Kann F. Combining mobile measurements on noise and soundscape evaluation in a University Campus after a renovation plan. Glasgow, UK: InterNoise; 2022.10.3397/IN_2022_0706Search in Google Scholar

[49] Leereveld T. Comparison of predicted and actual tranquility in Woonerf streets. Groningen: University of Groningen; 2023.10.2139/ssrn.4436203Search in Google Scholar

[50] Liu C, Liu J. Resilience of living streets in small and medium-sized towns: A grounded theory study of Yixing, China. Sustainability. 2023;15(15):4.10.3390/su151512084Search in Google Scholar

[51] Raifer M. Overpass Turbo: OpenStreetMap; [updated 15-1-2024; cited 11-6-2024]; 2024. Available from: https://overpass-turbo.eu/.Search in Google Scholar

[52] Planet dump retrieved from https://planet.osm.org: OpenStreetMap contributors; 2015 [Available from: https://www.openstreetmap.org/.Search in Google Scholar

[53] Camera vs the human eye. Cambridge in Colour; 2022. https://www.cambridgeincolour.com/tutorials/cameras-vs-human-eye.htm (accessed 30-12-2022).Search in Google Scholar

[54] Bocher E, Petit G, Picaut J, Fortin N, Guillaume G. Collaborative noise data collected from smartphones. Data Brief. 2017;14:498–503.10.1016/j.dib.2017.07.039Search in Google Scholar PubMed PubMed Central

[55] Graziuso G, Mancini S, Francavilla AB, Grimaldi M, Guarnaccia C. Geo-crowdsourced sound level data in support of the community facilities planning. A methodological proposal. Sustainability. 2021;13(10):5486.10.3390/su13105486Search in Google Scholar

[56] Roelandt N, Aumond P, Moisan L. Crowdsourced acoustic open data analysis with Foss4g tools. Int Arch Photogramm Remote Sens Spat Inf Sci. 2022;XLVIII-4/W1-2022:387–93.10.5194/isprs-archives-XLVIII-4-W1-2022-387-2022Search in Google Scholar

[57] Li X, Zhang C, Li W, Ricard R, Meng Q, Zhang W. Assessing street-level urban greenery using Google Street View and a modified green view index. Urban For Urban Green. 2015;14:675–85.10.1016/j.ufug.2015.06.006Search in Google Scholar

[58] Yang J, Zhao L, McBride J, Gong P. Can you see green? Assessing the visibility of urban forests in cities. Landsc Urban Plan. 2009;91(2):97–104.10.1016/j.landurbplan.2008.12.004Search in Google Scholar

[59] Can A, Dekoninck L, Botteldooren D. Measurement network for urban noise assessment: Comparison of mobile measurements and spatial interpolation approaches. Appl Acoust. 2014;83:32–9.10.1016/j.apacoust.2014.03.012Search in Google Scholar

[60] Helson H. Adaptation-level theory; an experimental and systematic approach to behavior. New York: Harper and Row; 1964.Search in Google Scholar

[61] Watts GR, Pheasant RJ, Horoshenkov KV, Ragonesi L. Measurement and Subjective Assessment of Water Generated Sounds. Acta Acust U Acust. 2009;95(6):1032–9.10.3813/AAA.918235Search in Google Scholar

[62] Mouratidis K. The impact of urban tree cover on perceived safety. Urban For Urban Green. 2019;44:2.10.1016/j.ufug.2019.126434Search in Google Scholar

[63] Yang W, Kang J. Acoustic comfort evaluation in urban open public spaces. Appl Acoust. 2005;66:211–9.10.1016/j.apacoust.2004.07.011Search in Google Scholar

[64] Mitchell A, Oberman T, Aletta F, Erfanian M, Kachlicka M, Lionello M, et al. The Soundscape Indices (SSID) Protocol: A method for urban soundscape surveys – questionnaires with acoustical and contextual information. Appl Sci. 2020;10:2397.10.3390/app10072397Search in Google Scholar

[65] Kang J, Aletta F, Oberman T, Mitchell A, Erfanian M. Subjective evaluation of environmental sounds in context – towards Soundscape Indices (SSID). Torino, Italy: ForumAcusticum; 2023.Search in Google Scholar

[66] Brown AL. Soundscapes and environmental noise management. Noise Control Eng J. 2010;58(5):493–500.10.3397/1.3484178Search in Google Scholar

[67] Fogola L, Grasso D, Masera S, Scordino P. OpeNoise: a free and open-source project for environmental noise assessment. Torino, Italy: ForumAcusticum; 11–15 September 2023.Search in Google Scholar

[68] D’Hondt E, Stevens M, Jacobs A. Participatory noise mapping works! An evaluation of participatory sensing as an alternative to standard techniques for environmental monitoring. Pervasive Mob Comput. 2013;9:681–94.10.1016/j.pmcj.2012.09.002Search in Google Scholar

[69] Ventura R, Mallet V, Issarny V, Raverdy P, Rebhi F. Estimation of urban noise with the assimilation of observations crowdsensed by the mobile application Ambiciti. INTER-NOISE; 27–30 Aug. Hong Kong, China: Institute of Noise Control Engineering; 2017. p. 5444–51.Search in Google Scholar

[70] Kardous CA, Shaw PB. Evaluation of smartphone sound measurement applications (apps) using external microphones-A follow-up study. J Acoust Soicety Am. 2016;140(4):328.10.1121/1.4964639Search in Google Scholar PubMed PubMed Central

[71] Aumond P, Lavandier C, Ribeiro C, Boix EG, Kambona K, D’Hondt E, et al. A study of the accuracy of mobile technology for measuring urban noise pollution in large scale participatory sensing campaigns. Appl Acoust. 2017;117:219–26.10.1016/j.apacoust.2016.07.011Search in Google Scholar

[72] Felixx Landscape Architects, GOUDAPPEL, STIPO, Universiteit van Amsterdam. New space for living: Design guideline – quality of public space. Groningen; December 2021.Search in Google Scholar

[73] Brown AL. A review of progress in soundscapes and an approach to soundscape planning. Int J Acoust Vib. 2012;17(2):73–81.10.20855/ijav.2012.17.2302Search in Google Scholar

[74] Bozovic R, Maksimovic C, Mijic A, Smith KM. Blue green solutions. A systems approach to sustainable, resilient and cost-efficient urban development. London: Imperial College; 2017.Search in Google Scholar

[75] Donovan J. Designing the compassionate city to overcome built-in biases and help us live better; 2018 [updated 4-10-2023; cited 15-3-2024]. https://theconversation.com/designing-the-compassionate-city-to-overcome-built-in-biases-and-help-us-live-better-92726.10.4324/9781315645551-16Search in Google Scholar

[76] Yanaky R, Tyler D, Guastavino C. City ditty: An immersive soundscape sketchpad for professionals of the built environment. Appl Sci. 2023;13(3):2.10.3390/app13031611Search in Google Scholar
Received: 2024-04-14
Revised: 2024-06-24
Accepted: 2024-06-25
Published Online: 2024-07-30

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Regular Articles
  2. Low-frequency cabin noise of rapid transit trains
  3. Utilizing the phenomenon of diffraction for noise protection of roadside objects
  4. Benchmarking the aircraft noise mapping package developed for a unified urban environmental modelling tool
  5. Acoustical analysis and optimization design of the hair dryers
  6. Methodologies for the prediction of future aircraft noise level
  7. Basics of meteorology for outdoor sound propagation and related modelling issues
  8. Predictive noise annoyance and noise-induced health effects models for road traffic noise in NCT of Delhi, India
  9. Modeling and mapping of traffic noise pollution by ArcGIS and TNM2.5 techniques
  10. A novel method for constructing large-scale industrial noise maps based on open source data
  11. Understanding perceived tranquillity in urban Woonerf streets: case studies in two Dutch cities
  12. Review Article
  13. A comprehensive review of noise pollution monitoring studies at bus transit terminals
  14. Rapid Communication
  15. The Environment (Air Quality and Soundscapes) (Wales) Act 2024
  16. Erratum
  17. Erratum to “Comparing pre- and post-pandemic greenhouse gas and noise emissions from road traffic in Rome (Italy): a multi-step approach”
  18. Special Issue: Latest Advances in Soundscape - Part II
  19. Soundscape maps of pleasantness in a university campus by crowd-sourced measurements interpolation
  20. Conscious walk assessment for the joint evaluation of the soundscape, air quality, biodiversity, and comfort in Barcelona
  21. A framework to characterize and classify soundscape design practices based on grounded theory
  22. Perceived quality of a nighttime hospital soundscape
  23. Relating 2D isovists to audiovisual assessments of two urban spaces in a neighbourhood
  24. Special Issue: Strategic noise mapping in the CNOSSOS-EU era - Part I
  25. Analysis of road traffic noise in an urban area in Croatia using different noise prediction models
  26. Citizens’ exposure to predominant noise sources in agglomerations
https://doi.org/10.1515/noise-2024-0009
Keywords for this article
Woonerf concept; soundscape; tranquillity; green view index; pedestrian-oriented design
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Low-frequency cabin noise of rapid transit trains
  3. Utilizing the phenomenon of diffraction for noise protection of roadside objects
  4. Benchmarking the aircraft noise mapping package developed for a unified urban environmental modelling tool
  5. Acoustical analysis and optimization design of the hair dryers
  6. Methodologies for the prediction of future aircraft noise level
  7. Basics of meteorology for outdoor sound propagation and related modelling issues
  8. Predictive noise annoyance and noise-induced health effects models for road traffic noise in NCT of Delhi, India
  9. Modeling and mapping of traffic noise pollution by ArcGIS and TNM2.5 techniques
  10. A novel method for constructing large-scale industrial noise maps based on open source data
  11. Understanding perceived tranquillity in urban Woonerf streets: case studies in two Dutch cities
  12. Review Article
  13. A comprehensive review of noise pollution monitoring studies at bus transit terminals
  14. Rapid Communication
  15. The Environment (Air Quality and Soundscapes) (Wales) Act 2024
  16. Erratum
  17. Erratum to “Comparing pre- and post-pandemic greenhouse gas and noise emissions from road traffic in Rome (Italy): a multi-step approach”
  18. Special Issue: Latest Advances in Soundscape - Part II
  19. Soundscape maps of pleasantness in a university campus by crowd-sourced measurements interpolation
  20. Conscious walk assessment for the joint evaluation of the soundscape, air quality, biodiversity, and comfort in Barcelona
  21. A framework to characterize and classify soundscape design practices based on grounded theory
  22. Perceived quality of a nighttime hospital soundscape
  23. Relating 2D isovists to audiovisual assessments of two urban spaces in a neighbourhood
  24. Special Issue: Strategic noise mapping in the CNOSSOS-EU era - Part I
  25. Analysis of road traffic noise in an urban area in Croatia using different noise prediction models
  26. Citizens’ exposure to predominant noise sources in agglomerations
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 29.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/noise-2024-0009/html?lang=en&srsltid=AfmBOooUojWGBApuAqk3QoZUQOfwlmoeDjpXYM7cP64js-un6Pxaonoc
Scroll to top button