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Example eye tracking results Lazarettgasse from participant no. 5 (heatmap left) and example eye tracking results Lessinggasse from participant no. 3 (heatmap right)
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Bicycling is a key component of sustainable mobility. This paper gives an insight into the importance of bicycle-friendliness, also known as “bikeability” and its qualitative components like perception and emotions of the riders. The research applies a multi-methodical framework on bicycling aiming to identify stressful events on a bicycle ride. Th...
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Context 1
... refurbishment, therefore street blocking of 2 sides, cycling against the one-way regulation allowed The example (see figure 4) shows that the signposts were only briefly spotted and the participant tried to analyze the alternative route options, or identify potential obstacles, but mostly fixed on the intersection. ...
Citations
... People who ride bikes express a fear of being involved in collisions with motorised traffic (Bauman et al., 2008;Sanders & Judelman, 2018) worry about unsafe road conditions, high traffic volumes & speeds, and the availability of cyclingspecific infrastructure (Cycling Promotion Fund and Heart Foundation, 2011). These perceptions cause people to feel unsafe and uncomfortable and act as substantial barriers to bicycling (Berger & Dörrzapf, 2018;Winters et al., 2011). These negative perceptions are reported across all age groups (Porter et al., 2020;Manaugh et al., 2017) and particularly for female riders (van Bekkum et al., 2011). ...
People who ride bicycles often feel unsafe and/or uncomfortable riding in various road conditions. Therefore, understanding the perceptions or experiences of bicyclists in real-world riding conditions is critical to inform interventions that enhance the experience of bike riding and therefore increase participation. A scoping review was undertaken to investigate methods used for capturing subjective experiences in the process of bicycling, or immediately post-ride. Six electronic databases and reference lists of the included studies were searched from inception to May 2021. Of the 11,904 non-duplicate articles, 53 full-text articles were included in the review. The review identified the following methods used to capture the subjective experiences: (i) on-ride method (n = 7), (ii) immediately post-ride method (n = 13), (iii) on-ride plus post-ride methods (n = 8), (iv) ride-along method (n = 4), (v) ride-along plus post-ride methods (n = 6), and (vi) intercept survey methods (n = 15). Some studies exclusively used naturalistic methods for capturing subjective experiences. There is a need to advance methods and standardise approaches to capture subjective user experiences. This is needed to ensure that we are able to understand the experiences and needs of people who ride bikes to inform the provision of safe and connected infrastructure for all ages and abilities.
... To address the limitations of self-reported PSC, there has been increasing interest in objective physiological measures of a psychological stress response for in situ evaluations of PSC (Berger and Dörrzapf, 2018;Caviedes and Figliozzi, 2018;Doorley et al., 2015;Fitch et al., 2020;Jones et al., 2016;Nuñez et al., 2018;Zeile et al., 2016). In addition to avoiding some of the reliability and validity issues of self-reported PSC, dynamic physiological stress marker measurements can be combined with GPS and video data for highresolution (in space and time) analysis of potential factors affecting PSC. ...
... Data on environmental factors or events that could be stressors for travellers were obtained through various means in these studies. Factors relating to the operating facility (facility type, lane width, posted speed limit, etc.) were extracted from existing spatial datasets (LaJeunesse et al., 2021;Teixeira et al., 2020), field measurements (Fitch et al., 2020), synchronous video (Caviedes and Figliozzi, Berger and Dörrzapf (2018) also conducted a field study using eye-tracking to identify objects that participants focused on during a ride. However, this part of the study did not record physiological markers of stress. ...
... Noise was measured with a mobile sensor carried on the bicycle (Teixeira et al., 2020), and proximity of other pedestrians or objects with a laser or infrared sensor attached to the traveller (Engelniederhammer et al., 2019;Kitabayashi et al., 2015). Other studies identified discrete events by extracting them from the synchronous video (Berger and Dörrzapf, 2018;Jones et al., 2016;Zeile et al., 2016). Examples of identified stressor events include interactions with unpredictable pedestrians like children and dog walkers, poor road surface conditions, vertical deflections on the road, unclear navigation instructions, cars passing closely, and waiting in a traffic queue. ...
Understanding perceptions of safety and comfort (PSC) while walking or cycling is essential to accommodating and encouraging active travel, but current measures of PSC, primarily surveys, suffer from validity and reliability issues. Physiological markers of stress like electrodermal activity and heart rate variability have been proposed as alternative, objective measures of PSC. This paper presents a literature summary and conceptual framework examining the use of physiological stress markers during walking and cycling. The existing studies of active traveller stress markers report inconsistent findings and account for limited controls. We propose a comprehensive conceptual framework to describe the array of dynamic stimuli experienced during active travel, with complex appraisals and multidimensional stress responses that feedback to travel behaviour and stimuli exposure, and culminate in a set of physiological outcomes triggered by activation of the autonomic nervous system – all moderated by numerous personal and trip-related factors. The key challenge of inferring traffic-related fear or discomfort from physiological markers measured on-road is potential confounding effects of: (1) non-traffic factors that induce or modify stress responses, (2) traffic factors that induce stress responses not associated with safety or comfort, and (3) personal and environmental factors that directly influence physiological measurements outside of a stress response. No physiological stress marker has yet been shown to be reliable for on-road active travellers, particularly not for inter-subject comparisons. Physiological markers have the potential to provide high-resolution, objective information about pedestrian and cyclist PSC, but further research, particularly controlled experiments, and more precise study framing are needed to ensure validity and address moderating and confounding factors.
... An interesting approach to this has been applied by Kazemzadeh et al. (2020), who compared cyclists' perceptions mentioned in "in-traffic" interviews on the street with those stated in an online questionnaire. Additionally, new technical methods, such as camera recordings from the cyclist's viewpoint and eye tracking, could provide new information on the perception of local environment stimuli when riding a bicycle (Berger and Dörrzapf, 2018;Liu et al., 2020;Oliver et al., 2013). Moreover, further research on perceptions in other environment settings and the impact of local environment changes due to interventions might contribute to a better understanding of cycling behaviour, attitudes and promotion. ...
The impact of local environment characteristics on individual cycling behaviour has been discussed in transport research for several years. Many previous studies have, however, primarily focused on the presence and distribution of built environment elements, considered using georeferenced or census data. This paper argues that not only is the objectively measured environment an influencing factor, but also the individual perception of this environment. Furthermore, besides built elements, the evaluation of perceived non-built attributes, such as discourses and policies, as well as the environment’s impact on cycling attitudes, should be taken into account for a more comprehensive view. For this purpose, this study examines the responses to a household survey in the German city of Offenbach am Main (n = 701). The impact of the perceived local environment on cycling behaviour and cycling attitudes has been analysed using 21 perception items as well as socio-demographics, travel mode availability and general travel attitudes. For a more detailed view on cycling behaviour, this study applies the stage model of self-regulated behavioural change (SSBC) indicating a level of openness to use a bicycle frequently in everyday life. The results of the multivariate analysis show that the perceptions of built and non-built environment characteristics interrelate. Furthermore, certain perceptions encourage bicycle use and positive attitudes towards cycling, such as perceived cycling safety and pleasure. Primarily, these perceptions are safe and appropriate cycling infrastructures, cycling as a common practice and the absence of vandalism, dirt and high car pressure.
... Many studies have explored various aspects of the built environment that can influence people's cycling behavior (Bauman et al., 2012;Nielsen and Skov-Petersen, 2018;Pritchard et al., 2019;Daraei et al., 2021;Kraus and Koch, 2021;Nazemi et al., 2021;McNeil, 2011;Ma and Dill, 2017;Cicchino et al., 2020;Nogal and Jiménez, 2020;Sottile et al., 2019;Berger and Dörrzapf, 2018;Porter et al., 2018;Aldred et al., 2020;Long and Zhao, 2020;Doubleday et al., 2021;Brüchert et al., 2020;Martin et al., 2021;Attard et al., 2021). Studies on the association of the built environment and cycling conditions became well-established, and many researchers developed indexes to assess specific aspects of the built environment that can affect cycling behavior and comprehensively quantify bikeability, i.e. the extent to which an environment is friendly for bicycling. ...
Studies evaluating bikeability usually compute spatial indicators shaping cycling conditions and conflate them in a quantitative index. Much research involves site visits or conventional geospatial approaches, and few studies have leveraged street view imagery (SVI) for conducting virtual audits. These have assessed a limited range of aspects, and not all have been automated using computer vision (CV). Furthermore, studies have not yet zeroed in on gauging the usability of these technologies thoroughly. We investigate, with experiments at a fine spatial scale and across multiple geographies (Singapore and Tokyo), whether we can use SVI and CV to assess bikeability comprehensively. Extending related work, we develop an exhaustive index of bikeability composed of 34 indicators. The results suggest that SVI and CV are adequate to evaluate bikeability in cities comprehensively. As they outperformed non-SVI counterparts by a wide margin, SVI indicators are also found to be superior in assessing urban bikeability and potentially can be used independently, replacing traditional techniques. However, the paper exposes some limitations, suggesting that the best way forward is combining both SVI and non-SVI approaches. The new bikeability index presents a contribution in transportation and urban analytics, and it is scalable to assess cycling appeal widely.
... Many studies have explored various aspects of the built environment that can influence people's cycling behavior (Bauman et al., 2012;Nielsen and Skov-Petersen, 2018;Pritchard et al., 2019;Daraei et al., 2021;Kraus and Koch, 2021;Nazemi et al., 2021;McNeil, 2011;Ma and Dill, 2017;Cicchino et al., 2020;Nogal and Jiménez, 2020;Sottile et al., 2019;Berger and Dörrzapf, 2018;Porter et al., 2018;Aldred et al., 2020;Long and Zhao, 2020;Doubleday et al., 2021;Brüchert et al., 2020;Martin et al., 2021;Attard et al., 2021). Studies on the association of the built environment and cycling conditions became well-established, and many researchers developed indexes to assess specific aspects of the built environment that can affect cycling behavior and comprehensively quantify bikeability, i.e. the extent to which an environment is friendly for bicycling. ...
Studies evaluating bikeability usually compute spatial indicators shaping cycling conditions and conflate them in a quantitative index. Much research involves site visits or conventional geospatial approaches, and few studies have leveraged street view imagery (SVI) for conducting virtual audits. These have assessed a limited range of aspects, and not all have been automated using computer vision (CV). Furthermore, studies have not yet zeroed in on gauging the usability of these technologies thoroughly. We investigate, with experiments at a fine spatial scale and across multiple geographies (Singapore and Tokyo), whether we can use SVI and CV to assess bikeability comprehensively. Extending related work, we develop an exhaustive index of bikeability composed of 34 indicators. The results suggest that SVI and CV are adequate to evaluate bikeability in cities comprehensively. As they outperformed non-SVI counterparts by a wide margin, SVI indicators are also found to be superior in assessing urban bikeability, and potentially can be used independently, replacing traditional techniques. However, the paper exposes some limitations, suggesting that the best way forward is combining both SVI and non-SVI approaches. The new bikeability index presents a contribution in transportation and urban analytics, and it is scalable to assess cycling appeal widely.
... The perception of cyclists on the surrounding environment was studied by Berger and Dörrzapf (2018), Marquart et al. (2020), Vedel et al. (2017) and Wahlgren and Schantz (2012). While green spaces, tree shades, and night lighting are found to be influencing factors that encourage cycling, traffic congestion and crowdedness have an opposite impact on cycling (Wahlgren and Schantz, 2012). ...
Previous studies on bikeability/cycling index have explored factors that influence cycling in cities, and developed indicators to characterize a bicycle-friendly environment. However, despite its strong influence on cycling behavior, cyclists’ exposure to traffic-related air pollution has been often disregarded. To close this knowledge gap, we propose a comprehensive bikeability index that comprises four sub-indices: accessibility, suitability, perceptibility, and prevailing air quality in the vicinity of cycling routes. We evaluate cyclists’ exposure to fine particulate matter and black carbon, and used open-source data, land-use regression models, deep neural networks and spatial analysis. The application of the proposed bikeability framework reveals that the inclusion of air quality makes a significant difference when calculating bikeability index in Singapore and hence it merits serious consideration. We believe that the newly developed framework will convince city planners to consider the importance of assessing cyclists’ exposure to airborne particles when planning cycling infrastructure ------------------------------------------------------------------------------(Free download within 50 days https://authors.elsevier.com/a/1bq-F4rgZigiSj).
... This methodology allows for a physiological measurement of stress on a previously determined route. A similar approach is used by Berger and Dörrzapf (2018), who apply biophysiological sensors and empirical data to measure stress on a previously determined route. However, physiological approaches can only identify conditions that affect cycling stress along the specific route measured, and it does not necessarily follow that those relationships hold in other environments, particularly where road conditions or social norms may be substantially different. ...
The Level of Traffic Stress (LTS) is an indicator that quantifies the stress experienced by a cyclist on the segments of a road network. We propose an LTS-based classification with two components: a clustering component and an interpretative component. Our methodology is comprised of four steps: (i) compilation of a set of variables for road segments, (ii) generation of clusters of segments within a subset of the road network, (iii) classification of all segments of the road network into these clusters using a predictive model, and (iv) assignment of an LTS category to each cluster. At the core of the methodology, we couple a classifier (unsupervised clustering algorithm) with a predictive model (multinomial logistic regression) to make our approach scalable to massive data sets. Our methodology is a useful tool for policy-making, as it identifies suitable areas for interventions; and can estimate their impact on the LTS classification, according to probable changes to the input variables (e.g., traffic density). We applied our methodology on the road network of Bogotá, Colombia, a city with a history of implementing innovative policies to promote biking. To classify road segments, we combined government data with open-access repositories using geographic information systems (GIS). Comparing our LTS classification with city reports, we found that the number of bicyclists’ fatal and non-fatal collisions per kilometer is positively correlated with higher LTS. Finally, to support policy making, we developed a web-enabled dashboard to visualize and analyze the LTS classification and its underlying variables.
... The subject of cycling ride comfort has been researched in many studies [ 9,25,49]. In general, we can classify the analysis of cycling comfort from various perspectives (e.g., measuring the vibration, emotion, etc.) with information technology, the second example contains an analysis from the cyclists' perspective, reflecting their opinions and feelings or evaluating the ride during cycling [10,24]. ...
... Rolling resistance was measured in previous research [25]. On the other hand, the study by [10] focuses on the measuring of the bio-physical activity of cyclists. The measuring of the cyclists' anger scale was the primary results of the study [41]. ...
... The first approach focuses on capturing data from various types of sensors and technical tools, such as accelerometers [41], GPS, video cameras or other sensors [42,53]. The potential technologies and tools are described in [10]. This will help conduct the dynamic analysis of vehicle [14], in this case, a bicycle. ...
Over recent years, cycling has emerged as a particularly desirable mode of transport in Europe. Many local authorities, together with cycling lobbyist and advocating groups strive to get cycling to an accepted level of urban mobility. Certain movements have already introduced the citizens' science approach aimed at improving the local conditions for cyclists with crowd-sourced data collection. With this in mind, this paper presents an entry-level analysis of vibration from various surfaces which might affect the comfort of cyclists in the city of Žilina. The emphasis is placed on the analysis of the road surface with a smartphone application and a so-called instrumental or a probe bicycle. The results of testing are presented in the context of the problematic issues that occur in the infrastructure. The results are aimed at drawing attention to the fact that not all infrastructure is properly built, designed or maintained. There is a relationship between properly planned and built cycling infrastructure and the cycling traffic. An Android smartphone with the Phyphox application was used in the analysis as an example of citizen's science.
... It yields a critical view about the specification of measurement indicators to be used for the survey of attitudes and beliefs and to test structural equation models as an exploration and data-mining tool. Berger and Dörrzapf (2018) gives an insight into the importance of bicycle-friendliness, also known as "bikeability" and its qualitative components like perception and emotions of the riders. The goal was to test new bio-physiological sensors (like EDA device, eye tracker etc.) to develop a methodology on how sensor technologies can be integrated in the data collection processes and to discuss their practicability. ...
This document presents an introduction to the ISCTSC Special Issue of Transport Research Procedia. It synthesizes the discussions held at the 11th International Conference on Transport Survey Methods, and describes the contents of the selected contributions. This conference has been held in different countries from all over the world, involving an increasing group of enthusiastic and generous specialists, willing to share their knowledge. This 11th conference was an opportunity to discuss the state of the art on transport survey methods, but also to question the way transport surveys are conducted in the era of big data. We took the opportunity to identify the main challenges, and the most important questions.
