Urban Emotions and Cycling Experience – enriching traffic planning for cyclists with human sensor data

Abstract

Even though much research has been conducted on the safety of cycling infrastructures, most previous approaches only make use of traditional and proven methods based upon datasets such as accident statistics, road infrastructure data, or questionnaires. Apart from typical surveys, which are known to face numerous limitations from a psychological and sociological viewpoints, the question of how perceived safety can best be assessed is still widely unexplored. Thus, this paper presents an approach for bio-physiological sensing to identify places in urban environments which are perceived as unsafe by cyclists. Specifically, a number of physiological parameters like ECG, skin conductance, skin temperature and heart rate variability are analysed to identify moments of stress. Together with data gathered through a People as Sensors app, these stress levels can be mapped to specific emotions. This method was tested in a pilot study in Cambridge, MA (USA), which is presented in this paper. Our findings show that our method can identify places with emotional peaks, particularly fear and anger. Although our results can be qualitatively interpreted and used in urban planning, more research is necessary to quantitatively and automatically generate recommendations from the measurements for urban planners.

Evaluating Urban Bicycle Infrastructures through Intersubjectivity of Stress Sensations Derived from Physiological Measurements

“…The stress detection method proposed by Zeile et al in [32] was extended by adding threshold values for exclusion of minor fluctuations in skin conductivity and skin temperature signals. Furthermore, a high sensitivity of their method to pre-processing of the raw signal was revealed.…”

“…The main contribution of this work is to prove the general applicability of automated detection of moments of stress from physiological measurements for deriving intersubjective indication for perceived bicycle infrastructure quality. By offering analysis on the infrastructure network level, more detailed conclusions for urban planning can be drawn compared to general aggregation in heatmaps as presented in [32]. Aggregation on edge and node level enables analysis based on travel direction and can provide a measure relative to the number of visits instead of absolute stress count.…”

“…Stress detection was based on identifying a rise of skin conductance followed by a decline of skin temperature within a 3-second window as proposed by Zeile et al in [32]. The original approach is sensitive to very small changes in amplitude.…”

“…It is thereby capable of offering a more holistic view on cyclists' stress perception under real-world traffic conditions. Previous research has already proved successful in identifying stress hot-spots within cities based on physiological measurements, targeting different application fields: Investigation was done on stressful locations for walking [29], cycling stress [30][31][32], stress of car drivers [33,34], and the emotional reactions of tourists [35]. Existing research commonly utilized single stress locations or hot-spot analysis based on spatial proximity of individual stress locations.…”

Combining Biosensing Technology and Virtual Environments for Improved Urban Planning

“…The use of environmental sensor data in combination with epidemiological data will be tested in the SmartAQnet project in Augsburg (Schäfer et al, 2017). As part of the Urban Emotion initiative, psychophysiological monitoring was carried out in the context of 'barrier-free planning' (Rodrigues da Silva et al, 2014;Beyel et al, 2016) and bicycle traffic (Groß et al, 2015;Groß & Zeile 2016;Zeile et al 2016).…”