Evaluation of low traffic neighbourhood (LTN) impacts on NO2 and traffic

Traffic restriction measures may create safer and healthier places for community members but may also displace traffic and air pollution to surrounding streets. Effective urban planning depends on understanding the magnitude of changes resulting from policy measures, both within and surrounding intervention areas; these are largely unstudied in the case of Low traffic Neighbourhoods (LTN). We evaluated impacts of three LTNs in the London Borough of Islington, UK, on air pollution and traffic flows in and around intervention areas, based on monthly Nitrogen Dioxide (NO2) and traffic volume data provided by the local authority. We identified pre- and post-intervention monitoring periods and intervention, boundary and control sites. We then adapted the generalised difference in differences approach to evaluate the effects within LTNs and at their boundary. We found that LTNs have the potential to substantially reduce air pollution and traffic in target areas, without increasing air pollution or traffic volumes in surrounding streets. These results provide sound arguments in favour of LTNs to promote health and wellbeing in urban communities

The effects of traveling in different transport modes on galvanic skin response (GSR) as a measure of stress : An observational study

Background Stress is one of many ailments associated with urban living, with daily travel a potential major source. Active travel, nevertheless, has been associated with lower levels of stress compared to other modes. Earlier work has relied on self-reported measures of stress, and on study designs that limit our ability to establish causation. Objectives To evaluate effects of daily travel in different modes on an objective proxy measure of stress, the galvanic skin response (GSR). Methods We collected data from 122 participants across 3 European cities as part of the Physical Activity through Sustainable Transport Approaches (PASTA) study, including: GSR measured every minute alongside confounders (physical activity, near-body temperature) during three separate weeks covering 3 seasons; sociodemographic and travel information through questionnaires. Causal relationships between travel in different modes (the “treatment”) and stress were established by using a propensity score matching (PSM) approach to adjust for potential confounding and estimating linear mixed models (LMM) with individuals as random effects to account for repeated measurements. In three separate analyses, we compared GSR while cycling to not cycling, then walking to not walking then motorized (public or private) travel to any activity other than motorized travel. Results Depending on LMM formulations used, cycling reduces 1-minute GSR by 5.7% [95% CI: 2.0–16.9%] to 11.1% [95% CI: 5.0–24.4%] compared to any other activity. Repeating the analysis for other modes we find that: walking is also beneficial, reducing GSR by 3.9% [95% CI: 1.4–10.7%] to 5.7% [95% CI: 2.6–12.3%] compared to any other activity; motorized mode (private or public) in reverse increases GSR by up to 1.1% [95% CI: 0.5–2.9%]. Discussion Active travel offers a welcome way to reduce stress in urban dwellers’ daily lives. Stress can be added to the growing number of evidence-based reasons for promoting active travel in cities