Search

Background: The harmful effects of outdoor air pollution on stroke incidence are becoming increasingly recognised. We examined the impact of different air pollutants (PM2.5, PM10, NO2, ozone, and SO2) on admission for all strokes in two Irish urban centres from 2013 to 2017. Methods: Using an ecological time series design with Poisson regression models, we analysed daily hospitalisation for all strokes and is-chaemic stroke by residence in Dublin or Cork, with air pollution level monitoring data with a lag of 0–2 days from exposure. Splines of temperature, relative humidity, day of the week, and time were included as confounders. Analysis was also performed across all four seasons. Data are presented as relative risks (RRs) and 95% confidence intervals (95% CI) per interquartile range (IQR) increase in each pollutant. Results: There was no significant association between all stroke admission and any individual air pollutant. On seasonal analysis, during winter in the larger urban centre (Dublin), we found an association between all stroke cases and an IQR increase in NO2 (RR 1.035, 95% CI: 1.003–1.069), PM10 (RR 1.032, 95% CI: 1.007–1.057), PM2.5 (RR 1.024, 95% CI: 1.011–1.039), and SO2 (RR 1.035, 95% CI: 1.001–1.071). There was no significant association found in the smaller urban area of Cork. On meta-analysis, there remained a significant association between NO2 (RR 1.013, 95% CI: 1.001–1.024) and PM2.5 (1.009, 95% CI 1.004–1.014) per IQR increase in each. Discussion: Short-term air pollution in winter was found to be associated with hospitalisation for all strokes in a large urban centre in Ireland. As Ireland has relatively low air pollution internationally, this highlights the need to introduce policy changes to reduce air pollution in all countries. [ABSTRACT FROM AUTHOR]

Abstract: The Irish government has outlined plans for 10% of the national road fleet to be powered by electricity by 2020. The objective of this paper is to evaluate the potential reduction in road traffic related emissions due to commuting in the Greater Dublin Area under different electric vehicle market penetration scenarios. The results indicate that the introduction of electric vehicles offers the potential for reductions in all road traffic related emissions. However, the time required for electric vehicles to acquire a significant share of the fleet, suggests that they will have a limited impact on climate change and urban air quality for at least the next decade. [Copyright &y& Elsevier]

The existing well-known short-term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily 'trend' of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time-of-the-day. A case study has been performed at two busy junctions at the city-centre of Dublin to validate the effectiveness of the strategy. [ABSTRACT FROM AUTHOR]

The paper reports on research using a new type of pedestrian waiting countdown timer to influence pedestrian behaviour at signalised pedestrian crossings in Dublin. The aim was to evaluate the impact of the timers on pedestrian crossing behaviour and in particular to see if it had any impact on the number of illegal crossings (during red man––do not walk signal). The timers inform the pedestrian how many seconds they have to wait until the green man appears. Two surveys were used to evaluate the impact: an attitude survey to evaluate the perception of the users and a video survey to estimate quantitatively the impacts on pedestrian behaviour but also to evaluate the awareness levels of the pedestrians towards the countdown timers. Some of the results include observance of more compliant behaviour by females and that pedestrians tend to overestimate their waiting time. Before the timers were installed 65% of pedestrians started to cross during the green man and amber phases but this rose to 76% after the timers were installed. [Copyright &y& Elsevier]

The objective of the work was to evaluate the potential user response to distance and time based road pricing of a sample of individuals drawn randomly from a group of volunteers in Dublin. The road use pricing charge levels were selected to match the marginal external costs of car transport i.e. those costs not currently paid by the car user. Such costs include marginal external costs of congestion, air pollution and noise. The project formed part of the EU DGXVII EUROPRICE project where one of the objectives was to evaluate the impact of road use pricing on private transport demand. Estimates of the marginal external costs of car travel had been previously made for Dublin in an EU DGVII project entitled TRENEN II STRAN and the results were used to select the road pricing charges in the trial. The distance travelled and travel time of a particular individual's work trip were noted. Charges per unit distance and time were applied so that the individual would incur a total charge for their average peak period work trip of 6.4 euro; the average marginal external cost of a peak period trip in Dublin, as estimated by the TRENEN model. Although the sample of individuals was relatively small, the indications from the results are worthy of note and further investigation on a larger sample. A significant reduction in the number of peak period trips was evident, of the order of 22%, resulting from trip suppression and transfer to other modes. [ABSTRACT FROM AUTHOR]

• Developing a novel framework for appraising shared electric micro-mobility hubs in Dublin. • Employing DEA and negative binomial regression and using six months of real-world data. • Proposing an e-bike sharing scalability measure using returns-to-scale analysis. • Assessing eco-efficiency based on trip and user characteristics and emission offset. • High scalability suggests system's expansion and improving infrastructure can boost performance. In car-dominated urban areas, shared electric micro-mobility offers a sustainable alternative to decarbonise and reshape mobility paradigms. This paper presents a comprehensive framework for evaluating Dublin's e-bike sharing system, comprising 12 stations (eHUBs). Using six months of real-world riding data, it employs data envelopment analysis to assess the eco-efficiency of each eHUB in utilising nearby infrastructure, population in the catchment area, and location to achieve desired economic, social, and environmental outputs. Results indicate an upward trajectory for the system's eco-efficiency. The returns-to-scale analysis provides insights into the system's scalability, suggesting that expanding the e-bike sharing system, along with infrastructural enhancements, would significantly increase ridership. Analysing decarbonisation, usage patterns, and spatial factors of eHUBs reveals the substantial potential of shared e-bikes if optimally used. The research highlights the need to reevaluate car-centric policies in favour of more inclusive and environmentally sustainable alternatives and proposes actionable policy recommendations to achieve this transformation. [ABSTRACT FROM AUTHOR]