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More than half of British voters chose to leave the European Union (EU) leading to a series of negotiations between the United Kingdom and the EU. The withdrawal of the UK from the EU is widely referred to as Brexit. As the only country that shares a land border with the UK, the impact of Brexit on Ireland is expected to be greater than on any other European country. The objective of the research is to evaluate the potential impact of Brexit on the transport sector in Ireland at a micro level by focusing on cross-border commuters and by also assessing the impact on road freight transport. Potential crossing scenarios are examined at six crossing locations. Assuming a hard border is implemented, each crossing is modelled in VISSIM, a microscopic traffic flow simulation software, using traffic data from Transport Infrastructure Ireland (TII) and dwell time estimated based on the US-Canada border crossings. Six scenarios are considered to determine the impact on cross-border traffic at different flow conditions and with varying levels of technology used in border infrastructure leading to short versus long processing times. The paper evaluates travel measures including delays, queue lengths and emissions. The worst-case scenario has a vehicle delay of 18.4 min and the highest delay-associated costs across all locations modelled are estimated at 60.7 million [euro] per year. Estimated emissions generated at the border crossings raise concerns about environmental impacts of a hard Brexit. Interviews with stakeholders emphasized the critical role of technology in reducing the impact of a hard Brexit on cross-border commuters and on the freight sector. A key finding is the importance of using technology tools to facilitate controls and reduce processing times. The results indicate that technology use leads to significant time and cost savings as well as reduced environmental impacts., 1. Introduction Over half of British voters chose to leave the EU by March 2019 and the UK government has been negotiating a deal with the EU regarding the details [...]

Irish rural areas saw an increase in the number of burglaries in the period following the completion of a network of high speed motorway interurban routes. To test the hypothesis that the new fast infrastructure was providing quick exits by urban based criminal gangs, Irish crime statistics along with census data were combined in a logistic regression analysis to assess the relative influence of accessibility, deprivation, population, and proximity to fast road infrastructure on the number of burglaries in areas. It was found that accessibility had a higher influence on crime than area deprivation and that proximity to fast road infrastructure was not significant., 1. Introduction In relation to the association between socioeconomic status and criminal offending, the literature suggests that poverty has an effect on serious crime [1]. Relative deprivation is more important [...]

The paper develops a short-term space-time traffic flow forecasting strategy integrating the empirical-based seasonal autoregressive integrated moving average (SARIMA) time-series forecasting technique with the theoretical-based first-order macroscopic traffic flow model--cell transmission model. A case study in Dublin city center which has serious traffic congestion is performed to test the effectiveness of the proposed multivariate traffic forecasting strategy. The results show that the forecasts at the junctions only deviate around 10% at a maximum from the original observations and seem to indicate that the proposed strategy is one of the effective approaches to predict the real-time traffic flow level in a congested network especially at the locations where no continuous data collection takes place. DOI: 10.1061/(ASCE)0733-947X(2009)135:9(658) CE Database subject headings: Traffic flow; Traffic models; Seasonal variations; Forecasting; Intersections; Data collections.

In the metropolitan cities of developed and developing countries, longer journeys are mostly performed by two or more modes. In the event of availability of suburban trains and public buses, commuters prefer to travel a longer stretch of their journeys by train, so as to avoid traffic congestion on roads, and the remaining part by buses to reach local areas if their final destination is not in close proximity to railway stations. Normally suburban trains have fixed corridors and buses have the flexibility to serve remote local areas. Thus design of feeder routes from railway stations to various destinations and the transfer time from trains to buses plays a very important role and can be controlled by transport planners. A considerable amount of research has been done on the independent design of a bus route network without considering the effect of train services. Researchers have made attempts using heuristics, simulation, expert systems, artificial intelligence, and optimization techniques for design of routes and schedules. So far, limited effort has been made in modeling coordinated operations. In this research, a new hybrid algorithm which exploits the benefits of genetic algorithms and a well tested heuristic algorithm for the study area is discussed. More convincing results in terms of feeder routes and coordinated schedules at the selected railway station are obtained by the proposed hybrid algorithm as compared to earlier approaches adopted by the writers for the same study area. DOI: 10.1061/(ASCE)0733-947X(2009)135:1(1) CE Database subject headings: Scheduling; Algorithms; Optimization; Hybrid methods; Railroad station; Suburbs.

The seasonal autoregressive integrated moving average (SARIMA) model is one of the popular univariate time-series models in the field of short-term traffic flow forecasting. The parameters of the SARIMA model are commonly estimated using classical (maximum likelihood estimate and/or least-squares estimate) methods. In this paper, instead of using classical inference the Bayesian method is employed to estimate the parameters of the SARIMA model considered for modeling. In Bayesian analysis the Markov chain Monte Carlo method is used to solve the posterior integration problem in high dimension. Each of the estimated parameters from the Bayesian method has a probability density function conditional to the observed traffic volumes. The forecasts from the Bayesian model can better match the traffic behavior of extreme peaks and rapid fluctuation. Similar to the estimated parameters, each forecast has a probability density curve with the maximum probable value as the point forecast. Individual probability density curves provide a time-varying prediction interval unlike the constant prediction interval from the classical inference. The time-series data used for fitting the SARIMA model are obtained from a certain junction in the city center of Dublin. DOI: 10.1061(ASCE)0733-947X(2007)133:3(180) CE Database subject headings: Traffic flow; Predictions; Auto-regressive moving-average model; Bayesian analysis; Forecasting.

Background: Links between air pollution and asthma are less well established for older adults than some younger groups. Nitrogen dioxide (NO2) concentrations are widely used as an indicator of transport-related air pollution, and some literature suggests NO2 may directly affect asthma.Methods: This study used data on 8162 adults >50 years old in the Republic of Ireland to model associations between estimated annual outdoor concentration of NO2 and the probability of having asthma. Individual-level geo-coded survey data from The Irish Longitudinal Study on Ageing (TILDA) were linked to model-based estimates of annual average NO2 at 50 m resolution. Asthma was identified using two methods: self-reported diagnoses and respondents' use of medications related to obstructive airway diseases. Logistic regressions were used to model the relationships.Results: NO2 concentrations were positively associated with the probability of asthma [marginal effect (ME) per 1 ppb of airborne NO2 = 0.24 percentage points asthma self-report, 95% confidence interval (CI) 0.06-0.42, mean asthma prevalence 0.09; for use of relevant medications ME = 0.21 percentage points, 95% CI 0.049-0.37, mean prevalence 0.069]. Results were robust to varying model specification and time period. Respondents in the top fifth percentile of NO2 exposure had a larger effect size but also greater standard error (ME = 2.4 percentage points asthma self-report, 95% CI -0. 49 to 5.3).Conclusions: Associations between local air pollution and asthma among older adults were found at relatively low concentrations. To illustrate this, the marginal effect of an increase in annual average NO2 concentration from sample minimum to median (2.5 ppb) represented about 7-8% of the sample average prevalence of asthma. [ABSTRACT FROM AUTHOR]

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]

Background Fine particulate matter (PM2.5) has been associated with disease incidence worldwide. In Ireland this is mainly produced by residential heating systems, particularly peat, coal and wood. This study aimed to explore the relationship between short-term exposure to PM2.5 and hospital admissions due to stroke, atrial fibrillation, myocardial infarction, atrial fibrillation (AF), heart failure, chronic obstructive pulmonary disease (COPD), asthma, Parkinson's disease, dementia and hip fracture in Dublin City and County between 2013 and 2017. Methods This was an ecological time series design utilising routine hospitalisation data collected from the national Health Service Executive (Hospital In-Patient Enquiry (HIPE)). Incidence of hospitalisation for each disease was identified by county of residence. Mean daily PM2.5 levels for Dublin were calculated using monitoring data from the Environmental Protection Agency's four monitoring sites. Disease incidence was organised by mean PM2.5 levels into 10μg/m3 categories and analysed using Poisson regression models correcting for minimum daily temperature at a lag of zero to five days. Results There was a significant increase in the short-term incidence of acute ischaemic stroke, AF, heart failure, Parkinson's disease, dementia and COPD when PM2.5 levels were above 50μg/m3 compared to when levels were below 10μg/m3 at one day post exposure when correcting for minimum temperature. Conclusion Controls need to be brought in to ensure that PM2.5 does not exceed a 50μg/m3 level due to the increase in disease incidence associated with same. The urban burning of solid fuels should be severely restricted. [ABSTRACT FROM AUTHOR]

Irish rural areas saw an increase in the number of burglaries in the period following the completion of a network of high speed motorway interurban routes. To test the hypothesis that the new fast infrastructure was providing quick exits by urban based criminal gangs, Irish crime statistics along with census data were combined in a logistic regression analysis to assess the relative influence of accessibility, deprivation, population, and proximity to fast road infrastructure on the number of burglaries in areas. It was found that accessibility had a higher influence on crime than area deprivation and that proximity to fast road infrastructure was not significant. [ABSTRACT FROM AUTHOR]

Understanding real-world driving conditions in the form of driving cycles is instrumental in the design of efficient powertrains and energy storage systems for electric vehicles. In addition, driving cycles serve as a standardised measurement procedure for the certification of a vehicle’s fuel economy and driving range. They also facilitate the evaluation of the economic and lifecycle costs of emerging vehicular technologies. However, discrepancies between existing driving cycles and real-world driving conditions exist due to a number of factors such as insufficient data, inadequate driving cycle development methodologies and methods to assess the representativeness of developed driving cycles. The novel aspect of the work presented here is the use of real-world data from electric vehicles, over a six month period, to derive a driving cycle appropriate for their assessment. A stochastic and statistical methodology is used to develop and assess the representativeness of the driving cycle against a separate set of real world electric vehicle driving data and the developed cycle performs well in that comparison. Although direct comparisons with internal combustion engine driving cycles are not that informative or relevant due to the marked differences between how they and electric vehicles operate, some discussion around how the developed electric vehicle cycle relates to them is also included. [ABSTRACT FROM AUTHOR]

The research uses a database of urban noise data collected continually from April 2013 to March 2014 at 10 sites in Dublin, Ireland. The first objective of the paper is to investigate if the morning daily noise level peak is related to transport characteristics of households, such as car ownership levels, the mode by which people travel to work, and morning work trip departure time. Data from the 2011 Irish census is used to provide the information on households and this is tested against noise measurement levels. The second objective is to examine the relative importance of the spatial and temporal variables of location, month of the year, weekday, and hour of the day in predicting urban noise levels using a multinomial logistic regression model. The results show that the transport household characteristics examined do not appear to influence noise levels. The outcome from the regression model demonstrates that location is the most important variable followed in order by hour of the day, month of the year, and weekday in predicting urban noise levels. [ABSTRACT FROM AUTHOR]

An algorithm for the implementation of short-term prediction of traffic with real-time updating based on spectral analysis is described. The prediction is based on the characterization of the flow based on modal functions associated with a covariance matrix constructed from historical flow data. The number of these modal functions used for prediction depends on the local traffic characteristics. Although the method works well for the examples in this paper using the lower frequency modes, it can be adapted to include modes of higher frequency, as traffic conditions dictate. This paper describes the intended online implementation of the method that predicts within-day traffic flow using a forecasting horizon of 1 h 15 min with a 15-min step. Thus, every 15 min, the traffic flow for a further 1 h 15 min is predicted. As well as forecasting to this horizon, a second algorithm incorporating a weighted averaging technique is developed, which allows the prediction of one 15-min step ahead by using current and previous predictions of traffic flows at the given time instant while placing more weight on the more recent predictions. This technique combines the features of a time-series-based prediction with spectral analysis. The development of an algorithm for the real-time implementation is described, and results are presented for a number of different schemes. [ABSTRACT FROM PUBLISHER]

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]

This article proposes optimization frameworks for discrete road network design considering the land-use transport interaction over time. Unlike existing models, the optimization frameworks can determine the optimal designs automatically without trial-and-error once the objective(s) is/are clearly defined. Moreover, these frameworks allow the evaluation of the impacts of the optimal designs on the related parties including landowners, toll road operators, transit operators, and road users, and help network planners and profit-makers with decision making by eliminating many alternative designs. A numerical study is set up to examine road network design's effects on these related parties under three road construction schemes: exact cost recovery, build-operate-transfer, and cross-subsidization. The results show that the changes in landowner profits are not the same after implementing any scheme. These unequal changes raise the issue of the landowner equity. This implies that the government has to consider trade-offs between parties’ objectives carefully. [ABSTRACT FROM AUTHOR]

This article studies travelers' mixed stochastic user equilibrium (SUE) behaviors under traveler information provision services with heterogeneous multiclass multicriteria decision making. A multiclass, multicriteria mixed SUE assignment model under traveler information provision services is proposed and modeled as a nonlinear complementary problem (NCP). The model considers heterogeneous road travelers who are assumed to be multicriteria decision makers. The route choice behaviors of equipped and unequipped travelers under traveler information provision services is then formulated as an optimization program, in which the net economic benefit is maximized and the total generated emissions are constrained. The optimization model has an interpretation from economic, behavioral, and environmental viewpoints. The solution of this program satisfies the logit-based SUE assignment. By deriving first-order optimality conditions from this program, the marginal cost pricing policy is obtained. This pricing is not only class dependent but also link dependent. [ABSTRACT FROM AUTHOR]

In acidifying lakes, pH decreases abruptly in response to acid precipitation events. We tested the hypothesis that, in comparison to a circumneutral lake, phytoplankton photosynthesis in an acidifying lake is less sensitive to a rapid decrease in pH (acid pH shock). Phytoplankton in Plastic Lake, which is undergoing acidification, was characterized by a predominance of Pyrophyta, and phytoplankton photosynthesis decreased to a lesser extent in response to an acid pH shock than the photosynthesis of populations from St. Nora Lake, a circumneutral lake located nearby, in which Pyrrophyta were not abundant. Rates of phytoplankton photosynthesis in acid pH shock experiments were significantly correlated with hydrogen ion but not with dissolved inorganic carbon (DIC) concentrations. Depression of photosynthesis following an acid pH shock occurred in axenic cultures of Chlorella Pyrenoidosa Chick but was not observed in axenic cultures of the acidophilic alga Chlorella saccharophila (Krug.) Nadson or in three species isolated from Plastic Lake. However, the three isolates were not acidophilic during growth. We conclude that phytoplankton in acidifying lakes consists predominantly of species which are tolerant to acid pH for short periods (hours) but cannot grow at these pHs. [ABSTRACT FROM AUTHOR]

SAW devices may be configured as strain sensors, providing passive, wireless strain measurement in demanding conditions. A key consideration is the modeling of the sensors, enabling different device designs to be considered. This paper presents a simulation scheme using coupling-of-modes (COM) analysis which allows both the frequency response of a SAW strain sensor and its bias sensitivity to be evaluated. Example applications are presented to demonstrate the use of the model. [ABSTRACT FROM AUTHOR]