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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 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. [ABSTRACT FROM AUTHOR]

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 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

New technologies are constantly being researched and developed to improve the sense of security of transport users. However, security technologies could also pose their own risks with regard to transport users’ privacy through the potential for data collection and storage. Transport, in particular, allows the data collector potential access to information on the users’ habits through the location information that could be collected during an individual’s interaction with technology during their travels. Some technologies, such as personal security apps (which essentially turn a smartphone into a type of panic alarm) also allow for the linking of the security technology and social networking apps. Given this link and the extent to which personal data, including location data, can be included in an individual’s social networking account, it was decided to investigate if the use of social networks could be related to individuals’ opinions on the use of their data through new technologies, such as personal security apps. This paper presents an exploration of the possibility of grouping respondents to a transport survey ( N = 469) based on their answers to questions on their social networking (SNS) use. It was hypothesised that if distinct groups exist within the SNS data, then they could be used as a supplementary personal variable for underlying privacy concerns and levels of engagement with technology in future transport studies. This would be in addition to the socio-demographic information collected in the survey. This paper presents the initial dimension reduction of the response variables to create composite variables. That is followed by the exploratory clustering of responses using two-step cluster analysis. Finally, the paper discusses the hypothesis testing of the resultant clusters against the socio-demographic responses to ascertain if additional information is provided by the clustering results. The transport survey responses were collected in Ireland, and a subset of responses, from the Greater Dublin Area, were used for the analysis in this paper. The results demonstrate that there is scope for this type of method to be used when researching new security technologies in transport. [ABSTRACT FROM AUTHOR]