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Assessing real-world emissions from buses and taxis is vital to comprehend their impact on urban air quality. Such vehicles differ significantly from the majority of the fleet owing to their higher mileage rates. However, few studies have focused on specifically assessing the emissions from this segment of the vehicle fleet. In this context, this study evaluated the real-world emissions of nitrogen oxides (NO x) from in-use buses and taxis in Dublin, Ireland, using crossroad remote sensing technology. The remote sensing system was deployed at strategic locations throughout the city to capture on-road emissions from passing vehicles. The collected data included vehicle related information such as emission standard, make, and mileage, and pollutants including NO x. Based on this data, analysis was aimed to understand the impact of Euro emission standard, ambient temperature, mileage, and make of the vehicle on NO x emissions. The results reveal that the average emissions from taxis reduce by 37% from Euro 5 to Euro 6b, and average emissions from Euro 6 buses are 87% lower compared to Euro 5. The trends in emission factors (EFs) of buses and taxis were similar during summer and winter sampling. Moreover, on comparing the emissions from the top five taxi manufacturers, different trends in the emission factors were observed. Finally, the study found that the effect of vehicle mileage on emissions was unclear for both buses and taxis. In any case, these findings provide valuable insights into the real-world emission performance of the existing fleet of buses and taxis in Dublin and highlight the need for targeted measures to reduce emissions from these vehicles. The results can assist policymakers and urban planners in formulating evidence-based strategies to improve air quality in Dublin and other cities facing similar challenges. • Evaluated real-world emissions from in-use buses and taxis in Dublin, Ireland. • On-road remote sensing technology was used to obtain NO x emission measurements. • Average emissions from taxis reduce by 37% from Euro 5 to Euro 6b. • Average emissions from Euro 6 buses are significantly lower (− 87 %) compared to Euro 5. [ABSTRACT FROM AUTHOR]

In recent years, cycling has been recognised by policy makers and practitioners as a sustainable alternative to motorised travel. However, a lack of perceived safety associated with cycling may have inhibited its growth as a popular mode of transportation in urban environments. A questionnaire-based survey, conducted in Dublin city, found cyclists to perceive cycling in the city as less safe than driving. The survey responses were analysed in an ordered logistic regression framework to identify the determinants of their perceived safety in order to recommend possible policy interventions for improvement of these perceptions. A cyclist safety index is also proposed, which allows the perceptions of cyclists relative to driving to be quantified for consideration in transport network design and adaptation for cyclists. [ABSTRACT FROM AUTHOR]

Short-term traffic-flow forecasting, the process of predicting future traffic conditions based on historical and real-time observations, is an essential aspect of intelligent transportation systems. The existing well-known algorithms used for short-term traffic flow include time-series analysis-based techniques, of which the seasonal auto-regressive moving average model is one of the most precise methods used in this field. The effectiveness of short-term traffic flow in an urban transport network can be fully realised only in its multivariate form where traffic flow is predicted at multiple sites simultaneously. In this paper, this concept in explored utilising an additive seasonal vector auto-regressive moving average model to predict traffic flow in the short-term future considering the spatial dependency among multiple sites. The dynamic linear model representation of the auto-regressive moving average model is used to reduce the number of latent variables. The parameters of the model are estimated in a Bayesian inference framework employing a Markov chain Monte Carlo sampling method. The efficiency of the proposed prediction algorithm is evaluated by modelling real-time traffic-flow observations available from a certain junction in the city centre of Dublin, Ireland. [ABSTRACT FROM AUTHOR]

Abstract: Ambient air quality monitoring, modeling and compliance to the standards set by European Union (EU) directives and World Health Organization (WHO) guidelines are required to ensure the protection of human and environmental health. Congested urban areas are most susceptible to traffic-related air pollution which is the most problematic source of air pollution in Ireland. Long-term continuous real-time monitoring of ambient air quality at such urban centers is essential but often not realistic due to financial and operational constraints. Hence, the development of a resource-conservative ambient air quality monitoring technique is essential to ensure compliance with the threshold values set by the standards. As an intelligent and advanced statistical methodology, a Structural Time Series (STS) based approach has been introduced in this paper to develop a parsimonious and computationally simple air quality model. In STS methodology, the different components of a time-series dataset such as the trend, seasonal, cyclical and calendar variations can be modeled separately. To test the effectiveness of the proposed modeling strategy, average hourly concentrations of nitrogen dioxide and nitrogen oxides from a congested urban arterial in Dublin city center were modeled using STS methodology. The prediction error estimates from the developed air quality model indicate that the STS model can be a useful tool in predicting nitrogen dioxide and nitrogen oxides concentrations in urban areas and will be particularly useful in situations where the information on external variables such as meteorology or traffic volume is not available. [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]

• Vehicle, cyclist & pedestrian count data from 44 neighboring or collocated stations were analyzed. • Pre & post-pandemic datasets showed pattern changes due to Covid-19 restrictions were mode-specific. • Land-use restrictions and altered trip purposes impacted daily and weekly travel patterns. • Cross-correlation, clustering, and Bayesian change-point analyses showed congruent inferences. • Timeline of pandemic restrictions could be matched using statistical analyses on traffic counts. The spread of Covid-19 and implementation of various restrictions changed how and why people travel. The present study analyzed three different modes of transportation, to understand the impact of Covid-19 mobility and land-use restrictions on chosen neighbourhoods in Dublin City, Ireland. Classification analysis, Spatial correlation analysis, and Bayesian change point analysis had been conducted using vehicle, cyclist & pedestrian count data from 44 neighbouring or collocated stations to explore the statistical changes in the traffic system characteristics. Apart from reduction in traffic counts, the other impacts were modal shift, rise in cyclist numbers, and similarity between weekday & weekend patterns observed. Analyses could identify that changes in the statistical aspects of traffic system are congruent with the changes in lockdown measures. Overall, this study presented a set of tools to identify the existence and degree of changes in traffic patterns over time due to any mobility or land-use policy changes. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, cycling has been recognized and is being promoted as a sustainable mode of travel. The perception of cycling as an unsafe mode of travel is a significant obstacle in increasing the mode share of bicycles in a city. Hence, it is important to identify and analyze the factors which influence the safety experiences of the cyclists in an urban signalized multi-modal transportation network. Previous researches in the area of perceived safety of cyclists primarily considered the influence of network infrastructure and operation specific variables and are often limited to specific locations within the network. This study explores the factors that are expected to be important in influencing the perception of safety among cyclists but were never studied in the past. These factors include the safety behavior of existing cyclists, the users of other travel modes and their attitude toward cyclists, facilities and network infrastructures applicable to cycling as well as to other modes in all parts of an urban transportation network. A survey of existing cyclists in Dublin City was conducted to gain an insight into the different aspects related to the safety experience of cyclists. Ordered Logistic Regression (OLR) and Principal Component Analysis (PCA) were used in the analysis of survey responses. This study has revealed that respondents perceive cycling as less safe than driving in Dublin City. The new findings have shown that the compliance of cyclists with the rules of the road increase their safety experience, while the reckless and careless attitudes of drivers are exceptionally detrimental to their perceived safety. The policy implications of the results of analysis are discussed with the intention of building on the reputation of cycling as a viable mode of transportation among all network users. [Copyright &y& Elsevier]

Emissions from real-world driving are often higher than estimated emission levels based on Euro emissions standards and type-approval tests. Consequently, the air quality in cities has not improved as originally anticipated from fleet renewal and stringent emission regulations for newer vehicles. This paper presents the real-world emission factors (EFs) for in-use cars, taxis, vans, and buses obtained using on-road remote sensing in Dublin city, Ireland. EFs from both petrol and diesel Euro 6 vehicles were significantly improved compared to the EFs of older emission standard vehicles. The analyses indicated that the presence of a few high emitting older vehicles skewed the overall distribution of EFs. Detailed description of variations in EFs for individual pollutants based on changes in emission standards, location, and other driving characteristics were studied and reported, which provide justification for implementing policies targeting fleet renewal. • First study to report real-world emission factors for in-use vehicles in Ireland. • This study considers cars, taxis, vans, and buses of varying makes and models. • Decreasing trends of NO EFs for all diesel vehicles with higher Euro standards. • Strong temperature dependence of NO x emissions for Euro 4 and Euro 5 diesel cars. • Remote sensing measurements can be used to inform vehicle emission control policies. [ABSTRACT FROM AUTHOR]