(156 words)The existing time series models used for short-term traffic condition forecasting are mostly univariate in nature. Generally the extension of the existing univariate time-

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 square 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 modelling. In Bayesian analysis, 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 classical inference. The time-series data used for fitting the SARIMA model are obtained from an intersection in Dublin's city centre.

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 towards 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.

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International audienceTo make visual data a part of quantitative assessment for infrastructure maintenance management, it is important to develop computer-aided methods that demonstrate efficient performance in the presence of variability in damage forms, lighting conditions, viewing angles, and image resolutions taking into account the luminous and chromatic complexities of visual data. This article presents a semi-automatic, enhanced texture segmentation approach to detect and classify surface damage on infrastructure elements and successfully applies them to a range of images of surface damage. The approach involves statistical analysis of spatially neighboring pixels in various color spaces by defining a feature vector that includes measures related to pixel intensity values over a specified color range and statistics derived from the Grey Level Co-occurrence Matrix calculated on a quantized grey-level scale. Parameter optimized non-linear Support Vector Machines are used to classify the feature vector. A Custom-Weighted Iterative model and a 4-Dimensional Input Space model are introduced. Receiver Operating Characteristics are employed to assess and enhance the detection efficiency under various damage conditions

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In the past several years, active travel (walking and cycling) has been increasingly recognised as an effective means of increasing physical activity and mitigating negative external impacts of motorised transport. As a result of this recognition, a range of methodologies for quantifying the benefits and risks of active travel have emerged in the literature. This review critically assesses studies which quantify the health impacts of transport scenarios involving increased active travel. It was found that the choice of methodology and assumptions employed in such studies may have a significant impact on the results and so care must be taken when planning these studies or interpreting their results. Increases in physical activity are the most important determinant of the health impacts of active travel but different methodologies for quantifying the health impacts of this physical activity can lead to substantial differences in the scale of the impact. Therefore, further research is required into the relationship between increased physical activity and health effects. Particular attention should also be paid to ensure that health impacts are assessed for both the individuals changing their travel behaviour and all other individuals in the study area. Where relative risk relationships are used to estimate health impacts, researchers should be mindful of the specific exposures used to develop these relationships in order to prevent double counting of health impacts. Extensive sensitivity analysis is necessary to compensate for the uncertainty inherent in models for predicting health effects.

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