Existing time-series models that are used for short-term traffic condition forecasting are mostly univariate in nature. Generally, the extension of existing univariate time-series models to a multivariate regime involves huge computational complexities. A different class of time-series models called structural time-series model (STM) (in its multivariate form) has been introduced in this paper to develop a parsimonious and computationally simple multivariate short-term traffic condition forecasting algorithm. The different components of a time-series data set such as trend, seasonal, cyclical, and calendar variations can separately be modeled in STM methodology. A case study at the Dublin, Ireland, city center with serious traffic congestion is performed to illustrate the forecasting strategy. The results indicate that the proposed forecasting algorithm is an effective approach in predicting real-time traffic flow at multiple junctions within an urban transport network.

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.

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.

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

The application of drones has recently revolutionised the mapping of wetlands due to their high spatial resolution and the flexibility in capturing images. In this study, the drone imagery was used to map key vegetation communities in an Irish wetland, Clara Bog, for the spring season. The mapping, carried out through image segmentation or semantic segmentation, was performed using machine learning (ML) and deep learning (DL) algorithms. With the aim of identifying the most appropriate, cost-efficient, and accurate segmentation method, multiple ML classifiers and DL models were compared. Random forest (RF) was identified as the best pixel-based ML classifier, which provided good accuracy (≈85%) when used in conjunction graph cut algorithm for image segmentation. Amongst the DL networks, a convolutional neural network (CNN) architecture in a transfer learning framework was utilised. A combination of ResNet50 and SegNet architecture gave the best semantic segmentation results (≈90%). The high accuracy of DL networks was accompanied with significantly larger labelled training dataset, computation time and hardware requirements compared to ML classifiers with slightly lower accuracy. For specific applications such as wetland mapping where networks are required to be trained for each different site, topography, season, and other atmospheric conditions, ML classifiers proved to be a more pragmatic choice.

Climate change is a prevalent issue facing the world today. Unexpected increase in rainfall intensity and events is one of the major signatures of climate change. Rainfall influences traffic conditions and, in turn, traffic volume in urban arterials. For improved traffic management under adverse weather conditions, it is important to develop a traffic prediction algorithm considering the effect of rainfall. This inclusion is not intuitive as the effect is not immediate, and the influence of rainfall on traffic volume is often unrecognizable in a direct correlation analysis between the two time-series data sets; it can only be observed at certain frequency levels. Accordingly, it is useful to employ a multiresolution prediction framework to develop a weather adaptive traffic forecasting algorithm. Discrete wavelet transform (DWT) is a well-known multiresolution data analysis methodology. However, DWT imparts time variance in the transformed signal and makes it unsuitable for further time-series analysis. Therefore, the stationary form of DWT known as stationary wavelet transform (SWT) has been used in this paper to develop a neurowavelet prediction algorithm to forecast hourly traffic flow considering the effect of rainfall. The proposed prediction algorithm has been evaluated at two urban arterial locations in Dublin, Ireland. This paper shows that the rainfall data successfully augments the traffic flow data as an exogenous variable in periods of inclement weather, resulting in accurate predictions of future traffic flow at the two chosen locations. The forecasts from the neurowavelet model outperform the forecasts from the standard artificial neural network (ANN) model.