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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.

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]