Frequent haze occurrences in Malaysia have made the management of PM (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM variation and good forecast of PM concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

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Bayesian forecasting in time and interpolation in space is a challenging task due to the complex nature of spatio-temporal dependencies that need to be modeled for better understanding and description of the underlying processes. The problem exacerbates further when the geographical study region, such as the one in the Eastern United States considered in this chapter, is vast and the training data set for forecasting, and modelling, is rich in both space and time. This chapter develops forecasting methods for three recently This is a Book Title Name of the Author/Editor c XXXX John Wiley & Sons, Ltd Bayesian Forecasting Using Spatio-temporal Models with Applications to Ozone Concentration Levels in the Eastern United Statesproposed hierarchical Bayesian models for spatio-temporal data sets. The chapter also develops Markov chain Monte Carlo based computation methods for estimating a number of relevant forecast calibration measures that facilitates rigorous comparisons of the Bayesian forecasting methods. The methods are illustrated with a test data set on daily maximum eight hour average ozone concentration levels observed over a study region in the Eastern United States. Forecast validations, using several moving windows, find a model developed using an approximate Gaussian predictive process to be the best and it is the only viable method for large data sets when computing speed is also taken into account. The methods are implemented in a recently developed software package, spTimer, which is a publicly available contributed R package that has wider applicability.

Lane-changing (LC) problem may cause serious accidents or create a painful traffic jam at multi-lane roads. Existing LC simulation model was created with some limitations (less fitted, without velocity and acceleration profiles, high curvature) by using well known trajectory curve such as Hyperbolic Tangent Curve (HTC), Sine-Based Curve (SC), Polynomial Curve (PC). In this study, a new parametric curve had been proposed by using curvilinear coordinate system and fitted against Next Generation Simulation (NGSIM) real dataset. Further, new profiles of velocity and acceleration were designed using the proposed LC trajectory curve. The curvature of proposed model was zero-based curvature both at LC starting and ending points. This proposed curvature was compared with two models such as HTC and SC. The average root-mean-square-error of proposed model decreased with 1.84% for left LC and 15.48% for right LC compared to HTC model and 1.74% for left LC and 15.60% for right LC compared to SC model. Similarly, the proposed model for velocity and acceleration profiles improved significantly from PC model. The proposed parametric curve solves the gap and collision points of LC vehicle with a front vehicle and rear vehicle at target lane and can be used in real LC path planning.

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