The ability to accurately model and predict the ambient concentration of Particulate Matter (PM) is essential for effective air quality management and policies development. Various statistical approaches exist for modelling air pollutant levels. In this paper, several approaches including linear, non-linear, and machine learning methods are evaluated for the prediction of urban PM 10 concentrations in the City of Makkah, Saudi Arabia. The models employed are Multiple Linear Regression Model (MLRM), Quantile Regression Model (QRM), Generalised Additive Model (GAM), and Boosted Regression Trees1-way (BRT1) and 2-way (BRT2). Several meteorological parameters and chemical species measured during 2012 are used as covariates in the models. Various statistical metrics, including the Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), the fraction of prediction within a Factor of Two (FACT2), correlation coefficient (R), and Index of Agreement (IA) are calculated to compare the predictive performance of the models. Results show that both MLRM and QRM captured the mean PM 10 levels. However, QRM topped the other models in capturing the variations in PM 10 concentrations. Based on the values of error indices, QRM showed better performance in predicting hourly PM 10 concentrations. Superiority over the other models is explained by the ability of QRM to model the contribution of covariates at different quantiles of the modelled variable (here PM 10 ). In this way QRM provides a better approximation procedure compared to the other modelling approaches, which consider a single central tendency response to a set of independent variables. Numerous recent studies have used these modelling approaches, however this is the first study that compares their performance for predicting PM 10 concentrations.

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Each year during the ninth month of the Muslim lunar calendar, more than 2 million Muslim pilgrims from around the world travel to the Holy City of Mecca in Saudi Arabia for Hajj, an annual religious pilgrimage. A significant milestone in the effort to improve the existing transport system in the Holy City was the introduction of the Southern Masha'er Rail Line during the 2010 pilgrimage season. In the first year of operation, the line operated at only 30% of its full capacity before full implementation in the following year, when the line operated at full capacity of 72,000 passengers per hour. Results are presented of a users’ survey that aimed to assess the performance of the rail line from the perspective of its users. The analysis revealed that rail users faced longer access, waiting, and egress times compared with regular rail operations standards. However, survey results showed that the majority of pilgrims found these times to be tolerable. Moreover, the majority of users found the rail line and its stations to be of excellent quality and gave positive recommendations for using the rail line in the future. The analysis also produced some interesting observations that may be of relevance to rail operation in similar crowded events. Those observations are highlighted.

Road traffic exhaust emission predictions are used to inform transport policy and investment decisions aimed at reducing emissions and achieving sustainable mobility. Emission predictions are also used as inputs when modelling air quality and human exposure to traffic-related air pollutants.To be effective, such policies and/or integration must be based on robust models that not only provide point-based predictions, but also inform these with an interval of confidence that properly accounts for the propagation of uncertainties through the complex chain of models involved. This paper develops a data-driven methodological framework which enables calculating the uncertainty in average speed-based emission predictions induced by uncertainty in its traffic data inputs which are most often predictions (or outputs) of traffic flow models. An ensemble-based optimisation approach is used to estimate both calibration and validation errors arising from uncertainty in the structure and parameterisation of the Cell Transmission Model (CTM); a discretised first-order macroscopic traffic flow model that is often integrated with average speed-based emission models. A Monte Carlo sampling approach is proposed to propagate the uncertainty in traffic flow inputs to emission predictions. To ensure transferability of findings, this methodology has been tested using multiple real data sets on three motorway road networks, one of which operates under Variable Speed Limits (VSL).