The considered problem has arisen as a result of collaboration with Riga Coach Terminal (Rīgas Starptautiskā Autoosta) authorities. Recent studies of the role of buses and coaches seem to confirm the already excellent safety, environmental and social record of bus and coach transport. In Latvia this mode of transport is in competition with railway (and also private cars) that's why the quality of services is very important from all points of view. In authors' previous researches different methods were applied to estimate functional form between overall quality of service and explanatory variables included questionnaire items related to the satisfaction accessibility (availability), information, time characteristics of service, customer service, comfort, safety, infrastructure and environment at Riga Coach Terminal. Such kind of model allows estimating influence of particular quality indicators on the overall quality assessment and simplifying the monitoring of quality indicators. In the given work ordinal regression method has been used to model the relationship between the ordinal outcome variable, e.g. estimates of overall quality of service -y i (these estimates are made on the basis of (1÷5) scale), and the 22 particular attributes of quality distributed on the mentioned above 7 groups. The main decisions involved in the model building for ordinal regression determine, which particular attributes should be included in the model, and choose the link function (e.g. logit link or complementary log-log link) that demonstrates the model appropriateness. The model fitting statistics, the accuracy of the classification results, and the validity of the model assumption, e.g., parallel lines, have been assessed for selecting the best model. The model was done on the basis of results of questionnaire of transport experts, which had been fulfilled in spring 2009. In total 44 questionnaires have been returned, however some questions remained without an answer; that's why different methods of data imputation have been applied to substitute skips in dataset and few models have been constructed for selecting the best one.

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Sustainable urban mobility remains an emerging research topic during last decades. In recent years, the smart card data collection systems have become widespread and many studies have been focused on usage of anonymized data from these systems for better understanding of mobility patterns of Public Transport (PT) passengers. Data-driven mobility patterns can benefit transport planners at strategic, tactical, and operational levels. A particular point of interest is a spatiotemporal dynamics of mobility patterns that highlights transformation of the PT passenger flows over the time continuously or in response to modifications of the PT system and policies. This study is aimed to estimation and analysis of the spatiotemporal dynamics of PT passenger flows in Riga (Latvia). A multi-stage methodology was proposed and includes three main stages: (1) estimation of individual trip vectors, (2) clustering of trip vectors into spatiotemporal mobility patterns, and (3) further analysis of mobility patterns’ dynamics. The best practice methods are applied at every stage of the proposed methodology: the smart card validation flow is used for extracting information on boarding locations; the trip chain approach is used for estimation of individual trip destinations; vector-based clustering algorithms are utilised for identification of mobility patterns and discovering their dynamics. The resulting methodology provides an advanced tool for observing and managing of PT demand fluctuation on a daily basis. The methodology was applied for mining of a large smart card data set (124 million records) for year 2018. Most important empirical results include obtained daily mobility patterns in Riga, their clusters, and within-cluster dynamics over the year. Obtained daily mobility patterns allows estimation of a city-level PT origin–destination matrix that is useful in many applied areas, e.g., dynamic passenger flow assignment models. Mobility pattern-based clustering of days allows effective comparison and flexible tuning of the PT system for different days of a week, public holidays, extreme weather conditions, and large events. Dynamics of mobility patterns allows estimating the effect of implementing changes (e.g., fare increase or road maintenance) and demand forecasting for user-focused development of PT system.