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It is well known that the definition of bus schedules is critical for the service reliability of public transports. Several proposals have been suggested, using data from Automatic Vehicle Location (AVL) systems, in order to enhance the reliability of public transports. In this paper we study the optimum number of schedules and the days covered by each one of them, in order to increase reliability. We use the Dynamic Time Warping distance in order to calculate the similarities between two different dimensioned irregularly spaced data sequences before the use of data clustering techniques. The application of this methodology with the K-Means for a specific bus route demonstrated that a new schedule for the weekends in non-scholar periods could be considered due to its distinct profile from the remaining days. For future work, we propose to apply this methodology to larger data sets in time and in number, corresponding to different bus routes, in order to find a consensual cluster between all the routes.

Travel time prediction is an important tool for the planning tasks of mass transit and logistics companies. ID this paper we investigate the use of regression methods for the problem of predicting the travel time of buses in a Portuguese public transportation company. More specifically, we empirically evaluate the impact of varying parameters on the performance of different regression algorithms, such as support vector machines (SVM), random forests (RF) and projection pursuit, regression (PPR). We also evaluate the impact of the focusing tusks (example selection; domain value definition and feature selection) in the accuracy of those algorithms. Concerning the algorithms, we observe that 1) RF is quite robust to the choice of parameters and focusing methods: 2) the choice of parameters for SVM can be made independently of focusing methods while 3) for PPR they should be selected simultaneously. For the focusing methods, we observe that a stronger effect is obtained using example selection, particularly in combination with SVM.

Integration methods for ensemble learning can use two different approaches: combination or selection. The combination approach (also called fusion) consists on the combination of the predictions obtained by different models in the ensemble to obtain the final ensemble predication. The selection approach selects one (or more) models from the ensemble according to the prediction performance of these models on similar data from the validation set. Usually, the method to select similar data is the k-nearest neighbors with the Euclidean distance. In this paper we discuss other approaches to obtain similar data for the regression problem. We show that using similarity measures according to the target values improves results. We also show that selecting dynamically several models for the prediction task increases prediction accuracy comparing to the selection of just one model.

Companies at the public sector have the mission to assure services which effectively contribute for the quality of life of the community and to make these services available to all citizens. Therefore, people expect that they offer services with quality and customer oriented, but using the available resources with parsimony, efficiency and effectiveness. To correspond to this expectation from the society, such companies need to incorporate new concepts of management, able to drive their strategy to the value creation for the community. In this context, this paper describes an adaptation of existent management control tools to the specificities of a passenger public transport company. The Balanced Scorecard (BSC) was originally designed for the private companies environment. Many researchers proposed modifications of the original metrics in order to adapt this tool to the public sector. This paper proposes a new perspective, the value creation for the Stakeholder, as the prime strategic objective of the BSC, in order to align the efforts of the company to the achievement of its role. The idea is to be able to get convergence between customer satisfaction and financial drivers. Other objective of this study is to reinforce in the culture of the company customer oriented principles. This new approach also allows the development of a strategic map for the company, which describes its strategy through a cause-effect chain of the objectives and actions on course.

The author describes a man-computer interactive mass transit crew scheduling module which was developed as a component of a Decision Support System (DSS) implemented in a personal computer at the Oporto Urban Transport Authority (STCP). The problem of crew scheduling is briefly discussed and set in the context of the broader process of the planning and control of urban transport services. The general philosophy and the structure of the module are analysed. A crew scheduling heuristic based method is presented. Particular attention is paid to the interactive nature of the approach adopted. Results of the actual implementation process at STCP are given.

This paper describes the study on example selection in regression problems using mu-SVM (Support Vector Machine) linear as prediction algorithm. The motivation case is a study done on real data for a problem of bus trip time prediction. In this study we use three different training sets: all the examples, examples from past days similar to the day where prediction is needed, and examples selected by a CART regression tree. Then, we verify if the CART based example selection approach is appropriate on different regression data sets. The experimental results obtained are promising.

This paper describes the methodology used in the construction of a Data Warehousing project to support planning and control on mass transit companies. The data sources used were the ones from the GIST98/EUROBUS system - a computer application for supporting operational planning in public transport companies. Firstly, the main Data Warehousing concepts and definitions are introduced. After the problem description and a brief analysis of different Data Warehousing methodologies, the analysis and development phases of the Data Warehousing project are presented. Finally, in the conclusions, the main advantages to implement such a system in the situation described are emphasized.

In this paper we discuss how trip time prediction can be useful foroperational optimization in mass transit companies and which machine learningtechniques can be used to improve results. Firstly, we analyze which departmentsneed trip time prediction and when. Secondly, we review related work and thirdlywe present the analysis of trip time over a particular path. We proceed by presentingexperimental results conducted on real data with the forecasting techniques wefound most adequate, and conclude by discussing guidelines for future work.

In this paper we discuss how trip time prediction can be useful for operational optimization in mass transit companies and how data mining techniques can be used to improve results. Firstly, we an- alyze which departments need trip time prediction and when. Secondly, we review related work and thirdly we present the analysis of trip time over a particular path. We proceed by presenting experimental results conducted on real data with the forecasting techniques we found most adequate, and conclude by discussing guidelines for future work.

Dada a complexidade inerente à avaliação de interfaces de transporte de passageiros, e sendo um interface um sistema influenciado pela envolvente e que a influencia, a aplicação de uma metodologia Soft parece ser a mais adequada para a estruturação correcta do problema. Uma vez estruturado o problema, é definida uma metodologia multicritério para a avaliação.

The Performance Indicators Board (PIB) is a top level oriented module for mass transport companies. The objective of PIB is to collect and to show the planning and control information to support the decision making process. In this paper, the authors describe the indicators identification process and the logic architecture of the module, in particular its query tool.

The Planning Indicators Board (PIB) is a top level oriented module for mass transit companies. The proposal is to collect and to show the planning and control information to support the decision making process. It uses Data Warehousing technology.

The organizations ability to manage corporate environmental performance is emerging as a strategic issue for companies. Environmental decision-making requires an explicit methodology in which it must be possible to articulate the involvement of various stakeholders, incorporate multi-disciplinary knowledge, and integrate criteria involving trade-offs in order to deal with different opinions and heterogeneous factors. The choice of sustainable options for the company and the creation of commitments among stakeholders are a must for businesses. The main motivation of this paper is to present results from the initial steps of the development of a methodology to support decision-making of corporate environmental strategies. Illustrations from an ongoing case study are presented. © 2014 The Authors. Published by Elsevier B. V. Selection and peer-review under responsibility of the Scientific Committee of EWGT2014.

In the present work we propose two bid-price based heuristic approaches to tackle a stochastic price-oriented demand of air cargo transportation. We assume fares are non-decreasing over time: the earlier the booking, the cheaper the fare. We consider a single-leg flight without overbooking practices or no-show customers. The proposed framework is suited for air cargo carriers providing a unique product to all its price-oriented customers. The business sustainability relies on a significant reduction in fares that would outperform other benefits, an earlier time of delivery above all. Nevertheless, our modelling framework may be easily extended to other modes of cargo transportation, such as maritime, where a given shipment receives the same service regardless the paid fare, which, in turn, only depends on the time the booking request is made.