Abstract

Abstract
Mining public transportation networks is a growing and explosive challenge due to the increasing number of information available. In highly populated urban zones, the vehicles can often fail the schedule. Such fails cause headway deviations (HD) between high-frequency bus pairs. In this paper, we propose to identify systematic HD which usually provokes the phenomenon known as Bus Bunching (BB). We use the PrefixSpan algorithm to accurately mine sequences of bus stops where multiple HD frequently emerges, forcing two or more buses to clump. Our results are promising: 1) we demonstrated that the BB origin can be modeled like a sequence mining problem where 2) the discovered patterns can easily identify the route schedule points to adjust in order to mitigate such events.

Abstract
In highly populated urban zones, it is common to notice headway deviations (HD) between pairs of buses. When these events occur in a bus stop, they often cause bus bunching (BB) in the following bus stops. Several proposals have been suggested to mitigate this problem. In this paper, we propose to find BBS (Bunching Black Spots) - sequences of bus stops where systematic HD events cause the formation of BB. We run a sequence mining algorithm, named PrefixSpan, to find interesting events available in time series. We prove that we can accurately model the BB trip usual pattern like a frequent sequence mining problem. The subsequences proved to be a promising way of identify the route' schedule points to adjust in order to mitigate such events. © 2012 Springer-Verlag.

Abstract
Long-term travel time prediction (TTP) can be an important planning tool for both freight transport and public transport companies. In both cases it is expected that the use of long-term TTP can improve the quality of the planned services by reducing the error between the actual and the planned travel times. However, for reasons that we try to stretch out along this paper, long-term TTP is almost not mentioned in the scientific literature. In this paper we discuss the relevance of this study and compare three non-parametric state-of-the-art regression methods: Projection Pursuit Regression (PPR), Support Vector Machine (SVM) and Random Forests (RF). For each one of these methods we study the best combination of input parameters. We also study the impact of different methods for the pre-processing tasks (feature selection, example selection and domain values definition) in the accuracy of those algorithms. We use bus travel time's data from a bus dispatch system. From an off-the-shelf point-of-view, our experiments show that RF is the most promising approach from the three we have tested. However, it is possible to obtain more accurate results using PPR but with extra pre-processing work, namely on example selection and domain values definition.

Abstract
The goal of ensemble regression is to combine several models in order to improve the prediction accuracy in learning problems with a numerical target variable. The process of ensemble learning can be divided into three phases: the generation phase, the pruning phase, and the integration phase. We discuss different approaches to each of these phases that are able to deal with the regression problem, categorizing them in terms of their relevant characteristics and linking them to contributions from different fields. Furthermore, this work makes it possible to identify interesting areas for future research.

Abstract
In this paper we study the deviation of bus trip duration and its causes. Deviations are obtained by comparing scheduled times against actual trip duration and are either delays or early arrivals. We use distribution rules, a kind of association rules that may have continuous distributions on the consequent. Distribution rules allow the systematic identification of particular conditions, which we call contexts, under which the distribution of trip time deviations differs significantly from the overall deviation distribution. After identifying specific causes of delay the bus company operational managers can make adjustments to the timetables increasing punctuality without disrupting the service. © Springer-Verlag Berlin Heidelberg 2012.