Abstract
Handling major challenges like traffic volume estimation, mobility pattern detection and feature extraction in mobility network usually form a weak balance among them. Most of the works are focused towards one of these areas which fail in improving altogether. In this paper, we present a model with modified conventional methods meeting all three above challenges to an extent. Extracting new temporal & directional feature, we introduce Reciprocity metric. It proves to be more informative and efficient in capturing mobility pattern of the network than existing metrics. We introduce the idea of network skeleton which is a reduced form of mobility network but captures approx 90% of its inherent characteristics. Network Skeleton can extract higher level of information from the network while enhancing network's short-term predictability. Our work has the following steps: 1) extracting and building "link reciprocity", a more informative feature; 2) pattern detection in random mobility introduced by "convergence of mobility network"; and 3) estimation of network skeleton formed using a link based approach for short-term forecasting. Our network convergence method outperforms conventional approaches and detects active regions at a very fast rate compared to other approaches. Long ShortTerm Memory (LSTM), a kind of Recursive Neural Networks (RNN) capable of learning long-term dependencies is used to estimate network traffic. Indicating link based network-skeleton helps to reduce short-term forecasting error up to 6% and 3/4 times in different time-slots. Our network skeleton approach can be used to meet the general problems of the traffic-rules formulation by characterizing important routes (links), detecting regions of high importance in less time and predicting short-term traffic volume in a more accurate way. Moreover, network skeleton with reduced network-size can be easily operable with existing methodologies, which is another essential contribution of our work.

Abstract
Transportation systems are very complex systems due to the characteristics of their components such as buses. Nowadays, buses are set up to follow a particular schedule that is very sensitive to the changes that occur inside the system. These schedules must frequently be updated, if necessary, due to many reasons. Among these reasons, we have the population growth inside the cities as well as traffic and congestions caused by unforeseen events. To solve the problem of system variability, companies such as the Public Transport Company in the city of Porto (STCP) usually fixes bus schedules with headways adapted to each type of bus lines (i.e., high/low-frequency bus lines). In this work, we adopt a robust optimization model from literature to improve the bus schedules using Automatic Vehicle Location Data collected along the year in the city of Porto. We apply the model to a high-frequency bus line case study. We present the model imperfections and propose new updates.

Abstract
Data stream is a challenging research topic in which data can continuously arrive with a probability distribution that may change over time. Depending on the changes in the data distribution, different phenomena can occur, for example, a concept drift. A concept drift occurs when the concepts associated with a dataset change when new data arrive. This paper proposes a new method based on k-Nearest Neighbors that implements a sliding window requiring less instances stored for training than existing methods. For such, a clustering approach is used to summarize data by placing labeled instances considered similar in the same cluster. Besides, instances close to the uncertainty border of existing classes are also stored, in a sliding window, to adapt the model to concept drift. The proposed method is experimentally compared with state-of-the-art classifiers from the data stream literature, regarding accuracy and processing time. According to the experimental results, the proposed method has better accuracy and less time consumption when fewer information about the concepts are stored in a single sliding window. © 2018, Springer Nature Switzerland AG.

Abstract
Human Activity Recognition has been primarily investigated as a machine learning classification task forcing it to handle with two main limitations. First, it must assume that the testing data has an equal distribution with the training sample. However, the inherent structure of an activity recognition systems is fertile in distribution changes over time, for instance, a specific person can perform physical activities differently from others, and even sensors are prone to misfunction. Secondly, to model the pattern of activities carried out by each user, a significant amount of data is needed. This is impractical especially in the actual era of Big Data with effortless access to public repositories. In order to deal with these problems, this paper investigates the use of Transfer Learning, specifically Unsupervised Domain Adaptation, within human activity recognition systems. The yielded experiment results reveal a useful transfer of knowledge and more importantly the convenience of transfer learning within human activity recognition. Apart from the delineated experiments, our work also contributes to the field of transfer learning in general through an exhaustive survey on transfer learning for human activity recognition based on wearables. © 2018, Springer Nature Switzerland AG.

Abstract
We present a method for improving the prediction accuracy using multiple predictive algorithms. Several techniques have been developed to tackle this issue such as bagging, boosting and stacking. In contrary to the first two that, usually, generate homogeneous ensembles of classifiers, stacking techniques have demonstrated success using heterogeneous ensembles. In our method, we adopt the stacking mechanism. Several models are generated using different learning algorithms. Forward stepwise selection is implemented to link each instance to its appropriate learning model. Experiments with three datasets benchmarked with four learning schemes show that this novel method improves prediction accuracy and can serve as a bridge to transfer knowledge between tasks given the same feature space but different data distributions. © 2018, Springer Nature Switzerland AG.

Abstract