Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset - based on the city of Porto -, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on Intelligent Transportation System

Chapter Self-Telling to Orientate Oneself

The guidance experience, pedagogically considered, is above all the process of identification, recognition and consolidation of individual resources of which the person is the bearer (attitudes, capabilities, inclinations, expectations, desires, projects), is the conquest of one’s own identity, the research of constituent elements of personality, the awareness of ‘being-different-from-everyone-else’. Within this theoretical assumption, the narration is a suitable placement as estimable and relevant device for self-guidance and transformation of the self. In the guidance actions the use of narrative, reflective and autobiographical paradigms can represent an effective aid in the direction of the redefinition of identity, empowerment and self-empowerment. Narration is the search for the truth about the self, or even more the process of re-thinking and re-design the self. It means to discover and achieve a deep knowledge about the self, to reconstruct one’s own life plan, is to de-form to re-emerge, to re-form it. In a broad sense, it is in some way to ‘die’ to be reborn regenerate

Coupling growth and mortality models to detect climate drivers of tropical forest dynamics

Climate models for the coming century predict rainfall reduction in the Amazonian region, including deep changes in water availability for tropical rainforests. Here, we develop an integrated modeling framework in order test the extent to which climate variables related to water regime, temperature and irradiance shape the long-term dynamics of neotropical forests. In a first step, a Bayesian hierarchical model was built to couple tree growth and tree mortality processes into a single modeling framework. Coupling a longitudinal growth model with a punctual mortality model was not an easy task. Past growth, related to the expected growth, was used as an indicator of the individual tree vigor, which is supposed to play a key role in the mortality process. A MCMC approach is used to estimate all the parameters simultaneously. The individual-centered model was explicitly designed to deal with diverse sources of uncertainty, including the complexity of the mortality process itself and the field data, especially historical data for which taxonomic determinations were uncertain. Functional traits are integrated as proxies of the ecological strategies of the trees and permit generalization among all species in the forest community. Data used to parameterize the model were collected at Paracou study site, a tropical rain forest in French Guiana, where 20,408 trees have been yearly censured over 18 years. Climate covariates were finally added as external drivers of the forest dynamics. These drivers are selected in a list of climate variables for which future predictions are available thanks to the IPCC scenario. Amongst climate variables, we highlight the predominant role of water availability in determining interannual variation in the dynamic of neotropical forests. And we stressed the need to include these relationships into forest simulators to test, in silico, the impact of different climate scenarios on the future dynamics of the rainforest. (Texte intégral