Assessing spatiotemporal correlations from data for short-term traffic prediction using multi-task learning

Traffic flow prediction is a fundamental problem for efficient transportation control and management. However, most current data-driven traffic prediction work found in the literature have focused on predicting traffic from an individual task perspective, and have not fully leveraged the implicit knowledge present in a road-network through space and time correlations. Such correlations are now far easier to isolate due to the recent profusion of traffic data sources and more specifically their wide geographic spread. In this paper, we take a multi-task learning (MTL) approach whose fundamental aim is to improve the generalization performance by leveraging the domain-specific information contained in related tasks that are jointly learned. In addition, another common factor found in the literature is that a historical dataset is used for the calibration and the assessment of the proposed approach, without dealing in any explicit or implicit way with the frequent challenges found in real-time prediction. In contrast, we adopt a different approach which faces this problem from a point of view of streams of data, and thus the learning procedure is undertaken online, giving greater importance to the most recent data, making data-driven decisions online, and undoing decisions which are no longer optimal. In the experiments presented we achieve a more compact and consistent knowledge in the form of rules automatically extracted from data, while maintaining or even improving, in some cases, the performance over single-task learning (STL).Peer ReviewedPostprint (published version

Improving adaptation and interpretability of a short-term traffic forecasting system

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyse the situation in a commercial real-time prediction system with its current problems and limitations. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. We test this predictive system in different real-work scenarios, and evaluate its performance integrating a multi-task learning paradigm for the sake of the traffic prediction task.Peer ReviewedPostprint (published version

Analyse du phénomène de retrait-gonflement dans les sols argileux sur les bâtiments légers: étude du t raitement à la chaux des argiles

Els danys provocats per l’expansió i contracció dels sols argilosos estan reconeguts com una de les grans catàstrofes naturals a França. Les cases individuals són especialment vulnerables al fenomen, principalment per les seves fonamentacions superficials. Aquesta tesina, desenvolupada al laboratori LGCIE de l’INSA de Lyon, es desenvolupa en 3 eixos: la caracterització d'una argila expansiva no tipus, l'estudi experimental del comportament hidro-mecànic de l’argila en qüestió, i l’anàlisi del tractament amb calç per reduir el caràcter expansiu de l’argila. La tesina té en tot moment en compte el baix pressupost que es pot dedicar als estudis geotècnics d’un habitatge nou de petites dimensions

Simulating fuel consumption and vehicle emissions in an Australian context

Road transport is a major source of air pollution and greenhouse gas emissions around the world. There is an increasing interest in accurate information on local vehicle emission levels for policy development and sustainable traffic management. Previous studies have shown that emission predictions for the Australian situation need to reflect both the Australian fleet and driving behaviour to avoid unreliable outcomes. This paper discusses a new Australian vehicle emission software (PΔP) and a case-study where traffic simulation software (Aimsun) is combined with PΔP to demonstrate how consistent results can be achieved for the Australian situation. The case-study is an Australian city modelled using the microscopic simulator to generate the required trajectory data of each individual vehicle for the emission model. The simulation results are used in a number of ways: to assess the impacts of urban driving behaviour on fuel consumption, to create maps showing where and when elevated emission levels occur and to compare results with another program (COPERT Australia). The paper will also discuss where further research is required

Estudi constructiu del vial de connexió de la BV-4316 (Sta. Eulàlia de Riuprimer) a la C-25 i la C-17 a través de la C-25D

El present Projecte de Final de Carrera té els següents objectius principals: - Justificar la necessitat de construcció del vial de connexió de la BV-4316 a la C-25 i la C-17, passant per la C-25D. - Realitzar un estudi d’alternatives i, a través d’un estudi multicriteri complet, escollir l’alternativa que respongui millor en termes de funcionalitat, minimització de l’impacte ambiental, integració territorial, desenvolupament econòmic, interès ciutadà i rendibilitat. - Desenvolupar l’alternativa escollida a nivell de projecte constructiu