Xiao He, Moreira-Matias L.: Patent Granted at US Office US10817543B2 - Method for automated scalable co-clustering (12/2020)

Moreira-Matias L., Cerqueira V.: Patent Granted at US Office US20170270413A1 - Real-time filtering of digital data sources for traffic control centers (10/2019)

Moreira-Matias L., Khiari J., Saadallah A.: Patent Granted at US Office US20180096606A1 - Method to control vehicle fleets to deliver on-demand transportation services (02/2019)

Kozodoi, N., Lessmann, S., Alamgir, M., Moreira-Matias, L., & Papakonstantinou, K. (2025). Fighting sampling bias: A framework for training and evaluating credit scoring models. European Journal of Operational Research, in Press. PDF
Kozodoi, N., Katsas, P., Lessmann, S., Moreira-Matias, L., Papakonstantinou, K.: “Shallow Self-Learning for Reject Inference in Credit Scoring". In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), pp. 516-532. Springer, Cham, 2019. (acceptance rate: 18.00%) PDF Poster PDF

Saadallah, A., Moreira-Matias, L., Sousa, R., Khiari, J., Jenelius, E., Gama, J.: "BRIGHT - Drift-Aware Demand Predictions for Taxi Networks". In: IEEE Transactions on Knowledge and Data Engineering vol. 32, no. 2, pp. 234-245, February (2020) PDF

Khiari J., Moreira-Matias L., Shaker A., Zenko B., Dzeroski S.: "MetaBags: Bagged Meta-Decision Trees for Regression". In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), pp. 637-652. Springer, Cham, 2018. (acceptance rate: 25.09%) PDF

Schimbinschi F., Moreira-Matias L., Nguyen V., Bailey J.: "Topology-regularized Universal Vector Autoregression for traffic forecasting in Large Urban areas". In: Expert Systems with Applications, vol. 82, pp. 301-316, October (2017) PDF

Moreira-Matias L., Gama J. and Mendes-Moreira J., ”Concept Neurons - Handling Drift Issues for Real-Time Industrial Data Mining“ In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, Proceedings, Part III, pp. 96-111, Springer (2016) PDF (acceptance rate: 24.10%)]

Moreira-Matias L., Cats O., Gama J., Mendes-Moreira J. and Sousa J.F., "An Online Learning Approach to Eliminate Bus Bunching in Real-Time". In: Applied Soft Computing, vol. 47, pp. 460-482 October (2016) PDF

Khiary J., Moreira-Matias L., Cerqueira V. and Cats O.,: "Automated Setting of Bus Schedule Coverage using Unsupervised Machine Learning". In: Advances in Knowledge Discovery and Data Mining 20th Pacific-Asia Conference (PAKDD), pp. 552-564, Springer (2016) acceptance rate: 17.26% (53/307) PDF

Moreira-Matias L., Gama J., Ferreira M., Mendes-Moreira J. and Damas L.,: "Time-Evolving OD Matrix Estimation using high-speed GPS data streams". In: Expert Systems with Applications, vol. 44, pp. 275-288, February (2016) PDF

Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L., ”Predicting Taxi–Passenger Demand Using Streaming Data“. In: IEEE Transactions on Intelligent Transportation Systems, vol.14, no.3, pp.1393-1402, September (2013) PDF - George N. Saridis Best Transactions Paper Award prize from IEEE Transacations on ITS

Editor

2017, JOURNAL: Special Issue on "Knowledge Discovery from Mobility Data for Intelligent Transportation Systems" from IEEE Transactions on Intelligent Transportation Systems

(Senior) Technical/Research Program Committee

Invited Reviewer

2025:

[1] Kozodoi, N., Lessmann, S., Alamgir, M., Moreira-Matias, L., & Papakonstantinou, K. (2025). Fighting sampling bias: A framework for training and evaluating credit scoring models. European Journal of Operational Research, in Press. PDF

2020:

[2] Saadallah, A., Moreira-Matias, L., Sousa, R., Khiari, J., Jenelius, E., Gama, J.: "BRIGHT - Drift-Aware Demand Predictions for Taxi Networks". In: IEEE Transactions on Knowledge and Data Engineering vol. 32, no. 2, pp. 234-245, February (2020) PDF

Presented at IEEE ICDE 2019 as a Poster PDF

2019:

[3] Lv, J., Sun, Q., Li, Q., Moreira-Matias, L. : ”Multi-Scale and Multi-Scope Convolutional Neural Networks for Destination Prediction of Trajectories“. In: IEEE Transactions on Intelligent Transportation Systems vol. 21, no. 8, pp. 3184-3195, August (2020) PDF

[4] Kozodoi, N., Katsas, P., Lessmann, S., Moreira-Matias, L., Papakonstantinou, K.: ”Shallow Self-Learning for Reject Inference in Credit Scoring". In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), pp. 516-532. Springer, Cham, 2019. (acceptance rate: 18.00%) PDF Poster PDF arxiv

2018:

[5] Moreira-Matias, L., Gama, J., Monreal, C., Nair, R., Trasarti, R.: "Guest Editorial Special Issue on Knowledge Discovery From Mobility Data for Intelligent Transportation Systems". In: IEEE Transactions on Intelligent Transportation Systems vol. 19, no. 11, pp. 3626-3629, November (2018) PDF

[6] Moreira-Matias, L. "ITSS Technical Activities Spotlight: Getting to Know the Big Data and AI for Mobility Technical Committee.“ IEEE Intelligent Transportation Systems Magazine 10.3 (2018): 205-205. PDF

[7] Cerqueira, V., Moreira-Matias, L., Khiari, J., Van Lint, H.: "On Evaluating Floating Car Data Quality for Knowledge Discovery". In: IEEE Transactions on Intelligent Transportation Systems vol. 19, no. 11, pp. 3698-3708, November (2018)

[8] Kong X., Li M., Tang T., Tian K., Moreira-Matias L., Xia F.: "Shared Subway Shuttle Bus Route Planning Based on Transport Data Analytics". In: IEEE Transactions on Automation Science and Engineering vol. 19, no. 11, pp. 3749-3760, November (2018) PDF

[9] Alesiani F., Moreira-Matias L., Faizrahnemoon M.: "On Learning from Inaccurate and Incomplete Traffic Flow Data". In: IEEE Transactions on Intelligent Transportation Systems vol. 19, no. 11, pp. 3698-3708, November (2018) PDF

[10] Khiari J., Moreira-Matias L., Shaker A., Zenko B., Dzeroski S.: "MetaBags: Bagged Meta-Decision Trees for Regression". In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), pp. 637-652. Springer, Cham, 2018. (acceptance rate: 25.09%) PDF

[11] He X., Moreira-Matias L.: “Robust Continuous Co-Clustering”. arXiv:1802.05036 (February, 2018) -

2017:

[12] Salanova J., Moreira-Matias L., Saadallah A., Tzenos P., Aifadopoulou G., Chaniotakis E., Romeu M.: “Informed versus Non-Informed Taxi Drivers: Agent-Based Simulation Framework for Assessing Their Performance” presented at 97th TRB Annual Meeting (2018) PDF

[13] Schimbinschi F., Moreira-Matias L., Nguyen V., Bailey J.: "Topology-regularized Universal Vector Autoregression for traffic forecasting in Large Urban areas". In: Expert Systems with Applications, vol. 82, pp. 301-316, October (2017) PDF

[14] Moreira-Matias L., Farah H.: "On Developing a Driver Identification Methodology Using In-Vehicle Data Recorders". In: IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 9, pp. 2387-2396, September (2017)PDF

[15] Hernandez A., Sanchez-Medina J., Moreira-Matias L.,: "A simple classification approach to traffic flow state estimation". In: Proceedings of at EUROCAST 2017 - 16th International Conference on Computer Aided Systems Theory, pp. 290-291, Las Palmas de Gran Canarias, Spain, February (2017) PDF

2016:

[16] Moreira-Matias L., Cerqueira V.,:"CJAMmer - Traffic Jam Cause Prediction using Boosted Trees". In: Proceedings of 19th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 743-748, Rio de Janeiro, Brazil, November (2016) PDF

[17] Moreira-Matias L., Gama J. and Mendes-Moreira J., ”Concept Neurons - Handling Drift Issues for Real-Time Industrial Data Mining“ In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, Proceedings, Part III, pp. 96-111, Springer (2016) PDF

[presented in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), (acceptance rate: 24.10%)]

[18] Moreira-Matias L., Cats O., Gama J., Mendes-Moreira J. and Sousa J.F., "An Online Learning Approach to Eliminate Bus Bunching in Real-Time". In: Applied Soft Computing, vol. 47, pp. 460-482 October (2016) PDF

[19] Hassan S., Moreira-Matias L., Khiari J. and Cats O., ”Feature Selection Issues in Long-Term Travel Time Prediction“. In: Advances in Intelligent Data Analysis XV, LNCS vol. 9897, pp. 98-109. Springer International (2016)

[presented at IDA - 15th International Symposium on International Data Analysis. Stockholm, Sweden (2016)] PDF

[20] Yousaf F. Z., Goncalves C., Moreira-Matias L. and Perez C., “RAVA - Resource Aware VNF Agnostic NFV Orchestration Method for Virtualized Networks”. In: Proceedings of IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), pp. 2331-2336, Valencia, Spain (2016) PDF

[21] Khiary J., Moreira-Matias L., Cerqueira V. and Cats O.,: "Automated Setting of Bus Schedule Coverage using Unsupervised Machine Learning". In: Advances in Knowledge Discovery and Data Mining 20th Pacific-Asia Conference (PAKDD), pp. 552-564, Springer (2016) acceptance rate: 17.26% (53/307) PDF

[presented at 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD)(2016)]

[22] Moreira-Matias L., and Cats O.,: "Toward a Demand Estimation Model Based on Automated Vehicle Location". In: Transportation Research Record: Journal of the Transportation Research Board, vol. 2544, pp.141-149, December (2016) PDF

[23] Moreira-Matias L., Gama J., Ferreira M., Mendes-Moreira J. and Damas L.,: "Time-Evolving OD Matrix Estimation using high-speed GPS data streams". In: Expert Systems with Applications, vol. 44, pp. 275-288, February (2016) PDF

2015:

[24] Moreira-Matias L., Alesiani F.,:”Drift3Flow: Freeway-Incident Prediction using Real-Time Learning“. In: Proceedings of 18th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 566-571, Las Palmas de Gran Canaria, Spain, September (2015) PDF

[25] Faizrahnemoon M., Alesiani F., Moreira-Matias L.:”A Scenario-Oriented approach for Noise detection on Traffic Flow data“. In: Workshop (WS06) Proceedings of 18th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 143-148, Las Palmas de Gran Canaria, Spain, September (2015) PDF

[26] Moreira-Matias L., Mendes-Moreira J., Sousa J.F. and Gama J.,: "On Improving Mass Transit Operations by using AVL-based Systems: A Survey". In: IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 4, pp. 1636-1653, July (2015) PDF

[27] Sousa, J.F., Mendes-Moreira J., Moreira-Matias, L., Gama, J., “Reliability Metrics for the Evaluation of the Schedule Plan in Public Transportation”. In: WAM 2015 - Workshop on Assessment Methodologies - Energy, Mobility and other real world applications, Coimbra, Portugal (2015) PDF

[28] Mendes-Moreira. J., Moreira-Matias. L., Gama. J. and Sousa. J.F., ”Validating the Coverage of Bus Schedules: A Machine Learning Approach“. In: Information Sciences, vol. 293, no. 1, pp. 299-313, February (2015) PDF

2014:

[29] Moreira-Matias, L., Gama, J., Mendes-Moreira, J., Freire de Sousa, J.: "An Incremental Probabilistic Model to Predict Bus Bunching in Real-Time". In: Advances in Intelligent Data Analysis XIII, LNCS vol. 8819, pp. 230-240. Springer International (2014)

Poster Version
[presented at IDA - 13th International Symposium on International Data Analysis. Leuven, Belgium (2014)] PDF

[30] Nunes, R., Moreira-Matias, L., Ferreira, M., “Using Exit Time Predictions to Optimize Self Automated Parking Lots”. In: Proceedings of 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 302-307, Qingdao, China (2014) PDF

[31] Moreira-Matias, L., Mendes-Moreira, J., Ferreira, M., Gama, J., Damas, L.: “An Online Learning Framework for Predicting the Taxi Stands Profitability”. In: Proceedings of 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 2009-2014, Qingdao, China (2014) PDF

[32] Moreira-Matias, L., Mendes-Moreira, J., Gama, J., Ferreira, M., “On Improving Operational Planning and Control in Public Transportation Networks using Streaming Data: A Machine Learning Approach”. In: Local Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 41-50, Phd Spotlight Session (2014) PDF

[33] Moreira-Matias, L., Nunes, R., Ferreira, M., Mendes-Moreira, J., Gama, J., ”On Predicting a Call Center's Workload: A Discretization-based Approach“. In: Foundations of Intelligent Systems, LNCS 8502, pp. 548-553 (2014).

[presented at ISMIS- 21st International Symposium on Methodologies for Intelligent Systems - Roskilde, Denmark (2014)] PDF

2013:

[34] Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L., ”On Predicting the Taxi-Passenger Demand: A Real-Time Approach“. In: Progress in Artificial Intelligence, LNCS 8154. Springer Berlin Heidelberg, pp. 54-65 (2013).

[presented at EPIA - 16th Portuguese Conference on Artificial Intelligence. Angra do Heroísmo, Portugal (2013)] PDF

[35] Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L., ”Predicting Taxi–Passenger Demand Using Streaming Data“. In: IEEE Transactions on Intelligent Transportation Systems, vol.14, no.3, pp.1393-1402, September (2013) PDF

[36] Moreira-Matias, L., Fernandes, R., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L., “On Recommending Urban Hotspots to Find Our Next Passenger”. In: Proceedings of the 3rd International Conference on Ubiquitous Data Mining - Volume 1088, pp. 17-23, Beijing, China (2013) PDF

2012:

[37] Moreira-Matias, L., Fernandes, R., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L., “An Online Recommendation System for the Taxi Stand choice Problem (Poster)”. In: IEEE Vehicular Network Conference (IEEE VNC), pp. 173-180, Seoul, South Korea (2012) PDF

[38] Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: "Online Predictive Model for Taxi Services". In: Advances in Intelligent Data Analysis XI, LNCS vol. 7619, pp. 230-240. Springer Berlin / Heidelberg (2012)

[presented at IDA - 11th International Symposium on International Data Analysis. Helsinki, Finland (2012)] PDF

[39] Moreira-Matias, L., Gama, J., Ferreira, M., Damas, L.: "A predictive model for the passenger demand on a taxi network". In: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1014-1019, Anchorage, Alaska (US) (2012) PDF

[40] Moreira-Matias L., Ferreira C., Gama J., Mendes-Moreira J., Sousa J.F.d., “Bus Bunching Detection: A Sequence Mining Approach”. In: 20th European Conference on Artificial Intelligence (ECAI) - Ubiquotous Data Mining (UDM) Workshop, Montpellier, France (2012) PDF

[41] Moreira-Matias L., Mendes-Moreira J., Gama J., Brazdil P. "Text Categorization Using an Ensemble Classifier Based on a Mean Co-association Matrix". In: Machine Learning and Data Mining in Pattern Recognition, LNCS vol. 7376, pp. 525-539: Springer Berlin / Heidelberg (2012)

[presented at MLDM - 9th International Conference on Machine Learning and Data Mining. Berlin, Germany (2012)] PDF

[42] Moreira-Matias L., Ferreira, C., Gama J., Mendes-Moreira J., Sousa J.F.d., "Bus Bunching Detection by Mining Sequences of Headway Deviations". In: Advances in Data Mining. Applications and Theoretical Aspects. LNCS vol. 7377, pp. 77-91. Springer Berlin / Heidelberg. (2012)

[presented at ICDM - 12th Industrial Conference on Data Mining. Berlin, Germany (2012)] PDF

[43] Ferreira, M., Fernandes, R., Conceição, H., Gomes, P., d’Orey, P.M., Moreira-Matias, L., Gama, J., Lima, F., Damas, L.: "Vehicular Sensing: Emergence of a Massive Urban Scanner". In: Sensor Systems and Software, LNCS vol. 102, pp. 1-14. Springer Berlin Heidelberg (2012)

[presented at S-Cube - 3rd International Conference on Sensor Systems and Software. Lisbon, Portugal (2012)] PDF

2011:

[44] Moreira-Matias L., “Bus Bunching Detection by Mining Sequences of Headway Deviations” - Phd Poster Session, IDA'2011 - The Tenth International Symposium on Intelligent Data Analysis, Porto, Portugal, October 28-31 (2011) PDF

2010:

[45] Matias L., Gama J., Mendes-Moreira J. and Sousa J.F., "Validation of both number and coverage of bus Schedules using AVL data.", 13th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC'2010), Madeira Island, Portugal, September 19-22: pp. 131-136 (2010). PDF

[46] Matias, L., "Developing Dynamic Reports using Rich Internet Applications". In: ENTERprise Information Systems, CCIS Vol. 110, pp. 436-445. Springer Berlin Heidelberg (2010) PDF

[47] Gama J., Ferreira C., Matias L., Botterud A., Wang J., Conzelmann G., "A Survey on Wind Ramp Forecasting", Technical Report (2010). PDF

PDF Document

This thesis is focused on improving both Operational Planning and Control of Public Road Transportation (PT) Networks (i.e. buses and taxis) using location-based data gathered through the Global Positioning System (GPS data). Its aim is to monitor the operations of these vehicular networks to infer useful information about their future status on both short-term and long-term horizons. To do it so, we undertook an explorative approach by surveying the data driven methods on this topic in order to identify research opportunities worthy to be further studied. The main idea is to provide sustainable frameworks (in a computational point of view) to handle this massive sources of data. Ultimately, we want to extract information useful to improve Human Mobility on the major urban areas.

As result of the abovementioned survey, three concrete problems were addressed on this thesis: (1) Automatic Evaluation of the Schedule Plan's Coverage; (2) Real-Time Mitigation of Bus Bunching occurrences; (3) Real-Time Smart Recommendations about the most adequate stand to head to in each moment according to the current network status. To do it so, we developed Machine Learning (ML) frameworks in order to advance the State-of-The-Art on such problems.

The first problem (1) concerns the days that are covered by the same schedule. This definition is usually made during the design of the network planning and it is based on the relationship between the demand profiles generated and the resources available to meet such demand. Consequently, at the best of our knowledge, there is no research work addressing this topic using GPS data. All the days covered by the same timetable have exactly the same daily profile due to the fact that they share the same departing/arrival times. However, the real values of such times may differ from the original ones (causing an undesired gap between the defined timetables and the real ones). To overcome this issue, we propose to evaluate if such coverage still meets the network behavior using a ML framework. It explores such differences by grouping each one of the days available into one of the possible coverage sets. This grouping is made according to a distance measured between each pair of days where the criteria rely on their profiles. As output, rules about which days should be covered by the same timetables are provided. Such rules can be used by the operational transportation planners to perform the abovementioned evaluation. These rules also provide insights on how the current coverage can be changed in order to achieve that.

The prevalence of (2) Bus Bunching (BB) is one of the most visible characteristics of an unreliable service. Two (or more) buses running together on the same route is an undeniable sign that something is going terribly wrong with the company's service. Most of the state-of-the-art on this topic departs from the assumption that the probability of BB events is minimized by maximizing headway stability. Notwithstanding its validity, this approach requires multiple control actions (e.g. speed modification, bus holding, etc.) which may impose high mental workload for drivers and result with low compliance rates. Hereby, we propose a proactive rather than a reactive operational control framework. The basic idea is to estimate the likelihood of a BB event occurring further downstream to then let an event detection threshold triggers the deployment of a corrective control strategy. To do it so, we propose a Supervised Online Learning framework. It is focused on exploring both historical and real-time AVL data to build automatic control strategies, which can mitigate BB from occurring while reducing the human workload required to make these decisions. State-of-the-art tools and methodologies such as Regression Analysis, Probabilistic Reasoning and Perceptron constitute building blocks of this predictive methodology.

The (3) taxi driver mobility intelligence is an important factor to maximize both profit and reliability within every possible scenario. Knowledge on where the services (transporting a passenger from a pick-up to a drop-off location) will actually emerge can be an advantage for the driver - especially when there is no economic viability of adopting random cruising strategies to find passengers. The stand-choice problem is based on four key variables: (i) the expected revenue for a service over time, (ii) the distance/cost relation with each stand, (iii) the number of taxis already waiting at each stand and (iv) the passenger demand for each stand over time. However, at the best of our knowledge, there is no work handling this recommendation problem by using these four variables simultaneously. The variable (iii) can be directly computed by the real-time vehicle's position - however, the remaining three need to be estimated for a short-term time horizon.

To estimate the short-term demand that will emerge at a given taxi stand is a complex problem. Such demand can be decomposed into two axis: the (iv) pick-up quantity (i.e. an integer representing the number of services to be demanded) and (i) the expected revenue for a service over time (i.e. a fare-based category). To do it so, we propose a framework based on both time series analysis and discretization techniques which are able to perform such supervised learning task incrementally.

The variable (ii) is related on how much time it will take to get to a given urban area/taxi stand where there are favorable service demand conditions (e.g. high service demand in terms of passenger quantity or revenue-based). Consequently, it is focused on apriori Travel Time Estimation. This problem is vastly covered on the literature - namely, by using Regression analysis. However, we propose a most general technique to address this problem. There are two motivations to do it so: (ii-1) to provide a sustainable way to handle these large amount of data in order to extract usable information from it independently of the problem we want to solve (namely, its variable of interest); (ii-2) to be able to include multiple data sources in order improve the penetration rate (i.e. the ratio of ground truth information) of our framework. To carry out such task, we propose incremental discretization techniques to maintain accurate statistics of interest over a time-evolving Origin-Destination matrix. These techniques include spatial clustering and incremental ML algorithms.

All these problems were addressed using real world data collected from two major public road transportation companies running in Porto, Portugal. These frameworks achieved promising results on the experiments conducted to validate them. This work resulted into sixteen high quality peer-reviewed publications at internationally known venues and journals.

Editor

Session Chair

Organizing Committee

Technical/International Program Committee (Conferences)

Steering/Technical Committee (Societies)

Invited Reviewer

2018, TU Kaiserlautern (Germany), Sourabh Parkala, “Ranking Feature Importance Through Representation Hierarchies” PDF

2017, EPT - Ecole Polytechnique de Tunisie, U. Carthage (Tunisia), Amine Tayari, “Automatic Labelling of Points Of Interest: A Data Mining Approach” PDF

2016, EPT - Ecole Polytechnique de Tunisie, U. Carthage (Tunisia), Amal Saadallah, “An Adaptive Learning Approach for Short-Term Taxi-Passenger Demand Prediction” PDF

2016, UPLGC - Universidad de Las Palmas de Gran Canarias, Aitor Saavedra Hernández, “Estimación del estado del flujo de tráfico mediante preprocesado y minería de datos. Aplicación de Dataset de posiciones GPS de taxis de Porto.” PDF (In Spanish)

2016, TUM - Technische Universitat Munchen (Germany), Syed Murtaza Hassan, “Long-Term Travel Time Prediction” PDF

2016, FEUP - Faculty of Engineering, U. Porto (Portugal), Leonel Araujo, “Recommended System for Optimizing Battery Energy Management with Floating Car Data” PDF

2015, ENSI - National School of Computer Science, U. Manouba (Tunisia), Jihed Khiary, “Improve the Bus Schedule using AVL and APC data” PDF

2014, FCUP - Faculty of Sciences of U. Porto (Portugal), Rafael Nunes, “Using Exit Time Predictions to Optimize Self-Automated Parking Lots” PDF

Selected Publications and Patents

Selected Service

Complete Publication List

Patents

Ph.D. Thesis

Complete Service List

Ms.C. Supervisions