Real-Time Mobility Data Mining

Abstract:

We live on a digital era. Weather, communications and social interactions start, happen and/or are triggered on some sort of cloud – which represent the ultimate footprint of our existence. Consequently, millions of digital data interactions result from our daily activities. The challenge of transforming such sparse, noisy and incomplete sources of heterogeneous data into valuable information is huge. Nowadays, such information is key to keep up a high modernization pace across multiple industries. Transportation is not an exception.

One of the key insights on mobility data mining are GPS traces. Portable digital devices equipped with GPS antennas are ubiquitous sources of continuous information for location-based decision support systems. The availability of these traces on the human mobility patterns is growing explosively, as industrial players modernize their infrastructure, fleets as well as the planning/control of their operations. However, to mine this type of data possesses unique characteristics such as non-stationarity, recurrent drifts or high communication rate. These latest issues clearly disallow the application of traditional off-the-shelf Machine Learning frameworks to solve these problems.

In this presentation, we approach a series of Transportation problems. Solutions involve near-optimal decision support systems based on straightforward Machine Learning pipelines which can handle the particularities of these problems. The covered applications include Mass Transit Planning (e.g. buses and subways), Operations of On-Demand Transportation Networks (e.g. taxis and car-sharing) and Freeway Congestion Prediction and Categorization. Experimental results on real-world case studies of NORAM, EMEA and APAC illustrate the potential of the proposed methodologies.

Bio:

Dr. Luis Moreira-Matias received his Ms.c. degree in Informatics Engineering and Ph.d. degree in Machine Learning from the University of Porto, in 2009 and 2015, respectively. During his studies, he won an International Data Mining competition held during a Research Summer School at TU Dortmund (2012). Luis served in the Program Committee and/or as invited reviewer of multiple high-impact research venues such as KDD, AAAI, IEEE TKDE, ESWA, ECML/PKDD, IEEE ITSC, TRB and TRP-B, among others. Moreover, he encloses a record of successful real-world deployment of AI-based software products across EMEA and APAC.

Currently, he is Senior Researcher at NEC Laboratories Europe (Heidelberg, Germany), integrated in the Intelligent Transportation Systems group. His research interests include Machine Learning, Data Mining and Predictive Analytics in general applied to improve Urban Mobility. He was fortunate to author 30+ high-impact peer-reviewed publications on related topics.