The recent technological advances on telecommunications create a new reality on mobility sensing. Nowadays, we live in an era where ubiquitous digital devices are able to broadcast rich information about human mobility in real-time and at a high rate. Such fact exponentially increased the availability of large-scale mobility data which has been popularized in the media as the new currency, fueling the future vision of our smart cities that will transform our lives. The reality is that we just began to recognize significant research challenges across a spectrum of topics. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders on build knowledge discovery pipelines over such data sources. However, such availability also raise privacy issues that must be considered by both industrial and academic stakeholders on using these resources. This workshop intends to be a top-quality venue to bring together transdisciplinary researchers and practitioners working in the related topics from multiple areas such as Data Mining, Machine Learning, Numerical Optimization, Public Transport, Traffic Engineering, Multi-Agent Systems, Human-Computer Interaction and Telecommunications, among others. The ultimate goal of this venue is to evaluate not only the theoretical contribution of the methodology proposed in each research work, but also its potential deployment/impact as well as its advances with respect to the State-of-the-Art/State-of-the-Practice in the domains of the related applications. The venue will take place at ECML/PKDD 2017, the largest and most reputed Machine Learning/Data Mining conference in Europe. The accepted formats are regular papers or demonstrations of production-ready software on related topics (soft max. limi of 20 pages on Springer’s LNCS format for both). Both formats will only be presented orally at the workshop (i.e., no local proceedings). The review process is single-blind. Accepted regular papers will be invited to submit an extended version to a Special Issue of IEEE Transactions on Intelligent Transportation Systems entitled Knowledge Discovery from Mobility Data for Intelligent Transportation Systems.

In order to better address the challenges in enabling insightful information in a heterogeneous city environment, we welcome topics grouped in three categories: The first category addresses the specific techniques in different areas of data mining and analytics that enable the discovery of patterns from heterogeneous city data. In the second category, we will focus on innovative techniques for using discovered patterns to enable missioncritical information to various city stakeholders via a seamless information access. Finally, the workshop aims to discuss the usage of the various techniques in particular application scenarios with the aim of revealing specific challenges of each application area. This workshop targets (but is not limited to) the following topics, grouped into these three categories:

• Different transportation modes and their interactions (road, rail, air and water-based);
• Intelligent and real-time public transport control and operational management;
• Transportation planning and management;
• Trajectory mining and related applications;
• Failures detection and preventive maintenance;
• Distributed and ubiquitous transport technologies and policies;
• Travel demand analysis and prediction;
• Advanced traveler information systems;
• Intelligent mobility models and policies for urban environments;
• Automatic assessment and/or evaluation on the transport reliability (planning, control and other related policies);
• Human mobility mining and pervasiveness applications;
• Privacy in collecting, storing and analyzing pervasive mobility/transportation data;
• Traffic control and demand forecasting for high-speed roadways;
• Pedestrians traffic analysis, prediction and safety issues;
• Social impact, land-use and trend analysis;
• Transit assignment and Activity-Choice models;
• Human Risk Factor Mining on Driving;