M1: Specifications document (01-08-2019)
Specifications of the most relevant use cases, task outputs, functionalities of the target prototype and key performance indicators to guarantee that contributions are developed in accordance with a well-defined set of requirements and design principles.

M2: Integrative data repository (01-01-2020)
Multi-dimensional data repository with updating and cleaning facilities that consolidates all available data sources with potential impact on city traffic analysis (including multi-source urban data and external data sources for gathering context information).

M3: Context-sensitive multi-source learning algorithms (01-05-2021)
Advanced algorithms able to learn descriptive and predictive models from heterogeneous urban data in the presence of situational context data; including spatiotemporal pattern miners to detect non-trivial traffic dynamics and deep learning methods to anticipate mobility problems.

M4: Traffic flow control system (01-09-2021)
Networks of reinforcement learning agents able to optimize traffic flow and adapt to context information; therefore combining state-of-the-art architectures for traffic simulation with richer reinforcement learning models based on recent advances on deep reinforcement learning.

M5: Decision support system for mobility optimization (01-12-2021)
Fully-functional prototype integrating descriptive, predictive and prescriptive algorithms made available in the two previous milestones; providing guarantees of adaptability, actionability and statistical significance in accordance with the specifications (Milestone 1).