Urban congestion is a challenge that cities commonly suffer across the globe. Traffic congestion and longer commutes are linked with poor cardiovascular and metabolic health, along with decreased energy and increased stress among the users. This is further translated into productivity and economic loss, an increase in health service expenses and a general decrease in the quality of social wellbeing. 
To improve this condition, the municipality administration has the role of implementing solutions to strategically address urban mobility. However, this is a complex task to achieve and normally involves limited resources, which make real-world deployments have a great inherited risk. Thus, decision-making is a task that has to be carefully addressed by different factors and scales. 
This thesis approaches multiple tools for analytics on urban mobility using skills in SQL, R and Python, and open-source software such as QGIS for spatial analysis and SUMO (Simulation of Urban Mobility) for microsimulation. The methodology includes the analysis of urban mobility in Montreal from different levels of analysis.
At the macro level, the MTL Trajet dataset provides insight of mobility behaviour of participants through their trip coordinates. Using geometry datasets of quarter and boroughs of Montreal, the analysis is framed and processed via SQL and QGIS. Data visualization is presented in Choropleth maps, Flow maps and Chord diagrams using origin and destination of trips. Supporting processing task such as reverse geocoding to join attributes between datasets are used. The macro analysis helps to identify a primary area of analysis seeking the most transited region. The quarter of René-Lévesque in/and the borough of Ville-Marie are the most accessed areas in this study. 
In the meso level, street network information from OpenStreetMap allows making relations among the elements of the area, such as universities and their proximity to pedestrian zones. Resulting maps aid decision-making from a meso perspective, choosing the area of Concordia University as a suitable space for microfocus.
At the micro-level, four areas of opportunity interpreted as transit policy-testing were identified. A custom micro-network and synthetic demand for this area were used to simulate the impacts of these scenarios. The measures tested to improve urban mobility in the area are the restriction of street lanes for specific vehicle types and the inclusion of pedestrian areas. Experimentations with different levels of user modal share and shift are presented.
Results of macro, meso and micro analyses are included to provide recommendations for the administration of the city of Montreal. The inclusion of multiple restrained lanes for buses and high-occupancy vehicles around Concordia University and a pedestrian zone will allow to save time to road users, as long as single-passenger vehicle shifts towards public transit and shared–vehicles.