This project evaluates the resilience of Milan’s road transport network to potential flooding of the Lambro River, setting a specific focus on the Tangenziale Est corridor and nearby zones. By integrating Python data analysis, GIS mapping, and PTV Visum scenario modeling, the project explores how disruptions affects the operation of the road network. It further explores potential short-term policies and long-term infrastructural measures to strengthen Milan’s mobility resilience.
A demand-side analysis was carried out mainly using Python to generate visual insights of the study area. The base model was already set up in PTV Visum. Maps were produced to show population distribution and general information of the study area, while demand matrices were processed into desire lines and chord graphs. These visualizations highlighted how trip demand was distributed and pointed out the most critical corridors in the study.
Using GIS, a detailed micromapping of the Tangenziale Est was performed to pinpoint roadway sections likely to be affected by rising Lambro River water levels. Flood exposure was defined based on return-period zones provided by local authorities, and water depth assumptions relied on documented past flood events.
The interaction between demand and supply was explored in PTV Visum. A baseline scenario representing normal conditions and two disrupted scenarios were defined: one for a high-disruption, lower-probability event, and another for a moderate-disruption, higher-probability event. Vehicle speed reductions due to water depth were applied following an equation proposed by Pregnolato et al. to the flooded links.
For each scenario, key performance indicators were extracted to allow comparisons with the baseline. Scenario creation and management were handled using Visum’s Scenario Management tool, where multiple modifications were applied to the disrupted cases to test interventions.
Finally, two categories of mitigation strategies were considered. Short-term measures focused on traffic management and rapid restoration of critical sections, while long-term measures explored upstream water retention basins to reduce flooding along the corridor. Iterative scenario testing made it possible to identify interventions with the best balance between effectiveness and cost.
For more detailed explanations of the project assessment, scenarios modeling, visual outputs and results, see the presentation slides.
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- Pregnolato, M., Ford, A., Wilkinson, S. M., & Dawson, R. J. (2017). The impact of flooding on road transport: A depth-disruption function. Transportation Research Part D: Transport and Environment, 55, 67–81. https://doi.org/10.1016/j.trd.2017.06.020
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For a complete list of references, see the presentation slides.
Daniel Avila, MSc Student in Mobility Engineering
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