Simulating the Impact of Shared, Autonomous Vehicles on Urban Mobility – a Case Study of Milan

Alazzawi, Sabina; Hummel, Mathias; Kordt, Pascal; Sickenberger, Thorsten; Wieseotte, Christian; Wohak, Oliver · 2018 · OpenAlex-citations

DOI: 10.29007/2n4h

archive: archived pipeline: cataloged verified

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Summary

This study investigates the potential of shared, autonomous vehicles (referred to as "robo-taxis") to alleviate urban traffic congestion and reduce emissions, using Milan, Italy, as a case study. Motivated by the growing challenges of metropolitan mobility—including high car ownership rates, severe congestion, and particulate matter pollution—the authors aim to quantify the impact of integrating on-demand mobility services, autonomous driving, and dynamic pricing. The research seeks to determine the critical transition rate from private car usage to robo-taxis required to achieve free-flowing traffic and to estimate the necessary fleet size to serve the city’s demand. The researchers employed the open-source simulation framework SUMO, enhanced with custom Python and PowerShell modules to model ride-sharing and autonomous driving behaviors. Input data for the simulation was derived from multiple sources: OpenStreetMap provided the road network; mobile phone usage data from Telecom Italia was used to generate time-dependent origin-destination matrices reflecting population density and movement; and traffic count data from the local authority (AMAT) calibrated vehicle volumes. The simulation modeled a mixed traffic scenario where robo-taxis, primarily six-seater vehicles, utilized a matching algorithm to pool passengers with similar routes. Key evaluation metrics included travel times, vehicle flux, passenger waiting times, and particulate matter emissions. The results indicate that a 30% reduction in peak-hour vehicle volume is necessary to restore free-flowing traffic conditions in Milan. Achieving this reduction requires approximately 50% of current car users to switch to robo-taxis. However, if dynamic pricing strategies are implemented to shift 10% of demand away from peak hours ("peak-shaving"), the required adoption rate drops to roughly 33%. The study estimates that a fleet of 9,500 six-seater robo-taxis would be sufficient to cover Milan’s peak mobility demand. Furthermore, the simulations suggest that introducing electric robo-taxis could reduce particulate matter emissions by 35–40% below current regulatory thresholds. The analysis also highlights that six-seater vehicles offer an optimal balance between route flexibility and passenger capacity, minimizing detour delays while maximizing vehicle utilization. The significance of this work lies in its demonstration that shared autonomous mobility, combined with smart incentive strategies, can effectively resolve urban congestion and environmental issues without requiring extensive infrastructure changes. The findings suggest that cities can transition to more sustainable mobility models by leveraging data-driven pricing and optimized fleet compositions. By reducing the number of private vehicles, this approach also frees up valuable urban space currently dedicated to parking, thereby improving overall quality of life. The study provides a quantitative basis for urban planners and policymakers to design future mobility systems and implement Mobility as a Service (MaaS) solutions.

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