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

Abstract

Recent technological advances in vehicle automation and connectivity have furthered the development of a wide range of innovative mobility concepts such as autonomous driving, on-demand services and electric mobility. Our study aimed at investigating the interplay of these concepts to efficiently reduce vehicle counts in urban environments, thereby reducing congestion levels and creating new public spaces to promote the quality of live in urban cities. For analysis, we implemented the aforementioned factors by introducing the concept of robo-taxis as an autonomous and shared mobility service. Using SUMO as the simulation framework, custom functionalities such as ride sharing, autonomous driving and advanced data processing were implemented as python methods via, and around, the TraCI interface. A passenger origin-destination matrix for our region of interest in Milan was derived from publically available mobile phone usage data and used for route input. Key evaluation parameters were the density-flow relationship, particulate-matter emissions, and person waiting- times. Based on these parameters, the critical transition rate from private cars to robo- taxis to reach a free-flow state was calculated. Our simulations show, that a transition rate of about 50% is required to achieve a significant reduction of traffic congestion levels in peak hours as indicated by mean travel times and vehicle flux. Assuming peak- shaving, e.g. through dynamic pricing promised by digitalization, of about 10%, the threshold transition rate drops to 30%. Based on these findings, we propose that introducing a robo-taxi fleet of 9500 vehicles, centered around mid-size 6 seaters, can solve traffic congestion and emission problems in Milan.

Shared autonomous vehicle services: A comprehensive review

“…As the name implies, the former category involves a simulation system, while in the latter, an equilibrium assignment model based on Wardrop's principles is used. For modelling on-demand SAV services, majority of the studies use simulation systems (e.g., Alam & Habib, 2018;Alazzawi et al, 2018;Bischoff & Maciejewski, 2016;Bösch et al, 2016;Fagnant & Kockelman, 2018;Hörl, 2017;Liu et al, 2017;Lokhandwala & Cai, 2018;Mahmassani, 2018;Moreno et al, 2018). For a list of simulation models being used in the reviewed studies, the readers are referred to Table A1 in the Appendix.…”

AMoDSim: An Efficient and Modular Simulation Framework for Autonomous Mobility on Demand

“…However, commercial tools are usually not available to researchers and their code is closed, impeding the verification and the reproduction of results. To the best of our knowledge, three simulation tools developed by academic institutions have been extended and employed in studies related to AMoD, namely SimMobility [8] and MATSim [10,9] and SUMO [6]. The main issue with the first two is the level of complexity that the researcher is required to handle and the performance.…”

Small Electric Vehicles (SEV)—Impacts of an Increasing SEV Fleet on the Electric Load and Grid

“…The impact as well as the design of the proposed concepts is predominantly modelled. For instance, Alazzawi et al determine the optimal fleet size of a shared autonomous vehicle concept for Milan [6].…”