Cristiana Botta

INTRODUCTION: As cities gradually begin integrating autonomous vehicles into existing transport systems, it becomes essential to assess their potential impacts on traffic dynamics and safety in a comprehensive and systematic manner — particularly through tools that can anticipate impacts before actual on-road deployment. OBJECTIVES: This paper aims to develop a data-driven and modular framework to evaluate the integration of autonomous mobility solutions in mixed traffic conditions. METHODS: A data-driven approach combining sensor data collected during autonomous shuttle trials with video-based behavioural analysis of road users and calibrated traffic microsimulation is employed to perform ex-ante assessment of different deployment scenarios. RESULTS: The framework enables the evaluation of the impacts of autonomous mobility solutions on traffic performance and safety, providing insights across multiple scenarios. CONCLUSION: The framework supports informed decision-making and enhances the understanding of how autonomous mobility can be effectively integrated into urban environments.

Integrating advanced technologies, such as Connected Autonomous Vehicles (CAVs) and Connected Vehicles (CVs), represents new strategies and solutions in urban mobility, particularly during unexpected urban events. Vehicle connectivity facilitates real-time communication between vehicles and infrastructure, enhancing traffic management by enabling dynamic rerouting to minimize delays and prevent bottlenecks. This study employs the SUMO (Simulation of Urban Mobility) microsimulation to analyze the impact of dynamic rerouting strategies during urban disruptions within the IN2CCAM project’s Turin Living Lab. The Living Lab integrates simulation with real-world testing, including autonomous shuttle operations, to evaluate new mobility solutions. In the initial phase, offline simulations examine street, lane, and intersection closures along shuttle routes to assess how penetration levels of CVs and CAVs influence mobility. The results indicate that higher connectivity penetration improves traffic flow, with the greatest benefits observed at increased levels of autonomous vehicles. These findings highlight the potential of dynamic routing strategies, supported by vehicle connectivity and autonomous driving technologies, to enhance urban mobility and effectively respond to real-time traffic conditions. Additionally, this work demonstrates the capabilities and flexibility of SUMO for simulating complex urban traffic scenarios.

This study identified the main factors affecting car selection decisions through discrete choice experiments based on a large dataset collected in four European countries in 2023 using stated choice questionnaires. The choice set includes six current and popular car powertrains with factors related to vehicle features, user characteristics, and specific geographical contexts, which can influence the adoption of vehicles with electrified powertrains. An easily applicable multinomial logit model was first proposed to explore the effects of selected attributes and the model’s ability to reproduce user preferences with different incentive policies, geographical contexts, and energy prices. A mixed logit model and a segmented multinomial logit model were introduced to consider the sample’s heterogeneity. The first captures the preference dispersion among respondents related to incentives and operational costs. The second, which specifically classifies users based on car market segments, showed a greater variation in factors related to the purchase cost and battery range. The models estimate the weight of nine factors, offering support for targeted policy recommendations. Cost-related factors confirm their relevance in choices, and the analysis shows that users who want to enhance their vehicle range by 1 km are willing to pay approximately EUR 80.

Positive Energy Districts (PEDs) are integral to achieving sustainable urban development by enhancing energy self-sufficiency and reducing carbon emissions. This paper explores energy balance calculations in four diverse case study districts within different climatic conditions—Fiat Village in Settimo Torinese (Italy), Großschönau (Austria), Beursplain in Amsterdam (Netherlands), and Lunca Pomostului in Reşiţa (Romania)—as part of the SIMPLY Positive project. Each district faces unique challenges, such as outdated infrastructure or heritage protection, which we address through tailored strategies including building renovations and the integration of renewable energy systems. Additionally, we employ advanced simulation methodologies to assess energy performance. Simulation results highlight the significance of innovative technologies like photovoltaic-thermal (PVT) systems, application of demand-side actions, and flexible grid usage. Furthermore, mobility assessments and resident-driven initiatives demonstrate the critical role of community engagement in reducing carbon footprints. This study underscores the adaptability of PED frameworks across varied urban contexts and provides actionable insights for scaling similar strategies globally, supporting net-zero energy targets.

The development and the diffusion of the electromobility is crucial for reducing air pollution and increase sustainable transport. In particular, electrification of private mobility has a significantly role in the energy transition within urban areas, since the progressive substitution of conventional passenger cars by electric vehicles (EVs) leads to the decarbonisation of transport sector without direct emissions. However, increasing EV penetration in the market forces an expansion of the existing charging infrastructure with potential negative impacts on the distribution grid. In this context, a simplified approach is proposed to estimate the energy and power demand owing to the recharge of electric passenger cars within the city of Turin in Italy. This novel approach is based on the usage of floating car data (FCD) to identify the travel behaviour and parking habits of a non-EV passenger car in the city. Mobility data were then used to evaluate EVs energy consumption and charging needs considering different charging options (public or domestic) and range anxiety in different scenarios of EV diffusion. Aggregated load profiles and demand were finally evaluated both for the whole and for each zone of the city as possible resource for city planner or distribution system operators (DSO).