Car ownership models are essential for understanding travel behavior and informing transportation policy decisions. However, previous research on car ownership modeling has not addressed the determinants of car ownership related to pollutant emissions such as fuel type or emission standard. This study seeks to fill this gap by comparing the performance of several classification models in predicting the number of cars owned by households, their fuel type and their Euro norm (i.e. car age), while also investigating the significance of explanatory variables. These variables include socioeconomic characteristics, as well as mobility-related variables such as commuting distance, parking availability, and public transportation accessibility to the home and workplace. The methodology is applied to the Paris region. We find that supervised learning models slightly outperform the multinomial logistic regression for the three models. Our results show that the main explanatory variables of the types of cars owned are related to the income, the household composition and the mobility-related characteristics of the household. For the fuel type, the household composition and regional accessibility of home city have a highly significant effect on petrol car ownership rather than diesel, while the income is an important predictor through non-linear relationships and interactions. Furthermore, the household income is the main determinant of the Euro norm, with a non-linear effect of the mobility-related variables (commuting distance and regional accessibility of home and work cities). This work paves the way for future research evaluating transportation policies related to household car ownership by allowing a deeper understanding of the determinants of the type of car owned by households and by providing the trained classifiers as open data.
We propose a Fast Fourier Transform based solver for estimating the thermoporomechanical behavior of porous materials, aiming at the periodic homogenization of microstructures with infinitely contrasted properties. Grounded on a thermoporomechanical framework, we first present the complete set of homogenized coupled properties. Subsequently, we isolate each physics, considering an elastoplastic formulation for the mechanical part, and fluid flow and heat conduction being governed by Darcy and Fourier laws before deriving the corresponding homogenized coupled operators. We employ, across the entire numerical framework, the Adaptive Eyre–Milton (AEM) algorithm as an enhanced iterative algorithm that guarantees convergence under infinitely contrasted material properties. Practical implementation aspects and illustrative applications in two- and three-dimensional settings are validated using analytical micromechanics solutions and a FEM-based solver. We emphasize the capability of the proposed framework to compute all the thermohydromechanical (THM) operators within a single tool while maintaining robustness even for materials with infinitely high contrasts in material properties for the three considered physics.
In multimodal transportation systems, shared mobility services (SMSs) are often promoted for their potential to enhance flexibility and reduce congestion. However, SMSs demand is often concentrated in high-density areas, which can limit the effectiveness and accessibility for various commuter groups across a city. This uneven integration challenges the efficiency of the transportation system, especially in terms of emissions and spatial equity. Addressing these issues requires coordination among multiple stakeholders whose objectives frequently conflict. Whereas authorities aim to ensure sustainable and equitable mobility, SMSs providers focus on revenue maximization, and travelers seek to minimize personal travel costs. This paper proposes a multi-agent deep reinforcement learning (RL) framework that captures these interactions and reconciles competing goals through dynamic pricing and incentivization strategies for SMSs and public transport. The framework integrates multimodal macroscopic simulation with two RL agents: (i) a public authority that allocates spatio-temporal public transport incentives to improve equity, emissions, and efficiency, and (ii) an SMS provider that dynamically adjusts its fares to optimize revenue. The agents interact iteratively with the simulated transportation system to learn optimal strategies in response to evolving demand, congestion, and network conditions. Numerical experiments conducted over a three-hour morning peak period, at a 20-minute temporal resolution, show that dynamic incentivization effectively reduces congestion peaks, lowers commuters’ costs by around 20% and emissions by approximately 10%, while nearly doubling public transport profit and supporting a more equitable distribution of benefits. When combined with dynamic SMS pricing, the two RL agents demonstrate the capacity to balance conflicting objectives between private providers and public authorities. The proposed approach provides a decision-support tool for guiding sustainable and equitable multimodal mobility planning.
The fracture resistance of structures is highly dependent on random defects that may arise, for example, due to the manufacturing process. Typically, cracks nucleate near these defects and can lead to catastrophic failure of the structure. However, the presence of random defects, far from known weak areas, is often overlooked in structural design. In this work, a procedure is proposed for designing reinforced structures with optimized fracture resistance, taking into account the initial presence of random defects (pores, cracks). The optimization aims to maximize structural fracture resistance in the presence of random defects, subject to a material volume fraction constraint. To this end, the SIMP method is integrated with a regularized phase field fracture model. The expectation or inverse P-norm aggregation of the fracture energy is quantified through Monte Carlo simulations of crack nucleation and propagation across an ensemble of random defect samples, enabling either the maximization of average performance or the emphasis on extreme failure responses. Optionally, a constraint on the standard deviation of the fracture energy may be imposed to control performance variability. Systematic comparisons with non-optimized structures and deterministic topology optimization are carried out using several numerical examples. The results show that, compared to deterministic designs, the optimized topologies obtained from the proposed framework that takes uncertainty into account not only achieve a higher average fracture energy, but also significantly reduce performance variance, resulting in increased robustness and reduced performance variability.
