arXiv subjects

Andrea Araldo

Publications and source records attributed to Andrea Araldo.

At least 19 recordsLinked to original sources

arXiv2026

Real-Time Design of Public Transport Lines: Reconciling Adaptivity and Efficiency

Demand-responsive transport (DRT) is typically routed by solving Dynamic Vehicle Routing Problems (DVRPs), where individual vehicle trajectories are adjusted on incoming requests. This limits demand consolidation and thus efficiency. On the other hand, Conventional Public Transport (CPT) bus systems are based on a network of lines and users find their routes on it, which provides high demand consolidation. However, such a network is built offline and cannot adapt to the demand. We propose a public transport management strategy that reconciles efficiency and adaptivity by dynamically designing a structured network of lines via a receding-horizon optimization approach. Using real-world trip requests, we show that we nearly double the fraction of served requests compared to DVRP-based routing, and we serve more requests than CPT with lower user trip times.

eess.SY↗

arXiv2026

Predict-then-Optimize Framework for Public Transport Line Redesign under Fluctuating Traffic Conditions

Public Transport (PT) lines are traditionally designed to optimize performance under nominal traffic conditions. In practice, operating conditions frequently deviate from nominal ones, leading to substantial performance deterioration. Existing adaptation mechanisms typically rely on reactive interventions, such as stop-skipping, which are insufficient under large or recurrent traffic fluctuations. In such contexts, incremental adjustments may not suffice. This paper evaluates the potential of deeper structural redesigns to preserve performance. We propose a method to proactively redesign appropriate parts of PT networks under high traffic fluctuations that would otherwise deteriorate operator and user performance. We adopt a predict-then-optimize paradigm in which PT lines are reconfigured based on traffic forecasts using the Non-dominated Sorting Genetic Algorithm III (NSGA-III). To ensure operational feasibility and avoid excessive structural changes, we enforce high Jaccard edge overlap between the original and redesigned networks. To assess prediction inaccuracies, we construct a statistical model of errors from a well-established deep learning predictor, the Diffusion Convolutional Recurrent Neural Network, trained on real-world data. Computational results on Mandl's benchmark and the large-scale Beijing network show that controlled PT line redesign yields substantial user-centric performance gains and operational cost reductions under high traffic fluctuations while limiting topological changes. On the Beijing network under high variability, average travel time improves by up to 25.8% while preserving over 85% line overlap. Unlike stop-skipping baselines, which break connectivity for many OD pairs, our redesign preserves full OD connectivity. These results support a shift from static planning toward continuous and adaptive PT network design.

math.OC↗

arXiv2026

From Individual to Shared Ownership: A Coalitional Game Approach to Sustainable Co-investment

This paper proposes a cooperative game-theoretic framework for sustainable co-investment in shared infrastructure under regulatory incentives. Multiple heterogeneous operators co-invest in a common infrastructure whose production capability evolves over time and is subject to operational variability. A regulator supports the deployment through incentive mechanisms designed to align individual economic investment objectives with the coalitional one. We formulate the co-investment problem as a transferable-utility (TU) coalitional game in which the value generated by cooperation depends on heterogeneous operational profiles, dynamic resource availability, investment costs, and regulatory incentive level. We show that the proposed coalitional game can be reformulated as a linear production game (LPG), whose dual prices yield a constructive and stable allocation of the cooperative surplus. Finally, we illustrate the proposed framework through a case study on co-investment among data center operators in shared renewable energy infrastructure, supported by government subsidies promoting renewable energy consumption.

cs.GT↗

arXiv2026

Shared Infrastructure Investment and Pricing: Stackelberg Equilibria in Risk-Aware Take-or-Pay Contracts

We study a shared infrastructure deployed by an Infrastructure Provider (InP) and used by multiple firms generating revenues through resource usage. We focus on a challenging setting where (i) infrastructure deployment requires substantial upfront investment, which the InP recovers via payments by firms that depend on their uncertain future revenues; (ii) firms' resource usage is jointly influenced by exogenous factors, infrastructure pricing, operational costs, and resource congestion; and (iii) firms exhibit heterogeneous risk aversion. These aspects are typical of emerging technologies, such as Mobile Edge Computing (MEC). Yet, their joint effect on the InP's capacity dimensioning and pricing decisions and the firms' usage commitments remains poorly understood. We establish conditions for the uniqueness of the equilibrium among the firms. We do so by introducing a Stackelberg game with risk-aware take-or-pay contracting and firm-side operational and congestion costs, in which the InP acts as the leader, while firms act as followers that share the infrastructure and commit upfront to future resource usage under uncertain revenues. Followers' heterogeneous risk aversion is modeled through Conditional Value-at-Risk (CVaR). We prove the existence of a Stackelberg equilibrium (SE), in which the followers' decisions constitute a generalized Nash equilibrium, and develop a polynomial-time algorithm that boundedly approximates the SE. We derive a lower bound on the followers' Probability of Profit (PoP). Simulations in a realistic MEC scenario show that higher followers' risk aversion reduces capacity, pricing, and leader profit, while increasing the lower bound on PoP.

cs.GT↗

arXiv2026

Continuity of VaR and Continuous Differentiability of CVaR under Decision-Dependent Losses

Value-at-risk (VaR) and conditional value-at-risk (CVaR) are widely used in risk-aware optimization and equilibrium models. When the loss depends on a decision variable, the induced distribution, the VaR threshold, and the CVaR tail set all change with the decision. This makes the regularity of the VaR and CVaR maps nontrivial. We give simple sufficient conditions under which the VaR map is continuous and the corresponding CVaR map is continuously differentiable. The assumptions are local around the VaR level and rely on dominated pathwise differentiability of the scenario-wise loss. We also derive the CVaR gradient formula, thereby justifying first-order analysis for decision-dependent tail-risk models.

cs.GT↗

arXiv2025

Restoring Accessibility During Urban Rail Disruptions via Bus Network Redesign

In broad terms, accessibility measures opportunities reachable (such as shops, residents, etc.) within a given time frame. Urban Rail Transit (URT) plays a crucial role in providing accessibility, but it is susceptible to disruptions. In city centers with dense public transport (PT) networks, travelers can often find alternative lines. However, in suburbs where PT is sparse, disruptions have a more significant impact on accessibility. The traditional approach consists in deploying bridge and replacement buses to mitigate URT disruptions without specific care to accessibility. Yet, the question arises: is this approach the most effective way to restore accessibility? To the best of our knowledge, our paper is the first to propose a bus re-routing method with the objective of restoring accessibility during URT disruptions. We formulate an integer program and develop a two-stage heuristic algorithm to maximize restored accessibility. The efficacy of our method is always the present assessed in Évry-Courcouronnes and Choisy-le-Roi, France. The results show that, compared to conventional replacement methods, our strategy improves accessibility in particular in the areas most affected by the disruption. Such results are observed even when no additional vehicles are deployed, and at the same time, achieving a reduction in the kilometers traveled. Despite it is well understood that accessibility is the most relevant benefit a transportation system can produce, this aspect is reflected by the traditional approaches in remediation to disruption. With this work, we show instead how to make accessibility the main guiding principle in remediation.

math.OC↗

arXiv2025

Co-Investment in Mobile Edge Computing with Infrastructure Update and Dynamic Participation

Mobile Edge Computing (MEC) requires Network Operators (NOs) to undertake substantial infrastructure investments, while most revenues are captured by Service Providers (SPs) offering end-user applications. This cost-revenue imbalance discourages NOs from investing in MEC deployment, despite increasing demand for low-latency and bandwidth-intensive services. This paper proposes a co-investment scheme in which players, i.e., one NO and multiple SPs, jointly deploy, maintain, and share MEC infrastructure over multiple decision epochs. We devise a new coalitional game model that captures the planning of resources, their allocation among players, and cost and revenue sharing. To address fluctuating user demand and evolving participation incentives, we design a mechanism that updates resources and allows the dynamic entrance and exit of players over time. We sustain cooperation through a compensation scheme. Numerical results show that combining resource updates with dynamic participation increases the total payoff and strengthens the NO's incentive to invest.

cs.GT↗

arXiv2025

Co-Investment under Revenue Uncertainty Based on Stochastic Coalitional Game Theory

The introduction of new services, such as Mobile Edge Computing (MEC), requires a massive investment that cannot be assumed by a single stakeholder, for instance the Infrastructure Provider (InP). Service Providers (SPs) however also have an interest in the deployment of such services. We hence propose a co-investment scheme in which all stakeholders, i.e., the InP and the SPs, form the so-called grand coalition composed of all the stakeholders with the aim of sharing costs and revenues and maximizing their payoffs. The challenge comes from the fact that future revenues are uncertain. We devise in this case a novel stochastic coalitional game formulation which builds upon robust game theory and derive a lower bound on the probability of the stability of the grand coalition, wherein no player can be better off outside of it. In the presence of highly dependent fluctuations of revenues however, stability can be too conservative. In this case, we make use also of profitability, in which payoffs of players are non-negative, as a necessary condition for co-investment, and we derive a lower bound on the probability that co-investment is profitable. The proposed framework is showcased for MEC deployment, where computational resources need to be deployed in nodes at the edge of a telecommunication network. Numerical results show high lower bound on the probability of stability when the SPs' revenues are of similar magnitude, even with high levels of uncertainty. In the case where revenues are highly variable however, the lower bound on stability can be trivially low whereas co-investment is still profitable.

cs.GT↗

arXiv2025

A Management Framework for Vehicular Cloudtoward Economic and Environmental Efficiency

Vehicular Cloud Computing (VCC) leverages the idle computing capacity of vehicles to execute end-users' offloaded tasks without requiring new computation infrastructure. Despite its conceptual appeal, VCC adoption is hindered by the lack of quantitative evidence demonstrating its profitability and environmental advantages in real-world scenarios. This paper tackles the fundamental question: Can VCC be both profitable and sustainable? We address this problem by proposing a management scheme for VCC that combines energy-aware task allocation with a game-theoretic revenue-sharing mechanism. Our framework is the first to jointly model latency, energy consumption, monetary incentives, and carbon emissions within urban mobility and 5G communication settings. The task allocation strategy maximizes the aggregate stakeholder utility while satisfying deadlines and minimizing energy costs. The payoffs are distributed via a coalitional game theory adapted to dynamic vehicular environments, to prevent disincentivizing participants with potentially negative contributions. Extensive simulations demonstrate that our approach supports low-latency task execution, enables effective monetization of vehicular resources, and reduces CO2 emissions by more than 99% compared to conventional edge infrastructures, making VCC a practical and sustainable alternative to edge computing.

cs.GT↗

arXiv2025

Quantifying the Improvement of Accessibility achieved via Shared Mobility on Demand

Shared Mobility Services (SMS), e.g., demand-responsive transport or ride-sharing, can improve mobility in low-density areas, which are often poorly served by conventional Public Transport (PT). Such improvement is generally measured via basic performance indicators, such as waiting or travel time. However, such basic indicators do not account for the most important contribution that SMS can provide to territories, i.e., increasing the potential, for users, to reach surrounding opportunities, such as jobs, schools, businesses, etc. Such potential can be measured by isochrone-based accessibility indicators, which count the number of opportunities reachable in a limited time, and are thus easy for the public to understand. % The potential impact of SMS on accessibility has been qualitatively discussed and implications on equity have been empirically studied. However, to date, there are no quantitative methods to compute isochrone-based indicators of the accessibility achieved via SMS. This work fills this gap by proposing a first method to compute isochrone accessibility of PT systems composed of conventional PT and SMS, acting as a feeder for access and egress trips to/from PT hubs. This method is grounded on spatial-temporal statistical analysis, performed via Kriging. It takes as input observed trips of SMS and summarizes them in a graph. On such a graph, isochrone accessibility indicators are computed. We apply the proposed method to a MATSim simulation study concerning demand-responsive transport integrated into PT, in the suburban area of Paris-Saclay.

cs.CY↗

arXiv2025

Vehicular Cloud Computing: A cost-effective alternative to Edge Computing in 5G networks

Edge Computing (EC) is a computational paradigm that involves deploying resources such as CPUs and GPUs near end-users, enabling low-latency applications like augmented reality and real-time gaming. However, deploying and maintaining a vast network of EC nodes is costly, which can explain its limited deployment today. A new paradigm called Vehicular Cloud Computing (VCC) has emerged and inspired interest among researchers and industry. VCC opportunistically utilizes existing and idle vehicular computational resources for external task offloading. This work is the first to systematically address the following question: Can VCC replace EC for low-latency applications? Answering this question is highly relevant for Network Operators (NOs), as VCC could eliminate costs associated with EC given that it requires no infrastructural investment. Despite its potential, no systematic study has yet explored the conditions under which VCC can effectively support low-latency applications without relying on EC. This work aims to fill that gap. Extensive simulations allow for assessing the crucial scenario factors that determine when this EC-to-VCC substitution is feasible. Considered factors are load, vehicles mobility and density, and availability. Potential for substitution is assessed based on multiple criteria, such as latency, task completion success, and cost. Vehicle mobility is simulated in SUMO, and communication in NS3 5G-LENA. The findings show that VCC can effectively replace EC for low-latency applications, except in extreme cases when the EC is still required (latency < 16 ms).

cs.NI↗

arXiv2025

Online Learning for Function Placement in Serverless Computing

We study the placement of virtual functions aimed at minimizing the cost. We propose a novel algorithm, using ideas based on multi-armed bandits. We prove that these algorithms learn the optimal placement policy rapidly, and their regret grows at a rate at most $O( N M \sqrt{T\ln T} )$ while respecting the feasibility constraints with high probability, where $T$ is total time slots, $M$ is the number of classes of function and $N$ is the number of computation nodes. We show through numerical experiments that the proposed algorithm both has good practical performance and modest computational complexity. We propose an acceleration technique that allows the algorithm to achieve good performance also in large networks where computational power is limited. Our experiments are fully reproducible, and the code is publicly available.

cs.LG↗

arXiv2025

Data-Driven Computation of the Accessibility Provided by Demand-Responsive Transport

Conventional Public Transport (PT) cannot support the mobility needs in weak demand areas. Such areas could be better served by integrating, within PT, Demand-Responsive Transport (DRT), in which bus routes dynamically adapt to user demand. While the literature has focused on the level of service of DRT, it has overlooked its contribution to accessibility, which measures the ease of accessing opportunities (e.g, schools, jobs, other residents). Therefore, the following simple question remains unanswered: How many additional opportunities per hour can be reached when DRT is deployed? However, no method exists to quantify the accessibility resulting from the integration of conventional PT and DRT. We propose a novel method to compute isochrone-based accessibility. The main challenge is that, while accessibility isochrones are computed on top of a graph-model of the transport system, no graph can model DRT, since its routes are dynamic and stochastic. To overcome this issue, we propose a data-driven method, based on the analysis of multiple days of DRT operation. The methodology is tested on a case study in Acireale (Italy) simulated in Visum, where a many-to-many DRT service is integrated with a metropolitan mass transit. We show that, regarding the currently deployed conventional bus lines, the accessibility provided by DRT is much higher and its geographical distribution more equal. While current DRT planning is based exclusively on level of service and cost, our approach allows DRT planners and operators to shift their focus on accessibility.

physics.soc-ph↗

arXiv2024

Can vehicular cloud replace edge computing?

Edge computing (EC) consists of deploying computation resources close to the users, thus enabling low-latency applications, such as augmented reality and online gaming. However, large-scale deployment of edge nodes can be highly impractical and expensive. Besides EC, there is a rising concept known as Vehicular Cloud Computing (VCC). VCC is a computing paradigm that amplifies the capabilities of vehicles by exploiting part of their computational resources, enabling them to participate in services similar to those provided by the EC. The advantage of VCC is that it can opportunistically exploit part of the computation resources already present on vehicles, thus relieving a network operator from the deployment and maintenance cost of EC nodes. However, it is still unknown under which circumstances VCC can enable low-latency applications without EC. In this work, we show that VCC has the potential to effectively supplant EC in urban areas, especially given the higher density of vehicles in such environments. The goal of this paper is to analyze, via simulation, the key parameters determining the conditions under which this substitution of EC by VCC is feasible. In addition, we provide a high level cost analysis to show that VCC is much less costly for a network operator than adopting EC.

cs.DC↗

arXiv2024

Data-driven assessment for the predictability of On-Demand Responsive Transit

By adapting bus routes to users' requests, Demand-Responsive Transit (DRT) can serve low-demand areas more efficiently than conventional fixed-line buses. However, a main barrier to its adoption of DRT is its unpredictability, i.e., it is not possible to know a-priori how much time a certain trip will take, especially when no large prebooking is imposed. To remove this barrier, we propose a data-driven method that, based on few previously observed trips, quantifies the level of predictability of a DRT service. We simulate different scenarios in VISUM in two Italian cities. We find that, above reasonable levels of flexibility, DRT is more predictable than one would expect, as it is possible to build a model that is able to provide a time indication with more than 90% reliability. We show how our method can support the operators in dimensioning of the service to ensure sufficient predictability.

physics.soc-ph↗

arXiv2024

Public Transport Network Design for Equality of Accessibility via Message Passing Neural Networks and Reinforcement Learning

Designing Public Transport (PT) networks able to satisfy mobility needs of people is essential to reduce the number of individual vehicles on the road, and thus pollution and congestion. Urban sustainability is thus tightly coupled to an efficient PT. Current approaches on Transport Network Design (TND) generally aim to optimize generalized cost, i.e., a unique number including operator and users' costs. Since we intend quality of PT as the capability of satisfying mobility needs, we focus instead on PT accessibility, i.e., the ease of reaching surrounding points of interest via PT. PT accessibility is generally unequally distributed in urban regions: suburbs generally suffer from poor PT accessibility, which condemns residents therein to be dependent on their private cars. We thus tackle the problem of designing bus lines so as to minimize the inequality in the geographical distribution of accessibility. We combine state-of-the-art Message Passing Neural Networks (MPNN) and Reinforcement Learning. We show the efficacy of our method against metaheuristics (classically used in TND) in a use case representing in simplified terms the city of Montreal.

cs.AI↗

arXiv2024

Online design of dynamic networks

Designing a network (e.g., a telecommunication or transport network) is mainly done offline, in a planning phase, prior to the operation of the network. On the other hand, a massive effort has been devoted to characterizing dynamic networks, i.e., those that evolve over time. The novelty of this paper is that we introduce a method for the online design of dynamic networks. The need to do so emerges when a network needs to operate in a dynamic and stochastic environment. In this case, one may wish to build a network over time, on the fly, in order to react to the changes of the environment and to keep certain performance targets. We tackle this online design problem with a rolling horizon optimization based on Monte Carlo Tree Search. The potential of online network design is showcased for the design of a futuristic dynamic public transport network, where bus lines are constructed on the fly to better adapt to a stochastic user demand. In such a scenario, we compare our results with state-of-the-art dynamic vehicle routing problem (VRP) resolution methods, simulating requests from a New York City taxi dataset. Differently from classic VRP methods, that extend vehicle trajectories in isolation, our method enables us to build a structured network of line buses, where complex user journeys are possible, thus increasing system performance.

cs.AI↗

arXiv2024

Joint Design of Conventional Public Transport Network and Mobility on Demand

Conventional Public Transport (PT) is based on fixed lines, running with routes and schedules determined a-priori. In low-demand areas, conventional PT is inefficient. Therein, Mobility on Demand (MoD) could serve users more efficiently and with an improved quality of service (QoS). The idea of integrating MoD into PT is therefore abundantly discussed by researchers and practitioners, mainly in the form of adding MoD on top of PT. Efficiency can be instead gained if also conventional PT lines are redesigned after integrating MoD in the first or last mile. In this paper we focus on this re-design problem. We devise a bilevel optimization problem where, given a certain initial design, the upper level determines stop selection and frequency settings, while the lower level routes a fleet of MoD vehicles. We propose a solution method based on Particle Swarm Optimization (PSO) for the upper level, while we adopt Large Neighborhood Search (LNS) in the lower level. Our solution method is computationally efficient and we test it in simulations with up to 10k travel requests. Results show important operational cost savings obtained via appropriately reducing the conventional PT coverage after integrating MoD, while preserving QoS.

eess.SY↗