arXiv subjects

Alain Y. Kibangou

Publications and source records attributed to Alain Y. Kibangou.

4 recordsLinked to original sources

arXiv2025

On the impact of coordinated fleets size on traffic efficiency

We investigate a traffic assignment problem on a transportation network, considering both the demands of individual drivers and of a large fleet controlled by a central operator (minimizing the fleet's average travel time). We formulate this problem as a two-player convex game and we study how the size of the coordinated fleet, measured in terms of share of the total demand, influences the Price of Anarchy (PoA). We show that, for two-terminal networks, there are cases in which the fleet must reach a minimum share before actually affecting the PoA, which otherwise remains unchanged. Moreover, for parallel networks we prove that, under suitable assumptions, the PoA is monotonically non-increasing in the fleet share.

cs.GT↗

arXiv2024

Potential detrimental effects of real-time route recommendations in traffic networks

Navigation apps have become pervasive in providing real-time route recommendations to travelers willing to minimize their travel times. However, such technologies introduce new complexities, raising concerns about their overall impact on traffic networks. This paper focuses on evaluating the effect of navigation apps on traffic flows, particularly examining how real-time route recommendations influence network efficiency and congestion. Using a dynamical network flow model, we study traffic dynamics between an origin-destination pair, where a fraction of drivers follow app recommendations while others rely on fixed route preferences. By incorporating supply-demand mechanisms to account for capacity and volume constraints on routes, we uncover partial demand transfer, i.e., only a portion of the traffic demand is able to traverse the network, while the rest builds up congestion at the origin. We prove that the dynamics converges to a globally stable equilibrium and we provide a detailed analysis of this equilibrium when the choices of the informed drivers follow a logit model, correlating the emergence of partial demand transfer to the penetration rate of navigation apps among users.

eess.SY↗

arXiv2024

Selfish routing on transportation networks with supply and demand constraints

Traditional non-atomic selfish routing games present some limitations in properly modeling road traffic. This paper introduces a novel type of non-atomic selfish routing game leveraging concepts from Daganzo's cell transmission model (CTM). Each network link is characterized by a supply and demand mechanism that enforces capacity constraints based on current density, providing a more accurate representation of real-world traffic phenomena. We characterize the Wardrop equilibria and social optima of this game and identify a previously unrecognized inefficiency in selfish routing: partially transferring Wardrop equilibria, where only part of the exogenous flow traverses the network.

math.OC↗

arXiv2014

Consensus-based In-Network Computation of the PARAFAC Decomposition

In this work, we present a new approach for the distributed computation of the PARAFAC decomposition of a third-order tensor across a network of collaborating nodes. We are interested in the case where the overall data gathered across the network can be modeled as a data tensor admitting an essentially unique PARAFAC decomposition, while each node only observes a sub-tensor with not necessarily enough diversity so that identifiability conditions are not locally fulfilled at each node. In this situation, conventional (centralized) tensor based methods cannot be applied individually at each node. By allowing collaboration between neighboring nodes of the network, we propose distributed versions of the alternating least squares (ALS) and Levenberg-Marquardt (LM) algorithms for the in-network estimation of the factor matrices of a third-order tensor. We assume that one of the factor matrices contains parameters that are local to each node, while the two remaining factor matrices contain global parameters that are common to the whole network. The proposed algorithms combine the estimation of the local factors with an in-network computation of the global factors of the PARAFAC decomposition using average consensus over graphs. They emulate their centralized counterparts in the case of ideal data exchange and ideal consensus computations. The performance of the proposed algorithms are evaluated in both ideal and imperfect cases.

math.NA↗