Abstract
The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ridesharing. Then using this framework, we assess the dynamic ride-sharing impact on two different network scales to find the answer to this question. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ridesharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Then we can assess the impact of such a service on the network. Most of the existing approaches focus on the first point, i.e., designing the demand matching while using basic assumptions for the second point, mainly constant travel times. The proposed method in this thesis can outperform the existing methods in the literature. The optimization algorithm can provide high-quality solutions in a short time. Our solution approach is designed to be exact for small samples. Then, to be able to handle the large-scale problems, it is extended with several heuristics that keep the general design for the solution method but significantly reduce its computation time. In the simulation component, a "Plant Model" is applied based on the "Trip-based Macroscopic Fundamental Diagram (MFD)" to represent the traffic dynamics reality and a "Prediction Model" is applied based on the mean-speed to be used during the assignment process. We perform an extensive simulation study (based on real-world traffic patterns) to assess the influence of dynamic ride-sharing systems on traffic congestion. In the medium-scale (Lyon 6 + Villeurbanne), the results showed that ride-sharing could not significantly improve the traffic situation. High levels of market-share add additional travel distance and travel time to the trips and lead to more traffic in the network. In large cities, the results are entirely different from those in small and medium-sized cities. In large-scale (Lyon city in France) simulations, the proposed dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Increasing the market-share and the number of sharing can enhance this improvement. Therefore, the proposed dynamic ride-sharing system is a viable option, alleviating stress on existing public transport, to reduce the network traffic in populated and large-scale cities.; The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ridesharing. Then using this framework, we assess the dynamic ride-sharing impact on two different network scales to find the answer to this question. When assessing the dynamic ride-sharing problem, two important points should be considered. First, how the ridesharing system serves the network demand and second, how the ride-sharing system is impacted by the network and in particular by congestion. Then we can assess the impact of such a service on the network. Most of the existing approaches focus on the first point, i.e., designing the demand matching while using basic assumptions for the second point, mainly constant travel times. The proposed method in this thesis can outperform the existing methods in the literature. The optimization algorithm can provide high-quality solutions in a short time. Our solution approach is designed to be exact for small samples. Then, to be able to handle the large-scale problems, it is extended with several heuristics that keep the general design for the solution method but significantly reduce its computation time. In the simulation component, a "Plant Model" is applied based on the "Trip-based Macroscopic Fundamental Diagram (MFD)" to represent the traffic dynamics reality and a "Prediction Model" is applied based on the mean-speed to be used during the assignment process. We perform an extensive simulation study (based on real-world traffic patterns) to assess the influence of dynamic ride-sharing systems on traffic congestion. In the medium-scale (Lyon 6 + Villeurbanne), the results showed that ride-sharing could not significantly improve the traffic situation. High levels of market-share add additional travel distance and travel time to the trips and lead to more traffic in the network. In large cities, the results are entirely different from those in small and medium-sized cities. In large-scale (Lyon city in France) simulations, the proposed dynamic ride-sharing system can significantly improve traffic conditions, especially during peak hours. Increasing the market-share and the number of sharing can enhance this improvement. Therefore, the proposed dynamic ride-sharing system is a viable option, alleviating stress on existing public transport, to reduce the network traffic in populated and large-scale cities.Abstract
The main goal of this dissertation is to answer one of the critical questions about dynamic ride-sharing services: Can dynamic ride-sharing reduce congestion? In this thesis, we propose a simulation-based optimization framework for dynamic ridesharing. Then using this framework, we [...]Abstract
Our research deals with the problem of the charging scheduling of electric vehicles (EV). The variation in the total power available to load vehicles, user the behaviour constraints and the uncertainties of daily energy demands require an efficient and secure scheduling. We defined five industrial configurations: ACPF (1,2) and ACPV (1a, 1b and 2), each of which corresponds to a set of technical constraints. Studies on formulations, including a conjunctive and a disjunctive, are based on the analysis of the strength of their LP-relaxation. The matrix form of the mathematical formula is composed of a partitioned matrix, which is decomposable by the Dantzig-Wolfe principles. The latter allows us to develop a Branch-and-Price Algorithm for the exact solution of the problem. A deterministic constructive heuristic was then designed for the allocation of the resource, which is very efficient: a quick resolution (less than a second) for a car park with about thirty EVs. Finally, to implement all algorithms in the microprocessor, and to establish a forecasting model and an online scheduling, we have created a stand-alone scheduler, based on the predictive-reactive rescheduling. The research carried out is part of the problems of energy reasoning. They, therefore, can combine with other works, including the smart grid problems; Notre travail de recherche traite de la problématique de l’ordonnancement de recharge des véhicules électriques (VE). La variation de la puissance totale disponible pour charger des véhicules, les contraintes de comportement des utilisateurs et l'incertitude des demandes énergétiques journalières demandent un ordonnancement efficace et sécurisé. Nous avons défini cinq configurations industrielles : ACPF (1,2) et ACPV (1a, 1b et 2) qui correspondent chacune à un ensemble de contraintes techniques. Les études sur les formulations, dont une conjonctive et une disjonctive, reposent sur l’analyse de la force de leurs relaxation-LP. La forme matricielle de la formule mathématique est composée d’une matrice partitionnée, qui est décomposable par le principe de Dantzig-Wolfe. Cette dernière nous permets de développer un algorithme de type Branch-and-Price pour la résolution exacte du problème. Une heuristique constructive déterministe a ensuite été conçue pour l’allocation de la ressource, qui se trouve très efficace : une résolution rapide (moins d’une seconde) pour un parking d’une trentaine VEs. Finalement, pour implémenter tous les algorithmes dans le microprocesseur, et pour établir un modèle prévisionnel et un ordonnancement en temps réel, nous avons créé un planificateur autonome, qui se base sur le réordonnancement prédictif-réactif. Les recherches effectuées font partie des problèmes de raisonnement énergétique. Elles possèdent donc la capacité de se combiner avec d’autres travaux, notamment le problème de smart gridAbstract
Our research deals with the problem of the charging scheduling of electric vehicles (EV). The variation in the total power available to load vehicles, user the behaviour constraints and the uncertainties of daily energy demands require an efficient and secure scheduling. We defined [...]