Multimodal Transport Systems.
Title:
Multimodal Transport Systems.
Author:
Hammadi, Slim.
ISBN:
9781118577301
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (278 pages)
Series:
Iste
Contents:
Cover -- Title page -- Table of Contents -- Preface -- Chapter 1. Dynamic Car-pooling -- 1.1. Introduction -- 1.2. State of the art -- 1.3. Complexity of the optimized dynamic car-pooling problem: comparison and similarities with other existing systems -- 1.3.1. Graphical modeling for the implementation of a distributed physical architecture -- 1.3.2. Collection of requests for car-pooling and data modeling -- 1.3.3. Matrix structure to collect information on requests -- 1.3.4. Matrix representation for modeling car-pooling offers -- 1.3.5. Modeling constraints of vehicles' allocation to users -- 1.3.6. Geographical network subdivision served and implementation of a physical distributed dynamic architecture -- 1.4. ODCCA: an optimized dynamic car-pooling platform based on communicating agents -- 1.4.1. Multi-agent concept for a distributed car-pooling system -- 1.5. Formal modeling: for an optimized and efficient allocation method -- 1.5.1. D3A: Dijkstra Dynamic Distributed Algorithm -- 1.5.2. ODAVe: Optimized Distributed Allocation of Vehicle to users -- 1.6. Implementation and deployment of a dynamic car-pooling service -- 1.6.1. Deployment of ODCCA: choosing a hybrid architecture -- 1.6.2. Layered architecture -- 1.6.3. Testing and implementation scenario -- 1.7. Conclusion -- 1.8. Bibliography -- Chapter 2. Simulation of Urban Transport Systems -- 2.1. Introduction -- 2.2. Context -- 2.3. Simulation of urban transport systems -- 2.3.1. Non-guided transport systems -- 2.3.2. Guided transport systems -- 2.4. The types of modeling -- 2.4.1. Nature of the models -- 2.4.2. Macrosimulation, mesoscopic simulation, micro simulation -- 2.5. Modeling approaches -- 2.6. Fields of application -- 2.7. Software tools -- 2.8. Simulation of the Valenciennes transport network with QUEST software -- 2.8.1. Problem -- 2.8.2. Network operation in normal mode.
2.8.3. Disturbed mode network function -- 2.9. The QUEST software -- 2.9.1. Presentation -- 2.9.2. Modeling -- 2.10. Network modeling in normal mode -- 2.10.1. Topology of traffic networks -- 2.10.2. Bus lines -- 2.10.3. Vehicles -- 2.10.4. Modeling -- 2.10.5. Stops -- 2.10.6. Passengers -- 2.10.7. The flow of connecting passengers -- 2.11. Network modeling in degraded mode -- 2.11.1. Disturbances -- 2.11.2. Regulatory procedures -- 2.12. Simulation results -- 2.13. Conclusion/perspectives -- 2.14. Self-organization of traffic - the FORESEE simulator -- 2.14.1. General problem -- 2.14.2. FORESEE simulator -- 2.14.3. Results -- 2.15. Conclusion - perspectives -- 2.15.1. Sustainability of the information -- 2.15.2. Information aggregation algorithms -- 2.15.3. Cooperation efficiency -- 2.15.4. Deployment of the proposed approach -- 2.16. Bibliography -- Chapter 3.Real-time Fleet Management: Typology and Methods -- 3.1. Introduction -- 3.2. General context of RTFMP -- 3.2.1. RTFMP characteristics -- 3.2.2. Application field of RTFMPs -- 3.3. Simulation platform for real-time fleet management -- 3.3.1. Dynamic management of vehicle routing -- 3.3.2. Routing management under time window constraints -- 3.3.3. General architecture of the simulation platform -- 3.3.4. Consideration of uncertainties on requests -- 3.3.5. Consideration of information linked to traffic -- 3.4. Real-time fleet management: a case study -- 3.4.1. General architecture of the optimization engine -- 3.4.2. Itinerary calculation and length estimation -- 3.4.3. The static route planning problem -- 3.4.4. Route planning and modification of the transport plan -- 3.5. Conclusion -- 3.6. Bibliography -- Chapter 4. Solving the Problem of Dynamic Routes by Particle Swarm -- 4.1. Introduction -- 4.2. Vehicle routing problems -- 4.2.1. The static vehicle routing problem.
4.2.2. The dynamic vehicle routing problem (DVRP) -- 4.2.3. Importance of dynamic routing problems -- 4.3. Resolution scheme of the dynamic vehicle routing problem -- 4.3.1. Event planner -- 4.3.2. Particle swarm optimization -- 4.4. Adaptation of the PSO metaheuristic for the dynamic vehicle routing problem -- 4.4.1. Representation of particles -- 4.4.2. Velocity and movement of particles -- 4.4.3. The APSO algorithm (Adaptive Particle Swarm Optimization) -- 4.4.4. Adaptive memory mechanism -- 4.5. Experimental results -- 4.5.1. Datasets -- 4.5.2. Experiments and analysis -- 4.5.3. Measure of dynamicity -- 4.6. Conclusion -- 4.7. Bibliography -- Chapter 5. Optimization of Traffic at a Railway Junction: Scheduling Approaches Based on Timed Petri Nets -- 5.1. Introduction -- 5.2. Scheduling in a railway junction -- 5.2.1. Classical scheduling -- 5.2.2. Flexible system scheduling -- 5.2.3. Dual Gantt diagram -- 5.2.4. The railway junction saturation problem -- 5.3. Petri nets for scheduling -- 5.3.1. Place/Transition Petri net -- 5.3.2. T-timed Petri nets -- 5.3.3. Controlled executions -- 5.3.4. Reachability problems in TPNs -- 5.3.5. Modeling of a railway junction with Petri nets -- 5.3.6. Approaches to solving the timed reachability problem -- 5.4. Incremental model for TPNs -- 5.4.1. Formulation operators "+" and "s" -- 5.4.2. Integer Mathematical Models -- 5.4.3. Numerical experiments -- 5.4.4. Study of the illustrative example of Figure 5.5 -- 5.4.5. Conclusion and future work -- 5.5. A (max,+) approach to scheduling -- 5.5.1. Introduction and production hypotheses -- 5.5.2. Construction of a simple event graph associated with the initial model -- 5.5.3. Resolution of resource sharing -- 5.5.4. Application -- 5.5.5. Overview -- 5.6. Conclusion -- 5.7. Bibliography -- List of Authors -- Index.
Abstract:
The use and management of multimodal transport systems, including car-pooling and goods transportation, have become extremely complex, due to their large size (sometimes several thousand variables), the nature of their dynamic relationships as well as the many constraints to which they are subjected. The managers of these systems must ensure that the system works as efficiently as possible by managing the various causes of malfunction of the transport system (vehicle breakdowns, road obstructions, accidents, etc.). The detection and resolution of conflicts, which are particularly complex and must be dealt with in real time, are currently processed manually by operators. However, the experience and abilities of these operators are no longer sufficient when faced with the complexity of the problems to be solved. It is thus necessary to provide them with an interactive tool to help with the management of disturbances, enabling them to identify the different disturbances, to characterize and prioritize these disturbances, to process them by taking into account their specifics and to evaluate the impact of the decisions in real time. Each chapter of this book can be broken down into an approach for solving a transport problem in 3 stages, i.e. modeling the problem, creating optimization algorithms and validating the solutions. The management of a transport system calls for knowledge of a variety of theories (problem modeling tools, multi-objective problem classification, optimization algorithms, etc.). The different constraints increase its complexity drastically and thus require a model that represents as far as possible all the components of a problem in order to better identify it and propose corresponding solutions. These solutions are then evaluated according to the criteria of the transport providers as well as those of the city transport
authorities. This book consists of a state of the art on innovative transport systems as well as the possibility of coordinating with the current public transport system and the authors clearly illustrate this coordination within the framework of an intelligent transport system. Contents 1. Dynamic Car-pooling, Slim Hammadi and Nawel Zangar. 2. Simulation of Urban Transport Systems, Christian Tahon, Thérèse Bonte and Alain Gibaud. 3. Real-time Fleet Management: Typology and Methods, Frédéric Semet and Gilles Goncalves. 4. Solving the Problem of Dynamic Routes by Particle Swarm, Mostefa Redouane Khouahjia, Laetitia Jourdan and El Ghazali Talbi. 5. Optimization of Traffic at a Railway Junction: Scheduling Approaches Based on Timed Petri Nets, Thomas Bourdeaud'huy and Benoît Trouillet. About the Authors Slim Hammadi is Full Professor at the Ecole Centrale de Lille in France, and Director of the LAGIS Team on Optimization of Logistic systems. He is an IEEE Senior Member and specializes in distributed optimization, multi-agent systems, supply chain management and metaheuristics. Mekki Ksouri is Professor and Head of the Systems Analysis, Conception and Control Laboratory at Tunis El Manar University, National Engineering School of Tunis (ENIT) in Tunisia. He is an IEEE Senior Member and specializes in control systems, nonlinear systems, adaptive control and optimization. The multimodal transport network customers need to be oriented during their travels. A multimodal information system (MIS) can provide customers with a travel support tool, allowing them to express their demands and providing them with the appropriate responses in order to improve their travel conditions. This book develops methodologies in order to realize a MIS tool capable of ensuring the availability of permanent multimodal information for customers before and while traveling,
considering passengers mobility.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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