Multimodal transport systems

Multimodal transport systems / edited by Slim Hammadi, Mekki Ksouri.

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...

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Bibliographic Details
Other Authors: Hammadi, Slim (Editor), Ksouri, Mekki (Editor)
Format: eBook
Language:English
Published: London, U.K. : Hoboken, N.J. : ISTE ; Wiley, 2014.
Series:Automation-control and industrial engineering series.
Subjects:
Transportation engineering.
Transportation.
transportation engineering (civil engineering)
transportation.
TECHNOLOGY & ENGINEERING > Engineering (General)
Transportation
Transportation engineering
Online Access:Click for online access
Table of Contents:
  • Machine generated contents note: ch. 1 Dynamic Car-pooling / Nawel Zangar
  • 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
  • ch. 2 Simulation of Urban Transport Systems / Alain Gibaud
  • 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. 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. 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. 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
  • ch. 3 Real-time Fleet Management: Typology and Methods / Gilles Goncalves
  • 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. static route planning problem
  • 3.4.4. Route planning and modification of the transport plan
  • 3.5. Conclusion
  • 3.6. Bibliography
  • ch. 4 Solving the Problem of Dynamic Routes by Particle Swarm / El Ghazali Talbi
  • 4.1. Introduction
  • 4.2. Vehicle routing problems
  • 4.2.1. static vehicle routing problem
  • 4.2.2. 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. 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
  • ch. 5 Optimization of Traffic at a Railway Junction: Scheduling Approaches Based on Timed Petri Nets / Benoit Trouillet
  • 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. 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. (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.

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