Advances in Artificial Transportation Systems and Simulation

Table of contents

1. Cover
2. Title page
3. Table of Contents
4. Copyright
5. List of Contributors
6. Preface
7. Chapter 1: ITSUMO: An Agent-Based Simulator for Intelligent Transportation Systems
   1. Abstract
   2. 1.1. Introduction and Motivation
   3. 1.2. Description of the Simulator
   4. 1.3. Control: Traffic Light Agent Module
   5. 1.4. Demand
   6. 1.5. Case-Study: Aggregating Intelligence to Traffic Simulation
   7. 1.6. Conclusion
   8. Acknowledgment
8. Chapter 2: A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems
   1. Abstract
   2. 2.1. Introduction
   3. 2.2. JASOF
   4. 2.3. Case Study: A Self-Organized Automatic Guided Vehicle
   5. 2.4. Related Work
   6. 2.5. Conclusions and Future Work
   7. Acknowledgment
9. Chapter 3: An Agent Methodology for Processes, the Environment, and Services
   1. Abstract
   2. 3.1. Introduction
   3. 3.2. Background
   4. 3.3. Analysis and Design for MAS
   5. 3.4. Discussion and Future Work
   6. Acknowledgment
10. Chapter 4: A Role-Based Method for Analyzing Supply Chain Models
    1. Abstract
    2. 4.1. Introduction
    3. 4.2. Related Work
    4. 4.3. A Framework of Supply Chain Roles, Responsibilities and Interactions
    5. 4.4. A Method for Analyzing Supply Chain Simulation Models
    6. 4.5. Applicability and Validity of the Framework and Analysis Method
    7. 4.6. Concluding Remarks and Future Work
11. Chapter 5: Applying Delegate MAS Patterns in Designing Solutions for Dynamic Pickup and Delivery Problems
    1. Abstract
    2. 5.1. Introduction
    3. 5.2. Related Work
    4. 5.3. Delegate MAS Patterns
    5. 5.4. Two Case Studies
    6. 5.5. Solutions
    7. 5.6. Conclusion
12. Chapter 6: Studying the Impact of the Organizational Structure on Airline Operations Control
    1. Abstract
    2. 6.1. Introduction
    3. 6.2. Background
    4. 6.3. Empirical Airline Operations
    5. 6.4. Organizational Structure Performance Assessment
    6. 6.5. Scenario and Experiments
    7. 6.6. Results and Conclusion
13. Chapter 7: A Multi-Agent System to Study the Internal Displacement of Passengers and Their Distribution on a Large-Capacity Bus
    1. Abstract
    2. 7.1. Introduction
    3. 7.2. The Model
    4. 7.3. Simulations and Results
    5. 7.4. Conclusions
    6. Acknowledgment
14. Chapter 8: Crowd Simulation Applied to Emergency and Evacuation Scenarios
    1. Abstract
    2. 8.1. Introduction
    3. 8.2. Related Work
    4. 8.3. ModP Pedestrian Simulator
    5. 8.4. Agent Editor Module
    6. 8.5. Preliminary Results
    7. 8.6. Trends and Future Work
    8. 8.7. Conclusions
    9. Acknowledgment
15. Chapter 9: Multi-Agent Active Collaboration Between Drivers and Assistance Systems
    1. Abstract
    2. 9.1. Introduction
    3. 9.2. Multi-Agent Architecture for Intelligent Control
    4. 9.3. Use Case
    5. 9.4. Architecture
    6. 9.5. More technical data
    7. 9.6. Discussion
    8. 9.7. Conclusion and Future Developments
    9. Acknowledgment
16. Chapter 10: Computational Traffic Experiments Based on Artificial Transportation Systems: An Application of the ACP Approach
    1. Abstract
    2. 10.1. Introduction
    3. 10.2. Growing Artificial Transportation Systems from Bottom Up
    4. 10.3. Modeling Environmental Impacts
    5. 10.4. Implementation of Intelligent Traffic Clouds
    6. 10.5. Experiments and Validation
    7. 10.6. Conclusions
    8. Acknowledgment
17. Chapter 11: Implementation of Transit Signal Priority and Predictive Priority Strategies in ASC/3 Software-in-the-Loop Simulation
    1. Abstract
    2. 11.1. Introduction
    3. 11.2. ASC/3 Controller and Software-In-the-Loop Applications
    4. 11.3. Project Description
    5. 11.4. Results
    6. 11.5. Discussion
    7. 11.6. Conclusions
18. Chapter 12: Mobility Network Evaluation in the User Perspective: Real-Time Sensing of Traffic Information in Twitter Messages
    1. Abstract
    2. 12.1. Introduction
    3. 12.2. Social Media as Artificial Sensors
    4. 12.3. Methodology
    5. 12.4. Fetching Data
    6. 12.5. Sensing Mobility – Tweet Classification
    7. 12.6. Named Entity Recognition
    8. 12.7. Sentiment Analysis
    9. 12.8. Experiment Setup and Results
    10. 12.9. Experimental Dataset
    11. 12.10. Traffic-Related Tweet Classifier
    12. 12.11. Evaluation Metrics
    13. 12.12. Results Discussion
    14. 12.13. Conclusions
19. Index