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Schedule-Based Dynamic Transit Modeling: Theory and Applications Book

Schedule-Based Dynamic Transit Modeling: Theory and Applications
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Schedule-Based Dynamic Transit Modeling: Theory and Applications, Schedule-Based Dynamic Transit Modeling: Theory and Applications outlines the new schedule-based dynamic approach to mass transit modeling. In the last ten years the schedule-based dynamic approach has been developed and applied especially for operational, Schedule-Based Dynamic Transit Modeling: Theory and Applications
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  • Schedule-Based Dynamic Transit Modeling: Theory and Applications
  • Written by author Wilson, Nigel H. M., Nuzzolo, Agostino
  • Published by Springer-Verlag New York, LLC, 12/7/2010
  • Schedule-Based Dynamic Transit Modeling: Theory and Applications outlines the new schedule-based dynamic approach to mass transit modeling. In the last ten years the schedule-based dynamic approach has been developed and applied especially for operational
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Authors

- Preface.
- General Aspects.
- 1: The Schedule-Based approach in dynamic transit modelling: a general overview; A. Nuzzolo, U. Crisalli. 1.1. Introduction. 1.2. User target time and demand temporal segmentation. 1.3. Transit supply models. 1.4. Schedule-based path choice models. 1.5. Schedule-based assignment models. 1.6. Conclusions.
- 2: A dynamic mode transit service choice model to design ex-urban transport service timetables; E. Cascetta, A. Papola. 2.1. Introduction. 2.2. The proposed joint mode-transit service choice model: general structure. 2.3. The database and choice set definition. 2.4. Estimation of the model. 2.5. Conclusion.
- 3: Finding shortest time-dependent paths in Schedule-Based transit networks: a Label Setting algorithm; M. Florian. 3.1. Introduction. 3.2. General problem definition. 3.3. The deterministic transit assignment algorithm. 3.4. Application issue. 3.5. Conclusions. - 4: A large scale Shastic Multi-Class Schedule-Based transit model with random coefficients; O. Anker Nielsen. 4.1. Background. 4.2. Modelling context. 4.3. Utility functions in the transit assignment model. 4.4. Solution algorithm. 4.5. Proposals to optimise MSA-based models. 4.6. Conclusions and recommendations.
- 5: Schedule-Based Dynamic Assignment models for public transport networks; F. Russo. 5.1. Introduction and general definitions. 5.2. Demand models. 5.3. Supply models. 5.4. Transit dynamic assignment models.
- Application To ITS.
- 6: Simulation-Based Evaluation of Advanced Public Transportation Systems; D. Morgan, H. Koutsopoulos, M. Ben-Akiva. 6.1. Introduction. 6.2. Model requirements. 6.3. Modeling framework. 6.4. Case study. 6.5. Conclusion.
- 7: Short-term prediction of vehicle occupancy in Advanced Public Transportation Information Systems (APTIS); P. Coppola, L. Rosati. 7.1. Introduction. 7.2. The case study of the city of Naples. 7.3. The overall modeling framework. 7.4. Preliminary applications to small scale examples networks. 7.5. Conclusion and research perspectives.
- 8: DY-RT: a tool for Schedule-Based planning of regional transit networks; U. Crisalli, L. Rosati. 8.1.Introduction. 8.2. DY-RT software architecture. 8.3. DY-RT: the system of models. 8.4. Application examples. 8.5. Conclusions.
- 9: A Schedule-Based transit assignment model addressing the passengers' choice among competing connections; M. Friedrich, S. Wekech. 9.1. Introduction. 9.2. Existing approaches. 9.3. Connection search. 9.4. Connection choice. 9.5. Application and outlook.
- 10: Estimation of transit passenger Origin-Destination matrices from passenger counts in congested transit networks; W.H K. Lam, Z.X. Wu. 10.1. Introduction. 10.2. Some useful concepts for transit networks and notations. 10.3. Model formulation. 10.4. Solution algorithm. 10.5. Numerical example. 10.6. Conclusions.
- 11: Evaluation of O/D trip matrices by traffic counts in transit systems; M.N. Postorino, G. Musolino, P. Velonà. 11.1. Introduction. 11.2. Notations. 11.3. Estimation of O/D levels by traffic counts. 11.4. Estimation of demand model parameters by traffic counts. 11.5. Application to a real case. 11.6. Conclusions.
- 12: Application for comparing frequency and Schedule-Based approaches in the simulation of a low frequency transit system; A. Vitetta, A. Cartisano, A. Comi. 12.1. Introduction. 12.2. Assignment models in optimal strategy and schedule-based approaches. 12.3. Experimentation in an ex-urban area. 12.4. Conclusions and indications for future developments.
- 13: Minimum path algorithms for a Schedule-Based transit network with a general fare structure; C.O. Tong, S.C. Wong. 13.1. Introduction. 13.2. Cheapest path algorithm. 13.3. Optimal path algorithm. 13.4. Test network. 13.5. Conclusion.
- 14: A Solution to the transit assignment problem; M.G.H. Bell, J.-D. Schmöcker. 14.1. Introduction. 14.2. Problem definition. 14.3. Assignment methods. 14.4. Fail to board probabilities. 14.5. Capacity constraint transit assignment. 14.6. Example. 14.7. Conclusions and discussion.


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Schedule-Based Dynamic Transit Modeling: Theory and Applications, Schedule-Based Dynamic Transit Modeling: Theory and Applications outlines the new schedule-based dynamic approach to mass transit modeling. In the last ten years the schedule-based dynamic approach has been developed and applied especially for operational, Schedule-Based Dynamic Transit Modeling: Theory and Applications

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Schedule-Based Dynamic Transit Modeling: Theory and Applications, Schedule-Based Dynamic Transit Modeling: Theory and Applications outlines the new schedule-based dynamic approach to mass transit modeling. In the last ten years the schedule-based dynamic approach has been developed and applied especially for operational, Schedule-Based Dynamic Transit Modeling: Theory and Applications

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Schedule-Based Dynamic Transit Modeling: Theory and Applications, Schedule-Based Dynamic Transit Modeling: Theory and Applications outlines the new schedule-based dynamic approach to mass transit modeling. In the last ten years the schedule-based dynamic approach has been developed and applied especially for operational, Schedule-Based Dynamic Transit Modeling: Theory and Applications

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