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 planning. It allows time evolution of on-board loads and travel times for each run of each line to be obtained, and uses behavioral hypotheses strictly related to transit systems and user characteristics. It allows us to open new frontiers in transit modelling to support network design, timetable setting, investigation of congestion effects, as well as the assessment of new technologies introduction, such as information to users (ITS technologies). The contributors and editors of the book are leading researchers in the field of transportation, and in this volume they build a solid foundation for developing still more sophisticated models. These future models of mass transit systems will continue to add higher levels of accuracy and sensitivity desired in forecasting the performance of public transport systems.
- 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 Stochastic 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. Velon?
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. Schm?cker.
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
