Schedule-Based Dynamic Transit Modeling

- 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