Operations Research/Computer Science Interfaces

Theory and Applications

By: Nigel H. M. Wilson (Editor), Agostino Nuzzolo (Editor)

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Hardcover | 31 December 2003

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300 Pages

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24.13 x 15.88 x 2.54

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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.

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Preface	p. ix
General Aspects
The Schedule-Based approach in dynamic transit modelling: a general overview	p. 1
Introduction	p. 1
User target time and demand temporal segmentation	p. 3
Transit supply models	p. 5
Schedule-based path choice models	p. 9
Schedule-based assignment models	p. 19
Conclusions	p. 22
A dynamic mode transit service choice model to design ex-urban transport service timetables	p. 25
Introduction	p. 25
The proposed joint mode-transit service choice model: general structure	p. 27
The database and choice set definition	p. 29
Estimation of the model	p. 31
Conclusion	p. 38
Finding shortest time-dependent paths in Schedule-Based transit networks: a Label Setting algorithm	p. 43
Introduction	p. 43
General problem definition	p. 44
The deterministic transit assignment algorithm	p. 45
Application issue	p. 50
Conclusions	p. 51
A large scale Stochastic Multi-Class Schedule-Based transit model with random coefficients	p. 53
Backround	p. 53
Modelling context	p. 55
Utility functions in the transit assignment model	p. 56
Solution algorithm	p. 64
Proposals to optimise MSA-based models	p. 67
Conclusions and recomendations	p. 73
Schedule-Based Dynamic Assignment models for public transport networks	p. 79
Introduction and general definitions	p. 79
Demand models	p. 81
Supply models	p. 85
Transit dynamic assignment models	p. 90
Application to Its
Simulation-Based Evaluation of Advanced Public Transportation Systems	p. 95
Introduction	p. 95
Model requirements	p. 96
Modeling framework	p. 98
Case study	p. 106
Conclusion	p. 111
Short-term prediction of vehicle occupancy in Advanced Public Transportation Information Systems (APTIS)	p. 113
Introduction	p. 113
The case study of the city of Naples	p. 116
The overall modeling framework	p. 118
Preliminary applications to small scale examples networks	p. 128
Conclusion and research perspectives	p. 133
DY-RT: a tool for Schedule-Based planning of regional transit networks	p. 135
Introduction	p. 135
DY-RT software architecture	p. 137
DY-RT: the system of models	p. 141
Application examples	p. 149
Conclusions	p. 157
A Schedule-Based transit assignment model addressing the passengers' choice among competing connections	p. 159
Introduction	p. 159
Existing approaches	p. 160
Connection search	p. 164
Connection choice	p. 167
Application and outlook	p. 172
Estimation of transit passenger Origin-Destination matrices from passenger counts in congested transit networks	p. 175
Introduction	p. 175
Some useful concepts for transit networks and notations	p. 177
Model formulation	p. 182
Solution algorithm	p. 186
Numerical example	p. 188
Conclusions	p. 194
Evaluation of O/D trip matrices by traffic counts in transit systems	p. 197
Introduction	p. 197
Notations	p. 200
Estimation of O/D levels by traffic counts	p. 203
Estimation of demand model parameters by traffic counts	p. 205
Application to a real case	p. 207
Conclusions	p. 214
Application for comparing frequency and Schedule-Based approaches in the simulation of a low frequency transit system	p. 217
Introduction	p. 218
Assignment models in optimal strategy and schedule-based approaches	p. 220
Experimentation in an ex-urban area	p. 228
Conclusions and indications for future developments	p. 235
Minimum path algorithms for a Schedule-Based transit network with a general fare structure	p. 241
Introduction	p. 241
Cheapest path algorithm	p. 243
Optimal path algorithm	p. 245
Test network	p. 248
Conclusion	p. 250
A Solution to the transit assignment problem	p. 263
Introduction	p. 263
Problem definition	p. 266
Assignment methods	p. 267
Fail to board probabilities	p. 269
Capacity constraint transit assignment	p. 272
Example	p. 273
Conclusions and discussion	p. 274
Index	p. 281
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Operations Research/Computer Science Interfaces

Theory and Applications

At a Glance

Hardcover

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