MODELLING INTELLIGENT MULTI-MODAL TRANSIT SYSTEMS

The growing mobility needs of travellers have led to the development of increasingly complex and integrated multi-modal transit networks. Hence, transport agencies and transit operators are now more urgently required to assist in the challenging task of effectively and efficiently planning, managing, and governing transit networks. A pre-condition for the development of an effective intelligent multi-modal transit system is the integration of information and communication technology (ICT) tools that will support the needs of transit operators and travellers. To achieve this, reliable real-time simulation and short-term forecasting of passenger demand and service network conditions are required to provide both real-time traveller information and successfully synchronise transit service planning and operations control.

Modelling Intelligent Multi-Modal Transit Systems introduces the current trends in this newly emerging area. Recent developments in information technology and telematics have enabled a large amount of data to become available, thus further attracting transport researchers to set up new models outside the context of the traditional data-driven approach. The alternative demand-supply interaction or network assignment modelling approach has improved greatly in recent years and has a crucial role to play in this new context.

Agostino NuzzoloAvishai (Avi) CederA. Comi and A. Nuzzolo and U. Crisalli and L. RosatiOded CatsA. Nuzzolo and A. ComiA. Comi and A. NuzzoloGuido GentileFrancesco Russo and Antonino VitettaWilliam H. K. Lam and Zhi-Chun LiK. An and H. K. Lo

Preface	p. v
List of Figures	p. xiii
1 Introduction to Modelling Multimodal Transit Systems in an ITS Context	p. 1
1.1 Introduction	p. 2
1.2 Ou-board Load Forecasting Methodologies	p. 3
1.3 Real-time Transit Assignment Modelling	p. 7
1.3.1 Transit Assignment Model Classification	p. 7
1.3.2 Mesoscopic Simulation-based Models	p. 8
1.3.3 General Requirements of Real-time Mesoscopic TAMs	p. 10
1.4 Advanced Path Choice Modelling	p. 10
1.4.1 Path Choice Modelling for Unreliable Networks	p. 10
1.4.2 Individual Path Choice Modelling	p. 11
1.5 Real-time Upgrading of the O-D Matrix and Model Parameters	p. 12
1.6 Concluding Remarks	p. 15
References	p. 15
2 New Applications of ITS to Real-time Transit Operations	p. 19
2.1 Introduction	p. 20
2.2 Multi-Agent Transit System (MATS)	p. 20
2.3 Synchronized Transfers	p. 24
2.3.1 Network Simulation	p. 25
2.4 Real-time Operational Tactics	p. 31
2.4.1 Holding and Skip-stop/Segment Tactics for Transfer Synchronization	p. 31
2.4.2 Case Study of Real-time Tactics Implementation	p. 39
2.4.3 A Robust, Tactic-based, Real-time Framework for Transfer Synchronization	p. 42
2.4.4 Case Study of Different Control Policies	p. 44
2.4.5 Analysis	p. 45
2.5 Customized Bus (CB)	p. 46
2.5.1 Demand-based CB Service Design	p. 47
2.5.2 CB Operations-planning Process	p. 50
2.6 Vehicle-to-Vehicle Communication and Predictive Control	p. 54
2.6.1 Vehicle-to-vehicle Communication	p. 55
2.6.2 Case Study of the Optimization Model	p. 67
2.6.3 Predictive Control	p. 71
2.6.4 Case Study of Predictive-control Modelling	p. 75
Acknowledgements	p. 77
References	p. 77
3 A New Generation of Individual Real-time Transit Information Systems	p. 80
3.1 Introduction	p. 81
3.2 Current Trip Planner Characteristics	p. 84
3.3 Utility-based Path Suggestions	p. 85
3.3.1 Individual Utility Function Modelling	p. 85
3.3.2 Individual Discrete Choice Modelling: Empirical Evidence	p. 87
3.3.3 Example of an Individual Utility-based Traveller Advisor	p. 90
3.3.4 Concluding Remarks and Research Issues in Individual Utility-based Path Suggestion	p. 93
3.4 Normative Strategy-based Real-time Path Suggestion in Unreliable Networks	p. 94
3.4.1 Introduction to Strategy-based Recommendation	p. 94
3.4.2 A Heuristic Methodology for Normative Strategy-based Path Recommendation	p. 98
3.5 Vehicle Occupancy Degree	p. 102
3.6 Concluding Remarks and Future Work	p. 105
References	p. 105
4 Real-time Operations Management Decision Support Systems: A Conceptual Framework	p. 108
4.1 Towards Decision Support Tools in Real-time Operations	p. 109
4.1.1 Real-time Operations Management	p. 109
4.1.2 Decision Support Systems for Real-time Operations Management	p. 110
4.2 Dynamic Modelling of Public Transport System Evolution	p. 112
4.2.1 Public Transport as Dynamic Systems	p. 112
4.2.2 The Agent-based Approach to Public Transport Assignment	p. 113
4.2.3 Modelling Public Transport Reliability and Information Provision	p. 114
4.3 Modelling Architecture	p. 116
4.3.1 Modelling Environment Components	p. 116
4.3.2 Network Initializer	p. 117
4.3.3 Traffic Flow	p. 118
4.3.4 Passenger Flow	p. 119
4.3.5 Real-time Strategies	p. 122
4.4 Embedding the Dynamic Public Transport Model in a Decision Support System	p. 123
4.4.1 Scenario Design	p. 124
4.4.2 Scenario Evaluation	p. 125
4.5 The Road Ahead: Future Prospects	p. 126
References	p. 128
5 Real-time Modelling of Normative Travel Strategies on Unreliable Dynamic Transit Networks: A Framework Analysis	p. 130
5.1 Introduction	p. 131
5.2 Factors Influencing Travel Decision Making	p. 132
5.3 Travel Strategies	p. 134
5.3.1 Uncertainty and Optimal Choice in Decision Theory	p. 134
5.3.2 Path Choice and Travel Strategies on 135 Unreliable Networks 5.3.3 Expected Experienced Utility of a Strategy	p. 137
5.3.4 Optimal Strategies	p. 138
5.4 Search Methods of an Objective Optimal Strategy Conditional on a Given Rule	p. 139
5.4.1 Search Method Classification	p. 139
5.4.2 Methods with Hyperpath Explicit Enumeration	p. 139
5.4.3 Methods Without Hyperpath Enumeration for Direct Conditional Optimal Strategy Search	p. 142
5.5 Normative Travel Strategy	p. 142
5.5.1 Normative Strategy Search Methods	p. 142
5.5.2 Dynamic Search for a Normative Strategy	p. 145
5.5.3 Real-time Search for a Normative Strategy	p. 147
5.6 Conclusions and the Road Ahead	p. 147
Appendix: Artificial Intelligence Methods for Optimal Strategy Search	p. 148
References	p. 149
6 A Dynamic Strategy-based Path Choice Modelling for Real-time Transit Simulation	p. 152
6.1 Introduction	p. 153
6.2 List of Notation	p. 156
6.3 General Behavioral Assumption Framework	p. 157
6.3.1 General Assumptions	p. 157
6.3.2 Strategies, Hyperpaths and Diversion Rules	p. 158
6.3.3 Master Hyperpaths	p. 159
6.3.4 Subjective Experienced Utilities and Optimal Master Hyperpath	p. 159
6.3.5 Diversion Nodes and Dynamic Diachronic Run Hyperpaths	p. 160
6.3.6 Diversion Link Choice Rule	p. 161
6.3.7 Anticipated Utility	p. 161
6.3.8 At-origin and At-stop Diversion Choice	p. 163
6.3.9 Non-expected Utility	p. 164
6.4 Path Choice Model Formulation	p. 166
6.4.1 From Behavioral Assumptions to Model Formulation	p. 166
6.4.2 Existing Methods of Choice Set Modelling	p. 166
6.4.3 Diversion Link Choice Probabilities	p. 169
6.5 Conclusions and the Road Ahead	p. 170
References	p. 171
7 Time-dependent Shortest Hyperpaths for Dynamic Routing on Transit Networks	p. 174
7.1 Introduction	p. 175
7.1.1 Motivations	p. 175
7.1.2 Classical Algorithms for Static Networks	p. 176
7.1.3 State-of-the-art on Algorithms for Dynamic Networks	p. 178
7.1.4 Dynamic Strategies on Transit Networks	p. 180
7.1.5 The Coexistence of Frequency-based and Schedule-based Services	p. 182
7.1.6 Contributions	p. 184
7.1.7 Future Research	p. 185
7.2 A Mathematical Framework for Dynamic Routing and Strategies	p. 185
7.2.1 Topology	p. 185
7.2.2 Performance	p. 186
7.2.3 The Space-time Network	p. 187
7.2.4 The Concept of Topological Order	p. 189
7.2.5 Path Costs in Presence of Random Arc Performance	p. 190
7.2.6 Modelling Strategies Through Hyperarcs	p. 191
7.2.7 The Cost of Hyperpaths	p. 194
7.2.8 Extension to Continuous Time Modelling	p. 196
7.3 Formulation and Solution of the Dynamic Routing Problem	p. 197
7.3.1 Route Search with Roots and Targets	p. 197
7.3.2 General Algorithm	p. 198
7.3.3 Extension to Departure and Arrival Time Choice	p. 201
7.3.4 Extension to Intermodal Routing	p. 202
7.3.5 Extension to Strategic Behavior and the Greedy Approach	p. 203
7.4 Algorithm Implementations	p. 205
7.4.1 Temporal-Layer Approach	p. 205
7.4.2 User-Trajectory Approach	p. 209
7.4.3 The Multi-Label Algorithm	p. 213
7.5 Implementation for a Journey Planner	p. 218
7.5.1 The Transit Network	p. 218
7.5.2 Timetable and Dynamic Attributes	p. 223
7.5.3 Application of the Multi-Label Algorithm	p. 224
7.5.4 Transit Arc Performance	p. 226
References	p. 228
8 Real-time Reverse Dynamic Assignment for Multiservice Transit Systems	p. 231
8.1 Introduction	p. 232
8.2 Assignment	p. 237
8.2.1 Supply and Demand: Definition and Notation	p. 237
8.2.2 Supply/Demand Interaction	p. 239
8.3 RDA Model	p. 243
8.3.1 Starting Values	p. 244
8.3.2 Optimization Variables	p. 244
8.3.3 Objective Function and Optimization Model	p. 245
8.3.4 Solution Procedure	p. 246
8.4 Numerical Test	p. 247
8.5 Conclusions and Further Developments	p. 250
References	p. 251
9 Optimal Schedules for Multimodal Transit Services: An Activity-based Approach	p. 253
9.1 Introduction	p. 254
9.2 Basic Consideration?	p. 257
9.2.1 Activity-time-space Network Representation	p. 257
9.2.2 Assumptions	p. 259
9.3 Model Formulation	p. 260
9.3.1 Transit Network Supply-Demand Equilibrium	p. 260
9.3.2 Transit-timetabling Problem	p. 267
9.4 Numerical Studies	p. 270
9.4.1 Scenario 1	p. 270
9.4.2 Scenario 2	p. 273
9.5 Conclusions and Further Studies	p. 279
Acknowledgments	p. 282
References	p. 282
10 Transit Network Design with Stochastic Demand	p. 286
10.1 Introduction	p. 287
10.2 Robust Model	p. 290
10.2.1 Problem Setting	p. 290
10.2.2 A Robust Formulation with Equilibrium Constraints	p. 292
10.2.3 Solution Algorithm	p. 301
10.3 Two-stage Stochastic Model	p. 303
10.3.1 Model Formulation	p. 304
10.3.2 Service Reliability-based Gradient Method	p. 306
10.4 Numerical Studies	p. 308
10.5 Conclusion	p. 314
Acknowledgements	p. 315
Appendix: Notation Table	p. 315
References	p. 319
Index	p. 321

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