Dynamic micro-assignment of travel demand with activity/trip chains

The fundamental objective of this dissertation is to develop an assignment framework for travel demand with activity/trip chains that can be used in conjunction with activity-based travel demand forecasting procedures as well as for traffic operational studies. Two groups of traffic assignment frameworks for travel demand with activity/trip chains are presented. The first group includes spatial micro-assignment models for known time-varying trip chain patterns. The second group includes temporal-spatial micro-assignment models in which trip chains are assigned over time and space.; Two spatial micro-assignment models are considered including: (a) Single-step simulation assignment procedure in which assignment is based on the prevailing travel time, and (b) An iterative simulation assignment procedure in which a User Equilibrium (UE) solution is obtained. These spatial assignment models are used to conduct a sensitivity analysis to study the effect of the different parameters of activity/trip chains on travel performance. Another group of experiments are considered to investigate activity/trip-chaining behavior. The objective of these experiments is to compare the travel performance of commuters with car-pooling trip-chaining travel pattern to that of those with single-occupant one-way-trip travel pattern. Finally, experiments are conducted to make the case of dynamic micro-assignment of activity/trip chains approach by contrasting it to current practice, and illustrating the pitfalls of inappropriately recognizing trip chains using current practice assignment.; Two temporal-spatial micro-assignment models are considered including: (a) Stochastic temporal-spatial micro-assignment of activity/trip chains, given sequence and location of stops along trip chains, (b) Stochastic temporal-spatial micro-assignment with activity sequencing. The first model represents the case when drivers simultaneously find their departure time and route at the origin to minimize their perceived travel cost over the chain. The second model extends the first by explicitly incorporating the capability of modeling activity sequencing. In other words, it represents the case when drivers simultaneously find their departure time, route, and the sequence of their intermediate activities at the origin to minimize their perceived travel cost. Different experiments are presented to illustrate the usefulness and the capabilities of these models.