Understanding how households make decisions with regards to their vehicle fleet based on their demographics, socio-economic status and travel patterns is critical for managing the financial, economic, social and environmental health of cities. Vehicle fleets therefore form a component of the Integrated Land Use, Transportation and Environment (ILUTE) modelling system under development at the University of Toronto. ILUTE is a year-by-year agent-based microsimulation model of demographics, land use and economic patterns, vehicle fleet decisions and travel choices in the Greater Toronto and Hamilton Area. This thesis extends previous work that modelled the quantity, class and vintage of vehicles in ILUTE households. This revised model offers three key improvements: transaction decisions are made sensitive to travel patterns, fuel costs are better represented, and vehicle purchases are considered in the context of the overall household budgeting. Results are promising, but further model validation is required. Potential extensions of the research are discussed.

Understanding how households make decisions with regards to their vehicle fleet based on their demographics, socio-economic status and travel patterns is critical for managing the financial, economic, social and environmental health of cities. Vehicle fleets therefore form a component of the Integrated Land Use, Transportation and Environment (ILUTE) modelling system under development at the University of Toronto. ILUTE is a year-by-year agent-based microsimulation model of demographics, land use and economic patterns, vehicle fleet decisions and travel choices in the Greater Toronto and Hamilton Area. This thesis extends previous work that modelled the quantity, class and vintage of vehicles in ILUTE households. This revised model offers three key improvements: transaction decisions are made sensitive to travel patterns, fuel costs are better represented, and vehicle purchases are considered in the context of the overall household budgeting. Results are promising, but further model validation is required. Potential extensions of the research are discussed.

Microsimulation is becoming more popular in transportation research. The purpose of this research is to explore the potential of microsimulation by integrating an existing activity-based travel demand model with an agent-based traffic simulation model. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modelling frameworks. The resulting model is then used for emission modelling where the traditional average-speed model is improved by exploiting agent-based traffic simulation results. Results from emission modelling have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation.

Microsimulation is becoming more popular in transportation research. The purpose of this research is to explore the potential of microsimulation by integrating an existing activity-based travel demand model with an agent-based traffic simulation model. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modelling frameworks. The resulting model is then used for emission modelling where the traditional average-speed model is improved by exploiting agent-based traffic simulation results. Results from emission modelling have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation.

This thesis presents a prism- and gap-based approach for modelling shopping destination choice in the Travel/Activity Scheduler for Household Agents (TASHA). The gap-location choice model improves upon TASHA’s existing destination choice model in 3 key ways: 1) Shifting from a zone-based to a disaggregate location choice model, 2) Categorizing shopping trips into meaningful types, and 3) Accounting for scheduling constraints in choice set generation and location choice. The model replicates gap and location choices reasonably well at an aggregate level and shows that a simple yet robust model can be developed with minimal changes to TASHA’s existing location choice model. The gap-based approach to destination choice is envisioned as a small but significant step towards a more comprehensive location choice model in a dynamic activity scheduling environment.

This thesis presents a prism- and gap-based approach for modelling shopping destination choice in the Travel/Activity Scheduler for Household Agents (TASHA). The gap-location choice model improves upon TASHA’s existing destination choice model in 3 key ways: 1) Shifting from a zone-based to a disaggregate location choice model, 2) Categorizing shopping trips into meaningful types, and 3) Accounting for scheduling constraints in choice set generation and location choice. The model replicates gap and location choices reasonably well at an aggregate level and shows that a simple yet robust model can be developed with minimal changes to TASHA’s existing location choice model. The gap-based approach to destination choice is envisioned as a small but significant step towards a more comprehensive location choice model in a dynamic activity scheduling environment.