Modeling travel demand by time of day is gaining increasing attention in travel demand forecasting practice. This is because time of day choice has important implications for mode choice and for quantifying potential modal and time of day shifts in response to traffic congestion and peak period travel demand management strategies. In this context, understanding the causal relationship between time of day (departure time) choice and mode choice behavior would be useful in the development of time of day based travel demand modeling systems both within the four-step modeling paradigm and within newer tour-based and activity-based microsimulation paradigms. This thesis investigates the relationship between departure time choice and mode choice for non-work trips as work trips tend to be constrained with respect to time of day choice. Two alternative causal structures are considered in this thesis: one structure in which departure time choice is determined first and mode choice is subsequently influenced by departure time choice and a second structure in which mode choice is determined first and affects departure time choice. These two causal structures are analyzed in a recursive bivariate probit modeling framework that allows random error covariance. The estimation is performed separately for worker and non-worker samples drawn from the 1999 Southeast Florida Regional Household Travel Survey. For workers, model estimation results show that the causal structure in which departure time choice precedes mode choice performs significantly better. For non-workers, the reverse causal relationship in which mode choice precedes departure time choice is found to be a more suitable joint modeling structure. These two findings can be reasonably explained from a travel behavior perspective and have important implications for advanced travel demand model development and application.

Technology used for real-time locating is being used to identify and track the movements of individuals in real time. With the increased use of mobile technology by individuals, we are now able to explore more potential interactions between people and their living environment using real-time tracking and communication technologies. One of the potentials that has hardly been taken advantage of is to use cell phone tracking information for activity-based transportation study. Using GPS-embedded smart phones, it is convenient to continuously record our trajectories in a day with little information loss. As smart phones get cheaper and hence attract more users, the potential information source for self-tracking data is pervasive. This study provides a cell phone plus web method that collects volunteer cell phone tracking data and uses an algorithm to identify the allocation of activities and traveling in space and time. It also provides a step that incorporates user-participated prompted recall attribute identification (travel modes and activity types) which supplements the data preparation for activity-based travel demand modeling. Besides volunteered geospatial information collection, cell phone users’ real-time locations are often collected by service providers such as Apple, AT&T and many other third-party companies. This location data has been used in turn to boost new location-based services. However, few applications have been seen to address dynamic human interactions and spatio-temporal constraints of activities. This study sets up a framework for a new kind of location-based service that finds joint-activity opportunities for multiple individuals, and demonstrates its feasibility using a spatio-temporal GIS approach.

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In the first chapter, we develop a dynamic model of collusion in city-pair routes for selected US airlines and specify the first order conditions using a state-space representation that is estimated by Kalman-filtering techniques using the Databank 1A (DB1A) Department of Transportation (DOT) data during the period 1979I-1988IV. We consider two airlines, American (AA) and United (UA) and four city pairs. Our measure of market power is based on the shadow value of long-run profits in a two person strategic dynamic game and we find evidence of relative market power of UA in three of the four city pairs we analyze. The second chapter explores three models of forecasting airline energy demand: Trend line, ARIMA and Structural Model based on results from Chapter 1 and find that none of them is a dominant winner in American (AA) and United (UA) between Chicago and Salt Lake City. In the third chapter, we use Model Averaging and Forecast Combination Techniques to provide a decisive conclusion focusing on discussing Equal Weighted Averaging, Mean Square Weighted Averaging and Optimized Weighted Averaging on UA and AA in City-Pairs Chicago -Seattle and Chicago-San Diego.

This thesis compared revealed-preference automobile morning work commute trip data from GPS-equipped instrumented vehicles of Commute Atlanta participants with transit commute alternatives identified in the regional planning model transit network. The Transit Capacity and Quality of Service Manual (TCQSM) travel time level of service (LOS) measure for transit was applied to these GPS automobile and modeled transit data. To quantify system-level transit availability, the TCQSM service coverage LOS was applied to the Atlanta region and Atlanta s transit service area LOS was calculated as C. Most of the commuters in this study would experience transit-auto travel time LOS of F. The analyses revealed that revealed automobile travel times were 45% shorter than the model-reported automobile travel time skims for the same origin and destination zones. Transit traces, calculated by manually tracing the trips from origin to destination via the most preferable transit mode, were about 24% longer than the minimum travel-demand-modeled transit skims. Only about 9% of commuters drove directly to work more than 95% of the time and only 6% of commuters left home within five minutes of their median departure time more than 95% of the time, indicating that the convenience and flexibility of the automobile is likely to be a significant element in these commute mode decisions. Commuters perceive the total transit trip time as between being 1.25 and 2.5 as long as the actual (modeled) time, and only about 25% of commuters could take transit without having to transfer. The calculated total cost of driving to work exceeded the cost of transit, but automobile operating costs alone did not exceed transit costs for about half the sample.

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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.