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.

This dissertation provides a comprehensive examination of the role of Transportation Networking Companies (TNC) across four dimensions. First, we examined the factors affecting Transportation Networking Companies (TNC) pricing and destination choice behavior using weekday TNC trip data from Chicago spanning January 2019 through December 2019. Towards achieving this goal, we developed a joint model framework where trip fare is modelled using linear regression model (LR) and destination choice is modelled using a multinomial logit model (MNL). Second, we build a systematic framework to analyze spatial TNC demand patterns (origins) across the urban region at the census tract (CT) level and compare them to overall transportation demand. We propose and compute a novel metric at the census tract level to identify the potential imbalance between overall transportation demand and TNC demand by developing a Generalized Ordered Logit. The model applicability is further illustrated through elasticity analysis. Third, based on earlier studies we identified that current TNC related macroscopic studies do not incorporate attributes at the microscopic resolution. We bridge the macroscopic and microscopic analysis using a bi-level modeling approaching that accommodates for the influence of microscopic attributes within the macroscopic modeling approach. In this proposed framework, the trip level destination choice model (microscopic model) takes the form of a multinomial logit model and the origin-destination flow model (macroscopic model) takes the form of a multinomial logit fractional split model. Finally, in our effort to incorporate TNCs into travel demand tools, we conducted a comprehensive literature review on studies examining the impact of TNCs on various components of travel demand models (TDM). We provide guidelines for potential travel demand model updates using three use case examples including vehicle ownership model, trip generation model, and trip level mode choice components.

Spatial and social dependence shape human activity-travel pattern decisions and their antecedent choices. Although the transportation literature has long recognized the importance of considering spatial and social dependencies in modeling individuals’ choice behavior, there has been less research on techniques to accommodate these dependencies in discrete choice models, mainly because of the modeling complexities introduced by such interdependencies. The main goal of this dissertation, therefore, is to propose new modeling approaches for accommodating flexible spatial and social dependency structures in discrete choice models within the broader context of activity-based travel demand modeling. The primary objectives of this dissertation research are three-fold. The first objective is to develop a discrete choice modeling methodology that explicitly incorporates spatial dependency (or correlation) across location choice alternatives (whether the choice alternatives are contiguous or non-contiguous). This is achieved by incorporating flexible spatial correlations and patterns using a closed-form Generalized Extreme Value (GEV) structure. The second objective is to propose new approaches to accommodate spatial dependency (or correlation) across observational units for different aspatial discrete choice models, including binary choice and ordered-response choice models. This is achieved by adopting different copula-based methodologies, which offer flexible dependency structures to test for different forms of dependencies. Further, simple and practical approaches are proposed, obviating the need for any kind of simulation machinery and methods for estimation. Finally, the third objective is to formulate an enhanced methodology to capture the social dependency (or correlation) across observational units. In particular, a clustered copula-based approach is formulated to recognize the potential dependence due to cluster effects (such as family-related effects) in an ordered-response context. The proposed approaches are empirically applied in the context of both spatial and aspatial choice situations, including residential location and activity participation choices. In particular, the results show that ignoring spatial and social dependencies, when present, can lead to inconsistent and inefficient parameter estimates that, in turn, can result in misinformed policy actions and recommendations. The approaches proposed in this research are simple, flexible and easy-to-implement, applicable to data sets of any size, do not require any simulation machinery, and do not impose any restrictive assumptions on the dependency structure. / text

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.

Das Thema der vorliegenden Bachelorarbeit ist, das Vier-Stufen-Modell der Verkehrsnachfrage nach Lohse, auch als Kennwertmodell bekannt, auf die Stadt Brandenburg an der Havel anzuwenden, um die Verkehrserzeugung, die Verkehrsverteilung und die Verkehrsmittelwahl zu berechnen. Dies ist für alle Leser interessant, die damit beginnen, sich mit der integrierten Verkehrsplanung zu beschäftigen, denn diese Arbeit stellt das theoretische, rechnerische und praktische Vorgehen formal vor. Die Berechnungen wurden mit dem Programm „Excel 2013“ realisiert. Zudem ist der Arbeit ein USB-Stick beigelegt, aus dem Sie die formalen Rechnungen aus der Bachelorarbeit besser nachvollziehen können, da auf dem USB-Stick alle Rechnungen hinterlegt sind, die dem Verfahren zugrunde liegen. Zusätzlich enthält der USB-Stick einige Grafiken, welche die Verteilung der Verkehrsmittel in der Stadt Brandenburg an der Havel darstellen.

Das Thema der vorliegenden Bachelorarbeit ist, das Vier-Stufen-Modell der Verkehrsnachfrage nach Lohse, auch als Kennwertmodell bekannt, auf die Stadt Brandenburg an der Havel anzuwenden, um die Verkehrserzeugung, die Verkehrsverteilung und die Verkehrsmittelwahl zu berechnen. Dies ist für alle Leser interessant, die damit beginnen, sich mit der integrierten Verkehrsplanung zu beschäftigen, denn diese Arbeit stellt das theoretische, rechnerische und praktische Vorgehen formal vor. Die Berechnungen wurden mit dem Programm „Excel 2013“ realisiert. Zudem ist der Arbeit ein USB-Stick beigelegt, aus dem Sie die formalen Rechnungen aus der Bachelorarbeit besser nachvollziehen können, da auf dem USB-Stick alle Rechnungen hinterlegt sind, die dem Verfahren zugrunde liegen. Zusätzlich enthält der USB-Stick einige Grafiken, welche die Verteilung der Verkehrsmittel in der Stadt Brandenburg an der Havel darstellen.