Activity-based travel demand models are generally considered superior to their trip-based counterparts, as activity-based models (ABMs) explicitly model individuals in contrast to the aggregate nature of trip-based models. There have been a number of comparisons between trip- and activity-based models, but these comparisons focus almost exclusively on the technical ability of the two model types, while not considering the practical benefits an ABM may or may not have to a transportation agency. This research performs a more holistic comparison between trip- and activity-based models, focused specifically on the practical differences between model types, both in terms of usability and capability for complex analysis. We use the existing Wasatch Front model as a representative trip-based model, and an ActivitySim implementation in the same area as a representative ABM. We create three hypothetical scenarios in both models: a change in land use, an improvement to commuter rail service, and an increase in remote work. We discuss the process of creating each scenario in both models, and perform several example analyses with each scenario and model. We find that many commonly-cited reasons for the lack of ABM adoption may not be as applicable as previously thought. ABMs are often considered more complicated than trip-based models, requiring more data and computational resources. While ABMs do require more input data, we found that in our case the complexity of the model and the computational resources required were similar between model types. Additionally, the ABM allows for much more intuitive and straightforward interpretation of results.

The advent of on-demand transport modes such as ride-hailing and microtransit has challenged forecasters to develop new methods of forecasting the use and impacts of such modes. In particular, there is some professional disagreement about the relative role of activity-based transportation behavior models -- which have detailed understanding of the person making a trip and its purpose -- and multi-agent demand simulations which may have a better understanding of the availability and service characteristics of on-demand services. A particular question surrounds how the relative strengths of these two approaches might be successfully paired in practice. Using daily plans generated by the activity-based model ActivitySim as inputs to the BEAM multi-agent simulation, we construct nine different methodological combinations by allowing the choice to use a pooled ride-hail service in ActivitySim, in BEAM with different utility functions, or in both. Within each combination, we estimate ride-hailing ridership and level of service measures. The results suggest that mode choice model structure drastically affects ride-hailing ridership and level of service. In addition, we see that multi-agent simulation overstates the demand interest relative to an activity-based model, but there may be opportunities in future research to implement feedback loops to balance the ridership and level of service forecasts between the two models.

The world is increasingly becoming urban. In fact, 80 percent of the US population is already living in cities. With the influx of a huge population in urban areas, the urban infrastructures are bound to be stressed. Furthermore, people from every walk of life live in urban areas in search of better economic opportunities. These diverse people have diverse needs. To make matters worse, governments have a limited budget. And, they are faced with the challenge of providing infrastructure and public services fair to everyone. This thesis attempts to respond to these challenges through two manuscripts. The first manuscript proposes a decision-support tool that responds to these challenges along with the flooding vulnerability due to sea-level-rise. As flooding events are getting more frequent and intense, coastal road network is vulnerable and can significantly affect daily mobility. Therefore, the paper proposes an optimization framework that minimizes the cost of mitigation measures for flooding while also considering social equity. As a result, the results of this optimization function is not only financially optimum but also equitable to all. The second manuscript proposes a novel framework for analyzing equity in terms of access to opportunity, rather than equity of outcomes. We showcase the use of a large-scale, high-fidelity agent-based, activity-based travel demand model to produce travel times to employment centers. This travel time is used as a proxy to access to opportunities. The results are visualized in a GIS heatmap. The model is applied to the Metropolitan Washington DC area. This manuscript contributes to the literature by analyzing the equity of opportunities without considering an individual’s socioeconomic characteristics. / Master of Science / The world is increasingly becoming urban. In fact, 80 percent of the US population is already living in cities. With the influx of a huge population in urban areas, the urban infrastructures are bound to be stressed. Furthermore, people from every walk of life live in urban areas in search of better economic opportunities. These diverse people have diverse needs. To make matters worse, governments have a limited budget. And, they are faced with the challenge of providing infrastructure and public services fair to everyone. This thesis attempts to respond to these challenges through two manuscripts. As flooding events are getting more frequent and of more intensity, coastal road network is vulnerable and can significantly affect day-to-day movements. Decision makers face the challenge of mitigating the flood risk under budget constraints and they need to make their decision fair to everyone. The first manuscript proposes a decision-support tool that not only optimizes the use of a limited budget but also ensures the decision is fair to everyone. The idea of what is fair to everyone is a contentious issue. Recently some people have argued against using socioeconomic characteristics of people in making investment decisions. Therefore, the second manuscript proposes a novel framework that analyzes access to employment centers using a higher fidelity advanced travel demand model without the explicit use of socioeconomic characteristics of individuals.