Latent modal preferences, or modality styles, are defined as behavioral predispositions towards a certain travel mode or set of travel modes that an individual habitually uses. They are reflective of higher-level orientations, or lifestyles, that are hypothesized to influence all dimensions of an individual's travel and activity behavior. For example, in the context of travel mode choice different modality styles may be characterized by the set of travel modes that an individual might consider when deciding how to travel, her sensitivity, or lack thereof, to different level-of-service attributes of the transportation (and land use) system when making that decision, and the socioeconomic characteristics that predispose her one way or another. Travel demand models currently in practice assume that individuals are aware of the full range of alternatives at their disposal, and that a conscious choice is made based on a tradeoff between perceived costs and benefits associated with alternative attributes. Heterogeneity in the choice process is typically represented as systematic taste variation or random taste variation to incorporate both observable and unobservable differences in sensitivity to alternative attributes. Though such a representation is convenient from the standpoint of model estimation, it overlooks the effects of inertia, incomplete information and indifference that are reflective of more profound individual variations in lifestyles built around the use of different travel modes and their concurrent influence on all dimensions of individual and household travel and activity behavior.

The objectives of this dissertation are three-fold: (1) to develop a travel demand model framework that captures the influence of modality styles on multiple dimensions of individual and household travel and activity behavior; (2) to test that the framework is both methodologically flexible and empirically robust; and (3) to demonstrate the value of the framework to transportation policy and practice.

In developing an appropriate framework, the dissertation builds on Latent Class Choice Models (LCCMs) used previously in the literature, synthesizing recent advances in the sub-domains of taste heterogeneity and choice set generation, and contributing methodologically to the sub-domains of preference endogeneity and simultaneous choice models. With regards to preference endogeneity, discrete choice models in the past have usually subscribed to the neoclassical assumption that preferences, as denoted by taste parameters and choice sets, are characteristics of the decision-maker that are exogenous to the choice situation and stable over time. Such a representation would be adequate if an individual's modality style were expected to be invariant across time. However, modality styles are subject to external influences, such as changes to the transportation system. For example, one would expect the introduction of transportation policies such as the London Congestion Charge or the implementation of infrastructural initiatives such as Bogota's Transmilenio bus rapid transit system to lead to changes in modality styles, and an apposite framework should be able to model and predict these changes. With regards to simultaneous choice models, discrete choice models in the past have introduced correlation across multiple dimensions through the covariance structure of the utility specification corresponding to each of the dimensions. Though such an approach has been shown to result in a significant improvement in fit, the covariance structure is a black box that it does not offer any insight to the underlying source of correlation. We introduce correlation through the modality styles construct, conditioning multiple dimensions of individual and household travel and activity behavior on a single overarching modality style, and thereby offering a behavioral rationale to the underlying source of correlation.

The proposed framework has the following structure: modality styles are specified as latent classes. Heterogeneity across modality styles is captured by allowing taste parameters and choice sets corresponding to the class-specific choice models to vary across classes. Preferences are endogenized by defining class membership as a function not only of the characteristics of the decision-maker, as is standard practice, but also of the consumer surplus offered by each class, which in turn is a function of alternative attributes, taste parameters and choice sets. Choices across multiple dimensions are correlated by conditioning the class-specific choice models for all dimensions of interest on the class membership model.

We apply the framework to study the relationship between individual modality styles and travel mode choice behavior using two very distinct travel diary datasets from two very culturally and geographically distinct regions. The first dataset was collected in Karlsruhe, Germany and comprises a relatively small sample of 119 individuals surveyed over a fairly long observation period of six weeks. Estimation results indicate the presence of habitual drivers who display a strong bias for using the automobile and multimodal individuals who exhibit variation in their modal preferences. Multimodal behavior is further distinguished by those who appear to be sensitive to travel times and those who appear to be insensitive. The second dataset was collected in the San Francisco Bay Area in the United States and consists of a relatively large sample of 26,350 individuals surveyed over a fairly short observation period of two days. Estimation results uncover six modality styles that are distinguishable from one another by the kinds of individuals that belong to each of the six modality styles, their latent preferences for different travel modes and the relative importance that they attach to different level-of-service attributes of each of the travel modes. For example, two of the six modality styles comprising 30% of the sample population only consider the car when deciding how to travel. These two modality styles, labeled inveterate drivers and car commuters, can further be distinguished from one another by their value of travel time. Inveterate drivers have a very low value of in-vehicle travel time of 0.55 $/hr for mandatory tours and are insensitive to in-vehicle travel times for non-mandatory tours. Car commuters have a value of in-vehicle travel time of 6.95 $/hr for mandatory tours and are insensitive to travel costs for non-mandatory tours, indicating a very high value of in-vehicle travel time for the same. Consistent with findings in the social sciences and multiple streams within economics that have shown preferences to be endogenous, the case study shows that a decision-maker's value of time is sensitive to the level-of-service of the transportation system, and an increase in overall travel times can induce decision-makers to lower their value of time.

The framework is subsequently adapted to study the evolution and persistence of modality styles and travel mode choice behavior in a dynamic context. Individual modality styles are still represented as latent classes, but an individual is allowed to have different modality styles at different time periods. The evolutionary path is hypothesized to be a Markov process such that an individual's modality style in the current time period is dependent only on her modality style in the previous time period. As before, travel mode choices for a particular time period are conditioned on the individual's modality style for that time period. The framework is empirically tested using travel diary data collected in Santiago, Chile. The dataset comprises a sample of 220 individuals surveyed over four one-week periods that span a time period of twenty-two months that includes the introduction of Transantiago, a complete redesign of the city's public transit system. Estimation results identify three modality styles: unimodal auto users who only consider the automobile, unimodal transit users who only consider the public transit system and have a low value of time, and multimodal users who consider all travel modes and have a high value of time. The case study further finds that the distribution of individuals across modality styles is highly sensitive to a shock to the transportation system such as that represented by the introduction of Transantiago. Results from a sample enumeration show that nearly a quarter of the sample population changed its modality style post-Transantiago.

For all three datasets, estimation results find that modality styles are strongly correlated with more long-term travel and activity decisions, such as level of vehicle ownership and residential location. In examining the influence of individual modality styles on travel mode choice behavior, the model framework for both the static and the dynamic context took one or more of these decisions as exogenous inputs. However, such a causal representation risks endogeneity, leading us to reverse the representation and include these dimensions explicitly as dependent variables. In doing so, we recognize that dimensions such as level of vehicle ownership represent decisions that are not made by individuals in isolation from other members of the household. An individual's preferences and choices are strongly shaped by the opinions and behaviors of the people around her, particularly when a choice is made collectively by a group of individuals, as in the case of a household. Therefore, interaction between household members must be understood to influence, among other things, individual modality styles. To reflect this influence, we introduce the household modality styles construct, characterized by the modality styles of the respective individuals that make up the household. We build upon the LCCM framework described previously, replacing the individual modality styles construct with the household modality styles construct and conditioning both individual and household level dimensions on the household's modality style, therefore introducing correlation between preferences of the individuals that constitute the household.

The framework is used to examine the relationship between household modality styles, level of vehicle ownership, transit season pass possession and travel mode choice behavior using travel diary data from Karlsruhe, Germany. The dataset comprises a sample of 96 male and female household heads belonging to 48 households surveyed over a six-week observation period. Estimation results identify four distinct household modality styles. The model uncovers both significant correlation between modal preferences of heads of the same household and notable differences as well. In general, female household heads are found to be less reliant on the automobile for their mobility requirements than their male counterparts. Short-term individual decisions, such as mode choice, are found to be inextricably linked with more long-term individual and household decisions, namely level of vehicle ownership and transit season pass possession, both of which vary considerably across different modality styles.

Modality styles have important implications for transportation policies and infrastructural initiatives seeking to change existing patterns of travel mode choice behavior. Travel demand models constitute an important component of the planning and policy-making process, being widely used to make forecasts, which in turn are driven by the assumptions that these models make about how individuals arrive at decisions. We use estimation results from the BATS 2000 dataset to compare forecasts from different model specifications for scenarios evaluating the impact of increased auto congestion and improvements to the public transit system on travel mode choice behavior. Findings reveal that models of travel mode choice behavior that ignore the influence of modality styles can overestimate expected gains from transport policies and infrastructural initiatives seeking to reduce automobile use by factors of between one-and-a-half and three. The dissertation further demonstrates how incremental improvements in the transportation system, unless accompanied by corresponding shifts in the distribution of individuals across different modality styles, will result in far smaller changes in travel behavior than would be predicted by a traditional model of travel mode choice. This dissertation makes the case that what is needed is a dramatic change to the transportation system that forces individuals to reconsider how they travel.

Though the applications presented in the dissertation restrict their attention to the influence of modality styles on four specific dimensions of individual and household travel and activity behavior - travel mode choice for work/mandatory tours, travel mode choice non-work/non-mandatory tours, transit season pass possession and level of vehicle ownership, our results serve as a good starting point for a more comprehensive framework that recognizes the influence of modality styles on all dimensions of individual and household travel and activity behavior. The model framework developed by this dissertation is shown to be both methodologically flexible and empirically robust. Using a model of individual modality styles and travel mode choices in a static context as the foundation, we were able to expand the framework in multiple directions, extending it to a dynamic context, including additional dimensions of decision-making such as transit season pass possession and level of vehicle ownership, and incorporating the influence of intra-household interactions on individual preferences. Despite differences in observation period, sample size, local topography and cultural context across the three datasets, the framework was consistently found to outperform traditional models of travel behavior in terms of both statistical measures of fit and behavioral interpretation. The dissertation concludes with a discussion on how the framework might be extended further to include dimensions such as destination choice, vehicle miles traveled and residential location. We identify some of the major hurdles to their inclusion and suggest possible solutions, laying out an extensive road map for future research in the area. When complete, the line of work initiated by this dissertation is expected to result in a comprehensive model of individual and household travel and activity behavior that integrates travel demand and land use analysis through the modality styles construct with the objective of offering a deeper understanding of decision-making and greater predictive power than current models in practice.

Every day, decision-makers make choices among finite and discrete sets of alternatives. For example, people decide whether to walk, bike, take transit, or drive to work; shoppers decide which of the available brands of toothpaste to buy; and firms decide which vacant buildings they will rent for office space. Across these disparate domains, discrete choice models mathematically represent the procedures that analysts believe decision-makers are using to make such choices.

Historically, the field of discrete choice modeling grew mainly out of economics, and this lineage has had long-lasting methodological ramifications. In particular, despite the great mathematical similarity between discrete choice models and models in statistics, machine learning, and causal inference, discrete choice research remains mostly siloed, seldom drawing from or contributing to methods in these related disciplines.

In this dissertation, we help demolish the methodological silo around discrete choice re- search. Drawing from recent techniques in statistics, machine learning, and causal inference, we remove substantive limitations on the decision-making processes that could be be represented and predicted with previously available discrete choice methods. At the same time, by addressing concerns of discrete choice modelers, we make methodological contributions to the fields of statistics and machine learning, and we identify future research areas where discrete choice modelers are well suited to advancing the state of the art in causal inference.

Importantly, the methodological advances described above were not divorced from to- day’s societal concerns. Given that more and more government agencies are (unsuccessfully) attempting to raise bicycle commuting rates in their jurisdictions, we guide our interactions with the statistics, machine learning, and causal inference literatures by trying to more accurately model an individual’s choice of commuting by bicycle. In particular, we use parametric link functions from statistics to better model the adoption and abandonment of bicycling. From machine learning, we use decision trees to represent the non-compensatory decision protocols that individuals appear to follow when deciding whether to commute by bicycle, and we use diagrams from the causal inference literature to gain insight into how we can bet- ter model the effects of bike lane investments on bicycle commute mode shares. All together, we not only make methodological contributions to the fields of discrete choice, statistics, machine learning, and causal inference, but we contribute to the efforts of transportation planners and modelers who are trying to make our cities and regions more sustainable and environmentally friendly. The methods developed in this dissertation have applications to strategic bicycle planning, helping analysts understand when certain interventions are not enough to cause people to abandon non-bicycle modes of travel at the desired rates and what alternative interventions might be more effective.

In total, the specific contributions of this dissertation are the following:

1. We create a new spatial unit of analysis (the zone of likely travel) for the incorporation of roadway-level variables such as presence and type of bicycle infrastructure, roadway slopes, and traffic speeds into mode choice models.

2. We propose and demonstrate the novel use decision-tree methods for directly including roadway-level variables in mode choice models.

3. We create a new class of closed-form, finite-parameter, multinomial choice models that avoid an undesirable symmetry property that we describe in Chapter 3.

4. We create a procedure for using this new class of models to extend many existing binary choice models to the multinomial setting for the first time.

5. We create methods for creating new, symmetric and asymmetric, binary choice models.

6. We provide a microeconomic framework for interpreting decision trees by showing that decision trees represent a non-compensatory decision rule known as disjunctions-of-conjunctions and that such rules generalize many of the non-compensatory rules used in the discrete choice literature so far.

7. We propose and estimate the first bayesian model tree, thereby combining decision trees and discrete choice models in the first two-stage, semi-compensatory model that jointly:

a) uses disjunctions-of-conjunctions for the choice-set generation stage,

b) allows for context-dependent preference heterogeneity in the choice stage, and

c) quantifies analyst uncertainty in the estimated disjunctions-of-conjunctions

8. We identify techniques such as the use of causal diagrams that can be borrowed from the causal inference literature to improve the ability of discrete choice modelers to predict outcomes under external changes or policy interventions such as investing in on-street bicycle lanes.

9. We identify areas of the causal inference literature that can be improved through the incorporation of techniques from discrete choice or through the application of causal inference techniques that are very relevant to discrete choice modellers yet only infrequently researched by traditional causal inference researchers.

Through this dissertation, we empirically demonstrate most of our contributions using commute mode choice data from the San Francisco Bay Area. In every case, we found that the new models developed as part of this dissertation fit our data better than traditional discrete choice models. These results were stable across all measures of fit that were used, whether the measures were in-sample or out-of-sample, frequentist or bayesian. Beyond fit, all of our new models also proved to be qualitatively different than traditional discrete choice methods. Our new models provided insights and forecasts that both made more sense and were more accurate than their traditional counterparts. Finally, our contributions related to causal inference are the only items from the list above without empirical demonstrations. Instead, these contributions are bolstered by substantial literature review, discussion, and thought exercises that show the (general and bicycle specific) benefits of merging discrete choice and causal inference techniques.

To address issues of climate change, information about greenhouse gas emissions is more and more often being presented to people. For example, labels and signs displaying pounds and kilograms of CO2 are showing up in trip planners, in car advertisements, and even in restaurant menus. This is being done under the assumption that the information about the environmental impacts of different alternatives will encourage more sustainable behavior. However, little is known about whether or not this strategy affects the choices people make. In order for people to change their behavior for the benefit of reduced emissions, they first need to place positive value on those reductions. This research aims to answer three questions regarding the efficacy of presenting people with this information: first, do people place significant value on reducing their greenhouse gas emissions, second, can people consistently value a pound of CO2, and third, how does this value of green vary across the population? To answer these questions, five experiments were designed and conducted using discrete choice experiments, a framework typically used to investigate how people value reducing their travel time. UC Berkeley students and residents of the San Francisco Bay Area made stated and revealed preference transportation choices from a set of alternatives. With knowledge of the attributes of their chosen alternative as well as those of their available alternates, their choices were analyzed using logit, mixed logit, and hybrid choice models. The findings include that not only can people consistently interpret and place value on the pounds of CO2 associated with their transportation alternatives, but also that there exists a discrete distribution in the value of green. For all except one experiment, the best models indicate that while a majority of the population does not act as though they care about reducing their greenhouse gas emissions, there is a small group with a willingness to pay of $2.68 per pound of CO2.

The COVID-19 pandemic presented an unprecedented challenge to public transit systems worldwide, testing the ability of transit agencies to make swift and difficult decisions. Balancing the need to provide essential workers with reliable transportation options while dealing with suddenly reduced revenue and increased operational costs forced many agencies to reduce transit services, making life difficult for countless commuters relying on transit. While the pandemic spotlighted the importance of decisive action during times of crisis, disruptions to public transit systems are a regular occurrence. Disruptions vary significantly in time, duration, location, scope, frequency, and impact. Terrorist attacks, the aftermath of violent protests, and natural disasters are a few examples that can drastically change the transit system and our travel needs. Responding quickly and effectively to these disruptions by making informed decisions is crucial not only for minimizing the impact of the disruption, but also for maintaining the reliability of public transit systems for those who depend on them, ensuring they remain available and accessible when needed most.

In recent years, transit agencies have increasingly begun to adopt automated fare collections (AFC) systems, also known as smartcards, to collect fares, making valuable data readily available for analysis. In response, the research community has proposed a number of inferential methods and predictive models to analyze transit patterns. More than 300 research papers have been published on short-term ridership prediction models in the last decade. Despite the abundance of literature, we, the research community, have yet to generate practical knowledge for decision-making. Lack of reproducibility due to inadequate documentation, inaccessible tools and resources, and code and data-sharing issues are prevalent; hindering our ability to create collective wisdom. The problem is further compounded by the lack of rigorous statistical analysis in many studies, making it difficult to identify relevant research, particularly when metrics across studies are incomparable.

Adding to this chaos, there is also a significant gap in inference methods and predictive models to account for disruptions in general. Current inference methods, which are mostly used to enrich transaction data with other data sources, are designed for stable periods and do not capture the changing transit behaviors of transit users during disruptions, such as those caused by the COVID-19 pandemic. Beyond the pandemic, station closures, a common cause of disruptions, are not accounted for in any predictive model. This further hinders the ability of transit agencies to utilize such models for decision-making during disruptions.

This dissertation demonstrates how to use readily available smartcard data to quickly support decision-making in highly dynamic conditions. The objectives of the dissertation are to:

\begin{enumerate} \item Enhance the value of smartcard data by integrating it with other data sources to provide more comprehensive, near-real-time insights during disruptions. \item Create an open-source repository for short-term ridership prediction models to facilitate accurate and reliable comparisons, and to accelerate advancements of the models. \item Compare and assess the performance of state-of-the-art methods for short-term ridership prediction in a highly dynamic condition. \item Improve the accuracy and reliability of short-term ridership prediction models during disruptions by integrating station closure information. \end{enumerate}

All work is done using the transaction data of 147 stations in the Bus Rapid Transit (BRT) system in Bogot {a}, Colombia, from August 2015 to May 2021. This dataset includes two highly dynamic conditions: a month-long protest that occurred in November-December 2019 and the COVID-19 pandemic that began in March 2020. Both conditions drastically changed the travel needs of the users and the transit system. Additionally, the dataset covers several station closures, which are not necessarily associated with these two primary events.

In the study described in Chapter \ref{chap:paper1}, I examined the changes in demand for the BRT system in Bogot {a} during the COVID-19 pandemic. Despite the disruptions caused by the pandemic, transit agencies had to continue providing services as it was a crucial transportation option for many essential workers. However, a comprehensive understanding of the remaining transit users, as well as how the demand adjusted during the recovery period across different population segments, was lacking, despite being critical information to inform the prioritization of service changes, station cleanings, and the effective implementation of safety measures. I enriched smartcard data transactions with block-level demographic data from the 2018 census to infer the socio-economic strata of BRT users, a variable that can be used as a proxy for income. I present a methodology that combines behavioral modeling techniques with well-established inference methods to capture the dynamic heterogeneity of transit use by strata at different points in time, and use this information to estimate a probability vector that assigns frequent transit users to a stratum. At the beginning of the pandemic, the reduction in transit use for all strata was similar, at around 85 to 90\%, but members of lower strata returned to transit at a pace five times faster than members of higher strata. The proposed method had the advantage of providing near real-time insights into the evolution of the COVID-19 pandemic using existing and available data.

Chapter \ref{chap:paper2} describes a study in which I explored predictive models to anticipate ridership and enable proactive resource allocation ahead of time. It was difficult to reliably compare and test models, even under stable conditions, let alone during highly dynamic conditions. Therefore, I built an open-source infrastructure with running code for five major methodologies commonly used in the literature, including econometric and deep learning approaches. The open-source code also provides the Bogot {a} BRT smartcard dataset for the five-year period for other researches to reproduce and replicate results.

While the literature on short-term ridership prediction focuses on relatively stable condition, I systematically compared their performance with two highly dynamic conditions, a one-month-long labour protest, and the COVID-19 pandemic. In stable conditions, most tested models performed relatively similarly, with forecasting errors varying from 8.5 to 12\%. However, all models performed significantly worse in both highly dynamic conditions relative to stable conditions. In the protest condition, increases in prediction error ranged from 14 to 24\%. During the COVID-19 pandemic, increases in prediction error ranged from 12 to 82\%. Notably, in the COVID-19 pandemic scenario, a Recurrent Neural Network (RNN) model, with a long-short term memory (LSTM) cell stood out by outperforming other models and adapting faster to disruptions. The prediction error stabilized within about 1.5 months, while other models had higher error rates even a year after the start of the pandemic.

In Chapter \ref{chap:paper3}, I noted that some disruptions resulted in temporary station closures, causing an increase in demand at nearby transit stations. But station closure information was not incorporated into any short-term predictive models. To improve predictions for stations affected by closures, I proposed a new model that uses graph theory and the attention mechanism. The model aimed to capture spatiotemporal correlations, so that the prediction at one station is sensitive to what happens in another station in a previous time step. By incorporating station closure information into the proposed model, I reduced the prediction errors by 3.3 to 23.5\% for station closures compared to existing models in the open-source codebase infrastructure developed in Chapter 3. These improvements were observed across multiple metrics and modeling strategies.

In summary, the contributions of this dissertation are to:

\begin{enumerate} \item Develop a method to enrich smartcard data with socio-economic information for highly dynamic conditions. \item Expand the understanding of the change in transit use by different socio-demographic groups during the COVID-19 pandemic in Bogot {a}. \item Create an open-source codebase infrastructure to consolidate short-term ridership prediction research to perform systematic, reliable and statistically rigorous benchmarks tests to accelerate the advancements of the models. \item Systematically compare the performance of state-of-the-art methods for short-term ridership prediction during both stable and highly dynamic conditions. \item Demonstrate the importance of including station closures to improve the prediction accuracy for nearby stations. \item Propose a novel modeling framework that captures spatiotemporal correlations of the transit network, improving overall performance and the prediction accuracy for stations impacted by other station closures. \item Integrate the new modeling framework in the open-source codebase infrastructure for other researchers to replicate, reproduce, compare, improve, and build upon our model. \end{enumerate} Smartcard data provides timely information about transit demand that can inform operational and tactical decisions of transit agencies. While not as rich in demographic information as surveys, these data provide timely and relevant information that is critical during disruptions. This information can help transit agencies understand how users adjust travel during disruptions and prioritize populations that may be disproportionately affected. Accurate forecasting can also give transit agencies additional time to plan resource reallocation and take appropriate actions. The creation of an open-source codebase infrastructure lowers the barrier to reproducibility. It allows for reliable and systematic comparisons of model performance, identifying relevant research and ultimately generating knowledge to improve decision-making.

Thorough this dissertation, I empirically demonstrate the contributions by leveraging publicly available smartcard data from the BRT system in Bogot {a} spanning more than five years, including multiple highly dynamic periods of time affecting transit ridership. I showcased the power of smartcard data in understanding the evolution of the impact of the COVID-19 pandemic on frequent transit users from different population segments. In the spirit of collaboration, I built an open-source codebase to foster more innovative solutions, enabling researchers, practitioners, and transit agencies to generate collective wisdom that advances scientific knowledge. I proposed a new modeling framework that leverages station closure information to improve overall short-term ridership forecast. In conclusion, my work demonstrates the potential of utilizing smartcard data to support

Smartphones are becoming an increasingly interesting survey medium for behavioral research due to their value for collecting long-term panel observations and supplementary data on the choice environment. Thanks to the sensor data, it becomes possible to survey participants based on whether or not a certain activity has been carried out. By fusing the phone-generated sensor data and survey responses with data from outside sources, substantial data sets can be generated which can be used to investigate choices in complex environments. Computational systems for behavior research take advantage of automation and scalability opportunities, thereby building also on pertinent bodies of literature regarding machine learning on large data sets and crowdsourcing. The importance of comprehensive, long-term data sets in understanding behavior has been highlighted in the choice theory literature, specifically with respect to capturing an individual decision-maker’s history of choices and personal experiences with those choices. To date, however, relatively few studies have capitalized on emerging technologies to create or analyze such data sets.

Rich data sets which combine panel information on the decision-maker with information on the choice environment can support the study of dynamic phenomena, which is especially important in a rapidly changing world where behavioral adaptation can take place on a relatively small time scale and, once habits are formed, have long-lasting effects. Some examples of pressing questions in the field of transportation involve understanding how travelers are responding to the emerging sharing economy, to new ride sharing services and new information systems, how time use and travel patterns will change due to automated vehicles, and how more sustainable travel behavior can be promoted through incentive or pricing strategies. This dissertation aims to support the adoption of smartphone-based survey technology in travel behavior research in order to lay the groundwork for research aimed at answering the above questions. It describes the design and implementation of a smartphone-based study, presents a system for fusing smartphone data with externally acquired data, and demonstrates how these ample data sets can be leveraged to generate new behavioral insights. The problem chosen for study is the link between transit service quality, rider satisfaction and ridership retention on public transit. This is motivated by the fact that many transit agencies in the United States continue to see large rates of ridership turnover, and that to date, very little is known about what drives transit use cessation.

The six-week San Francisco Travel Quality Study (SFTQS) was conducted in autumn 2013. It collected a data set that included high-resolution phone locations, a number of daily mobile surveys on specific trip experiences, responses to online entry and exit surveys, and transit vehicle locations. By fusing the phone location data with transit vehicle locations, individual-level automatic transit travel diaries could be created without the need to ask participants. The reduced respondent burden, in turn, facilitated a longer term data collection. Initial recruitment proved to be challenging, with response rates to some of the email and direct mailing lists around 1%, and response rates to in-person recruiting between 8 and 15%. On the other hand, attrition was lower than expected, considering the length of the study: The initial enrollment was 856 participants, of which 555 (65%) participants completed all required surveys and 637 (74%) completed the entry and exit survey as well as at least one daily mobile survey. Interestingly, 36% of participants later stated they would have preferred to fill out mobile surveys more frequently (e.g., one per trip rather than one per day) than what was required in the study.

A central part of the computational infrastructure used to collect the data was the system of integrated methods to reconstruct and track travelers’ usage of transit at a detailed level by matching location data from smartphones to automatic transit vehicle location (AVL) data and by identifying all out-of-vehicle and in-vehicle portions of the passengers’ trips. This system is presented in detail in this dissertation, where it is shown how high-resolution travel times and their relationships with the timetable are derived. Approaches are presented for processing relatively sparse smartphone location data in dense transit networks with many overlapping bus routes, distinguishing waits and transfers from non-travel related activities, and tracking underground travel in a metro network. While transit agencies have increasingly adopted systems for collecting data on passengers and vehicles, the ability to derive high-resolution passenger trajectories and directly associate them with vehicles has remained a challenge. The system presented in this dissertation is intended to remedy this situation, and it enables a range of different analyses and applications. Results are presented from an implementation and deployment of the system during the SFTQS. An analysis of out-of-vehicle travel times shows that (a) longer overall travel times in trips involving a transfer are strongly driven by transfer times, and (b) median wait times at the origin stops are consistently low regardless of the headway. The latter can be seen as an effect of real-time information, as it appears that wait times are increasingly spent at locations other than the stop and that passengers time their arrivals at the stop. Given these shifts, the traditional assumption that the average wait time at a transit stop of a high-frequency route is half the headway due to random arrivals may need to be revisited.

This dissertation presents two applications to derive new behavioral insights from the SFTQS data set and to demonstrate the power and value of these new types of data. The analyses were based on participants’ individual history of transit usage and experiences with service quality. The first analysis used the data from the daily mobile surveys to model the link between participants' reported satisfaction with travel times on specific trips (i.e., their subjective assessment) and objective measures of those travel times. Thanks to the tracking data, it was possible to decompose observed travel times into their in-vehicle and out-of-vehicle components, and to compare the observed in-vehicle travel times to scheduled in-vehicle travel times to identify delays suffered while the participant was on board. The estimation results show that on average, a minute of delay on board a vehicle contributed more to passenger dissatisfaction than a minute of waiting time either at the origin stop or at a transfer stop, and that delays on board metro trains are perceived as more onerous than delays on board buses. Furthermore, the models included participants' baseline satisfaction levels as reported in the entry survey and a daily measure of their subjective well-being. Both variables are relatively new elements in travel surveys, and both are seen to be significant in the estimation results. These results indicate that satisfaction with travel times may be composed of a baseline satisfaction level and a variable component that depends on daily experiences, and that there may be non-negligible interactions between subjective well-being and travel satisfaction. Therefore, it is recommended that future survey designs should include measures for both these variables.

The second application builds on the results of the first to empirically investigate the causes for cessation of transit use, with a specific focus on the influence of personal experiences that users have had in the past, on resulting levels of satisfaction, and subsequent behavioral intentions. A latent variable choice model is developed to explain the influence of satisfaction with travel times, including wait times at the origin stop, in-vehicle travel times, transfer times and overall reliability, and satisfaction with the travel environment on behavioral intentions. The group of variables summarized as ``travel environment'' includes crowding, cleanliness, the pleasantness of other passengers, and safety. Satisfaction is modeled as a latent variable, and the choice consists of participants’ stated desire and intention to continue using public transportation in the future. In addition to the delay types captured in the first analysis, a set of negative critical incidents is included, namely being left behind at stops and arriving late to work, school or a leisure activity. The results of the model and descriptive analysis show that operational problems resulting in delays and crowding are much stronger drivers of overall dissatisfaction and cessation than variables related to the travel environment. The importance of baseline satisfaction, mood and the relatively larger impact of in-vehicle delays are confirmed by this model. Thanks to the framework, the critical incidents can be expressed in terms of equivalent delay minutes. For instance, being left behind at a bus stop is found to cause the same amount of dissatisfaction as approximately 18 minutes of wait time. Furthermore, the effect of delays or incidents on ridership can be quantified, as is demonstrated in a set of simulations using the San Francisco transit network (Muni) as a basis. It is shown that if all passengers were subjected to one hypothetical on-board delay of 10 minutes per person, the resulting loss of riders would account for approximately 9.5% of Muni's yearly ridership turnover.

In summary, the contributions and impact of this dissertation are as follows: It presents a framework and system that allows the researcher to gather detailed information on an individual and on the decision environment through phone-based survey apps in combination with sensor data from the phone and from external sources. In the public transit context, an innovative system is presented to match AVL data with smartphone location data in order to measure the personal experiences of travelers with respect to travel times. With repeated measurements, these data can be used to calculate personalized reliability metrics for individual travelers, reflecting the sum of their travel experiences, or they can be used to derive aggregate travel time distributions across all travelers by time of day, origin-destination pair or location on the network. These metrics can capture the true door-to-door travel times experienced by travelers and can serve as the basis for user-centric performance metrics to supplement system-level performance metrics commonly used by transit agencies. The long-term nature of this data collection and low respondent burden facilitate the observation of behavioral dynamics such as habit formation or lifestyle adjustments in response to changes of the choice set. The low cost and scalability of these data collection methods permits relatively short lead times for studies and frequent data collection. Thus, these systems can be deployed at the early stages of a new product's or technology's emergence (e.g., new ride sharing services or traveler information systems) to gain insights into how they affect consumer choices in a real-world setting, both on short-term and longer-term time scales. Moreover, personalized interactions between the researcher and the participant via the smartphone facilitate behavioral interventions and allow for targeted incentives to change behavior.

By elaborating on the researchers’ experiences in designing and implementing the SFTQS, this dissertation provides a powerful example of how these new types of data sets can be harnessed and supports the development of future studies. The application of the methodology and framework to the transit context were motivated by a desire to learn more about the drivers of satisfaction among transit riders and the causes of transit use cessation. The model estimation results underscore the importance of investments into run time stability measures such as transit signal priority systems and dedicated rights of way. Furthermore, thanks to the modeling framework, the cost of holding vehicles with passengers on board and the cost of vehicles not stopping at stops due to overcrowding can be quantified; this can directly impact operating and control policies. On the other hand, the model results provide evidence that investments into stop amenities may be becoming less important as passengers spend less time at stops (except in the case of transfers), but that conversely, the benefits of transit-oriented development may be increasing as passengers can choose to spend their wait times elsewhere thanks to real-time information. The data and modeling framework allow transit agencies to create service quality metrics that can appropriately capture individual users' experiences in real-time, and improve researchers' ability to compare the level of service on public transportation with the level of service of private modes of transportation. Together, this set of methods and results is an important step toward aligning the service provided by transit agencies more closely with the needs of customers.

The framework and methodologies described in this dissertation are useful beyond the specific transit application presented as a case study. Thanks to the flexibility and scalability of the smartphone-based data collection and automated post-processing, they can be used by researchers to quickly gather insights on emerging trends and on travelers' adaptation to new services and new technologies, and to study the dynamics of behavior change over longer time periods. In particular, they can be applied to understand how flexible, shared-ride transportation systems and future automated mobility on demand systems will shape travel demand and how users will interact with those systems. This, in turn, will be a critical input to policy-making and system design.

The exposure to the toxic products of the incomplete combustion of wood, charcoal, crop residues and dung used as cooking and heating fuels kills 1.6 million people every year. This leading environmental health risk also accounts for about one-third of the global human-caused black carbon emissions. Stove technologies that vent smoke to the outside of a house and have verified improved combustion efficiencies have been identified as a solution to the household air pollution problem. However, a persistent barrier to success has been the lack of systematic and proven ways to ensure that households continue using the stoves in the long-term and reduce their open fire practices.

Over thirty-five years of experience with cookstove dissemination have demonstrated that providing access to the stoves is necessary but not sufficient: people need to accept bringing the stoves into the home (initial acceptance), use them on a the long-term basis (sustained use), incorporate them into their cooking practices, reduce their open-fire use, and importantly, the devices must maintain their performance through time. These seemingly obvious requirements imply that objective metrics need to be developed for these system parameters and ways found to optimize them. The optimization requires better understanding of the cooking technology-behavior interface and an integrative systems perspective to analyze the socio-cultural, economic, ecological and infrastructural factors that regulate the performance of cookstove programs.

This dissertation presents a framework of analysis to characterize stove adoption and it introduces the field methods, signal analysis, metrics and visualization tools to quantify the adoption process using low-cost temperature dataloggers as Stove Use Monitors (SUMs). The SUMs enable the parameterization of stove usage behavior as a critical stove performance parameter that can be objectively measured, unobtrusively monitored and systematically evaluated together with the reductions in air pollution exposures, fuel consumption and greenhouse gas emissions. Data from two biomass chimney-stove case studies are presented: the Guatemala CRECER-RESPIRE stove trial and the Mexico Patsari Stove Project.

In the first two chapters I review the main approaches to study the stove adoption process. I propose a new framework that redefines the emphasis previously placed solely on the stove technology, extending it to include the whole cooking system, thereby including the dynamic complex interactions between stoves, fuels and household behavior with the greater socioeconomic and ecological contexts. I argue that at the household level the innovation being introduced is not only the stove technology, but a set of modified or new cooking practices. When a new stove is brought into the home, the interactions between the user and the previous cooking fuels and devices are redefined. Each fuel-device combination creates its own niche and is used for the cooking tasks where it best fits the needs of the user ("the adoption niche"). This adoption process commonly leads to the combined use (or "stacking") of new and traditional stoves and fuels and to the redistribution of the cooking tasks that are performed with each device. Drawing from field data and well-documented experiences at the two study sites I find that at the population level the adoption process has three stages: initial adoption, sustained use, and disadoption. Further, I find that the process can be characterized by the following critical parameters: level of acceptance, level of initial use, level of sustained use, time for stabilization, and magnitude of the seasonal fluctuations.

In the third chapter I introduce the concept of Stove Use Monitors (SUMs), devices that objectively quantify stove use through direct measurements of physical or chemical parameters on stoves, cookware or food. Using Thermochron iButton temperature dataloggers as SUMs I recorded the initial adoption and sustained use by 82 households in the Guatemala CRECER stove trial. During the 32 months of the study, I recorded stove temperature signals from a total of 31,112 stove-days, with a 10% data loss rate. I present the protocols for sensor placement, data collection and data management specific to these sensors. I implemented a peak detection algorithm based on the instantaneous derivatives and the statistical long-term behavior of the stove and the ambient temperature signals to count the number of daily meals and determine daily usage. The fraction of days in use from all stoves and days monitored in a period (the "percent stove-days") and other key quantitative metrics are defined and their population-averaged behavior and statistical variability are modeled. Using robust Poisson regression models I detected small (3-12%) but statistically significant seasonal variations in the population level of use, while the age of the stove and household size at baseline did not produce statistically significant variations. Usage was highest during the warm-dry period from February to April, with 92% stove-days (95% CI: 87%, 97%) and 2.56 daily meals (95% CI: 2.40, 2.74); and it was lower during the warm-rainy season of May to November and the cold-dry season of November to February. The high levels of sustained use found reflect optimal conditions for stove adoption in the CRECER trial. The narrow confidence intervals highlight the precision and accuracy of the SUMs to detect the small seasonal fluctuations. Modeling the variability with a random effects model I find an intraclass correlation coefficient of 76%, confirming the stability of daily use behavior within households, whereas the main sources of variance are found between homes.

In the fourth chapter I study the critical role of behavior in the adoption process and in the delivery of benefits from cookstoves. I focus on the patterns of stove stacking found in the RESPIRE and CRECER studies. Using graphic representations for hierarchical clustering analysis ("heat maps"), I identify groups of households that had similar combined stove-fire use behavioral patterns ("stove stacking clusters"). Despite the high levels of stove use measured with the SUMs, fifty-percent of the households reported continued use of an open fire. The preparation of animal feed was the task most commonly performed with the fire (46% of the households) followed by the boiling of corn kernels to prepare tortillas (nixtamal, 25%), space heating (13%), boiling water (11%) and preparing food for the family (11%). The stratification of the SUMs-measured stove use was strongly driven by the preferences of the households for using the stove or the fire for specific tasks. Clusters that performed a larger number of tasks with the fire had lower stove usage. This explains why the between-household differences in the SUMs-measured stove use in the regression models could not be accounted for by the fixed characteristics of the households.

Chapter five presents an integrated discussion and the concluding remarks of the dissertation. I conclude that when the energy end-uses of the open fires extend beyond cooking, cookstoves become imperfect substitutes for all fire tasks. Therefore, in addition to monitoring cookstove use it is crucial to include the residual use of traditional fires in the assessment of the impacts brought by the sustained use of new a cookstove. I describe current research efforts in the development of monitoring tools for stove adoption and discuss the need for open source platforms to scale up stove use monitoring and to merge fuel consumption, air pollution and emissions data with stove use parameters. The Stove Use Monitors can herald a new era of research to elucidate the behavioral determinants of stove usage and cookfire prevalence, which had not been possible previously at larger scales due to a lack of objective measurements. It is equally critical to develop analytical frameworks and quantitative monitoring tools to identify the distinct behaviors affecting each of the stages of the stove adoption process and to understand the role of other behaviors that influence the exposure to household air pollutants, such as the time-activity and time-location patterns of the household members, kitchen ventilation, fuel preparation, stove operation and stove maintenance practices.