Indian cities have been witnessing rapid transformation due to the synergistic effect of industrialisation, flourishing-economy, motorisation, population explosion, and migration. The alarming increase in travel demand as an after effect of the transformation, and the scarcity in transport infrastructures have exacerbated urban transport issues such as congestion, pollution, and inequity. Due to the escalating cost of transport infrastructure and the scarcity of resources such as space, there has been an increasing interest in promoting sustainable transportation policy measures for the optimum use of existing resources. Such policy measures mostly target the activitytravel behaviour of individuals to bring about desired changes in the transport sector. However, the responses of individuals to most of the measures are complex or unknown. The current ‘commute trip-based’ aggregate travel demand analysis strategy followed in most of the Indian cities is inadequate for providing basic inputs to understand the activity-travel behaviour of individuals under such policy interventions. Furthermore, the current analysis strategy also ignores the activitytravel behaviour of non-workers – who include homemakers, unemployed, and retired individuals – whose inclusion to transportation planning is relevant when the proposed policies are mostly ‘citizen-centric’. Analysis of activity-travel behaviour of non-workers provide important inputs to transportation planning as their activity-travel behaviour, and responses to transportation policies are different from that of workers. However, case studies exploring the activity-travel behaviour of non-workers from Indian cities are very limited. Appraising the practical importance of this subject, the current research undertakes a comprehensive analysis of the activity-travel behaviour of non-workers from a developing country’s context. To fulfil the goal, a series of empirical analysis are conducted on a primary activity-travel weekday survey data collected from Bangalore city. The analysis provides insightful findings and interpretations consistent with a developing country’s perspective. The day-planner format of time use diary, which was observed to have satisfactory performances in developed countries, is apparently have inferior performances in a developing country’s context. Further, the face-to-face method of survey administration is observed to have higher operating and economic efficiencies compared to the drop-off and pick-up method. The comprehensive analysis of activity-travel behaviour of non-workers indicate that comparing with their counterparts in the developed world (e.g. the U.S.), non-workers in Bangalore city are observed to have lower activity participation level (in terms of time allocation and number of stops), higher dependency on walking, lower trip chaining tendency, and a distinct time-of-day preference for departing to activity locations. On the other hand, the analysis shows similarities (mode use and trip chaining) and differences (time allocation and departure time choice) with the findings of the case studies from the developing world (e.g. China). Activity-travel behaviour of non-workers belonging to low-income households is characterised by lower activity participation level, higher dependency on sustainable transport modes, and lower trip chaining propensity, compared to other two income groups (middle and high-income groups). The research also suggests that built environment measures have their highest impacts on non-workers’ travel decisions related to shopping. Finally, the joint analysis of activity participation and travel behaviour of non-workers indicate that in-home maintenance activity duration drives the time allocation and travel behaviour of non-workers, and non-workers trade in-home discretionary activity duration with travel time. The joint analysis also shows that the time spent on children’s and elders’ activity is an important time allocation of its own. Keywords: Activity-travel behaviour, Non-worker, Time Use, Income Groups, India

The health and well-being of individuals is related to their activity-travel patterns. Individuals who undertake physically active episodes such as walking and bicycling are likely to have improved health outcomes compared to individuals with sedentary auto-centric lifestyles. Activity-based travel demand models are able to predict activity-travel patterns of individuals at a high degree of fidelity, thus providing rich information for transportation and public health professionals to infer health outcomes that may be experienced by individuals in various geographic and demographic market segments. However, models of activity-travel demand do not account for the attitudinal factors and lifestyle preferences that affect activity-travel and mode use patterns. Such attitude and preference variables are virtually never collected explicitly in travel surveys, rendering it difficult to include them in model specifications. This paper applies Bhat’s (2014) Generalized Heterogeneous Data Model (GHDM) approach, whereby latent constructs representing the degree to which individuals are health conscious and inclined to pursue physical activities may be modeled as a function of observed socio-economic and demographic variables and then included as explanatory factors in models of activity-travel outcomes and walk and bicycle use. The model system is estimated on the 2005-2006 National Health and Nutrition Examination Survey (NHANES) sample, demonstrating the efficacy of the approach and the importance of including such latent constructs in model specifications that purport to forecast activity and time use patterns. / text

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