| dc.description.abstract | Ridership forecasting models are valuable tools for public transit agencies to assess and
quantify the possible impacts of their operational strategies and service improvements on transit
demand and revenue. Although a rich body of literature exists on this topic, there has not been
much study on transit ridership forecasting for cities in emerging economies such as India.
Further, the literature in this area is saddled with one or more of the following methodological
and substantive issues:
(a) the difficulty of relating spatially aggregate demand data (typically available with transit
agencies) to disaggregate, transit stop-level catchment characteristics,
(b) inadequate consideration of inter-route interactions such as competition and
complementarity for transit riders,
(c) neglect of the endogeneity and nonlinearity of the influence of service frequency on
ridership,
(d) ad-hoc ways to address the overlapping of catchment areas among closely spaced stops,
(e) inadequate efforts toward quantifying the impact of service reliability on ridership and
revenue, and
(f) limited efforts on understanding the role of subjective perceptions of service quality on
transit usage (and the extent of usage).
The overarching goal of this dissertation is to advance the formulation of bus transit
ridership models to address the methodological and substantive issues discussed above. To this
end, first, the study develops a direct demand modeling framework for forecasting bus transit
ridership at a spatially disaggregate stop-route level. The proposed model is applied to a rich
empirical dataset put together from various sources, including the bus ticket sales and service
data from Bengaluru Metropolitan Transport Corporation (BMTC). Empirical findings from
the model indicate that while service frequency is one of the significant drivers of transit
ridership even after controlling for endogeneity, routes with low current frequency (1 to 3 buses
per hour) in Bengaluru benefit the most from increasing frequency than those with high current
frequency. In addition, the results suggest that ignoring endogeneity between service frequency
and ridership would result in overestimating the impact of service frequency on ridership.
Next, the study expands the above direct demand modeling framework to examine both
theoretically and empirically the relationships between transit ridership, service frequency (or
headways), and service variability (variability in headways) using a tri-variate equations
framework that considers endogeneity relationships between demand and supply. In addition,
auxiliary supply models are developed to understand the determinants of service frequency and
service variability in Bengaluru. The proposed models are applied for policy analyses to
quantify the ridership impacts of service modification strategies, such as an increase in service
frequency and an improvement in service reliability, on bus transit ridership in Bengaluru.
Empirical results from these models demonstrate that headway variability negatively impacts
transit ridership and passenger-kilometres, with the adverse effect exacerbating as variability
increases. This trend is in contrast to the diminishing benefits of increasing service frequency
at high current frequency levels. Further, the empirical results and policy simulations indicate
that transit agencies can potentially gain greater ridership and revenue if they can lower
headway variability rather than by merely adding more buses to high-frequency routes.
Finally, the study develops a disaggregate, integrated choice and latent variable model of
individual-level transit usage and extent of usage to complement the direct demand modeling
frameworks discussed above. Specifically, the model considers the influence of individuals’
demographics and their subjective perceptions of transit service quality on the usage and extent
of usage of bus transit. The role of subjective perceptions is incorporated using a generalized
heterogeneous data modeling framework that treats individuals’ perceptions as latent
explanatory variables identified using Likert scale measurements of the perceptions. In doing
so, the study addresses the methodological issue of disentangling individuals’ unfamiliarity
from their informed opinions underlying the Likert scale measurements of individuals’
subjective perceptions. The proposed model is applied to examine individuals’ transit usage
and the extent of usage using empirical data from Bengaluru, India. The empirical results shed
light on the role of individuals’ demographic characteristics and perceptions of transit service
quality on their usage and extent of usage of bus transit in Bengaluru. Additionally, comparing
the empirical model with another model that did not consider individuals’ unfamiliarity with
transit service highlighted the importance of separating the influence of unfamiliarity from that
of informed perceptions of service quality on transit usage.
To the best of our knowledge, this dissertation is the first attempt to (a) develop a
comprehensive demand modeling framework for analyzing bus transit ridership in an Indian
city using empirical data from a large-scale transit system, (b) address several methodological
and substantive issues identified in existing bus transit ridership studies, (c) examine both
theoretically and empirically the endogeneity and non-linear effects of two important transit
service attributes – service frequency and variability in headways – on demand for public
transit, (d) shed light on individuals’ use and extent of use of bus transit in an Indian city while
also focusing on the role of their subjective perceptions, and (e) address the issue of separating
unfamiliarity from informed opinions when analyzing the role of subjective perceptions on
individuals’ transit usage.
Although the application of the models in this dissertation is for Bengaluru, the proposed
methodological frameworks and empirical strategies are potentially applicable for studying bus
transit demand in other cities in India and other countries. Specifically, the models can be used
as descriptive tools to understand the factors that influence bus transit ridership, and as
predictive tools to forecast bus ridership in response to a variety of what-if scenarios of interest
to transit agencies – as long as empirical parameters are estimated using relevant data from the
city of interest. In this context, it will be useful to apply the models to other cities to examine
their applicability and the generalizability of substantive findings. | en_US |
