<div>
<p>Public
transportation is vital for many people who do not have the means to use other
forms of transportation. In small
communities, transit service is often limited, due to funding constraints of
the transit agency. In order to maximize
the use of available funding resources, agencies strive to provide effective
and efficient service that meets the needs of as many people as possible. To do this, effective service planning is
critical.</p>
<p> </p>
<p>Unlike
traditional road-based transportation projects, transit service modifications
can be implemented over the span of just a few weeks. In planning for these
short-term changes, the traditional four-step transportation planning process
is often inadequate. Yet, the
characteristics of small communities and the resources available to them limit
the applicability of existing transit demand models, which are generally
intended for larger cities.</p>
<p> </p>
<p>This
research proposes a methodology for using population and demographic data from
the Census Bureau, combined with stop-level ridership data from the transit
agency, to develop models for forecasting transit ridership generated by a
given geographic area with known population and socioeconomic
characteristics. The product of this
research is a methodology that can be applied to develop ridership models for
transit agencies in small cities. To
demonstrate the methodology, the thesis built ridership models using data from
Lafayette, Indiana.</p>
<p> </p>
<p>A total
of four (4) ridership models are developed, giving a transit agency the choice
to select a model, based on available data and desired predictive power. More complex models are expected to provide
greater predictive power, but also require more time and data to
implement. Simpler models may be
adequate where data availability is a challenge. Finally, examples are provided to aid in
applying the models to various situations.
Aggregation levels of the American Community Survey (ACS) data provided
some challenge in developing accurate models, however, the developed models are
still expected to provide useful information, particularly in situations where
local knowledge is limited, or where additional information is unavailable.</p>
</div>
<br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8011214 |
| Date | 10 June 2019 |
| Creators | Nathaniel J Shellhamer (6611465) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Direct_Demand_Estimation_for_Bus_Transit_in_Small_Cities/8011214 |
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