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Title: Fusion of large continuously collected data sources : understanding travel demand trends and measuring transport project impacts
Author: Erhardt, G. D.
ISNI:       0000 0004 7659 4896
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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This research combines several large, continuously collected data sets to understand recent travel demand trends in San Francisco, and it develops a tool for measuring transport project impacts. Because they are continuously collected, these data provide an opportunity to measure change in a way that is not available in traditional, cross-sectional travel surveys. The data used are from San Francisco and cover performance of the transit system and associated factors expected to drive transit demand. This study employs a two stage methodology to derive insight from these data. First, a performance monitoring tool is developed to process the raw data and report meaningful performance indicators. This tool encapsulates the necessary data cleaning functionality, and manages a multi-stage data expansion process to ensure that data are representative of the system as a whole. Second, time series models of transit ridership are estimated from the outputs of the performance monitoring tool. These time series models provide a means of quantifying the portion of the ridership changes due to service changes versus background factors, such as employment growth. The estimated models are applied to understand the drivers of recent ridership trends in the San Francisco, where ridership on the San Francisco Municipal Railway (MUNI) bus system remains flat in spite of population and employment growth, while ridership on the Bay Area Rapid Transit (BART) system grows faster than employment. In addition, the models are applied to several planning case studies, including both ex-post ridership evaluations and short-term forecasting applications. The outcome of this research is to establish and test a tool to facilitate the use of passively collected data for retrospective travel demand analyses. It provides insight into the effects of transport projects, and lays the groundwork for a future studies that further our ability to observe and understand travel behaviour.
Supervisor: Batty, M. ; Arcaute, E. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available