Developing Demand Model for Commuter Rail while Analyzing Underlying Attitudes of the System

*PhD Defense*
Time
08/12/2021 15:00–17:00
Location
Zoom meeting - https://uci.zoom.us/j/97944077091?pwd=NEYxNnQ4WDlxN1pMNldZalRMZ2JJQT09
De'Von Jennings
De'Von Jennings
TSE PhD Candidate
Abstract

There have been laws passed in California (SB32) that would require the State to cut its Greenhouse Gas Emissions (GHG) to 40% of 1990 levels by 2030 in order to combat climate change. With cars contributing to 41% of GHG emissions in California it is clear that to reach that goal there will need to be a significant reduction in Vehicle Miles Travelled (VMT). A way to quickly reduce VMT is to invest in existing rail systems specifically commuter rails. An investigation was conducted to model the potential effects of improving commuter rail services on
a state vs. national level, station-by-station level, and a regional level. To conduct the research data was gathered from the National Transit Database, Longitudinal Employer-Household Dynamics site, and the Environmental Protection Agencies Smart Location Database (EPA-SLD) for the year 2014.
The California Model unlinked passenger trips are more sensitive to the hours of service than the National Model. Also, the California Model is more sensitive to log peak vehicles operated which would imply that the more vehicles or frequency of the vehicles servicing people can have a large impact on passenger trips.
The Station boarding and egress models were the best when there were exogenous latent variables in the regression model. The latent variables Mixed-Use Density and Work Opportunity play a significant role in transit boardings and egress by stating that if the mixed-use density increases the employment, employment entropy, and ratio of jobs accessible in 45 minutes increases.
Model 2 is superior of the SEM models created. The ridership factors that the passenger rates to all the observed variables and the measure of their satisfaction with the variables can be a tool to use for improving service quality and for planning for future services. In the long run, this could have cost savings because if there is information about the riders’ preferences there can be improvements made specific to what is valued as important. This model can be easily modified to fit other transit services in many different regions or countries because of the framework structure which can be used for analyzing any type of service from survey responses.