Improving Demand Modeling in California's Rail Transit System

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Improving Demand Modeling in California Rail Transit System

Abstract: 

This paper analyzes urban rail-fare elasticity and compares the results across four California transit systems. A method of Internet search is adopted to collect monthly transit-fare records from 2002 to 2013. This paper contributes towards improving demand modeling for public transit using more precise and monthly data and applies econometric techniques involving autoregressive integrated moving average (ARIMA) and panel data models. Results show that demand for public transit in California is very inelastic. Any ridership promotion policy may have a heterogeneous impact across transit systems.

Authors: 

RUI LIU, PhD

Dr. Rui Liu is an Assistant Professor of Economics at San Jose State University. She is also a faculty member at the Center of Smart Technology, Computing, and Complex Systems at San Jose State University. She completed her PhD in Economics in 2013 at the University of California, Irvine (with a specialization in time series econometrics). She has several publications and research papers in the fields of applied econometrics, macroeconometrics, and education economics.

Published: 
May 2018
Keywords: 
Public transit
Fare elasticity
ARIMA
Panel data models

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MCTM
CSUTC
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NTFC

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