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Service improvements on Capitol Corridor and impacts on ridership
Public transit has undergone turbulent changes in the US. Between 2012 and 2018, bus ridership in the US declined 15% and rail ridership declined 3%, losses which are widespread and in contrast to trends observed in other countries. Subsequently, the Covid-19 pandemic shifted millions of Americans into working from home, reducing rail ridership by 89% from February 2020 to April 2020 in response to stay-at-home suggestions. While there has been some improvement in ridership, rail passenger trips remain significantly below pre-pandemic levels. The Capital Corridor, a 166-mile passenger train route servicing Northern California, has been no exception to this trend. Rail operators all over the country need to understand how to meet evolving needs for transportation. With work from home becoming the norm, it is vital for transit agencies to understand new market segments and produce well-tailored solutions.
Our research aims to explore the demand for service improvements using survey data collected from households near Capitol Corridor stations. The research objectives of this study are:
1. To assess which service improvements rail agencies need to encourage people to switch their transportation mode choice from driving to trains. The service improvements include discounted tickets, off-peak and peak frequency, reliability, train schedule (time of departure of last train of the day) and travel time.
2. To investigate how subsidized station access modes such as bikeshare can impact people’s mode choice, and to calculate the willingness-to-pay for these partnerships.
3. To present a comparison of current commute vs non-commute travel behavior to develop post-pandemic demand elasticities by broad trip purpose.
To conduct this work, we have designed a stated preference choice experiment, which is a set of alternatives characterized by different attributes, from which a respondent must choose the best option for their travel. We are using the revealed preference of trip purpose and destination to inform the stated preference experiment. Although this research method is not new, it has traditionally been focused on work travel only due to the complexity of asking about all travel. Therefore, we have designed a complex survey that limits the burden on the respondent while allowing us to ask about non-commute travel. The results of our analysis will be disaggregated by market segments to draw broader implications for regional rail services attempting to attract new trips for non-commute purposes.
The survey collects data about respondents’ current commute and non-commute travel choices, their stated mode choice for hypothetical travel options (choice experiment), attitudes, perceptions about rail service and socio-demographic information. The data will be analyzed using an Integrated Choice and Latent Variable (ICLV) model as it offers greater explanatory power for latent variables such as perceptions and attitudes by allowing the decomposition of observable variables into constituent effects.
