Better Understanding the Potential Market of Metro Transit's Ridership and Service

Abstract

Ridership is a key element in the transit industry. Conventional travel analysis focuses on two types of transit users: captive and choice riders. Captive riders are typically those who lack an alternative to transit; they therefore use it as their primary mode of transportation to reach their destination. Choice riders are those who have realistic alternatives (e.g., driving) but choose to use transit for various trips. Service reliability and availability affects the ridership of both populations. However, substantial increases in ridership are usually assumed to be derived only from choice riders. Populations not using transit may be further considered as two distinct populations: auto captives and potential riders. Auto captives are mainly auto users who don’t have transit as a potential mode of transportation or would not even realistically consider using transit. Potential riders are currently not using transit for certain reasons and/or concerns, but may consider the idea of using transit based on certain criteria. This research analyzes results from two surveys conducted in the Twin Cities metropolitan region: one of existing riders and the other of non-riders. The aim is to understand the characteristics of both captive and choice riders, with an eye toward the factors that can increase ridership of the latter population. This research classifies riders and non-riders differently from previous research. In addition to the captivity of modes, the classification considers regularity of commuting. Accordingly, transit riders are classified as one of four categories: captive riders with regular commuting habits, captive riders with irregular commuting habits, choice riders with regular commuting habits, and choice riders with irregular commuting habits. Similarly, there are four types of non-riders: auto captives with regular commuting habits, auto captives with irregular commuting habits, potential riders with regular commuting habits, and potential riders with irregular commuting habits. Using the survey data to uncover such population, this research then comments on how using advanced forms of technology could increase the ridership from various populations.