Understanding the Transit Market: A Persona-Based Approach for Preferences Quantification

Gamal Eldeeb,Moataz Mohamed

doi:10.3390/su12093863

Abstract

The study aims at utilizing a persona-based approach in understanding, and further quantifying, the preferences of the key transit market groups and estimating their willingness to pay (WTP) for service improvements. The study adopted an Error Component (EC) interaction choice model to investigate personas’ preferences in a bus service desired quality choice experiment. Seven personas were developed based on four primary characteristics: travel behaviour, employment status, geographical distribution, and Perceived Behavioural Control (PBC). The study utilized a dataset of 5238 participants elicited from the Hamilton Street Railway Public Engagement Survey, Ontario, Canada. The results show that all personas, albeit significantly different in magnitude, are negatively affected by longer journey times, higher trip fares, longer service headways, while positively affected by reducing the number of transfers per trip and real-time information provision. The WTP estimates show that, in general, potential users are more likely to have higher WTP values compared to current users except for at-stop real-time information provision. Also, there is no consensus within current users nor potential users on the WTP estimates for service improvements. Finally, shared and unique preferences for service attributes among personas were identified to help transit agencies tailor their marketing/improvement plans based on the targeted segments.

Highlights

Introduction and BackgroundLuring people out of their cars into public transit is vital for making cities liveable, sustainable, and equitable
The aim of this paper is twofold: (1) Understanding the preferences of the dominant transit market segments considering a persona-based approach, and (2) Advancing the use of the persona-based approach through quantifying personas’ preferences and estimating their willingness to pay for service improvements
Where Xijt is the observable component of the utility function, which is a vector of explanatory variables, and β is a vector of estimated fixed parameters, while ηijt is a vector of random elements with a distribution, assigned by the modeller, and Yijt is a vector of unknown attributes. εijt is the error term, which is assumed to be identically and independently distributed (IID)

Summary

Introduction

Introduction and BackgroundLuring people out of their cars into public transit is vital for making cities liveable, sustainable, and equitable. Understanding transit service desired quality for a wide spectrum of nontransit users is vital to increase transit market share and reduce car dependency. In this regard, the transit market is often classified, among other classifications, into current and potential transit users [4,5,6,7], and/or captive and choice users [8,9,10,11]. Other studies adopted a cluster analysis approach to extract homogenous customer groups with respect to preferences towards transit service quality [12,15,16] All these prior classification approaches are often utilized to provide additional layers of information to better understand the preferences of different customer groups within the transit market

Objectives

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Results

Conclusion