Understanding factors influencing public transport passengers\u2019 pre-travel information-seeking behaviour

Godwin Yeboah,Milan Markovic,Caitlin D Cottrill,David Corsar,Peter Edwards,John D Nelson

doi:10.1007/s12469-019-00198-w

Godwin Yeboah, Milan Markovic + Show 4 more

Open Access

https://doi.org/10.1007/s12469-019-00198-w

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Abstract

This paper investigates factors influencing public transport passengers’ pre-travel information-seeking behaviours in a British urban environment. Public transport traveller surveys were conducted to better understand the journey stages at which information was sought and the information sources used. A multivariate explanatory model of pre-travel information-seeking behaviour was developed using binomial logistic regression. Explanatory factors considered include socio-demographics, trip context, frequency of public transport use, information sources used, and smartphone ownership and use. Findings suggest that travel behaviour (5 + trips weekly, and < 1 trip weekly), socio-demographics (unemployment/unknown employment), trip context (journey planning stages, mode of transport), and preferred information sources (Internet site, word-of-mouth, visits to travel shop/centre/library) were significant predictors of pre-travel information-seeking behaviours among surveyed travellers. While the final model found that bus users are significantly associated with the use of Internet sites as a source of pre-travel information, rail users rely significantly on a multiplicity of sources comprising Internet sites, word-of-mouth, and visits to a travel shop/centre/library. The final model suggests that metro (light rail) users tend not to seek pre-travel information. The odds of seeking pre-travel public transport information are 2.512 times greater for respondents who reported < 1 trip per week as opposed to those who reported 5 + trips per week. These findings are relevant for passenger information strategies deployed by operators and authorities and can be used to caution against a “one size fits all” strategy for travel information service provision. Implications and suggestions for future research are discussed.

Highlights

Despite on-going attention to the ways in which information is made available to public transport passengers, in many places the use of such information by travellers can be relatively low, suggesting that merely making information available does not necessarily lead to its use (DfT 2007; Burkhard et al 2013)
The analysis addresses the following questions: where do public transport passengers seek information? At what stage of the journey do they seek information? Are there particular user characteristics that contribute to these behaviours and, if so, how may these impact upon decision-making by public transport operators? Using a multivariate binomial logistic regression model, factors related to socio-demographics, the trip context, public transport trip frequency, preferred information sources when using bus/rail/metro, smartphone ownership and use, and their relationship to pre-travel information-seeking behaviour are examined
The main purpose of this study was to examine pre-travel information-seeking behaviours of public transport passengers using data collected during an extensive public transport on-board survey in Birmingham

Summary

Introduction

Despite on-going attention to the ways in which information is made available to public transport passengers, in many places the use of such information by travellers can be relatively low, suggesting that merely making information available does not necessarily lead to its use (DfT 2007; Burkhard et al 2013). The studies by Farag and Lyons (2010, 2012) appear to be among the few that tend to use a multivariate explanatory model, rather than the use of descriptive statistics, or bivariate statistics (e.g. Kambele et al 2015), to explain pre-travel information-seeking behaviours. They examined long distance business/leisure journeys while comparing cars with public transport for unfamiliar trips and found that travel behaviour and socio-demographics were significant predictors. Unlike descriptive studies (Mulley et al 2017; Cain 2007), the use of multivariate explanatory models provide a better ( not absolute) explanation of pretravel information-seeking behaviours by combining different variables

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