Mobile and personalised versus traditional travel information: A case study of how London commuters acquire and use information sources

In today’s technology-driven environment, travel information has become more abundant and more complex. As a consequence personalised and simplified information is more recurrently needed by travellers (e.g. privately developed phone applications, itinerary-based journey planner, etc.) than traditional ways to look for information (e.g. network map, timetable, etc.). When using mobile devices, digital information about the entire transport system can be accessed from almost any location. And different information providers compete to develop the best technological tools and efficiently supply travel information to travellers. While travel information has been introduced in travel behaviour models for decades ( Lyons et al. 2007 ), models of how people acquire and process travel information have been recently developed ( Wang et al. 2009 ; Chorus et al. 2013 ). However, to date, most of the studies in the literature have focused on the acquisition of travel information using different sources, mainly in the tourism domain ( Ratchford et al. 2007 ; Kulkarni et al. 2012 ). The use of smartphone applications to inform travel decisions has grown spectacularly in the past decade yet the relationship between mobile and personalised information and travel behaviour remains largely unexplored. In this paper, we address this gap by analysing commuters’ preferences for use of smartphones versus fixed platforms and of services allowing various degrees of personalisation in information provision. In particular, public transport commuters regularly consult a portfolio of travel information sources and make an active decision about which source(s) they seek before choosing their travel option. A self-administered internet-based survey was used to collect data from London public transport commuters. In the survey, respondents list the combination of sources of information they regularly use for commuting (e.g. app on phone, Transport for London journey planner on phone, Google Maps on computer, etc.), as well as those that are available to them but are not used. Additionally, information regarding their commuting journey is collected. Generalised extreme value discrete choice models are used to analyse the revealed preference dataset obtained, accounting for correlations between sources with similar level of mobility or personalisation level. Travel patterns such as transport modes, total travel and waiting times, number of transfers and transport reliability, as well as demographics such as age, income, and gender, are accounted for as explanatory variables. Correlations between sources using the smartphone media are explored using a nested structure. Cross-correlations between sources from similar media and providers are explored using a cross-nested structure. Results on substitution rates between correlated alternatives are interpreted to understand the extent to which traditional media could be replaced by digital media. Finally the consumption of information – here interpreted as monthly frequency of use – for each source is estimated using a Multiple Discrete Continuous Extreme Value model ( Bhat 2005 ), in the aim of predicting the amount of information consulted for London commuters. The implications drawn from the findings are discussed, elaborating on potential applications for various competing information providers, especially for phone application developers and service-based transport authorities.  In addition, it is also important for policy makers to understand in full details how people acquire and use information and apply the results towards more efficiently designed travel information tools. Bhat, C.R. (2005). "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions." Transportation Research Part B: Methodological 39 (8): 679-707. Chorus, C.G., Walker, J.L. and Ben-Akiva, M. (2013). "A joint model of travel information acquisition and response to received messages." Transportation Research Part C: Emerging Technologies 26 (0): 61-77. Kulkarni, G., Ratchford, B.T. and Kannan, P.K. (2012). "The Impact of Online and Offline Information Sources on Automobile Choice Behavior." Journal of Interactive Marketing 26 (3): 167-175. Lyons, G., Avineri, E., Farag, S. and Herman, R. (2007). "Strategic Review of Travel Information Research." The Department for Transport, London . Ratchford, Brian T., Talukdar, D. and Lee, M.S. (2007). "The Impact of the Internet on Consumers’ Use of Information Sources for Automobiles." Journal of Consumer Research 34 (1): 111-119. Wang, X., Khattak, A. and Fan, Y. (2009). "Role of Dynamic Information in Supporting Changes in Travel Behavior." Transportation Research Record: Journal of the Transportation Research Board 2138 (-1): 85-93.