Response to Travel Information: A Behavioural Review

Abstract

Innovation in information and communication technologies (ICTs) is providing us with a myriad of travel information sources. Knowledge on the influence of information on human travel behaviour (mainly route and mode choice) and their implications on network levels of service remains fragmented. We distinguish between experiential, descriptive, and prescriptive information sources. We draw on recently developed theoretical concepts in behavioural and cognitive sciences to examine the state of the knowledge on information and travel behaviour. Key theoretical concepts used to explore the relationship between information and travel behaviour include: reinforced learning; framing; risk and loss aversion; probability weighting; affect; anchoring and ambiguity aversion; and regret aversion. We review studies focusing on individual travel behaviour as well as network studies involving collective behaviours. While information seems to assist individual travellers in coping with uncertainty, the impacts relating to collective behaviour on networks remain unclear. Many open questions remain, yet research provides important insights and suggests that ICTs will enable the design of persuasive information systems that motivate cooperative and efficient use of the transportation network beyond what is possible today.

Keywords:

• travel behaviour
• choice modelling
• bounded rationality
• information
• persuasion
• cooperation

Acknowledgements

The comments and suggestions of four anonymous reviewers are highly appreciated.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

The corresponding author kindly thanks the support of the Returning Scientist Fellowship (2013–2014) of the Israeli Ministry of Immigration and Absorption as Senior Research and Teaching Fellow at Tel-Aviv University.

ORCID

Eran Ben-Elia http://orcid.org/0000-0002-4169-7129

Notes

1. RFID is the acronym for radio-frequency identification device.

2. Google's recent purchase of the social navigation start-up company WAZE, for a colossal sum of more than $1.3 billion, elucidates the lucrative aspects of this new market. See Google Maps and Waze, outsmarting traffic together. Posted on 11 June 2013. Retrieved on 12 December 2014. http://googleblog.blogspot.co.il/2013/06/google-maps-and-waze-outsmarting.html.

3. Readers interested in the literature preceding this period will find an excellent review by Chorus, Molin, and Van Wee (2006).

4. Regarding information effects on generation and destination choice, the interested readers can find an elucidating account in Mokhtarian and Tal's (2013) book on ICT and travel behaviour.

5. Note that random utility models (RUM) is not derived from EUT as it usually overlooks uncertainty or risk per se. Any uncertainty included in RUM is attributed to the analyst not behaviour. Michea and Polak (2006) provide an example where EUT is integrated within an RUM specification.

6. Van de Kaa (2010a, 2010b) examines PT applications in transport studies and presents a meta-analysis of different studies.

7. Ramos, Daamen, and Hoogendoorn (2014) recently published an excellent review on the contribution of random utility, prospect theory, and regret theory for understanding travel behaviour under uncertainty.

8. Kaplan and Prato (2012) discuss the use of semi-compensatory decisions rules in route-choice choice set formation and selection but without specific reference to travel information.

9. To illustrate this peculiarity consider the choice problem ‘choose between: (A) there is an 80% chance of losing $4000’, and (B) ‘lose 3200$ with certainty (100%)’. Although the expected values are identical most people prefer the gamble or risky prospect because it provides a chance of avoiding the unpleasantness of a sure loss.

10. For a discussion of risk aversion, regret aversion and travel choice inertia, and translation into a model of ex-ante and ex-post information, see Chorus (2014a, 2014b).

11. The interested readers can find more on the relation of such theories to travel information in the editorial by Ben-Elia and Shiftan (2013).

12. The difficulties of selection of one or more reference points have been recognised in the travel behaviour literature, albeit not in studies of travel information (Avineri, 2009; Rose & Masiero, 2010; Schwanen, 2008).

13. Ben-Elia, Ishaq, and Shiftan (2013) reveal significant differences in learning effects based on group scales’ estimates and their evolution over the experiment.

14. Fogg (2002) suggests seven types of persuasive tools: tailoring, reduction, self-monitoring, tunnelling, suggestion, surveillance, and conditioning.

15. Examples of such applications include: Peacox — Schrammel, Busch, and Tscheligi (2012); Quantified traveller — Jariyasunant et al. (2014), and Matkahupi — Jylhä, Nurmi, Sirén, Hemminki, and Jacucci (2013).

16. A travel satisfaction scale has been developed by Ettema et al. (2011) but does not relate to availability of information.