Modelling travellers’ route switching behaviour in response to variable message signs using the technology acceptance model
Abstract
Recent studies adopted models of user acceptance of information technology to predict and explain drivers’ acceptance of traffic information. Among these frameworks, the most commonly used is the Technology Acceptance Model (TAM). However, TAM is too general and does not consider drivers’ response in specific traffic conditions or choice scenarios. This study combines an extended TAM with different choice scenarios displayed by Variable Message Signs (VMS) into a Hybrid Choice Model (HCM). Two models are proposed. The first model takes into account the causal relationships among latent variables based on the following hypotheses: Information Quality (IQ) has a positive effect on Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) which, in turn, have a positive effect on the Behavioural Intention (BI) to use traffic information. In the second model, the four latent variables PU, PEOU, IQ, and BI are directly added to the utility function without any causal relationships. 339 drivers with valid licence were interviewed via Stated Preference (SP) survey and the results show that TAM can explain travellers’ response to VMS if the causal relationships among latent variables are taken into account. In addition, all hypothesized relationships are strongly supported. Practical and academic implications are also discussed.
First published online 27 April 2020
Keyword : travel behaviour, route choice model, traffic information, variable message signs, hybrid choice model, technology acceptance model, attitudes, perceptions
How to Cite
Diop, E. B., Zhao, S., Song, S., & Duy, T. V. (2020). Modelling travellers’ route switching behaviour in response to variable message signs using the technology acceptance model. Transport, 35(5), 533-547. https://doi.org/10.3846/transport.2020.12498
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Dingus, T. A.; Hulse, M. C. 1993. Some human factors design issues and recommendations for automobile navigation information systems, Transportation Research Part C: Emerging Technologies 1(2): 119–131. https://doi.org/10.1016/0968-090X(93)90009-5
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Fishbein, M.; Ajzen, I. 1975. Belief, Attitude, Intention and Behavior: an Introduction to Theory and Research. Addison-Wesley. 578 p.
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Gan, H.; Ye, X. 2013. Investigation of drivers’ diversion responses to urban freeway variable message signs displaying freeway and local street travel times, Transportation Planning and Technology 36(8): 651–668. https://doi.org/10.1080/03081060.2013.851504
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Ghazizadeh, M.; Peng, Y.; Lee, J. D.; Boyle, L. N. 2012. Augmenting the technology acceptance model with trust: commercial drivers’ attitudes towards monitoring and feedback, Proceedings of the Human Factors and Ergonomics Society Annual Meeting 56: 2286–2290. https://doi.org/10.1177/1071181312561481
Hair, J. F.; Black, W. C.; Babin, B. J.; Anderson, R. E. 2009. Multivariate Data Analysis. 7th Edition. Pearson. 816 p.
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Jou, R.-C.; Lam, S.-H.; Liu, Y.-H.; Chen, K.-H. 2005. Route switching behavior on freeways with the provision of different types of real-time traffic information, Transportation Research Part A: Policy and Practice 39(5): 445–461. https://doi.org/10.1016/j.tra.2005.02.004
Kamargianni, M.; Ben-Akiva, M.; Polydoropoulou, A. 2014. Incorporating social interaction into hybrid choice models, Transportation 41(6): 1263–1285. https://doi.org/10.1007/s11116-014-9550-5
Kantowitz, B. H. 1992. Heavy vehicle driver workload assessment: lessons from aviation, Proceedings of the Human Factors and Ergonomics Society Annual Meeting 36(15): 1113–1117. https://doi.org/10.1518/107118192786749883
Kattan, L.; Nurul Habib, K. M.; Nadeem, S.; Islam, M. T. 2010. Modeling travelers’ responses to incident information provided by variable message signs in Calgary, Canada, Transportation Research Record: Journal of the Transportation Research Board 2185: 71–80. https://doi.org/10.3141/2185-10
Kim, J.; Rasouli, S.; Timmermans, H. 2014. Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: application to intended purchase of electric cars, Transportation Research Part A: Policy and Practice 69: 71–85. https://doi.org/10.1016/j.tra.2014.08.016
Kline, R. B. 2015. Principles and Practice of Structural Equation Modeling. 4th Edition. Guilford Press. 534 p.
Koutsopoulos, H.; Polydoropoulou, A.; Ben-Akiva, M. 1993. Public Acceptance and User Response to ATIS Products and Services: the Use of Travel Simulators to Investigate the Response to Traffic Information. US Deptartment of Transportation, Washington, DC, US. 90 p.
Lai, K.-H.; Wong, W.-G. 2000. SP approach toward driver comprehension of message formats on VMS, Journal of Transportation Engineering 126(3): 221–227. https://doi.org/10.1061/(ASCE)0733-947X(2000)126:3(221)
Larue, G. S.; Rakotonirainy, A.; Haworth, N. L.; Darvell, M. 2015. Assessing driver acceptance of Intelligent Transport Systems in the context of railway level crossings, Transportation Research Part F: Traffic Psychology and Behaviour 30: 1–13. https://doi.org/10.1016/j.trf.2015.02.003
Levinson, D. 2003. The value of advanced traveler information systems for route choice, Transportation Research Part C: Emerging Technologies 11(1): 75–87. https://doi.org/10.1016/S0968-090X(02)00023-2
Li, X.; Cao, Y.; Zhao, X.; Xie, D. 2015. Drivers’ diversion from expressway under real traffic condition information shown on variable message signs, KSCE Journal of Civil Engineering 19(7): 2262–2270. https://doi.org/10.1007/s12205-014-0692-y
Lin, T.-W.; Lin, C.-Y.; Hsu, W.-H. 2014. Effects of system charac-teristics on adopting web-based advanced traveller information system: evidence from Taiwan, Promet – Traffic & Transportation 26(1): 53–63. https://doi.org/10.7307/ptt.v26i1.1224
Ma, Z.; Shao, C.; Song, Y.; Chen, J. 2014. Driver response to information provided by variable message signs in Beijing, Transportation Research Part F: Traffic Psychology and Behaviour 26: 199–209. https://doi.org/10.1016/j.trf.2014.07.006
Majumder, J.; Kattan, L.; Nurul Habib, K.; Fung, T. S. 2013. Modelling traveller response to variable message sign, International Journal of Urban Sciences 17(2): 259–280. https://doi.org/10.1080/12265934.2013.776288
Mariel, P.; Meyerhoff, J. 2016. Hybrid discrete choice models: Gained insights versus increasing effort, Science of The Total Environment 568: 433–443. https://doi.org/10.1016/j.scitotenv.2016.06.019
NBSC. 2016. China Statistical Yearbook. National Bureau of Statistics of China (NBSC), China Statistics Press. Available from Internet: http://www.stats.gov.cn/tjsj/ndsj/2016/indexeh.htm
Park, E.; Kim, H.; Ohm, J. Y. 2015. Understanding driver adoption of car navigation systems using the extended technology acceptance model, Behaviour & Information Technology 34(7): 741–751. https://doi.org/10.1080/0144929X.2014.963672
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