Share:


Modelling travellers’ route switching behaviour in response to variable message signs using the technology acceptance model

    El Bachir Diop Affiliation
    ; Shengchuan Zhao Affiliation
    ; Shuo Song Affiliation
    ; Tran Van Duy Affiliation

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
Published in Issue
Dec 29, 2020
Abstract Views
945
PDF Downloads
598
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abou-Zeid, M.; Ben-Akiva, M. 2011. The effect of social comparisons on commute well-being, Transportation Research Part A: Policy and Practice 45(4): 345–361. https://doi.org/10.1016/j.tra.2011.01.011

Ahn, T.; Ryu, S.; Han, I. 2007. The impact of Web quality and playfulness on user acceptance of online retailing, Information & Management 44(3): 263–275. https://doi.org/10.1016/j.im.2006.12.008

Anderson, J. C.; Gerbing, D. W. 1988. Structural equation modeling in practice: a review and recommended two-step approach, Psychological Bulletin 103(3): 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Ayeh, J. K.; Au, N.; Law, R. 2013. Predicting the intention to use consumer-generated media for travel planning, Tourism Management: 132–143. https://doi.org/10.1016/j.tourman.2012.06.010

Bagozzi, R. P.; Yi, Y. 1988. On the evaluation of structural equation models, Journal of the Academy of Marketing Science 16(1): 74–94. https://doi.org/10.1007/BF02723327

Bekhor, S.; Albert, G. 2014. Accounting for sensation seeking in route choice behavior with travel time information, Transportation Research Part F: Traffic Psychology and Behaviour 22: 39–49. https://doi.org/10.1016/j.trf.2013.10.009

Ben-Akiva, M.; McFadden, D.; Gärling, T.; Gopinath, D.; Walker, J.; Bolduc, D.; Börsch-Supan, A.; Delquié, P.; Larichev, O.; Morikawa, T.; Polydoropoulou, A.; Rao, V. 1999. Extended framework for modeling choice behavior, Marketing Letters 10(3): 187–203. https://doi.org/10.1023/A:1008046730291

Bierlaire, M. 2003. BIOGEME: a free package for the estimation of discrete choice models, in STRC: 3rd Swiss Transport Research Conference, 19–21 March 2003, Ascona, Switzerland, 1–24. Available from Internet: https://transp-or.epfl.ch/documents/proceedings/Bier03.pdf

Bonsall, P. 1992. The influence of route guidance advice on route choice in urban networks, Transportation 19(1): 1–23. https://doi.org/10.1007/BF01130771

Byrne, B. M. 1998. Structural Equation Modeling with Lisrel, Prelis, and Simplis: Basic Concepts, Applications, and Programming. Lawrence Erlbaum Associates Inc. 432 p.

Cantillo, V.; Arellana, J.; Rolong, M. 2015. Modelling pedestrian crossing behaviour in urban roads: a latent variable approach, Transportation Research Part F: Traffic Psychology and Behaviour 32: 56–67. https://doi.org/10.1016/j.trf.2015.04.008

Chang, I.-C.; Li, Y.-C.; Hung, W.-F.; Hwang, H.-G. 2005. An empirical study on the impact of quality antecedents on tax payers’ acceptance of Internet tax-filing systems, Government Information Quarterly 22(3): 389–410. https://doi.org/10.1016/j.giq.2005.05.002

Chatterjee, K.; Hounsell, N. B.; Firmin, P. E.; Bonsall, P. W. 2002. Driver response to variable message sign information in London, Transportation Research Part C: Emerging Technologies 10(2): 149–169. https://doi.org/10.1016/S0968-090X(01)00008-0

Chen, C.-F.; Chen, P.-C. 2011. Applying the TAM to travelers’ usage intentions of GPS devices, Expert Systems with Applications 38(5): 6217–6221. https://doi.org/10.1016/j.eswa.2010.11.047

Chen, H.-H.; Chen, S.-C. 2009. The empirical study of automotive telematics acceptance in Taiwan: comparing three technology acceptance models, International Journal of Mobile Communications 7(1): 50–65. https://doi.org/10.1504/IJMC.2009.021672

Chorus, C. G.; Kroesen, M. 2014. On the (im-)possibility of deriving transport policy implications from hybrid choice models, Transport Policy 36: 217–222. https://doi.org/10.1016/j.tranpol.2014.09.001

Daly, A.; Hess, S.; Patruni, B.; Potoglou, D.; Rohr, C. 2012. Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour, Transportation 39(2): 267–297. https://doi.org/10.1007/s11116-011-9351-z

Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly 13(3): 319–340. https://doi.org/10.2307/249008

Davis, F. D.; Bagozzi, R. P.; Warshaw, P. R. 1989. User acceptance of computer technology: a comparison of two theoretical models, Management Science 35(8): 982–1003. https://doi.org/10.1287/mnsc.35.8.982

Davis, F. D.; Venkatesh, V. 1996. A critical assessment of potential measurement biases in the technology acceptance model: three experiments, International Journal of Human-Computer Studies 45(1): 19–45. https://doi.org/10.1006/ijhc.1996.0040

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

Dutta, A.; Fisher, D. L.; Noyce, D. A. 2004. Use of a driving simulator to evaluate and optimize factors affecting understandability of variable message signs, Transportation Research Part F: Traffic Psychology and Behaviour 7(4–5): 209–227. https://doi.org/10.1016/j.trf.2004.09.001

Feng, C. M.; Kuo, Y. W. 2007. Comparative analysis of stated enroute switching behavior under various information scenarios, in 2007 International Conference on Wireless Communications, Networking and Mobile Computing, 21–25 September 2007, Shanghai, China, 6150–6153. https://doi.org/10.1109/WICOM.2007.1508

Fishbein, M.; Ajzen, I. 1975. Belief, Attitude, Intention and Behavior: an Introduction to Theory and Research. Addison-Wesley. 578 p.

Fornell, C.; Larcker, D. F. 1981. Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research 18(1): 39–50. https://doi.org/10.2307/3151312

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

Gan, H.; Ye, X. 2012. Urban freeway user’ diversion response to variable message sign displaying the travel time of both free-way and local street, IET Intelligent Transport Systems 6(1): 78–86. https://doi.org/10.1049/iet-its.2011.0070

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.

Hu, L.-T.; Bentler, P. M. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Structural Equation Modeling: a Multidisciplinary Journal 6(1): 1–55. https://doi.org/10.1080/10705519909540118

Isa, M. H. M.; Deros, B. M.; Kassim, K. A. A. 2015. A review of empirical studies on user acceptance of driver assistance systems, Global Journal of Business and Social Science Review 4(1): 182–189.

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

Paulssen, M.; Temme, D.; Vij, A.; Walker, J. L. 2014. Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice, Transportation 41(4): 873–888. https://doi.org/10.1007/s11116-013-9504-3

Peeta, S.; Ramos, J. L. 2006. Driver response to variable message signs-based traffic information, IEE Proceedings – Intelligent Transport Systems 153(1): 2–10. https://doi.org/10.1049/ip-its:20055012

Prato, C. G.; Bekhor, S.; Pronello, C. 2012. Latent variables and route choice behavior, Transportation 39(2): 299–319. https://doi.org/10.1007/s11116-011-9344-y

Rämä, P.; Schirokoff, A.; Luoma, J. 2004. Potential harmonisation of variable message signs in Viking countries, Nordic Road and Transport Research 16(3): 4–5.

Richards, A.; Mcdonald, M.; Fisher, G.; Brackstone, M. 2004. Investigation of driver comprehension of traffic information on graphical congestion display panels using a driving simulator, European Journal of Transport and Infrastructure Research 4(4): 417–435. https://doi.org/10.18757/ejtir.2004.4.4.4276

Roberts, S. C.; Ghazizadeh, M.; Lee, J. D. 2012. Warn me now or inform me later: drivers’ acceptance of real-time and post-drive distraction mitigation systems, International Journal of Human-Computer Studies 70(12): 967–979. https://doi.org/10.1016/j.ijhcs.2012.08.002

Shah, V. P.; Wunderlich, K.; Toppen, A.; Larkin, J. 2003. Potential of advanced traveler information system to reduce travel disutility: assessment in Washington, D.C., region, Transportation Research Record: Journal of the Transportation Research Board 1826: 7–15. https://doi.org/10.3141/1826-02

Toledo, T.; Beinhaker, R. 2006. Evaluation of the potential benefits of advanced traveler information systems, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 10(4): 173–183. https://doi.org/10.1080/15472450600981033

Venkatesh, V. 2000. Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model, Information Systems Research 11(4): 342–365. https://doi.org/10.1287/isre.11.4.342.11872

Venkatesh, V.; Davis, F. D. 2000. A theoretical extension of the technology acceptance model: four longitudinal field studies, Management Science 46(2): 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venkatesh, V.; Morris, M. G.; Davis, G. B.; Davis, F. D. 2003. User acceptance of information technology: toward a unified view, MIS Quarterly 27(3): 425–478. https://doi.org/10.2307/30036540

Vredin Johansson, M.; Heldt, T.; Johansson, P. 2006. The effects of attitudes and personality traits on mode choice, Transportation Research Part A: Policy and Practice 40(6): 507–525. https://doi.org/10.1016/j.tra.2005.09.001

Wardman, M.; Bonsall, P. W.; Shires, J. D. 1997. Driver response to variable message signs: a stated preference investigation, Transportation Research Part C: Emerging Technologies 5(6): 389–405. https://doi.org/10.1016/S0968-090X(98)00004-7

Xu, C.; Wang, W.; Chen, J.; Wang, W.; Yang, C.; Li, Z. 2010. Analyzing travelers’ intention to accept travel information: structural equation modeling, Transportation Research Record: Journal of the Transportation Research Board 2156: 93–100. https://doi.org/10.3141/2156-11

Yáñez, M. F.; Raveau, S.; Ortúzar, J. de D. 2010. Inclusion of latent variables in mixed logit models: modelling and forecasting, Transportation Research Part A: Policy and Practice 44(9): 744–753. https://doi.org/10.1016/j.tra.2010.07.007

Zhong, S.; Zhou, L.; Ma, S.; Jia, N. 2012. Effects of different factors on drivers’ guidance compliance behaviors under road condition information shown on VMS, Transportation Research Part A: Policy and Practice 46(9): 1490–1505. https://doi.org/10.1016/j.tra.2012.05.022