Trust, Intention, and Behavioral Adaptation to Real Time Traffic Information Systems: A Latent Variable Model of Urban Mode Choice in Toronto
DOI:
https://doi.org/10.66578/btis.v2i1.20Keywords:
Artificial intelligence, real time traffic information, mode choice, system trust, travel intention, urban mobility, PLS-SEMAbstract
The rapid diffusion of artificial intelligence (AI)-enabled real time traffic information systems is reshaping how urban travelers form perceptions, develop trust, and adapt their mode choice behavior. While existing transportation research has extensively examined the operational and network level impacts of intelligent mobility technologies, comparatively limited attention has been devoted to the latent cognitive and motivational mechanisms through which these systems influence individual travel decisions. This study develops and empirically tests an integrated behavioral framework that examines how AI information quality, system reliability, perceived safety, and accessibility perception are associated with system trust, travel intention, and self reported mode choice adaptation. Using survey data from 412 commuters in the Greater Toronto Area, Canada, the proposed model is estimated through Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping and predictive validation procedures. The results indicate that system trust serves as a central mediating construct linking AI system attributes to travel intention, which in turn demonstrates a strong and systematic association with behavioral mode adaptation. The model exhibits substantial explanatory and predictive performance, with coefficients of determination reaching up to R² = 0.64 for key endogenous constructs. From a policy perspective, the findings highlight the role of trust centered and accessibility-oriented design of digital mobility platforms as mechanisms for reinforcing public transport strategies and promoting more sustainable travel behavior in metropolitan transport systems.
References
Alam, M., Ferreira, J., & Fonseca, J. (2016). Introduction to intelligent transportation systems Intelligent transportation systems: Dependable vehicular communications for improved road safety (pp. 1-17): Springer.
Amekudzi-Kennedy, A., Singh, P., Yang, Z., & Garrett, A. (2024). A performance-based approach to developing capabilities for building resilience to climate hazards in transportation systems. Smart and Resilient Transportation(ahead-of-print).
Bilal, M., Son, S., & Jang, K. (2025). Traveler’s interactive decision-making behavior between itinerary and mode choice using Copula-based discrete-count joint modeling. Transportation, 52(4), 1249-1266.
Bouzaghrane, M. A., Obeid, H., Hayes, D., Chen, M., Li, M., Parker, M., . . . Sengupta, R. (2025). Tracking the state and behavior of people in response to COVID-19 through the fusion of multiple longitudinal data streams. Transportation, 52(3), 1059-1090.
Carter, C. R., Kaufmann, L., & Michel, A. (2007). Behavioral supply management: a taxonomy of judgment and decision‐making biases. International Journal of Physical Distribution & Logistics Management, 37(8), 631-669.
Castaño-Herrera, D., Gomez, J., Garrido, L., Tapiador, L., & Vassallo, J. M. (2025). How does e-commerce impact shopping mobility behavior? Transportation, 1-34.
Chen, F., & Costa, A. B. (2024). Exploring the causal effects of the built environment on travel behavior: a unique randomized experiment in Shanghai. Transportation, 51(1), 215-245.
Chen, Q., Wang, X., Jiang, Z. L., Wu, Y., Li, H., Cui, L., & Sun, X. (2023). Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environments. Neural Computing and Applications, 35(22), 16193-16222.
Cheng, M., & Lu, Y. (2017). Investment efficiency of urban infrastructure systems: Empirical measurement and implications for China. Habitat international, 70, 91-102.
Coit, D. W., & Zio, E. (2019). The evolution of system reliability optimization. Reliability Engineering & System Safety, 192, 106259.
De Vos, J., Cheng, L., Zhang, Y., Wang, K., Mehdizadeh, M., & Cao, M. (2025). The effect of ease of travel on travel behaviour and perceived accessibility: A focus on travel to university campus. Transportation Research Part F: Traffic Psychology and Behaviour, 109, 1170-1181.
Galkin, A. (2024). Sustainable Transportation and Mobility Handbook of Technological Sustainability (pp. 152-168): CRC Press.
Haghighat, A. K., Ravichandra-Mouli, V., Chakraborty, P., Esfandiari, Y., Arabi, S., & Sharma, A. (2020). Applications of deep learning in intelligent transportation systems. Journal of Big Data Analytics in Transportation, 2(2), 115-145.
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027.
Heiskanen, E., Apajalahti, E.-L., Matschoss, K., & Lovio, R. (2018). Incumbent energy companies navigating energy transitions: strategic action or bricolage? Environmental Innovation and Societal Transitions, 28, 57-69.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135.
Hodge, K., Subramaniam, N., & Stewart, J. (2009). Assurance of sustainability reports: Impact on report users' confidence and perceptions of information credibility. Australian accounting review, 19(3), 178-194.
Karner, A., Pereira, R. H., & Farber, S. (2024). Correction to: Advances and pitfalls in measuring transportation equityTransportation. Transportation, 51(3), 1179-1179.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1-10.
Lee, K.-S., & Eom, J. K. (2024). Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility. Transportation, 51(5), 1907-1961.
Liengaard, B. D., Becker, J.-M., Bennedsen, M., Heiler, P., Taylor, L. N., & Ringle, C. M. (2025). Dealing with regression models’ endogeneity by means of an adjusted estimator for the Gaussian copula approach. Journal of the academy of marketing science, 53(1), 279-299.
Lizardo, O. (2009). Formalism, behavioral realism and the interdisciplinary challenge in sociological theory. Journal for the Theory of Social Behaviour, 39(1), 39-79.
Manousiadou, A. (2024). Behavioral theories in inventory management and forecasting: Driving supply chain resilience. Journal of Supply Chain Management Science, 5(3-4), 143-158.
Nordin, F., & Ravald, A. (2023). The making of marketing decisions in modern marketing environments. Journal of business research, 162, 113872.
Omotoye, O. O., Olusesi, L. D., & Adigun, U. O. (2025). Smart transport technologies and Urban mobility: enhancing transportation satisfaction in African cities–The case of Lagos Transport Services Management in Africa (pp. 17-42): Routledge.
Oshiro, K., Kainuma, M., & Masui, T. (2016). Assessing decarbonization pathways and their implications for energy security policies in Japan. Climate Policy, 16(sup1), S63-S77.
Parra, J. D. (2023). Realist evaluation and its role in the stages of explanatory research based on critical realism. Journal of Critical Realism, 22(5), 859-881.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
Pourhashem, G., Georgouli, C., Malichová, E., Straka, M., & Kováčiková, T. (2024). Factors influencing the perceived value of travel time in European urban areas. Transportation, 51(4), 1525-1545.
Ranjan, R., & Sinha, S. (2025). Evaluating commuter mode choice behaviour and value of travel time for fuel-based bus alternatives in urban areas. Innovative Infrastructure Solutions, 10(9), 397.
Raue, M., Streicher, B., & Lermer, E. (2019). Perceived safety: Springer.
Robinsha, S. D., & Amutha, B. (2024). Velocious: A resilient IoT architecture for 6G based intelligent transportation system with expeditious movement mechanism. Wireless Personal Communications, 1-22.
Sarin, S., Im, S., Di Benedetto, A., Sego, T., & Chitturi, R. (2025). Contingent influences of innovation characteristics and firm capabilities on early-mover advantage: A conceptual framework for high-tech environments. AMS Review, 1-15.
Shafi, R., Delbosc, A., & Rose, G. (2023). The role of culture and evolving attitudes in travel behaviour assimilation among south asian immigrants in Melbourne, Australia. Transportation, 50(4), 1261-1287.
Sheng, M. L. (2017). A dynamic capabilities-based framework of organizational sensemaking through combinative capabilities towards exploratory and exploitative product innovation in turbulent environments. Industrial marketing management, 65, 28-38.
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European journal of marketing, 53(11), 2322-2347.
Tushman, M. L., & Anderson, P. (2018). Technological discontinuities and organizational environments Organizational innovation (pp. 345-372): Routledge.
Visan, M., Negrea, S. L., & Mone, F. (2022). Towards intelligent public transport systems in Smart Cities; Collaborative decisions to be made. Procedia Computer Science, 199, 1221-1228.
Wang, L., Chen, X., Ma, Z., Zhang, P., Mo, B., & Duan, P. (2025). Data-driven analysis and modeling of individual longitudinal behavior response to fare incentives in public transport. Transportation, 52(1), 263-286.
Zhao, C. (2024). The power of technology innovation: can smart transportation technology innovation accelerate green transportation efficiency? Smart and Resilient Transportation, 6(2), 94-114.
Downloads
Published
Versions
- 2026-04-12 (3)
- 2026-04-07 (2)
- 2026-03-31 (1)
Issue
Section
License
Copyright (c) 2026 Meisam Karami
This work is licensed under a Creative Commons Attribution 4.0 International License.
Licensing
Articles published in the Business, Technology & Innovation Studies Journal (BTISJ)
are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0),
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.
