Causal Modeling of Factors Affecting the Performance of Autonomous Vehicles in Intelligent Transportation Systems (ITS)

Document Type : Scientific - Research

Author
Elham Asadamraji
Assistant Professor, Department of Information Technology Management, North Tehran Branch, Islamic Azad University, Tehran, Iran
10.22119/jte.2026.575462.2761
Abstract
Autonomous vehicles, as one of the key components of Intelligent Transportation Systems (ITS), play a significant role in enhancing the safety, efficiency, and sustainability of urban transportation networks. Despite considerable technological advancements in automation, sensor technologies, and communication systems, a comprehensive understanding of the causal relationships among the technical, human, infrastructural, and institutional factors influencing the performance of autonomous vehicles remains limited. The present study aims to develop and empirically test a comprehensive causal–statistical model within the ITS framework. The research data were collected from 320 experts and potential users through purposive and convenience sampling methods. Based on the 10-times rule in Partial Least Squares Structural Equation Modeling (PLS-SEM), the sample size was considered adequate for accurate parameter estimation. Data analysis was conducted using the PLS-SEM approach. The findings revealed that the level of autonomy, sensor reliability, and the quality of ITS communications have a positive and significant effect on transportation system efficiency through the mediating variables of trust, perceived safety, and technology acceptance. Furthermore, the results indicate that user trust and perceived safety play a decisive role in the acceptance and effective utilization of autonomous vehicles. Overall, the findings of this study provide a scientific and practical framework to support policymaking, infrastructure planning, and the successful development of intelligent transportation systems based on autonomous vehicles.
Keywords
Technology acceptance
Autonomous vehicles
Intelligent Transportation Systems
Causal modeling
Structural equation modeling
Subjects
- رحمانی، م.، احمدی، ح.، و کریمی، س. (1401). بررسی عوامل مؤثر بر پذیرش خودروهای خودران در سیستم‌های حمل‌ونقل هوشمند شهری. فصلنامه مهندسی حمل‌ونقل، 13(4)، 45.60.
 
- Anderson, J. M., Kalra, N., Stanley, K. D., Sorensen, P., Samaras, C., & Oluwatola, O. A. (2016). Autonomous vehicle technology: A guide for policymakers. RAND Corporation.
 
- Chen, T., Liu, Z., Wang, D., & Zhang, H. (2020). Traffic flow analysis for autonomous vehicles in intelligent transportation systems. Transportation Research Part C, 113, 21–35.
 
- Khan, M. A., Zhang, Y., & Li, H. (2025). Modeling trust and perceived risk in autonomous vehicle adoption. Expert Systems with Applications, 211, 118608.
 
- Hewitt, C., Politis, I., Amanatidis, T., & Sarkar, A. (2019). Assessing public perception of self-driving cars: The autonomous vehicle acceptance model. In Proceedings of the 24th International Conference on Intelligent User Interfaces (pp. 518–527). ACM.
https://doi.org/10.1145/3301275.3302268
 
- Katrakazas, C., Quddus, M., Chen, W. H., & Deka, L. (2015). Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions. Transportation Research Part C, 60, 416–442.
 
- Kyriakidis, M., Happee, R., & de Winter, J. C. F. (2015). Assessing public perception of self-driving cars: The autonomous vehicle acceptance model. Transportation Research Part F, 30, 70–84.
 
- Li, Z., Chai, Y., & Li, Q. (2019). Impact of V2X communication on traffic efficiency. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3928–3939.
 
- Litman, T. (2020). Autonomous vehicle implementation predictions. Victoria Transport Policy Institute.
 
- Liu, G., Sheng, J., & Tao, Z. (2025). Application and design of a decision-making model in ethical dilemma for self-driving cars. Scientific Reports, 15, Article 8187. https://doi.org/10.1038/s41598-025-91921-0
 
- Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press.
- Raats, K., Lund, J., & Brodersen, M. (2024). Trust in autonomous vehicles: Insights from a Swedish suburb. Journal of Responsible Innovation, 11(1), 2318825
 
- SAE International. (2021). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (J3016).
 
- Scientific Reports. (2025). Public trust and behavioral intention toward autonomous vehicles. Scientific Reports, 15, Article 8187. https://doi.org/10.1038/s41598-025-91921-0
 
- Shladover, S. E. (2018). Connected and automated vehicle systems: Introduction and overview. Journal of Intelligent Transportation Systems, 22(3), 190–200.
 
- Talebpour, A., & Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C, 71, 143–163.
 
- Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
 
- Zhang, Y., Jing, P., & Li, M. (2020). User acceptance of autonomous vehicles: An empirical study. Transportation Research Part F, 72, 1–15.
Quarterly Journal of Transportation Engineering
Volume 17, Issue 4 - Serial Number 69
Spring 2026
Pages 5769-5781

  • Receive Date 22 February 2026
  • Revise Date 23 May 2026
  • Accept Date 27 June 2026