Journal of Transportation Research

Journal of Transportation Research

Institutional and Legal Challenges of Applying Artificial Intelligence to Road Safety in Iran Content Analysis of Interviews with Experts

Document Type : Original Article

Authors
Mehran Gholami 1
Parisa Bazdar Ardebili 2
Peyman Pejmanzad 3
1 Assistant Professor, Road, Housing and Urban Development Research Center, Tehran, Iran.
2 Assistant Professor, Housing & Urban Development Research Center, Tehran, Iran.
3 Ph.D., Grad., Housing & Urban Development Research Center, Tehran, Iran.
10.22034/tri.2026.610657.3539
Abstract
The use of artificial intelligence (AI) in road safety depends not only on technical capabilities but also on data quality and accessibility, inter-agency coordination, and clear legal arrangements. This study identifies institutional and legal challenges to AI adoption in road safety in Iran and explores perceived relationships among them. Data were collected through semi-structured interviews with 20 experts selected purposively from four groups: traffic police, academia, knowledge-based companies working on intelligent transport systems, and knowledge-based companies active in the road sector. The reported thematic analysis produced 45 initial codes, 19 subthemes, and five main themes. This article examines the institutional and governance theme and the legal and regulatory theme. Participants described weak coordination, difficulties in data sharing, and unclear responsibilities as interconnected barriers. A review of selected Iranian legal instruments also indicates that some duties concerning the registration of crash data and access to related databases are already provided for in existing laws and regulations. The issue should therefore not be framed simply as an absence of law; rather, the adequacy, coherence, implementation, and applicability of existing rules to AI-enabled road-safety systems require assessment. The article presents an interpretive framework and policy recommendations concerning legal review, data governance, and inter-agency coordination. The proposed relationships are interpretive propositions grounded in interview accounts, not empirically tested causal relationships.
Keywords
Artificial Intelligence
Road Safety
Data Governance
Legal Barriers
Thematic Analysis
Subjects
-قانون مسئولیت مدنی(1۳۳۹) مصوب ۷ اردیبهشت ۱۳۳۹، مواد ۱ و ۲.
-قانون مجازات اسلامی(۱۳۹۲) مصوب مجلس شورای اسلامی، مواد ۱۴۰ تا ۱۴۵.
-قانون تجارت الکترونیکی(۱۳۸۲) مصوب مجلس شورای اسلامی، مواد ۵۸ تا ۶۱.
-قانون رسیدگی به تخلفات رانندگی (1۳۸۹) مصوب مجلس شورای اسلامی، ماده ۲۷.
-قانون بیمه اجباری خسارات واردشده به شخص ثالث در اثر حوادث ناشی از وسایل نقلیه (۱۳۹۵) مصوب مجلس شورای اسلامی، مواد ۴۱ و ۵۴.
-آیین‌نامه مدیریت ایمنی حمل‌ونقل و سوانح رانندگی(۱۴۰۰) مصوب هیئت وزیران، ۲۹ دی ۱۴۰۰.
-دستورالعمل اجرایی بهبود حفاظت از حریم خصوصی کاربران و شیوه جمع‌آوری، پردازش و نگهداری اطلاعات کاربران در سامانه‌ها و سکوهای فضای مجازی(۱۴۰۲) مصوب کمیسیون عالی تنظیم مقررات فضای مجازی کشور، ۱۱ دی ۱۴۰۲. 
-قانون جرایم رایانه‌ای (۱۳۸۸) مصوب مجلس شورای اسلامی.
 
-Abraham, R., Schneider, J., & vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, 424–438.
doi.org/10.1016/j.ijinfomgt.2019.07.008
-Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
 doi.org/10.1191/1478088706qp063oa
-Cinar, E., Trott, P., & Simms, C. (2019).A systematic review of barriers to public sector innovation process. Public Management Review, 21(2), 264–290.
 doi.org/10.1080/14719037.2018.1473477
-Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice:
A consolidated framework for advancing implementation science. Implementation Science, 4(1), 50.
 doi.org/10.1186/1748-5908-4-50
-D’Este, P., Iammarino, S., Savona, M., & von Tunzelmann, N. (2012). What hampers innovation? Revealed barriers versus deterring barriers. Research Policy, 41(2), 482–488. doi.org/10.1016/j.respol.2011.09.008
-DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. doi.org/10.2307/2095101
-European Parliament and Council of the European Union. (2016). Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union.
-European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union.
-Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Applied thematic analysis. SAGE Publications.
Hadjimanolis, A. (2003). The barriers approach to innovation. In L. V. Shavinina (Ed.),The International Handbook on Innovation, Elsevier.559–573.
-Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148–152.
doi.org/10.1145/1629175.1629210
-PIARC (World Road Association). (2024). Artificial Intelligence in the Road Sector:A PIARC Special Project (2024SP02).
 -Raj, A., Dwivedi, G., Sharma, A., Lopes de Sousa Jabbour, A. B., & Rajak, S. (2020). Barriers to the adoption of Industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, 107546.
doi.org/10.1016/j.ijpe.2019.107546
-Sandberg, B., & Aarikka-Stenroos, L. (2014). What makes it so difficult? A systematic review on barriers to radical innovation. Industrial Marketing Management, 43(8), 1293–1305. doi.org/10.1016/j.indmarman.2014.08.003
-Scott, W. R. (1995). Institutions and organizations. SAGE Publications.
Journal of Transportation Research
Volume 23, Issue 4
Autumn 2026
Pages 571-582