ABSTRACT
Intelligent Transportation Systems (ITS) represent one of the most significant innovations in urban mobility, leveraging advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) to enhance traffic management, alleviate congestion, improve road safety, and promote urban sustainability. Nevertheless, conventional transportation systems continue to dominate in many Iranian cities, including Ahvaz, resulting in persistent challenges such as traffic congestion, environmental pollution, and operational inefficiency. This study aims to identify and evaluate the key factors influencing the implementation of Intelligent Transportation Systems in Ahvaz. This study employed a descriptive–analytical methodology, utilizing a structured questionnaire as the primary data collection instrument. The statistical population comprised Ahvaz residents and urban transport stakeholders. Based on Sample Power software, a sample of 300 respondents was selected using random sampling. The validity of the questionnaire was confirmed through expert evaluation, while its reliability was verified utilizing Cronbach's alpha coefficient. Data were analyzed using SPSS and AMOS software through Structural Equation Modeling (SEM). The findings revealed that socio-behavioral, managerial-institutional, policy-regulatory, and technological dimensions exerted significant effects on ITS implementation, with the proposed model explaining 68% of the variance in the dependent variable. Among these dimensions, socio-behavioral (β = 0.86) and managerial-institutional (β = 0.85) factors demonstrated the strongest effects, whereas the technological factor (β = 0.38) exhibited the weakest influence. The results indicate that the successful implementation of ITS in Ahvaz depends not only on the development of technical infrastructure but also on strengthening public trust, enhancing institutional coordination, ensuring sustainable financial resources, and establishing supportive policy and regulatory frameworks.
Extended Abstract
Introduction
Intelligent Transportation Systems (ITS) have emerged as one of the fundamental pillars of smart cities by integrating advanced technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), big data analytics, cloud computing, Geographic Information Systems (GIS), and advanced communication networks to enable real-time, intelligent, and integrated transportation management. The implementation of these systems has significantly enhanced transportation network efficiency, reduced travel time, improved traffic safety, lowered energy consumption and environmental emissions, and improved the quality of public transportation services. Consequently, ITS has become one of the most effective strategies for achieving sustainable urban development. Simultaneously, rapid urbanization, increasing private vehicle ownership, growing urban travel demand, and limited transportation infrastructure capacity have rendered conventional traffic management approaches increasingly ineffective, making the adoption of intelligent transportation technologies an urgent necessity.
Although technological advances have created unprecedented opportunities for implementing ITS, international experiences indicate that successful deployment depends on much more than technological infrastructure. Recent studies emphasize that factors such as social acceptance, institutional coordination, managerial capability, regulatory frameworks, sustainable financial resources, and governance capacity are equally critical determinants of successful ITS implementation. Consequently, the focus of contemporary research has shifted from purely technological perspectives toward comprehensive frameworks that simultaneously consider technological, social, institutional, managerial, and policy dimensions.
In Iran, despite the urgent need to modernize urban transportation systems to address severe traffic congestion, air pollution, and excessive energy consumption, transportation management in many cities continues to rely predominantly on traditional approaches. Ahvaz, one of the largest metropolitan areas in southwestern Iran, faces particularly acute transportation challenges due to rapid population growth, increasing private vehicle ownership, inadequate roadway capacity, severe air pollution, and unique climatic conditions. Despite several initiatives aimed at developing intelligent transportation infrastructure, the full implementation of ITS programs in Ahvaz remains constrained by numerous technical and non-technical barriers.
A review of the existing literature reveals that most previous studies have primarily concentrated on technological and infrastructural aspects of ITS, while relatively few have simultaneously examined the combined effects of technological, socio-behavioral, managerial-institutional, and policy-regulatory factors within a comprehensive analytical framework, particularly in the context of Ahvaz. Therefore, this study aims to identify, analyze, and prioritize the factors influencing the implementation of Intelligent Transportation Systems in Ahvaz. The primary contribution of this research is the development of an integrated Structural Equation Modeling (SEM)-based conceptual framework that simultaneously evaluates the effects of four major dimensions on ITS implementation, thereby providing an evidence-based decision-support framework for urban planners and policymakers responsible for smart transportation development.

Methodology
This research is an applied study employing a descriptive–analytical approach to identify and analyze the determinants of Intelligent Transportation System implementation in the metropolitan city of Ahvaz. The theoretical framework was established on the premise that successful ITS deployment depends not only on technological infrastructure but also on socio-behavioral, managerial-institutional, and policy-regulatory factors. Accordingly, a four-dimensional conceptual model was developed in which technological, socio-behavioral, managerial-institutional, and policy-regulatory dimensions served as independent variables, while successful ITS implementation constituted the dependent variable.
Data collection was conducted in two stages. Primarily, an extensive review of academic literature, scientific publications, technical reports, and policy papers was undertaken to identify the principal indicators and variables affecting ITS implementation and to construct the theoretical framework. Subsequently, empirical data were collected via a researcher-developed questionnaire consisting of 23 items distributed across the four principal constructs. Responses were measured using a five-point Likert scale ranging from "Strongly Disagree" to "Strongly Agree," allowing respondents' perceptions to be quantitatively assessed regarding ITS implementation factors.
The study population comprised Ahvaz residents, transportation managers, transportation experts, urban planning specialists, and other stakeholders involved in urban transportation management. Purposive and convenience sampling techniques were employed given the absence of a comprehensive sampling frame. A pilot study involving 30 questionnaires was initially conducted to assess item clarity and preliminary reliability. Sample size was subsequently determined using SamplePower software, considering a 95% confidence level and the statistical requirements of Structural Equation Modeling. A final sample of 300 respondents was determined to be adequate for estimating structural relationships and testing the proposed hypotheses.
Content and face validity were evaluated by ten university faculty members and transportation planning experts, whose recommendations were incorporated into the final questionnaire. Instrument reliability was assessed using Cronbach's alpha coefficient, which yielded a value of 0.87, indicating satisfactory internal consistency and high measurement reliability.
Data analysis was performed employing both descriptive and inferential statistical techniques. Preliminary analyses evaluated respondent characteristics and examined assumptions required for multivariate analysis, including normality, sampling adequacy, and correlation structure. The results of skewness and kurtosis statistics, the Kaiser-Meyer-Olkin (KMO) measure, and Bartlett's Test of Sphericity confirmed that the dataset was suitable for factor analysis and SEM.
Construct validity was evaluated through Confirmatory Factor Analysis (CFA). Following confirming the measurement model's validity, the structural model was estimated using AMOS software. Model fit was assessed using several goodness-of-fit indices, including CMIN/DF, GFI, AGFI, NFI, CFI, PNFI, PCFI, and RMSEA, all of which demonstrated acceptable model fit. Finally, Structural Equation Modeling was employed to estimate path coefficients, evaluate the influence of each construct on ITS implementation, and determine the explanatory power of the conceptual model.

Results and discussion
Structural Equation Modeling was employed to evaluate the proposed conceptual model and examine the determinants of Intelligent Transportation System implementation in Ahvaz. Confirmatory Factor Analysis demonstrated that all observed indicators exhibited statistically significant factor loadings, while goodness-of-fit indices—including CMIN/DF, GFI, AGFI, NFI, CFI, and RMSEA—fell within recommended thresholds, confirming the validity and reliability of the measurement model.
The structural model revealed that all four dimensions—socio-behavioral, managerial-institutional, policy-regulatory, and technological—exerted statistically significant positive effects on ITS implementation. The coefficient of determination (R² = 0.68) indicated that the proposed model accounts for 68% of the variance in successful ITS implementation, demonstrating substantial explanatory power. These findings confirm that successful ITS deployment depends on the interaction of technological, managerial, institutional, social, and regulatory factors rather than technological infrastructure alone.
Among the examined dimensions, the socio-behavioral construct exhibited the strongest influence (β = 0.86). Public trust in intelligent technologies, acceptance of smart services, public awareness, willingness to modify travel behavior, and satisfaction with user experience emerged as the most influential determinants of efficacious ITS implementation. User satisfaction represented the strongest indicator within this construct, highlighting the importance of public engagement, digital literacy, awareness campaigns, and citizen participation alongside technological investment.
The managerial-institutional dimension ranked second (β = 0.85). Sustainable financial resources, effective management structures, inter-agency coordination, strategic planning, professional capacity building, and monitoring mechanisms were identified as the principal components of this construct. The findings indicated that even advanced technological infrastructure cannot ensure successful ITS implementation without integrated governance and institutional coordination. Consequently, strengthening collaboration among municipalities, traffic police, transportation organizations, and other governmental agencies is essential for sustainable ITS development.
The policy-regulatory dimension also demonstrated a strong positive effect (β = 0.83). Transparent legislation, comprehensive legal frameworks, supportive governmental policies, data governance regulations, cybersecurity provisions, and privacy protection were identified as critical prerequisites for successful implementation. As smart transportation systems increasingly rely on large-scale data generation and exchange, robust legal and regulatory frameworks become indispensable for improving public trust and reducing implementation risks.
Although the technological dimension exhibited a statistically significant positive effect, it recorded the smallest standardized coefficient (β = 0.38). This finding does not diminish the importance of technological infrastructure; rather, it indicates that current ITS development in Ahvaz is constrained more by managerial, institutional, and social barriers than by technological limitations. Investments in communication infrastructure, intelligent traffic management systems, sensors, and data-sharing platforms remain necessary; however, their effectiveness depends largely on complementary institutional, managerial, and social capacities.
Comparison with previous national and international studies demonstrated broad consistency regarding the importance of public trust, technology acceptance, governance capacity, and institutional coordination. Nevertheless, one of the principal contributions of this research is the identification of socio-behavioral and managerial factors as more influential than purely technological considerations, reflecting the realities of many developing cities where governance weaknesses, limited institutional capacity, and insufficient public trust represent more significant barriers than technological deficiencies.
Overall, the findings suggest that sustainable ITS development in Ahvaz requires an integrated strategy combining technological advancement with institutional reform, governance improvement, legal development, sustainable financing, and enhanced public participation.

Conclusion
The results demonstrate that successful Intelligent Transportation System implementation in metropolitan Ahvaz is a multidimensional process requiring the simultaneous interaction of technological, socio-behavioral, managerial-institutional, and policy-regulatory factors. Structural Equation Modeling revealed that the proposed framework accounted for 68% of the variance in ITS implementation, confirming its strong explanatory capability. Consequently, smart transportation development extends far beyond technological infrastructure and requires comprehensive institutional, managerial, social, and legal support.
Among the investigated dimensions, socio-behavioral factors exerted the strongest influence (β = 0.86), emphasizing the importance of citizens' trust, technology acceptance, public awareness, and user satisfaction. Managerial-institutional (β = 0.85) and policy-regulatory (β = 0.83) dimensions ranked second and third, highlighting the critical roles of integrated governance, organizational coordination, sustainable financing, transparent legislation, and supportive regulatory frameworks. Although the technological dimension demonstrated a significant positive influence, its comparatively minor effect (β = 0.38) indicates that managerial and social constraints currently outweigh technological restrictions in Ahvaz.
From a theoretical perspective, this study contributes to the ITS literature by proposing an integrated framework that simultaneously incorporates technological, governance, policy, and socio-behavioral dimensions. Practically, the findings suggest that successful ITS implementation requires comprehensive policies integrating digital infrastructure development, institutional capacity building, public awareness enhancement, regulatory reform, sustainable financial mechanisms, and effective inter-organizational coordination. Such an integrated approach can significantly improve the effectiveness of intelligent transportation investments and facilitate sustainable urban mobility and smarter transportation governance in Ahvaz.

Funding
There is no funding support.

Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.

Conflict of Interest
Authors declared no conflict of interest.

Acknowledgments
We are grateful to all the scientific consultants of this paper.