Ridoy, Md Jahidul Islam and , Al Akhir (2025) Smart Transportation Solutions Using AI to Optimize Traffic Flow and Reduce Urban Congestion for Improved Urban Mobility. International Journal on Orange Technologies, 7 (2). pp. 116-123. ISSN 2615-8140

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Abstract

Background: The fast growth of cities has led to worsen traffic problems and longer wait times and increased pollution which requires smart transportation solutions to address these issues. Smart transportation systems which use Artificial Intelligence (AI) provide an effective solution to enhance traffic management and protect road users while achieving better urban transportation system performance. Methods: The research applied a quantitative approach to study smart transportation systems which use AI technology affect various aspects. Data from 195 participants who included transportation engineers and urban planners. Descriptive statistics together with percentage analysis and correlation techniques to study traffic patterns and their effects on reducing delays and boosting safety and improving fuel consumption and public transportation contentment. Results: AI-based transportation systems positively because 91.3% of respondents observed better traffic movement and 88.7% experienced shorter wait times and 85.1% reported improved safety and 83.6% achieved better fuel efficiency and 87.2% expressed higher satisfaction levels. Correlation analysis demonstrates strong positive connections between essential variables which show that traffic flow relates to delay reduction through r = 0.78 and safety through r = 0.71 and satisfaction through r = 0.74. The data shows that fuel consumption levels create negative relationships with efficiency-related variables because users need less energy to operate these systems. Conclusion: AI-based smart transportation networks deliver better urban mobility results which improve safety performance and generate sustainable advantages. Multiple operational difficulties because it requires expensive equipment and users struggle to protect their data privacy while lacking sufficient expertise.

Item Type:	Article
Subjects:	A General Works > AI Indexes (General)
Depositing User:	admin eprints
Date Deposited:	28 Jun 2026 23:39
Last Modified:	28 Jun 2026 23:39
URI:	http://eprints.umsida.ac.id/id/eprint/16664

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