| dc.description.abstract | Traffic congestion in Sri Lanka is a pressing issue that leads to wasted time, financial
burdens, and disruption of personal schedules. The inadequate road infrastructure,
underutilization of various travel information sources, and transportation systems coupled
with the increasing number of vehicles contribute to this problem. Despite the presence
of two international airports, a railway system, and waterways for transportation, the
populace predominantly relies on the road network, leading to consequential challenges
such as traffic accidents, property damage, and environmental pollution. The objective
of this research is to put forth and assess a smart system for managing traffic known
as the Intelligent Traffic Management System (ITMS). This system utilizes technological
progressions like Artificial Intelligence (AI), cloud computing, the Internet of Things, and
data analytics to enhance traffic management and control. The objective is to optimize
traffic flow, reduce wait times, alleviate congestion, minimize travel expenses, and mitigate
air pollution levels. The proposed system employs machine learning algorithms to forecast
optimal routes based on traffic patterns, vehicle classification, frequency of accidents, and
weather conditions. The development and implementation of the ITMS demonstrate
the potential of AI-driven solutions in addressing traffic-related problems and improving
daily commuting experiences. In conclusion, integrating AI technologies into the ITMS
presents a promising approach to mitigating traffic congestion challenges in Sri Lanka.
By forecasting optimal routes and incorporating data-driven decision-making, the ITMS
offers a solution to improve traffic management and alleviate the negative effects of
congestion | en_US |
