Traffic Demand Regulation by an Intelligent Management System: A Case Study in Dhaka

Traffic congestion in urban cities is acute, resulting in extensive delays, elevated fuel usage, and pollution due to mismanaged signals and unregulated traffic systems. These inhibitions primarily stem from ungoverned signal timing and unpredictable traffic patterns. An Intelligent Traffic Control System (ITCS) is developed here as a pilot study by utilizing field-observed traffic data from Gulshan-2 Circle, Dhaka. Machine learning (ML) techniques were used to recreate a daylong traffic demand profile from data obtained in specific time periods, while preserving temporal variability and realistic demand features. Three ways of synchronized traffic control are offered. First, a Traffic Demand Regulation Model (TDRM) based on incentives is proposed to transfer part of the trips in the peak periods to the nearby off-peak periods. Second, staggered scheduling is proposed for academic institutions, offices, and commercial organizations by altering opening and closing times to lessen simultaneous travel demand. Third, an Adaptive Traffic Signal Control (ATSC) technique is proposed to reduce the queue length and delay by dynamically adjusting the signal timing according to the traffic conditions. Mathematical models for TDRM, staggered scheduling, and ATSC have been built and assessed inside a MATLAB simulation environment. Comparative simulations of baseline and intervention scenarios show that transferring only 1-10% of peak period demand to off-peak periods may significantly reduce congestion and coordinated control methods can cut fuel consumption and CO2 emissions by about 10-20%. Integrated demand management, scheduling, and adaptive control can improve urban traffic efficiency under realistic heterogeneous urban traffic conditions and support sustainable transportation in Dhaka, contributing to SDGs 11 and 13.