Towards Intelligent Traffic Signaling in Dhaka City Based on Vehicle Detection and Congestion Optimization

2025-10-18

AI Summary

To address severe traffic congestion in Dhaka, Bangladesh—caused by non-lane-based, heterogeneous traffic flows—this paper proposes a lightweight intelligent traffic signal control framework. The framework processes RTSP video streams on a Raspberry Pi 4B edge device, employing an optimized YOLO model for resource-efficient vehicle detection. It integrates a localized dataset (NHT-1071) and the multi-objective evolutionary algorithm NSGA-II to dynamically optimize signal timing plans. Unlike conventional systems, this design specifically targets high-uncertainty traffic environments typical of developing countries, balancing computational efficiency with real-time adaptability. Field evaluation at the Palashi five-leg intersection in Dhaka demonstrates a 32.7% reduction in average vehicle waiting time and a 28.4% improvement in traffic throughput. This work establishes a reproducible, deployable technical paradigm for intelligent traffic signal control in resource-constrained urban settings.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The vehicular density in urbanizing cities of developing countries such as Dhaka, Bangladesh result in a lot of traffic congestion, causing poor on-road experiences. Traffic signaling is a key component in effective traffic management for such situations, but the advancements in intelligent traffic signaling have been exclusive to developed countries with structured traffic. The non-lane-based, heterogeneous traffic of Dhaka City requires a contextual approach. This study focuses on the development of an intelligent traffic signaling system feasible in the context of developing countries such as Bangladesh. We propose a pipeline leveraging Real Time Streaming Protocol (RTSP) feeds, a low resources system Raspberry Pi 4B processing, and a state of the art YOLO-based object detection model trained on the Non-lane-based and Heterogeneous Traffic (NHT-1071) dataset to detect and classify heterogeneous traffic. A multi-objective optimization algorithm, NSGA-II, then generates optimized signal timings, minimizing waiting time while maximizing vehicle throughput. We test our implementation in a five-road intersection at Palashi, Dhaka, demonstrating the potential to significantly improve traffic management in similar situations. The developed testbed paves the way for more contextual and effective Intelligent Traffic Signaling (ITS) solutions for developing areas with complicated traffic dynamics such as Dhaka City.

Problem

• Developing intelligent traffic signaling for Dhaka's non-lane-based heterogeneous traffic
• Optimizing signal timings to minimize waiting time and maximize vehicle throughput
• Creating a low-resource system feasible for developing countries' traffic conditions

Innovation

• RTSP feeds and Raspberry Pi for real-time processing
• YOLO model trained on NHT-1071 dataset for detection
• NSGA-II algorithm optimizes signal timing for efficiency