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Naushin Nower
University of Dhaka
Computer Science & IT Engineering

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A Comprehensive Analysis of Reward Function for Adaptive Traffic Signal Control Abu Rafe Md Jamil; Naushin Nower
Knowledge Engineering and Data Science Vol 4, No 2 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i22021p85-96

Abstract

Adaptive traffic control systems (ATCS) can play an important role to reduce traffic congestion in urban areas. The main challenge for ATSC is to determine the proper signal timing. Recently, Deep Reinforcement learning (DRL) is used to determine proper signal timing. However, the success of the DRL algorithm depends on the appropriate reward function design. There exist various reward functions for ATSC in the existing research.  In this research, a comprehensive analysis of the widely used reward function is presented. The pros and cons of various reward algorithms are discussed and experimental analysis shows that multi-objective reward function enhances the performance of ATSC.
Long-Term Traffic Prediction Based on Stacked GCN Model Atkia Akila Karim; Naushin Nower
Knowledge Engineering and Data Science Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i12023p92-102

Abstract

With the recent surge in road traffic within major cities, the need for both short and long-term traffic flow forecasting has become paramount for city authorities. Previous research efforts have predominantly focused on short-term traffic flow estimations for specific road segments and paths. However, applications of paramount importance, such as traffic management and schedule routing planning, demand a deep understanding of long-term traffic flow predictions. However, due to the intricate interplay of underlying factors, there exists a scarcity of studies dedicated to long-term traffic prediction. Previous research has also highlighted the challenge of lower accuracy in long-term predictions owing to error propagation within the model. This model effectively combines Graph Convolutional Network (GCN) capacity to extract spatial characteristics from the road network with the stacked GCN aptitude for capturing temporal context. Our developed model is subsequently employed for traffic flow forecasting within urban road networks. We rigorously compare our method against baseline techniques using two real-world datasets. Our approach significantly reduces prediction errors by 40% to 60% compared to other methods. The experimental results underscore our model's ability to uncover spatiotemporal dependencies within traffic data and its superior predictive performance over baseline models using real-world traffic datasets.
Co-Authors Abu Rafe Md Jamil Atkia Akila Karim