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Title	Traffic Forecasting & Route Optimization In Smart Environment Using Graph Representation Learning
ID_Doc	58598
Authors	Shah S.M.A.H.; Shah S.F.H.; Hussain S.; Turrakheil K.S.
Year	2023
Published	Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023
DOI	http://dx.doi.org/10.1109/ISC257844.2023.10293287
Abstract	Route optimization and traffic forecasting play pivotal roles in efficient traffic management and the development of intelligent transportation systems in smart cities. In tandem, traffic forecasting offers real-time insights into traffic conditions, empowering proactive decision-making and efficient resource allocation. In this paper, we introduce a novel approach to address the critical challenges of traffic forecasting and route optimization by harnessing the power of Graph Representation Learning (GRL). Utilizing advanced Graph Representation Learning methodologies enables the customization of traffic management systems to accommodate the distinct mobility requirements of individuals with disabilities. This involves optimizing routes that prioritize both efficiency and accessibility, accounting for features like wheelchair accessibility, curb cuts, and pedestrian-friendly routes. The integration of real-time traffic forecasting offers additional empowerment to People with Disabilities (PwD), furnishing timely information about traffic conditions. This assists them in making informed decisions for more seamless and secure commuting encounters. The integration of GRL in traffic management fosters the establishment of sustainable, accessible, and optimized systems, leading to reduced congestion and an elevated commuting experience in smart cities. © 2023 IEEE.
Author Keywords	GNNs; Graph Representation Learning; Multimodality data; Route Optimization; Traffic Forecasting

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