Efficient Traffic Routing with Temporal Fusion Transformers: Addressing Urban Congestion Challenges

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : Sreelekha M, Midhunchakkaravarthy Janarthanan

10.14445/23488549/IJECE-V11I11P117

How to Cite?

Sreelekha M, Midhunchakkaravarthy Janarthanan, "Efficient Traffic Routing with Temporal Fusion Transformers: Addressing Urban Congestion Challenges," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 11, pp. 196-212, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I11P117

Abstract:

The exponential increase in urban population necessitates the emergence of transportation systems that are both effective and sustainable, using the potential modern technology. The issue of dynamic traffic flow significantly impedes the movement of vehicles. Traffic congestion is a critical issue affecting urban mobility and efficiency in cities worldwide, with Bangalore no exception. This study addresses the challenge of leveraging advanced predictive analytics and intelligent transport systems to manage traffic congestion. The proposed research aims to address the limitations of traditional traffic management strategies by integrating the Temporal Fusion Transformer (TFT) model into an Intelligent Transport System (ITS) framework. The research employs rigorous data preprocessing techniques to leverage extensive data from multiple online map service providers and traffic monitoring platforms, spanning from January 1, 2019, to December 31, 2023. The TFT model forecasts traffic congestion with notable precision, achieving a Mean Absolute Error (MAE) of 0.39, Mean Squared Error (MSE) of 0.30, Root Mean Squared Error (RMSE) of 0.55, Mean Absolute Percentage Error (MAPE) of 7.2%, and an R-squared (R²) value of 0.87. The outcomes obtained clearly illustrate the model’s superior accuracy and efficacy. Integrating TFT predictions into the ITS framework enhances real-time traffic control by improving the timings of traffic signals, recommending alternative routes, and improving incident management. This proactive approach significantly reduces traffic congestion and enhances travel efficiency, substantially advancing urban traffic management solutions.

Keywords:

Temporal Fusion Transformer, Intelligent Transport System, Traffic congestion, Traffic volume, Smart city.

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