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<p>In this study, a new approach will be discussed in which routing is done by predicting future traffic and the learning algorithm is optimized during navigation. Traffic has a complex structure that is constantly changing. Especially for long-term travel, it is not an optimum approach to suggest a route only by considering the traffic situation at the time the navigation request is made. For this reason, the proposed algorithm recommends a route by taking into account future saturation conditions on the vehicle’s route. Singapore was chosen as the study area. The tests were carried out in a simulation environment. The four selected algorithms were tested spatially and temporally. Especially in long-term travels, the superior success of the proposed method compared to other selected methods has been demonstrated.</p>

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A novel dynamic path planning method TD learning supported modified spatiotemporal GNN-LSTM model on large urban networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月29日

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  • Abdullah Karaağaç

摘要

In this study, a new approach will be discussed in which routing is done by predicting future traffic and the learning algorithm is optimized during navigation. Traffic has a complex structure that is constantly changing. Especially for long-term travel, it is not an optimum approach to suggest a route only by considering the traffic situation at the time the navigation request is made. For this reason, the proposed algorithm recommends a route by taking into account future saturation conditions on the vehicle’s route. Singapore was chosen as the study area. The tests were carried out in a simulation environment. The four selected algorithms were tested spatially and temporally. Especially in long-term travels, the superior success of the proposed method compared to other selected methods has been demonstrated.