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Traffic signal optimization framework using interpretable machine learning technique under heterogeneous-autonomy traffic environment

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Abstract

Recent advancements in the industrial revolution and artificial intelligence have aided in the development of novel approaches that have considerable potential for increasing the efficiency of traffic networks. Emerging concepts of autonomous driving and machine learning can be incorporated intelligently for improving traffic operations and control. In the coming years, traffic composition can vary in terms of autonomous vehicle (AV) penetration rate, which can lead to a heterogeneous traffic environment. Traffic control methods for such complex networks need to be designed effectively for accommodating the positive effects of AV implementation without compromising the safety and level of service in the presence of regular vehicles (RVs). An intelligent-based optimization framework for traffic signal control under heterogeneous AV-based traffic is proposed in this paper. This framework utilizes state-of-the-art machine-learning approaches to represent and design different components of the optimization process of cycle length. Further, SHapley Additive exPlanations (SHAP) is used to enhance model interpretability. The proposed optimization framework improves performance under congested traffic conditions compared with that of the conventional optimization methods. Compared to pure RV-based traffic, the penetration rates of 25%, 50%, and 100% can decrease optimized cycle lengths by 26%, 39%, and 53%, respectively, which can result in delay reductions of approximately 18%, 31%, and 56%, respectively. The proposed framework when applied in an adaptive-based manner can help in the generalization of controlling existing signalized intersections with the gradual penetration of AVs without the extra infrastructure or specific operational and connectivity capabilities of the involved vehicles.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Civil Engineering, Jubail Industrial College, 31961, Jubail Industrial City, Saudi Arabia

    Mohammed Al-Turki

  2. Civil Engineering Department, Engineering Faculty, Kabul University, Jamal-e-Mina, Kabul, Afghanistan

    Mohammad Tamim Kashifi

  3. Research Center, Rana University, Second District, Kabul, Afghanistan

    Mohammad Tamim Kashifi

  4. Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Nedal T. Ratrout

  5. Research Institute, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Syed Masiur Rahman

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  1. Mohammed Al-Turki
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Al-Turki, M., Kashifi, M.T., Ratrout, N.T. et al. Traffic signal optimization framework using interpretable machine learning technique under heterogeneous-autonomy traffic environment. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09694-y

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