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Route intersection reduction with connected autonomous vehicles

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Abstract

A common cause of traffic congestions is the concentration of intersecting vehicle routes. It can be difficult to reduce the intersecting routes in existing traffic systems where the routes are decided independently from vehicle to vehicle. The development of connected autonomous vehicles provides the opportunity to address the intersecting route problem as the route of vehicles can be coordinated globally. We prototype a traffic management system for optimizing traffic with connected autonomous vehicles. The system allocates routes to the vehicles based on streaming traffic data. We develop two route assignment algorithms for the system. The algorithms can help to mitigate traffic congestions by reducing intersecting routes. Extensive experiments are conducted to compare the proposed algorithms and two state-of-the-art route assignment algorithms with both synthetic and real road networks in a simulated traffic management system. The experimental results show that the proposed algorithms outperform the competitors in terms of the travel time of the vehicles.

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Acknowledgments

We thank the anonymous reviewers whose comments helped improve and clarify this study.

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Authors and Affiliations

  1. University of Melbourne, Melbourne, Australia

    Sadegh Motallebi, Hairuo Xie, Egemen Tanin, Jianzhong Qi & Kotagiri Ramamohanarao

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  1. Sadegh Motallebi
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  2. Hairuo Xie
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  3. Egemen Tanin
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  4. Jianzhong Qi
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  5. Kotagiri Ramamohanarao
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Correspondence to Sadegh Motallebi.

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Motallebi, S., Xie, H., Tanin, E. et al. Route intersection reduction with connected autonomous vehicles. Geoinformatica 25, 99–125 (2021). https://doi.org/10.1007/s10707-020-00420-z

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  • DOI: https://doi.org/10.1007/s10707-020-00420-z

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