Dynamic coordinated strategy for parking guidance in a mixed driving parking lot involving human-driven and autonomous vehicles

Zhiyuan Wang; Chu Zhang; Shaopei Xue; Yinjie Luo; Jun Chen; Wei Wang; Xingchen Yan; Zhiyuan Wang; Chu Zhang; Shaopei Xue; Yinjie Luo; Jun Chen; Wei Wang; Xingchen Yan

doi:10.3934/era.2024026

Electronic Research Archive

2024, Volume 32, Issue 1: 523-550. doi: 10.3934/era.2024026

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Dynamic coordinated strategy for parking guidance in a mixed driving parking lot involving human-driven and autonomous vehicles

1.
School of Transportation, Southeast University, Jiangning District, Nanjing 211189, China
2.
Division of Engineering, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, United Arab Emirates
3.
Key Laboratory of Transport Industry of Comprehensive Transportation Theory (Nanjing Modern Multimodal Transportation Laboratory), Ministry of Transport of the PRC, No. 56 Baoshansi Road, Jiangning District, Nanjing 211100, China
4.
College of Automobile and Traffic Engineering, Nanjing Forestry University, No. 159 Longpan Road, Nanjing 210037, China

Academic Editor: Wei Zhang

Received: 29 November 2023 Revised: 21 December 2023 Accepted: 26 December 2023 Published: 03 January 2024

The advent of autonomous vehicles (AVs) poses challenges to parking guidance in mixed driving scenarios involving human-driven vehicles (HVs) and AVs. This study introduced a dynamic and coordinated strategy (DCS) to optimize parking space allocation and path guidance within a mixed driving parking lot, aiming to enhance parking-cruising efficiency. DCS considers the distinctive characteristics of HVs and AVs and dynamically formulates parking guiding schemes based on real-time conditions. The strategy encompasses four main steps: Triggering scheme formulation, identifying preoccupied parking spaces, updating the parking lot traffic network and optimizing the vehicle-path-space matching scheme. A programming model was established to minimize the total remaining cruising time, and iterative optimization was conducted with vehicle loading test based on timing. To elevate computational efficiency, the concept of parking-cruising path tree (PCPT) and its updating method were introduced based on the dynamic shortest path tree algorithm. Comparative analysis of cases and simulations demonstrated the efficacy of DCS in mitigating parking-cruising duration of different types of vehicles and minimizing forced delays arising from lane blocking. Notably, the optimization effect is particularly significant for vehicles with extended cruising durations or in parking lots with low AV penetration rates and high saturation, with an achievable optimization rate reaching up to 18%. This study addressed challenges related to drivers' noncompliance with guidance and lane blocking, thereby improving overall operational efficiency in mixed driving parking lots.
- mixed driving,
- parking guidance,
- dynamic coordinated strategy (DCS),
- iterative optimization,
- efficiency,
- dynamic shortest-path tree
Citation: Zhiyuan Wang, Chu Zhang, Shaopei Xue, Yinjie Luo, Jun Chen, Wei Wang, Xingchen Yan. Dynamic coordinated strategy for parking guidance in a mixed driving parking lot involving human-driven and autonomous vehicles[J]. Electronic Research Archive, 2024, 32(1): 523-550. doi: 10.3934/era.2024026

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Abstract

The advent of autonomous vehicles (AVs) poses challenges to parking guidance in mixed driving scenarios involving human-driven vehicles (HVs) and AVs. This study introduced a dynamic and coordinated strategy (DCS) to optimize parking space allocation and path guidance within a mixed driving parking lot, aiming to enhance parking-cruising efficiency. DCS considers the distinctive characteristics of HVs and AVs and dynamically formulates parking guiding schemes based on real-time conditions. The strategy encompasses four main steps: Triggering scheme formulation, identifying preoccupied parking spaces, updating the parking lot traffic network and optimizing the vehicle-path-space matching scheme. A programming model was established to minimize the total remaining cruising time, and iterative optimization was conducted with vehicle loading test based on timing. To elevate computational efficiency, the concept of parking-cruising path tree (PCPT) and its updating method were introduced based on the dynamic shortest path tree algorithm. Comparative analysis of cases and simulations demonstrated the efficacy of DCS in mitigating parking-cruising duration of different types of vehicles and minimizing forced delays arising from lane blocking. Notably, the optimization effect is particularly significant for vehicles with extended cruising durations or in parking lots with low AV penetration rates and high saturation, with an achievable optimization rate reaching up to 18%. This study addressed challenges related to drivers' noncompliance with guidance and lane blocking, thereby improving overall operational efficiency in mixed driving parking lots.

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