Quantifying Impacts of Connected and Autonomous Vehicles on Traffic Operation using Micro-simulation in Dubai, UAE

Alozi, Abdul Razak; Hamad, Khaled

doi:10.5220/0007753905280535

Cited by 4 publications

(1 citation statement)

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“…Compared to outskirts, travel costs at city centres would drop by 12.1% [10]. Additionally, the total travel time is likely to decrease by 20%∼25% during off-peak hours [11], which will have a substantial positive impact on V/C conditions.…”

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confidence: 99%

Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions Using Reinforcement Learning

Feng

Macias

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The deployment of Autonomous Vehicles (AVs) poses considerable challenges and unique opportunities for the design and management of future urban road infrastructure. In light of this disruptive transformation, the Right-Of-Way (ROW) composition of road space has the potential to be renewed. Design approaches and intelligent control models have been proposed to address this problem, but we lack an operational framework that can dynamically generate ROW plans for AVs and pedestrians in response to real-time demand. Based on microscopic traffic simulation, this study explores Reinforcement Learning (RL) methods for evolving ROW compositions. We implement a centralised paradigm and a distributive learning paradigm to separately perform the dynamic control on several road network configurations. Experimental results indicate that the algorithms have the potential to improve traffic flow efficiency and allocate more space for pedestrians. Furthermore, the distributive learning algorithm outperforms its centralised counterpart regarding computational cost (49.55%), benchmark rewards (25.35%), best cumulative rewards (24.58%), optimal actions (13.49%) and rate of convergence. This novel road management technique could potentially contribute to the flowadaptive and active mobility-friendly streets in the AVs era.

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confidence: 99%