The increasing degree of connectivity between vehicles and infrastructure, and the impending deployment of autonomous vehicles (AV) in urban streets, presents unique opportunities and challenges regarding the on-street parking provision for AVs. This study develops a novel simulationoptimisation approach for intelligent curbside management, based on a metaheuristic technique. This approach is tested using an idealised grid layout with a range of flow rates and parking policies. The hybrid method balances curb lanes for driving or parking, aiming to minimise the average traffic delay. Results demonstrate delays decreased by 9%-27% from the benchmark case. Additionally, the delay distribution indicates the trade-offs between expanding road capacity and minimising traffic demand through curb management, demonstrating the interplay between curb parking and traffic management in the AV era.

Complete streets scheme makes seminal contributions to securing the basic public right-of-way (ROW), improving road safety, and maintaining high traffic efficiency for all modes of commute. However, such a popular street design paradigm also faces endogenous pressures like the appeal to a more balanced ROW for non-vehicular users. In addition, the deployment of Autonomous Vehicle (AV) mobility is likely to challenge the conventional use of the street space as well as this scheme. Previous studies have invented automated control techniques for specific road management issues, such as traffic light control and lane management. Whereas models and algorithms that dynamically calibrate the ROW of road space corresponding to travel demands and place-making requirements still represent a research gap. This study proposes a novel optimal control method that decides the ROW of road space assigned to driveways and sidewalks in real-time. To solve this optimal control task, a reinforcement learning method is introduced that employs a microscopic traffic simulator, namely SUMO, as its environment. The model was trained for 150 episodes using a four-legged intersection and joint AVs-pedestrian travel demands of a day. Results evidenced the effectiveness of the model in both symmetric and asymmetric road settings. After being trained by 150 episodes, our proposed model significantly increased its comprehensive reward of both pedestrians and vehicular traffic efficiency and sidewalk ratio by 10.39%. Decisions on the balanced ROW are optimised as 90.16% of the edges decrease the driveways supply and raise sidewalk shares by approximately 9%. Moreover, during 18.22% of the tested time slots, a lane-width equivalent space is shifted from driveways to sidewalks, minimising the travel costs for both an AV fleet and pedestrians. Our study primarily contributes to the modelling architecture and algorithms concerning centralised and real-time ROW management. Prospective applications out of this method are likely to facilitate AV mobility-oriented road management and pedestrian-friendly street space design in the near future.

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