Time-dependent on-street parking planning in a connected and automated environment. (September 2022)

• Record Type: Journal Article
• Authors: Yan, Huimin; Li, Meng; Lin, Xi
• Is Part Of: Transportation research. Volume 142(2022)
• Publication Date: 2022-09
• Subjects: On-street parking -- Connected and automated vehicles -- Flexible utilization of road space -- Period planning; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103745
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Flexible lane usage as on-street parking spaces in connected and automated environments. Time-dependent design of optimal locations, capacities and opening durations of on-street parking lots. Balancing the reduction of vehicle miles traveled and the compromise of network mobility. 10%–27% reduction of total vehicle miles traveled with numerical experiments. Abstract: Parking remains a serious difficulty in metropolitan transport mainly because of the supply shortage in downtown areas. The parking experience will be improved with the support of connected and automated vehicle (CAV) technologies, enabled by remote parking functionality. Nevertheless, remote parking of CAVs not only results in additional surface traffic but also increases the total vehicle miles traveled (VMT). On the other hand, it is possible to manage the overall traffic by a centralized system with the assistance of CAV technology, which brings the potential for using road resources more flexibly. Inspired by the above ideas, this paper investigates a novel scheme to transfer some lanes to provide on-street parking spaces for CAVs within a necessary period to reduce "cruise-for-parking" travel; as such transfer compromises road capacity, a methodological framework is required to optimally plan the scheme for maximizing certain social goals. We formulate the on-street planning problem as a multi-period mixed-integer linear program with inter-period constraints, which determines the locations and opening durations of on-street parking lots, as well as the assignment of all parking demands across the network. Approximate approaches are adopted to solve the problem for large-scale instances. Numerical experiments are conducted on a real-world network in Wangjing District, Beijing. The results indicate that appropriate time-dependent planning of on-street parking can reduce the total VMT by 10%–27% under various demand levels.