传输(计算)
运输工程
航空学
工程类
业务
计算机科学
并行计算
作者
Younghun Bahk,Michael Hyland,Sunghi An
标识
DOI:10.1016/j.tra.2024.104009
摘要
Cities implemented park-and-ride (PNR) systems to decrease congestion in dense urban areas while providing transit options to travelers who live in a city's low- to medium-density regions. The success of PNR systems is mixed, as they suffer from several disadvantages, namely, the uncertainty of parking locations and infrequent and/or unreliable transit services, and the fact that travelers still need to walk to their destination. Motivated by the premise of PNR systems and the potential of automated vehicles (AVs), to address each of the shortcomings of PNR systems, this study proposes a future system with near-ubiquitous AVs where travelers transfer from privately owned AVs (PAVs) to shared-use, shared-ride AVs (SAVs), called a PAV-SAV transfer system. This study proposes a modeling framework to assess the potential market share of the PAV-SAV transfer system and the network impacts (e.g., congestion, vehicle miles traveled) of the proposed system. Finally, the study identifies good designs for the PAV-SAV transfer system using scenario analysis. The critical design variables are the location of transfer stations, the capacity of SAVs, and the transfer station connector links. For the Greater Los Angeles area, the computational results show a market share for PAV-SAV of almost 18% for person trips terminating in downtown Los Angeles. In all scenarios, the proposed PAV-SAV system decreases vehicle hours traveled (VHT) across the whole network with significant decreases in the urban core. For all designs, the PAV-SAV system decreases vehicle miles traveled (VMT) compared to a network without PAV-SAV transfer stations, albeit only slightly. Locating transfer stations closer to the urban core, increasing vehicle capacities, and connecting transfer stations to both arterial links and highway links improves network performance (i.e., VMT and VHT) and increases the market share of the PAV-SAV system.
科研通智能强力驱动
Strongly Powered by AbleSci AI