Abstract

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the National Renewable Energy Laboratory (NREL) recently developed the Automated Mobility District (AMD) toolkit. The AMD toolkit—comprising of a travel micro-simulation model and an energy estimation model—estimates the mobility and energy impacts of a given shuttle configuration within an AMD. Early-stage AMD deployments need to find optimal operational configurations that include: (a) passenger capacity of an AES, (b) time-dependent routes, and (c) fleet size (AES units) to satisfy the demand for the region. This research extends the AMD toolkit functionality by developing an optimization-based planning module that will assist in the operations of AES units. We developed a constrained mixed-integer program accounting for passenger waiting time, battery range, and passenger capacity of AES units. For scalability, we demonstrated the Tabu search-based solution technique for a real-world network—a proposed AMD deployment in Greenville, South Carolina, USA. Compared to rule-based operations, our developed solution yields higher travel time and energy savings for the network at different demand levels. The sensitivity analyses for waiting time thresholds indicate nonlinearity in the system performance, underscoring the need to meet shared-use mobility user-level expectations. The developed optimization framework can be adapted and extended to accommodate different categories of shared-use on-demand mobility services.

摘要

世界各地的地区都在采用和探索低速自动电动班车 (AES) 服务作为密集地理围栏城市地区的按需共享移动服务。在此概念的基础上，国家可再生能源实验室 (NREL) 最近开发了自动移动区 (AMD) 工具包。AMD 工具包——包括一个旅行微观模拟模型和一个能量估计模型——估计 AMD 内给定穿梭配置的移动性和能量影响。早期 AMD 部署需要找到最佳运营配置，包括：(a) AES 的载客量，(b) 依赖时间的路线，以及 (c) 机队规模（AES 单位）以满足该地区的需求。这项研究通过开发一个基于优化的规划模块来扩展 AMD 工具包的功能，该模块将有助于 AES 单元的操作。我们开发了一个受约束的混合整数程序，用于考虑乘客等待时间、电池续航时间和 AES 装置的乘客容量。对于可扩展性，我们展示了现实世界网络的基于禁忌搜索的解决方案技术——在美国南卡罗来纳州格林维尔的 AMD 部署计划。与基于规则的操作相比，我们开发的解决方案为不同需求水平的网络带来了更高的旅行时间和能源节省。等待时间阈值的敏感性分析表明系统性能存在非线性，强调需要满足共享使用移动用户级别的期望。