摘要: |
Public transportation provides a safe, convenient, affordable, and eco-friendly mobility service. However, due to its fixed routes and limited network coverage, it is sometimes difficult or impossible for passengers to walk from a transit stop to their destination. This inaccessibility problem is also known as the “transit last mile connectivity problem”. Such a lack of connectivity forces travelers to drive and hence increase the vehicle ownership and Vehicle Miles Traveled (VMT) on roads. The autonomous mobility-on-demand (AMoD) service with characteristics such as quick fleet repositioning and demand responsiveness, has the potential to provide coverage in low-density areas where the fixed route transit can only provide limited coverage. We propose to conduct research on designing an AMoD service to solve the transit last mile problem in low-density regions of the Greater Minnesota. The project will explore two different alternatives for providing the last-mile service, namely, traditional fixed-route circulator and online demand-responsive autonomous service. Furthermore, we propose to survey travelers in a focus area to estimate the induced (extra) demand for this new service. The work will employ techniques from state-of-the-art optimization and machine learning to optimize the system as a whole. By conducting this study, the project would pave a way for low-density Minnesota regions to adopt future mobility options. |