Simulation-based optimization considering energy consumption for assisted station locations to enhance flex-route transit

Abstract

As an emerging mode of public transit, flex-route transit (FRT) is regarded as the key to reducing energy consumption and promoting sustainable development in low-density suburban cities. However, its performance in cases with unexpectedly high demand levels is usually unsatisfactory. To address this issue, a simulation-based optimization method is proposed for assisted station locations in FRT and to reduce the system costs, including that for energy consumption. First, considering the uncertain travel demands of passengers, this location problem is modelled as a simulation-based optimization (SO) problem based on Monte Carlo simulation (MCS). Then, a multiple metamodel-based efficient global optimization (MMEGO) algorithm is proposed to effectively solve the SO problem. The numerical results demonstrate that the proposed MMEGO algorithm can obtain good results with lower computing cost and stronger robustness than counterpart algorithms (EGO and MISK). Finally, simulation tests of FRT based on data from the operation of 320 buses in Shenzhen city and the related OD data are conducted to test the feasibility of the proposed method. The results show that the proposed optimization method can be used to identify suitable locations for assisted stations and reduce the total system cost by 26.6% compared with that for an FRT system with unoptimized assisted station locations.