Open AccessReforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework
Author(s) -
Duan Mengyuan,
Qi Geqi,
Guan Wei,
Lu Chaoru,
Gong Congcong
Publication year - 2021
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/itr2.12098
Subject(s) - scheduling (production processes) , schedule , trips architecture , beijing , operations research , public transport , transport engineering , computer science , genetic algorithm , monte carlo method , alternative fuel vehicle , engineering , automotive engineering , operations management , statistics , mathematics , diesel fuel , china , machine learning , alternative fuels , law , political science , operating system
Bus scheduling plays a significant role in public transportation and supports the sustainable development of transportation systems. Challenges are beginning to appear with the newly emerging electric buses (EBs), as scheduling changes due to fleet composition make traditional fixed timetables no longer able to satisfy operational needs. Moreover, the fixed‐trip time hypothesis has been inappropriate for large cities due to the variety of urban traffic statuses. This paper proposes an optimal framework for reforming the mixed operation schedule for electric buses and traditional fuel buses under stochastic trip times. Based on the primary grouping genetic algorithm (GGA), a straightforward framework with a Monte Carlo simulation is presented to optimize the scheduling scheme. Case studies based on the operating environment and service trips of real bus lines in Beijing are conducted to verify the effectiveness of the proposed model by considering both the composition of fleet types and time stochasticity. Additionally, the impacts of stochasticity, fleet composition, government subsidies and cost factors on operational costs are investigated. Considering stochastic trip times, the achieved scheduling strategies can provide the optimal proportion of electric and traditional fuel buses and make a crucial impact on operational costs.