Abstract
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.
Highlights
Considering climate change and health impacts, air quality has attracted more attention worldwide [1]
We investigate the impacts of stochasticity, fleet composition, government subsidies and cost factors on operational costs
We propose a methodology for solving the bus scheduling problem of mixed fleets with electric buses and conventional buses under stochastic trip time
Summary
Considering climate change and health impacts, air quality has attracted more attention worldwide [1]. From the environmental benefit and operational costs aspects, comprehensively formulating a dispatching strategy is necessary for evaluating the impact of the electrification process on the bus operating company Another problem that should be settled is the fixed-trip time hypothesis, especially for large cities. To fill the research gap, this study comprehensively considers the environmental effects, cost benefits and passenger service levels, as well as setting the bus purchase costs, energy consumption costs, recharging costs, emission costs, and expected waiting time and delay time costs as objective values to establish a mixed fleet scheduling model under stochastic road conditions. All costs are monetized and converted into a single-objective optimization model This model is explicitly formulated to consider mixed bus operations under stochastic service trip times and can be solved efficiently using the grouping genetic algorithm (GGA) with a crossover and mutation strategy suitable for a mixed fleet.
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