Eco-conscious decision-support model for optimizing stopping patterns in the mass transit system
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Abstract
The existing train operational efforts focus on need-based criteria for serving passenger demand while maintaining travel time satisfaction. Although mass transit contributes to reduced private vehicle fuel consumption, the current mass transit operations reveal a lack of environmental consciousness. This study introduced a newly developed model for optimizing sustainable train scheduling based on skip-stop operations. The model was applied based on the genetic algorithm technique to identify optimal stopping patterns for the Bangkok Mass Transit System (BTS) on the SkyTrain Silom Line in Bangkok, Thailand. The results presented a wide range of optimal or near-optimal solutions for environmentally friendly stopping patterns for trains and illustrated the tradeoff relationship between the passenger travel time and environmental impacts. This optimization model should prove useful as a decision-support tool for train operation agencies when implementing energy-efficient and environmentally friendly train schedules to improve sustainability.
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