Research on Case-Based Reasoning for Energy-Efficient Rail Transit Emergency Timetables

Abstract

The continuous expansion of rail transit lines has led to a significant increase in energy consumption. Choosing an appropriate timetable based on external conditions of train operation can reduce energy consumption by more than 5%. When emergencies occur in rail transit, external conditions change, rendering the original timetable unable to meet energy-saving requirements. This paper primarily explores how to quickly determine an energy-efficient timetable based on historical energy-saving timetable data and current external conditions using case-based reasoning methods. The paper represents emergency event timetable cases using a framework approach, and then employs a retrieval algorithm based on the grey correlation degree method to search for cases. The energy-efficient timetable for the target case is selected from the retrieved source cases, considering the one with the lowest energy consumption.