Plan Adjustment Model and Algorithm for Inserting Extra Trains in Urban Rail Transit Based on Predictable Large Passenger Flow

Objective Holidays, large-scale cultural and sports activities and other events will lead to large predictable passenger flow in urban rail transit. Under the premise of ensuring the train operation safety, extra trains should be inserted into the existing train operation plan according to the predicted passenger flow so as to strike a balance between operation cost, operational organization complexity and passenger service level. Method The train operation process is formally described by using event-activity network. Three objective functions and related constraints are established, and then the train operation schedule adjustment models under independent operation and mixed running strategies are further constructed. On this basis, a search algorithm based on hybrid genetic taboo is proposed. According to the actual operation data of Shenzhen Metro Line 11, different scenarios for peak hours and off-peak hours are established respectively to verify the effectiveness of the proposed models and algorithm. Result & Conclusion The proposed models meet the demands of inserting extra trains into operation in different operation periods, routes, and connection strategies, and are applicable to the operation adjustment with predictable large passenger flow. Under the condition of relatively limited vehicle resources, the mixed running strategy can be flexibly adopted to effectively alleviate the passenger flow pressure during peak hours and maximize the transportation capacity of the line.