To improve punctuality, reduce delay, and solve bunching of urban buses, an optimization method for bus scheduling was studied based on a group-gathered passenger flow. The travel willingness, ride attributes, and passenger arrival regularity were all identified to feed the model. The bunching scene was described based on the vehicle carrying restrictions, delay at stops, arrival rate, and passenger alighting rate. Constraints such as punctuality, passenger flow demand, and control strategy were all considered in the development of a real-time mixed control strategy, which was adopted for multi-objective optimization of the minimum headway deviation and total passenger travel time. To prevent potential bus bunching, a scheduling method of bus bunching that meets the travel demands of periodic group-gathered passenger flows at bus stops is herein described. The method considers the uncertainty of passenger arrival rate, holding time at bus stops, and average driving speed across different sections. To solve the model, the difference in the view of bi-objective optimization was considered when the overtaking rule was used to reorder outbound vehicles in the bunching scene. To design the NSGA-II algorithm, the order relation was calibrated by the crowding distance, and a new population was obtained by the elite strategy to improve the crossover operator. The resulting Pareto solution set was then optimized based on the TOPSIS method. Finally, an actual bus line was used as an example to experimentally verify the accuracy of the model and its algorithm. The results show that the optimization model of bus bunching based on group-gathered passenger flow at stops systematically predicts the passenger riding attributes and vehicle carrying restrictions. An optimal scheduling scheme for vehicle holding and speed adjustment is obtained by the model, which allows for calculating a series of operational indicators, such as vehicle departure time, headway deviation, punctuality rate, passenger waiting time, and passenger travel time. Comparing the system before and after the model optimization, it is found that total headway deviation is shortened by 56%, reducing the total travel time of passengers by 11. 7%, passenger average waiting time by 12. 5%, and increasing the punctuality rate of bus stops by 24. 1%. A reduction of 36 in the number of bunches is also observed. An experiment to verify randomness found that the average declined ratio of the two objective functions is 50. 4% and 13. 7%, which is a large reduction range and a good optimization effect. The results show that implementation of this model in real-life can greatly improve bus operational efficiency and effectively solve the problem of bus bunching; the solution is robust and reliable, and this method is both practical and feasible to implement. © 2023 Xi'an Highway University. All rights reserved.
