Bus-Pooling: Demand-Driven Flexible Scheduling for Intercity Transit

The mismatch between static bus schedules and dynamic passenger travel demand results in increased passenger waiting time and inefficient operation for intercity transit. To address this issue, a cross-route bus-pooling scheduling method is proposed to merge passengers from low-efficiency routes into alternative routes within a shared operation segment at a key station with significant ridership fluctuations. An optimization model is established with the objective of maximizing operational efficiency and minimizing total passenger waiting time considering the restrictions of load factor and arrival time window. Operational efficiency is primarily enhanced by reducing the opportunity cost and the energy consumption cost. The real-time bus operation status and ridership distribution characteristics are analyzed using an adaptive genetic algorithm (AGA) to obtain the optimal solution. Using real data, the case study shows that the proposed bus-pooling schedule obtains an increase of 10 bus runs with a significant rise in ridership by 22.75% within fleet size constraints. The average waiting time of suburban passengers decreases by 15.88%. Sensitivity analysis on the load factor threshold is discussed. This approach facilitates a dynamic equilibrium between feasible operation resources and travel demand, enhancing the effectiveness and sustainability of public transportation systems.