Optimization of Transit Scheduling Combined with Short-Turn Service Based on Real-Time Passenger Flow

In view of a series of problems, such as unable to meet the needs of passengers, high full load ratio or waste of carrying capacity on unbalanced passenger flow sections caused by the all-stop fleet scheduling in the urban public transit system, this paper proposed a bus combination scheduling strategy with considering short-turn service based on the imbalance coefficient of passenger flow and a method to determine the turning back point. A combined dispatching optimization model is established with the objective function of minimizing the total system cost which includes the waiting time cost of passengers, the congestion feeling cost and the operation cost of public transit enterprises. The headways of short-turn and all-stop scheme are optimized by the combined scheduling model, and the solution method is proposed. Taking Beijing No. A bus line as an empirical analysis object, the real-time passenger flow and vehicle data in a working day are collected and analyzed, and the optimized scheme of short-turn service combination scheduling is obtained. The results show that compared with the traditional all-stop fleet scheduling, the optimized short-turn service combination scheduling can reduce the fleet size by 4.9% and effectively improve the operation efficiency and system benefits.

Modeling and optimization of urban rail transit scheduling with adaptive fruit fly optimization algorithm

Despite the rapid development of urban rail transit in China, there are still some problems in train operation, such as low efficiency and poor punctuality. To realize a proper allocation of passenger flows and increase train frequency, this paper has proposed an improved urban rail transit scheduling model and solved the model with an adaptive fruit fly optimization algorithm (AFOA). For the benefits of both passengers and operators, the shortest average waiting time of passengers and the least train frequency are chosen as the optimization objective, and train headway is taken as the decision variable in the proposed model. To obtain higher computational efficiency and accuracy, an adaptive dynamic step size is built in the conventional FOA. Moreover, the data of urban rail transit in Zhengzhou was simulated for case study. The comparison results reveal that the proposed AFOA exhibits faster convergence speed and preferable accuracy than the conventional FOA, particle swarm optimization, and bacterial foraging optimization algorithms. Due to these superiorities, the proposed AFOA is feasible and effective for optimizing the scheduling of urban rail transit.

Urban Rail Transit Scheduling under Time-Varying Passenger Demand

In view of the conflict between the time-variation of urban rail transit passenger demand and the homogeneity of the train timetable, this paper takes into account the interests of both passengers and operators to build an urban rail transit scheduling model to acquire an optimized time-dependent train timetable. Based on the dynamic passenger volumes of origin-destination pairs from the automatic fare collection system, the model focuses on minimizing the total passenger waiting time with constraints on time interval between two consecutive trains, number and capacity of trains available, and load factor of trains. A hybrid algorithm which consists of the main algorithm based on genetic algorithm and the nested algorithm based on train traction calculation and safety distance requirement is designed to solve the model. To justify the effectiveness and the practical value of the proposed model and algorithm, a case of Nanjing Metro Line S1 is illustrated in this paper. The result shows that the optimized train timetable has advantage compared to the original one.

Transit Scheduling Model Research Based on Time-Space Network

This paper try to develop a transit scheduling model based on the time-space network. The object is using the minimum cost to finish the required trips in time-table with the consideration of certain assumptions and rules. In this paper we deal with the multi-depot vehicle schedule optimization problem, addressed as MDVSP, using one time-space network instead of the traditional connection-based networks. Furthermore, we introduce the concept of open-depot, which means the bus may not return the depot where it started.