Abstract
In recent years, with the development of high-speed railway in China, the operating mileage and passenger transport capacity have increased rapidly in transportation industry. Due to the high density of trains in the daytime, we usually set up skylights at night (0:00–6:00 am) on high-speed railway for comprehensive maintenance. However, this arrangement contradicts with the operation demand of D-series overnight high-speed trains (overnight D-trains for short). In order to adjust the operation plan of overnight D-trains with skylights coordinately, it is necessary to predict the passenger demand of newly added overnight D-trains. Therefore, in this paper, a mixed logit model based on nonlinear random utility functions for different transport modes is proposed, in order to predict transfer passenger demand. According to Maximum Simulated Likelihood Method, the likelihood function of this mixed logit model is proposed to maximize the overall utility value of different passenger groups while Metropolis–Hastings algorithm is adopted to iteratively solve the probabilities of discrete random variables in utility functions. After that, the unknown distributions of parameters are estimated and the optimal solution of this model is provided by traditional algorithms, basic heuristic algorithms and improved heuristic algorithms including improved fireworks-simulated annealing algorithm proposed in this paper, respectively. Finally, a real-world instance with related data of Beijing–Shanghai corridor is implemented to demonstrate the performance and effectiveness of the proposed approaches.
Highlights
With the continuous improvement of railway network, the connectivity across the regions and travel demand of passenger are growing with an unprecedented speed
We estimate the unknown distributions of elements in parameter vectors and solve the optimal solution of this model by traditional algorithms, basic heuristic algorithms and improved heuristic algorithms including Imporved Fireworks-Simulated Annealing Algorithm which is proposed in this paper, respectively
Mixed logit model based on nonlinear random utility functions: The probabilities of discrete random variables can be solved and the coefficient distributions and the optimal solutions of the model are estimated by an improved heuristic algorithm
Summary
With the continuous improvement of railway network, the connectivity across the regions and travel demand of passenger are growing with an unprecedented speed. Due to the heavily congested passenger flow in peak hours, sometimes passenger demand still cannot be satisfied even with the maximum departure frequency. To release traffic pressure and solve the transportation problems, such as the mismatch between transportation resource allocation and passenger demand, overnight D-trains have been constructed and operated between cities (e.g., Beijing-Guangzhou, ShanghaiShenzhen, Beijing-Shanghai, etc.). The skylight time of the railway is closely related to practical operation plan of overnight D-trains, e.g., the carrying capacity, departure quantity and departure frequency, in essence determines the number of passengers allowed to board. Considering the optimal combination utility value with transfer modes and transport modes of different passenger groups in the prediction methods is a new problem, which can improve the accuracy of the prediction results with more practical to a great extent. Keywords Newly-added overnight D-trains · Nonlinear random utility functions · Metropolis-Hastings Algorithm · Imporved Fireworks-Simulated Annealing Algorithm
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