Abstract
In recent years, with the development of high-speed railway in China, the operating mileage and passenger transport capacity have increased rapidly. Due to the high density of trains in the daytime, we usually set up skylights at night within 0:00-6:00 on high-speed railway for comprehensive maintenance, which contradict with the operation demand of D-series overnight high-speed trains (overnight D-trains for short). In order to dynamically 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, the purpose of this paper is to predict transfer passenger demand by formulating a mixed logit model based on nonlinear random utility functions for different transport modes. Firstly 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. And then we adopt Metropolis-Hastings Algorithm to iteratively solve the probabilities of discrete random variables in utility functions. After that, 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. Finally, a real-world instance with related data of Beijing-Shanghai corridor, is implemented to demonstrate the performance and effectiveness of the proposed approaches.
Mixed Logit Model Based Diagnostic Analysis of Bicycle-Vehicle Crashes at Daytime and Nighttime
