Holiday Passenger Flow Forecasting Based on the Modified Least-Square Support Vector Machine for the Metro System

Abstract

Holiday passenger flow forecasting is essential to transportation plan-making and passenger flow organization in metro systems during holidays. Usually, daily passenger flow characteristics show a great difference between holidays and normal days, and the annual growth of holiday passenger flow seems more complicated. Least-square support vector machine (LSSVM) is able to handle the complex fluctuations in holiday daily passenger flow, but it suffers from critical parameter selection, and sparseness is also lost in the LSSVM solution. In an attempt to forecast holiday passenger flow accurately, this paper proposes an approach based on the modified LSSVM, in which an improved particle-swarm optimization (IPSO) algorithm is developed to optimize parameters and pruning algorithm is used to achieve sparseness, as well as a new evaluation indicator based on the k-fold cross-validation method to evaluate the training performance. Finally, passenger flow data for Guangzhou Metro stations in China during the National Day holiday from 2011 to 2014 are applied as numerical examples to validate the performance of the proposed approach. The results show that the modified LSSVM model is an effective forecasting approach with higher accuracy than other alternative models.