Abstract
Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station.
Highlights
In recent decades, metros with the characteristics of a large volume, high speed, low pollution, low resource consumption, low energy consumption, having a convenient and comfortable ride, and being in line with the principles of sustainable development have become the first choice for major cities to solve traffic congestion and develop public transportation [1]
We propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), which is comprised of a cycle-based least squares support vector machine (W-LSSVM) and a day-based least squares support vector machine (D-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow, respectively
This paper establishes a novel SN-LSSVM hybrid model for short-term subway passenger flow prediction, which is composed of three stages: traffic data profiling, passenger flow analysis, and predictive modelling
Summary
Metros with the characteristics of a large volume, high speed, low pollution, low resource consumption, low energy consumption, having a convenient and comfortable ride, and being in line with the principles of sustainable development have become the first choice for major cities to solve traffic congestion and develop public transportation [1]. In metro-related research, short-term ridership forecasting plays a crucial role in improving the efficiency of metro systems, which has been an important part of intelligent transportation systems, for example, motivations and benefits that include alleviating station congestion, informing travelers about traffic conditions and providing real-time traffic monitoring and management. An increasing number of studies have been conducted to address the metro ridership pressure using short-term predictions, improving the metro service quality. Short-term passenger flow prediction methods in the existing literature primarily include three types: linear prediction methods (LPs) [4], nonlinear prediction methods (NLPs) [5,6,7,8,9,10,11,12], and combined model prediction methods (CMs) [13,14,15,16,17,18,19]. Motivated by concerns for the environment and Promet – Traffic&Transportation, Vol 33, 2021, No 2, 217-231
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