Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach

Abstract

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.

Model Hibrid Genetic-Xgboost Dan Principal Component Analysis Pada Segmentasi Dan Peramalan Pasar

“…terutama pada kasus-kasus non linier (H. Wang, Lei, Zhang, Zhou, & Peng, 2019). Model-model machine learning yang juga populer pada masalah segmentasi adalah artificial neural network (ANN) (Pourdaryaei et al, 2019), support vector regression (SVR) (Li, Wang, Cheng, & Bai, 2020), dan metode Convolutional Neural Network (CNN). Disisi lain, metode-metode di atas sering terjebak pada optimum lokal dan overfitting (Cai et al, 2020).…”

A Generic and Efficient Globalized Kernel Mapping-Based Small-Signal Behavioral Modeling for GaN HEMT

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Short-Term Load Forecasting Based on Integration of SVR and Stacking

“…The SVR method has been widely investigated [22][23][24]. In [22], the method adopted the load features and the temperature features extracted by individual LSTM networks provides good forecasting results.…”

“…In [23], a method based on EMD and SVR was used to forecast wind speed. In [24], a model based on affinity propagation SVR was used to forecast power load, and seek the optimal hyperparameters by particle swarm optimization. With regard to the recent research works, two problems arise in the use of SVR to forecast the data.…”