Bus Single-Trip Time Prediction Based on Ensemble Learning

Abstract

The prediction of bus single-trip time is essential for passenger travel decision-making and bus scheduling. Since many factors could influence bus operations, the accurate prediction of the bus single-trip time faces a great challenge. Moreover, bus single-trip time has obvious nonlinear and seasonal characteristics. Hence, in order to improve the accuracy of bus single-trip time prediction, five prediction algorithms including LSTM (Long Short-term Memory), LR (Linear Regression), KNN (K-Nearest Neighbor), XGBoost (Extreme Gradient Boosting), and GRU (Gate Recurrent Unit) are used and examined as the base models, and three ensemble models are further constructed by using various ensemble methods including Random Forest (bagging), AdaBoost (boosting), and Linear Regression (stacking). A data-driven bus single-trip time prediction framework is then proposed, which consists of three phases including traffic data analysis, feature extraction, and ensemble model prediction. Finally, the data features and the proposed ensembled models are analyzed using real-world datasets that are collected from the Beijing Transportation Operations Coordination Center (TOCC). Through comparing the predicting results, the following conclusions are drawn: (1) the accuracy of predicting by using the three ensemble models constructed is better than the corresponding prediction results by using the five sub-models; (2) the Random Forest ensemble model constructed based on the bagging method has the best prediction accuracy among the three ensemble models; and (3) in terms of the five sub-models, the prediction accuracy of LR is better than that of the other four models.

Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG

“…These results deviate from those of previous studies that indicated better predictive capabilities of LSTM than the RF on time-series data [ 31 ]. However, these results agree with the findings of other investigations that reported comparable predictive performance between these two ML models [ 32 , 33 ]. We postulate that one contributing factor to this inconsistency may be the limited sample size and relatively uncomplicated model structure employed in this study [ 32 ].…”

Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19

“…Unlike the GBDT, the complexity of the model is controlled by using a regular function, so it is an "upgraded" version of the GBDT model. The objective function of the model is shown in Equation (6).…”

“…Sun et al [5] used the data of Xi'an Metro Line 2 to prove that the prediction junction effect of XGBoost is better than that of the back propagation neural network model and ARMA model. Huang et al [6] used three integration methods, including Adaboost, to integrate five models: long short-term memory (LSTM), linear regression, K-nearest neighbor (KNN), XGBoost and gate recursive unit (GRU), and then predicted the single bus trip time. Xu et al [7] realized short-term passenger flow prediction by establishing the GBDT model and proved that the model was superior to the linear model and back propagation neural network.…”