Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

Abstract

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

Heterogeneity in Personal Attitudes and Preferences for High-Speed Metropolitan Railway Service by Travel Contexts in South Korea: A Hybrid Choice Modeling Approach

“…Studies on urban or suburban express railway services like GTX have found that the properties of express railways such as mobility (3) and punctuality (4) have a great influence on the mode choice process. In addition, the possibility of using the suburban express rail is also increased through reducing the out-vehicle time (5) by improving accessibility or strengthening the linkage of modes and enhancing convenience by reducing congestion (6,7) in the vehicle and similar results are obtained from studies on Gyeong-in Line and Line 9 (8)(9)(10) in the metropolitan area of Seoul based on transportation card data. Although these studies could provide meaningful insights about properties of express rail or suburban rail services in mode choice process, diverse travel contexts which might affect the utility of using the express railway services tend to be ignored.…”

Prediction Models for Individuals' Control Skill Development and Retention using XGBoost and SHAP

“…This section will briefly summarize the gradient boosting regression tree architecture and the XGBoost model structure as described in detail in [31][32][33][34][35]. GBRT is an advanced decision tree model that uses the concept of boosting, i.e., combining weak learners with other, iteratively formed, weak learners (decision trees) to form a strong predictor.…”

Equity in Active and Shared Transportation: An Investigation of Barriers and Individual and Contextual Factors

“…This technique has been used extensively in traffic safety studies, including crash injury prediction, to better interpret risk factors ( 45 , 46 ). In recent years, SHAP has also been used to estimate and interpret transportation mode preferences from a variety of data sources, including smart cards and travel surveys ( 47 , 48 ).…”