Time prediction model of subway transfer

Area of Science:

• Transportation Engineering
• Urban Planning
• Predictive Modeling

Background:

• Subway systems face challenges in managing schedules due to unpredictable passenger flow.
• Accurate prediction of passenger walking times is crucial for efficient subway operations and passenger experience.

Purpose of the Study:

• To develop a practical and effective model for predicting passenger walking times in subway transfer stations.
• To provide a quantitative basis for optimizing subway schedule management and passenger guidance.

Main Methods:

• Utilized transfer passenger flow and pedestrian facility data from Beijing's Chaoyangmen station for model calibration.
• Employed curve fitting techniques to determine model parameters based on various pedestrian facility types.
• Validated the model using data from four large-volume transfer stations.

Main Results:

• Established a robust relationship between transfer walking speed and passenger volume.
• Demonstrated the model's effectiveness and practicality in real-world subway environments.
• Achieved accurate predictions for walking times, aiding in real-time operational adjustments.

Conclusions:

• The developed walking time prediction model offers a valuable tool for enhancing subway operational efficiency.
• The model provides real-time transfer scheme references for passengers and supports improved subway scheduling.
• This research contributes to more effective management of urban public transportation systems.