A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger-Train Matching...

Area of Science:

• Transportation Science
• Operations Research
• Urban Planning

Background:

• Increasing travel demand strains subway systems, necessitating scientific operational planning.
• Accurate passenger flow estimation is vital for optimizing subway operations and management.
• Existing methods often overlook passenger-train matching probability and struggle with large-scale networks.

Purpose of the Study:

• To develop an efficient method for estimating passenger flow distribution in large-scale subway networks.
• To incorporate passenger-train matching probability into passenger flow distribution models.
• To address the computational complexity limitations of current approaches.

Main Methods:

• Analyzing passenger travel behavior and train operations in spatio-temporal dimensions.
• Formulating passenger-train matching probability using automated fare collection (AFC) data, train timetables, and network topology.
• Proposing a reverse derivation method to enhance computational efficiency by reducing train combinations.
• Presenting an estimation method for passenger flow distribution based on calculated probabilities.

Main Results:

• The proposed method demonstrates good accuracy in estimating passenger flow distribution.
• The reverse derivation approach significantly improves computational efficiency.
• The method is effective for practical application in large-scale subway networks.
• Experimental validation using synthetic and real-world Beijing subway data confirms effectiveness.

Conclusions:

• The developed method accurately estimates passenger flow distribution by considering passenger-train matching probability.
• The approach offers improved computational efficiency, making it suitable for large-scale subway systems.
• This research provides a valuable tool for subway operators to enhance planning and management strategies.