Dynamic schedule-based assignment model for urban rail transit network with capacity constraints.

Area of Science:

• Transportation Engineering
• Urban Planning
• Network Analysis

Background:

• Estimating passenger flow distribution in urban rail transit is crucial but challenging.
• Existing models often lack capacity constraints and overload delay factors for schedule-based networks.
• In-vehicle crowding and its impact on passenger experience require further investigation.

Purpose of the Study:

• To develop a stochastic user equilibrium model for passenger flow assignment in schedule-based rail transit networks.
• To incorporate capacity constraints and overload delay factors to accurately reflect in-vehicle crowding.
• To dynamically estimate passenger flow distribution considering time-space paths and passenger cost minimization.

Main Methods:

• Developed a dynamic schedule-based assignment model by splitting origin-destination demands into a time-space path network.
• Formulated stochastic user equilibrium conditions equivalent to passenger overload delay and crowding penalty.
• Utilized a numerical example of the Beijing Urban Rail Transit (BURT) network for validation.

Main Results:

• The proposed model effectively estimates passenger flow temporal and spatial distribution.
• It dynamically accounts for train capacity constraints and their impact on passenger choices.
• The model provides a more reasonable and realistic estimation of passenger flow compared to previous methods.

Conclusions:

• The stochastic user equilibrium model offers a robust approach for passenger flow assignment in schedule-based transit networks.
• Considering capacity constraints and crowding is essential for accurate passenger flow estimation.
• The model enhances the understanding of passenger behavior and network performance under capacity limitations.