Capacity-Constrained Network Performance Model for Urban Rail Systems

Abstract

This paper proposes a general network performance model (NPM) for monitoring the performance of urban rail systems using smart card data. NPM is a schedule-based network loading model with strict capacity constraints and boarding priorities. It distributes passengers over the network given origin-destination demand, operations, route choice, and effective train capacity. A Bayesian simulation-based optimization method for calibrating the effective train capacity is introduced, which explicitly recognizes that capacity may be different at different stations depending on congestion levels. Case studies with data from the Mass Transit Railway network in Hong Kong are used to validate the model and illustrate its applicability. NPM is validated using survey data on left-behind passengers and exiting passenger flow extracted from smart card data. The use of NPM for performance monitoring is demonstrated by analyzing the spatial-temporal crowding patterns in the system and evaluating dispatching strategies.

Modeling epidemic spreading through public transit using time-varying encounter network

“…3) This study did not model the contacts of passengers at trains due to difficulties in identifying the vehicles that passengers belong to. Future research can incorporate a transit assignment model ( e.g., Zhu et al, 2017 , Mo et al, 2020 ) to infer passengers’ boarding trains and construct the PEN by trains. Meanwhile, due to the large space in a train, the variation of transmission probability due to passengers’ spatial distribution should also be captured.…”

“…The direct contact in trains is, however, difficult to obtain from smart card data because the transactions are recorded at the station level. To identify the car that passengers boarded on, a transit assignment or simulation model is required ( Zhu et al, 2017 , Mo et al, 2020 , which is beyond the scope of this study.…”

Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data

“…Transit network loading (TNL) models aim to assign passengers over a transit network given the (dynamic) OD entry demand and path choices. In this study, we adopt an event-driven schedulebased TNL model proposed by Mo et al [13]. e model takes OD entry demand (number of tap-in passengers by time), path choices, train arrival and departure times from stations, train capacity, and infrastructure information (e.g., network topology) as inputs and outputs the passengers' tapout times, train loads, waiting times, and other network performance indicators of interest.…”

“…Figure 2 illustrates the main functions of the TNL model [13]. ree objects are defined: train, waiting queue (on the platform), and passengers.…”

“…Xu and Yong proposed a passenger boarding model which revealed that the number of actually boarding passengers in a crowded train was closely related to the number of queuing passengers and train load. Mo et al [13] proposed an effective capacity model that recognized train capacity may vary across stations depending on the corresponding number of queuing passengers and train load. e calibration of train capacity or WTB usually requires the AFC data with passengers' boarding and journey time information.…”