Use of Agent-Based Crowd Simulation to Investigate the Performance of Large-Scale Intermodal Facilities: Case Study of Union Station in Toronto, Ontario, Canada

Abstract

When planning complex transit terminals, hubs, and stations, it is critical to analyze a facility’s capacity to handle expected passenger movements and volumes. In Toronto, Ontario, Canada, the revitalization of Union Station, the country’s busiest transit facility, involved the development of a set of high-fidelity pedestrian microsimulation models that were used to plan the improvement of this major intermodal hub. The pedestrian models were first created with MASSMOTION software, and construction plans and projected transit schedules were applied to represent various scenarios during and after the station revitalization. These models were then calibrated and validated against data from passenger counts and transit usage surveys to provide an accurate base for future scenarios. Through the use of a fully constructed model of the station with 2031 transit demand projections, a series of stress experiments was performed to evaluate the station’s capability to handle future passenger volumes. Under projected 2031 circumstances, it was found that Union Station could operate reasonably well. However, an increase in inbound transit passenger volumes of a mere 10% over 2031 projections could lead to severe levels of congestion within constrained sections of the station. This paper reports on the development of the microsimulation models and the model results for future scenarios.

Capacity Analysis of a Passenger Rail Hub Using Integrated Railway and Pedestrian Simulation

“…In passenger modelling, emphasis is placed on smooth flows. Hoy et al [14] examined passenger flow in Toronto's Union Station using future demand scenarios, indicating that unplanned increases in demand can significantly disrupt passenger flow and station operations. Wang et al [31] attempted an optimization of passenger flow lines at the Zhongchuan high-speed rail station in China, demonstrating that lengthening walk distances in the station can increase overall flow by reducing interference in the station.…”

“…The complexity of modelling both train and pedestrian movements, even individually, results in studies that focus on one or the other, assuming simplified models for the interactions between the two. For example, fixed dwell times that ignore passenger volumes are often assumed in railway simulation [19], while fixed arrival and departure times for trains are assumed in pedestrian simulation [14].…”

Spatial Agents for Geological Surface Modelling

“…Agent applications are extensively used in the entertainment industry (Damiano et al 2013); computer games for sports and battle simulation (Zuparic et al 2017, Guo andSprague 2016), landscape and land use design, management and visualization (Tieskens et al 2017, Valbuena et al 2010; Urban planning (Motieyan.and Mesgari 2018, Levy et al 2016; crowd modelling for public transport and community infrastructure design (Dickinson et al 2019, Hoy andShalaby 2016); Climate change and adaptation modelling (Amadou et al 2018); Architecture and Engineering design Li 2017, Van Dyke Parunak et al 2001) as well as hazard response and real-time three-dimensional mapping (Schlögl et al 2019, Bürkle 2009; transportation and surveillance using semi-automated or fully-autonomous vehicles such as drones and automobiles (Fagnant.and Kockelman 2014, de Swarte et al 2019 (Azam et al 2015) to name a few examples.…”

Spatial agents for geological surface modelling

“…de Kemp: Spatial agents for geological surface modelling their roots in the development of cellular automata and complexity theory, which has been able to model complex natural and artificial systems with simple neighbourhood algorithms (Cervelle and Formenti, 2009;Wolfram, 1994;Von Neumann, 1966). Agent applications are extensively used in the entertainment industry (Damiano et al, 2013); in computer games for sports and battle simulation (Zuparic et al, 2017;Guo and Sprague, 2016), landscape and land use design, management, and visualization (Tieskens et al, 2017;Valbuena et al, 2010); urban planning (Motieyan and Mesgari, 2018;Levy et al, 2016); crowd modelling for public transport and community infrastructure design (Dickinson et al, 2019;Hoy and Shalaby, 2016); climate change and adaptation modelling (Amadou et al, 2018); architecture and engineering design (Guo and Li, 2017;Parunak et al, 2001) as well as hazard response and real-time 3D mapping (Schlögl et al, 2019;Bürkle, 2009); and transportation and surveillance using semi-automated or fully autonomous vehicles, such as drones and automobiles (Fagnant and Kockelman, 2014;de Swarte et al, 2019). Agent-based modelling has been used in the Earth sciences for spatial-temporal, more process-oriented modelling, such as solar storm and flare activity (Schatten, 2013), groundwater modelling (Jaxa-Rozen et al, 2019), and earthquake prediction (Azam et al, 2015) to name a few examples.…”