Integrated Microsimulation Framework for Dynamic Pedestrian Movement Estimation in Mobility Hub

TL;DR

This paper introduces an integrated microsimulation framework that dynamically estimates pedestrian movement in mobility hubs using limited data, combining activity-based modeling, Markov chains, and an iterative convergence approach.

Contribution

The novel framework combines activity-based microsimulation with a Markov chain and Metropolis-Hastings algorithm to estimate demand and trajectories with limited data.

Findings

Successfully calibrated and tested at Montreal Central Station

Can model demand reactions to exogenous scenario changes

Provides dynamic demand estimates with partial observational data

Abstract

We present an integrated microsimulation framework to estimate the pedestrian movement over time and space with limited data on directional counts. Using the activity-based approach, simulation can compute the overall demand and trajectory of each agent, which are in accordance with the available partial observations and are in response to the initial and evolving supply conditions and schedules. This simulation contains a chain of processes including: activities generation, decision point choices, and assignment. They are considered in an iteratively updating loop so that the simulation can dynamically correct its estimates of demand. A Markov chain is constructed for this loop. These considerations transform the problem into a convergence problem. A Metropolitan Hasting algorithm is then adapted to identify the optimal solution. This framework can be used to fill the lack of data or to model the reactions of demand to exogenous changes in the scenario. Finally, we present a case study on Montreal Central Station, on which we tested the developed framework and calibrated the models. We then applied it to a possible future scenario for the same station.