Large-scale application of MILATRAS: case study of the Toronto transit network

Abstract

This paper documents the efforts to operationalize the conceptual framework of MIcrosimulation Learning-based Approach to TRansit Assignment (MILATRAS) and its component models of departure time and path choices. It presents a large-scale real-world application, namely the multi-modal transit network of Toronto which is operated by the Toronto Transit Commission (TTC). This large-scale network is represented by over 500 branches with more than 10,000 stops. About 332,000 passenger-agents are modelled to represent the demand for the TTC in the AM peak period. A learning-based departure time and path choice model was adopted using the concept of mental models for the modelling of the transit assignment problem. The choice model parameters were calibrated such that the entropy of the simulated route loads was optimized with reference to the observed route loads, and validated with individual choices. A Parallel Genetic Algorithm engine was used for the parameter calibration process. The modelled route loads, based on the calibrated parameters, greatly approximate the distribution underlying the observed loads. 75% of the exact sequence of transfer point choices were correctly predicted by the offstop/on-stop choice mechanism. The model predictability of the exact sequence of route transfers was about 60%. In this application, transit passengers were assumed to plan their transit trip based on their experience with the transportation network; with no prior (or perfect) knowledge of service performance.

Agent-Based Modeling and Simulation for the Bus-Corridor Problem in a Many-to-One Mass Transit System

“…Wahba and Shalaby [26] took a significant step toward the advancement of TAP modeling by providing the operational integrated dynamic modeling framework MILATRAS; departure time and path choices are considered in the framework. MILATRAS has been applied to a largescale real-world transit network and exhibited promising predictability [27]. The core of MILATRAS is a departure time and transit path choice model based on the Markovian decision process, which can be found in [28].…”

A Predictive Model of Ride Satisfaction in Mass Transit Users

“…Predicting the effects of service changes on objective ride experiences is becoming more feasible via the use of agent-based models (ABMs). These computational models simulate each vehicle and/or passenger as an independent agent, and so capture aggregate trends by modeling individual-level behaviours and interactions (McDonnell & Zellner, 2011;Othman, Legara, Selvam, & Monterola, 2015;Wahba & Shalaby, 2011). This micro-level simulation means ABMs are able to capture higher-level phenomena which emerge from lower-level interactions and feedback loops.…”

Analysing improvements to on-street public transport systems: a mesoscopic model approach

“…And Fernandez et al (2010) evaluates the effects of station layouts and operational strategies in terms of passenger interchanges, bus operations at stops and stop capacity within busways. Other studies have analysed the applicability of mesoscopic models on large-scale test networks (Nuzzolo et al 2016) and real networks (Wahba and Shalaby 2011;Neumann et al 2012). …”