caa a22 4500 445890452 CHVBK 20180317145614.0 cr unu---uuuuu 170323e20111101xx s 000 0 eng 10.1007/s11116-011-9358-5 doi (NATIONALLICENCE)springer-10.1007/s11116-011-9358-5 Large-scale application of MILATRAS: case study of the Toronto transit network [Elektronische Daten] [Mohamed Wahba, Amer Shalaby] 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 off-stop/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. Springer Science+Business Media, LLC., 2011 Public transportation nationallicence Transportation models nationallicence Travel demand nationallicence Experiential learning nationallicence Genetic algorithm nationallicence Wahba Mohamed Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, V6T 1Z4, Vancouver, BC, Canada aut Shalaby Amer Department of Civil Engineering, University of Toronto, 35 St. George Street, M5S 1A4, Toronto, ON, Canada aut Transportation Springer US; http://www.springer-ny.com 38/6(2011-11-01), 889-908 0049-4488 38:6<889 2011 38 11116 https://doi.org/10.1007/s11116-011-9358-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11116-011-9358-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wahba Mohamed Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, V6T 1Z4, Vancouver, BC, Canada aut NATIONALLICENCE 700 1- Shalaby Amer Department of Civil Engineering, University of Toronto, 35 St. George Street, M5S 1A4, Toronto, ON, Canada aut NATIONALLICENCE 773 0- Transportation Springer US; http://www.springer-ny.com 38/6(2011-11-01), 889-908 0049-4488 38:6<889 2011 38 11116 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer