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- W3022903754 abstract "This paper documents the efforts to operationalize the conceptual framework of 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). The transit service is represented by over 500 branches with more than 10,000 stops. About 332,000 passenger agents are modeled 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 modeling 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. A Parallel Genetic Algorithm engine was used for the parameter calibration process. The modeled 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." @default.
- W3022903754 created "2020-05-13" @default.
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- W3022903754 date "2009-01-01" @default.
- W3022903754 modified "2023-09-24" @default.
- W3022903754 title "Large-Scale Application of MILATRAS: Case Study of the Toronto Transit Network" @default.
- W3022903754 hasPublicationYear "2009" @default.
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