Ontario’s Multimodal Province-Wide Model: Validation, Challenges and Data Gaps

Abstract
The Ontario Ministry of Transportation (MTO) uses an integrated, multimodal passenger and freight forecasting model for the province in long-range planning and to support various applications such as corridor-level highway planning and design, highway project prioritization and modal studies. This model – termed Transport and Regional Economic Simulation of Ontario, TRESO – is a multimodal macroscopic model, used to forecast person and freight transportation demand throughout the province of Ontario.
This model connects five key components, namely: a macroeconomic model that is spatially disaggregated, person travel models dealing with urban as well as inter-urban/long-distance travel by residents and visitors, freight models that deal with commodity flows by main modes of rail, marine, intermodal and truck flows on the road, and the underlying “supply-side” common element of the transportation network.


The focus of this paper is “model validation”, which is a process through which model outputs for the base year are compared with observed data to gauge how well a model represents the reality. This paper discusses the details of validation of different components of the TRESO model, data used to perform such validation, while pointing out the data gaps. Validation efforts focus on the following key areas:


• modeled road network volumes versus traffic count
• use of GPS data to compare network performance
• long-distance rail passenger demand compared to rail ridership data
• urban transit travel demand compared to transit ridership data for major urban areas of Ontario

The paper concludes with key observations from the analysis, key challenges, and potential areas of improvements in future model re-calibration and validation exercises.

Author

Chowdhury, Tufayel
Nateghinia, Ehsan
Khan, Mohammad Rashid
Damodaran, Sundar

Session title

Innovations in Transportation Systems Modelling

Category

Transportation Systems Technology

Year

2023

Format

Paper

File

 


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