pabloguarda/pesuelogit
Parameter Estimation of LOGIT-based Stochastic User Equilibrium models using computational graphs and day-to-day system-level data
This helps transportation planners and traffic engineers understand and predict how traffic flows through a network. It takes system-level traffic data, collected across various times and days, to estimate both the origin-destination patterns of trips and critical parameters influencing route choices. The output helps in better planning for congestion, infrastructure needs, and policy impacts.
No commits in the last 6 months.
Use this if you need to accurately estimate unobserved travel demand (Origin-Destination matrices) and calibrate traffic assignment model parameters using real-world traffic counts and system data.
Not ideal if you are looking for a real-time traffic prediction system or if your primary need is individual driver behavior modeling rather than aggregate flow estimation.
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10
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Language
Python
License
MIT
Category
Last pushed
Jan 31, 2024
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