pabloguarda/pesuelogit

Parameter Estimation of LOGIT-based Stochastic User Equilibrium models using computational graphs and day-to-day system-level data

28
/ 100
Experimental

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.

transportation-planning traffic-engineering urban-mobility origin-destination-estimation network-flow-analysis
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 7 / 25

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Stars

10

Forks

1

Language

Python

License

MIT

Last pushed

Jan 31, 2024

Commits (30d)

0

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