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This project constructs a Nested Logit-based Stochastic User Equilibrium (SUE) transportation analysis framework for Tehran, supporting multi-modal transportation system modeling, MSA solution algorithm, two-stage parameter calibration, and policy scenario analysis.
Section 01
This project constructs a Nested Logit-based Stochastic User Equilibrium (SUE) transportation analysis framework for Tehran, supporting multi-modal transportation system modeling, MSA solution algorithm, two-stage parameter calibration, and policy scenario analysis. It aims to address challenges such as traffic congestion and air pollution in megacities.
Section 02
Traditional traffic assignment models assume users choose the shortest path, ignoring the randomness and heterogeneity of travel behavior. The Stochastic User Equilibrium (SUE) theory considers the random distribution of perception errors, describing a state where no traveler can unilaterally improve their perceived utility. It is more realistic and provides a robust foundation for policy analysis.
Section 03
The Nested Logit (NL) model handles the correlation of choice alternatives through a hierarchical structure: the top layer selects travel modes (private car, public transit, etc.), and the bottom layer selects specific routes. Compared to the Multinomial Logit model, it can capture differences in substitution elasticity between different modes (e.g., the substitutability between subway and bus is higher than between subway and private car).
Section 04
The Method of Successive Averages (MSA) is used to iteratively approximate equilibrium, updating traffic allocation in each iteration. Through a path storage strategy plus column generation technology to dynamically add new paths, the optimized model can handle large-scale networks with thousands of nodes in Tehran.
Section 05
Two-stage calibration: The first stage uses observed traffic flow to estimate link cost parameters, and the second stage uses travel surveys to calibrate NL choice parameters. It integrates automatic counter data, GPS trajectory data, and household survey data, quantifies parameter uncertainty through Bayesian inference, and supports sensitivity analysis.
Section 06
Adapted to Tehran's characteristics: subway-bus transfer system, downtown congestion, morning and evening peak tidal phenomena; considers non-motorized transportation (walking/bicycling in old urban areas) and the impact of gender differences on travel behavior; inputs include road network geometry, public transit schedules, fares, and land use data.
Section 07
Supports policy simulations: road capacity adjustment, public transit service optimization, pricing strategies (congestion/parking fees), demand management (flexible working hours). Output evaluation indicators: average travel time, total vehicle kilometers traveled, emission estimates, changes in consumer surplus.
Section 08
Technical implementation: Python ecosystem (NumPy/SciPy/NetworkX/Pandas), modular design, supports multi-core/cluster computing; open-source contribution reduces the learning threshold. Extension directions: migration to other cities, dynamic traffic assignment, integration with demand forecasting models; provides low-cost analysis tools for developing countries.
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