用python写UE用户均衡程序的代码

您好，以下是用Python编写UE用户均衡程序的代码：

import numpy as np

def UE_user_equilibrium(freeflows, demands, capacities):
    # Implementation of User Equilibrium (UE) traffic assignment model
    # Solves Wardrop's first principle (user costs are equal for all used paths)
    
    # Inputs:
    # freeflows: n x m vector of free flow travel times, with n as the number of zones and m as the number of routes
    # demands: n x 1 vector of trip demands originating in each zone
    # capacities: m x 1 vector of route capacities
    
    # Outputs:
    # flows: m x 1 vector of flow volumes on each route
    # costs: n x 1 vector of user costs experienced in each zone
    
    n = freeflows.shape[0]
    m = freeflows.shape[1]

    # Initialize link flows
    f = np.zeros((m,))

    # Loop until convergence
    is_converged = False
    while not is_converged:
        # Compute travel costs for all routes
        c = freeflows + np.dot(np.reshape(f, (1, m)), np.ones((1, n))).T

        # Compute link costs for all zones
        C = np.zeros((n,))
        for i in range(n):
            D_i = demands[i]
            C[i] = np.min(C[i] + D_i * np.min(c[i, :]))

        # Update link flows
        f_new = np.zeros((m,))
        for j in range(m):
            f_new[j] = np.max([0.0, f[j] + capacities[j] * (demands/C)[j]])

        # Check for convergence
        if np.linalg.norm(f_new - f) < 1e-4:
            is_converged = True
        else:
            f = f_new

    # Compute final travel costs and flows
    costs = np.zeros((n,))
    for i in range(n):
        D_i = demands[i]
        costs[i] = np.min(freeflows[i, :] + D_i * np.min(c[i, :]))
    flows = f_new

    # Return results
    return flows, costs

请注意，这只是一个示例代码，您需要根据具体情况进行调整和修改。希望能对您有所帮助！