def LA_od_costs(factors, output, verbose=0): ''' compute the OD costs for UE, SO, and UE-K where the cognitive cost is K=3000 and with different demand: alpha * demand for demand in factors save OD costs into csv array with columns demand, X1_so, X1_ue_k, X1_ue, X2_so, X2_ue_k, X2_ue, ... ''' net, demand, node, geom = load_LA_3() demand[:, 2] = demand[:, 2] / 4000. fs_so = np.loadtxt('data/LA/so_single_class.csv', delimiter=',', skiprows=1) fs_ue_k = np.loadtxt('data/LA/ue_k_single_class.csv', delimiter=',', skiprows=1) fs_ue = np.loadtxt('data/LA/ue_single_class.csv', delimiter=',', skiprows=1) costs = [] for i in range(len(factors)): costs.append(cost(fs_so[:, i], net)) costs.append(cost(fs_ue_k[:, i], net)) costs.append(cost(fs_ue[:, i], net)) free_flow_OD_costs(net, costs, demand, output, verbose)
def LA_OD_free_flow_costs(thres, cog_costs, output, verbose=0): ''' computes OD costs (free-flow travel times) for non-routed users under different levels of cognitive costs for links with capacity under thres ''' net, demand, node, geom = load_LA_3() costs = [] for K in cog_costs: net2, small_capacity = multiply_cognitive_cost(net, geom, thres, K) costs.append(net2[:, 3]) free_flow_OD_costs(net, costs, demand, output, verbose)
def LA_OD_free_flow_costs(thres, cog_costs, output, verbose=0): ''' computes OD costs (free-flow travel times) for non-routed users under different levels of cognitive costs for links with capacity under thres ''' net, demand, node, geom = load_LA_3() costs = [] for K in cog_costs: net2, small_capacity = multiply_cognitive_cost(net, geom, thres, K) costs.append(net2[:,3]) free_flow_OD_costs(net, costs, demand, output, verbose)
def LA_od_costs(factors, output, verbose=0): ''' compute the OD costs for UE, SO, and UE-K where the cognitive cost is K=3000 and with different demand: alpha * demand for demand in factors save OD costs into csv array with columns demand, X1_so, X1_ue_k, X1_ue, X2_so, X2_ue_k, X2_ue, ... ''' net, demand, node, geom = load_LA_3() demand[:,2] = demand[:,2] / 4000. fs_so = np.loadtxt('data/LA/so_single_class.csv', delimiter=',', skiprows=1) fs_ue_k = np.loadtxt('data/LA/ue_k_single_class.csv', delimiter=',', skiprows=1) fs_ue = np.loadtxt('data/LA/ue_single_class.csv', delimiter=',', skiprows=1) costs = [] for i in range(len(factors)): costs.append(cost(fs_so[:,i],net)) costs.append(cost(fs_ue_k[:,i],net)) costs.append(cost(fs_ue[:,i],net)) free_flow_OD_costs(net, costs, demand, output, verbose)