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notebook to trim the transit demand for congested assignment
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notebooks/trim-demand-for-congested-transit-assignment.ipynb
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 1, | ||
"metadata": { | ||
"show_input": true | ||
}, | ||
"outputs": [], | ||
"source": [ | ||
"import inro\n", | ||
"import numpy as np\n", | ||
"import pandas as pd" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 2, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# this notebook is run from inside EMME environment. Therefore it picks up the emmebank and scenario from the project that user opened. \n", | ||
"modeller = inro.modeller.Modeller()\n", | ||
"\n", | ||
"emmebank = modeller.emmebank" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 4, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"scenario = emmebank.scenario(12)\n", | ||
"scen_emmebank = scenario.emmebank" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 5, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"matrices_name_id_map = {\n", | ||
" \"AM_WLK_TRN_WLK_DEMAND\": \"6\",\n", | ||
" \"AM_PNR_TRN_WLK_DEMAND\" : \"7\",\n", | ||
" \"AM_WLK_TRN_PNR_DEMAND\" : \"8\",\n", | ||
" \"AM_KNR_TRN_WLK_DEMAND\" : \"9\",\n", | ||
" \"AM_WLK_TRN_KNR_DEMAND\" : \"10\",\n", | ||
" \"AM_WLK_TRN_WLK_TRIM\": \"155\",\n", | ||
" \"AM_PNR_TRN_WLK_TRIM\" : \"177\",\n", | ||
" \"AM_WLK_TRN_PNR_TRIM\" : \"199\",\n", | ||
" \"AM_KNR_TRN_WLK_TRIM\" : \"221\",\n", | ||
" \"AM_WLK_TRN_KNR_TRIM\" : \"243\",\n", | ||
" \"AM_WLK_TRN_WLK_IVT\": \"146\",\n", | ||
" \"AM_PNR_TRN_WLK_IVT\" : \"168\",\n", | ||
" \"AM_WLK_TRN_PNR_IVT\" : \"190\",\n", | ||
" \"AM_KNR_TRN_WLK_IVT\" : \"212\",\n", | ||
" \"AM_WLK_TRN_KNR_IVT\" : \"224\",\n", | ||
"}" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 6, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"matrix_calc = modeller.tool(\"inro.emme.matrix_calculation.matrix_calculator\")\n" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 7, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"output_file_name = \"D:/kulshresthaa/MTC/TM2.2.1.1/output_summaries/trimmed_demand_report_am.csv\"" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 8, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"report = []\n", | ||
"period = \"AM\"\n", | ||
"class_names = [\"WLK_TRN_WLK\", \"PNR_TRN_WLK\", \"WLK_TRN_PNR\", \"KNR_TRN_WLK\", \"WLK_TRN_KNR\"]" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 9, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"Trimming Demand for: WLK_TRN_WLK ... \n", | ||
" Prev Demand = 281040.4\n", | ||
" Current Demand = 276198.6\n", | ||
" Trimmed Demand = 4841.8125\n", | ||
"Trimming Demand for: PNR_TRN_WLK ... \n", | ||
" Prev Demand = 53121.586\n", | ||
" Current Demand = 52774.56\n", | ||
" Trimmed Demand = 347.02734\n", | ||
"Trimming Demand for: WLK_TRN_PNR ... \n", | ||
" Prev Demand = 1037.2283\n", | ||
" Current Demand = 1024.8384\n", | ||
" Trimmed Demand = 12.389893\n", | ||
"Trimming Demand for: KNR_TRN_WLK ... \n", | ||
" Prev Demand = 37145.855\n", | ||
" Current Demand = 36810.797\n", | ||
" Trimmed Demand = 335.0586\n", | ||
"Trimming Demand for: WLK_TRN_KNR ... \n", | ||
" Prev Demand = 7373.4683\n", | ||
" Current Demand = 7295.744\n", | ||
" Trimmed Demand = 77.72412\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"for name in class_names: \n", | ||
" skim_mat_name = \"%s_%s_IVT\" % (period, name)\n", | ||
" demand_mat_name = \"%s_%s_DEMAND\" % (period, name)\n", | ||
" trim_mat_name = \"%s_%s_TRIM\" % (period, name)\n", | ||
" \n", | ||
" demand_id = matrices_name_id_map[demand_mat_name]\n", | ||
" ivt_id = matrices_name_id_map[skim_mat_name]\n", | ||
" trim_id = matrices_name_id_map[trim_mat_name]\n", | ||
" \n", | ||
" matrix = emmebank.matrix(f'mf{demand_id}')\n", | ||
" prev_demand = matrix.get_numpy_data()\n", | ||
" \n", | ||
" print(\"Trimming Demand for\", name, \"... \")\n", | ||
" print(\" Prev Demand =\", np.sum(prev_demand))\n", | ||
" \n", | ||
" spec_list = [\n", | ||
" { # initialize TRIM skim\n", | ||
" \"type\": \"MATRIX_CALCULATION\",\n", | ||
" \"constraint\": None,\n", | ||
" \"result\": f'mf{trim_id}',\n", | ||
" \"expression\": '0',\n", | ||
" },\n", | ||
" \n", | ||
" { # matrix used for trimming demands, set value to 1 if IVT is not infinite\n", | ||
" \"type\": \"MATRIX_CALCULATION\",\n", | ||
" \"constraint\": {\n", | ||
" \"by_value\": {\n", | ||
" \"od_values\": f'mf{ivt_id}',\n", | ||
" \"interval_min\": 0,\n", | ||
" \"interval_max\": 9999999,\n", | ||
" \"condition\": \"INCLUDE\"}\n", | ||
" },\n", | ||
" \"result\": f'mf{trim_id}',\n", | ||
" \"expression\": '1',\n", | ||
" },\n", | ||
" \n", | ||
" { # update demand tables\n", | ||
" \"type\": \"MATRIX_CALCULATION\",\n", | ||
" \"constraint\": None,\n", | ||
" \"result\": f'mf{demand_id}',\n", | ||
" \"expression\": f'(mf{trim_id} * mf{demand_id})',\n", | ||
" },\n", | ||
" ]\n", | ||
"\n", | ||
" matrix_calc(spec_list, scenario=scenario)\n", | ||
" \n", | ||
" matrix = emmebank.matrix(f'mf{demand_id}')\n", | ||
" current_demand = matrix.get_numpy_data()\n", | ||
" \n", | ||
" print(\" Current Demand =\", np.sum(current_demand))\n", | ||
" \n", | ||
" trimmed_demand = np.sum(prev_demand) - np.sum(current_demand)\n", | ||
" \n", | ||
" print(\" Trimmed Demand =\", trimmed_demand)\n", | ||
" \n", | ||
" report.append((name,trimmed_demand))\n" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 10, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"data": { | ||
"text/plain": [ | ||
"[('WLK_TRN_WLK', 4841.8125),\n", | ||
" ('PNR_TRN_WLK', 347.02734),\n", | ||
" ('WLK_TRN_PNR', 12.389893),\n", | ||
" ('KNR_TRN_WLK', 335.0586),\n", | ||
" ('WLK_TRN_KNR', 77.72412)]" | ||
] | ||
}, | ||
"execution_count": 10, | ||
"metadata": {}, | ||
"output_type": "execute_result" | ||
} | ||
], | ||
"source": [ | ||
"report" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 11, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"pd.DataFrame(report).to_csv(output_file_name, index=False, header=['class','trimmed_demand'])" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.7.6" | ||
}, | ||
"modeller": { | ||
"desktop_app_port": "4242" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 2 | ||
} |