2020-10-08 01:37:44 +03:00
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#!/usr/bin/python3
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# This example loads an exported JSON map, finds different buildings, and
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# generates a simple travel demand model.
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#
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2021-02-14 02:45:59 +03:00
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# 1) cargo run --bin dump_map data/system/us/seattle/maps/montlake.bin > montlake.json
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2020-10-08 01:37:44 +03:00
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# 2) ./headless/examples/generate_traffic.py --map=montlake.json --out=traffic.json
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2021-02-14 02:45:59 +03:00
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# 3) cargo run --bin import_traffic -- --map=data/system/us/seattle/maps/montlake.bin --input=traffic.json
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# 4) Use data/system/us/seattle/scenarios/montlake/monday.bin in the game or from the API.
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2020-10-08 01:37:44 +03:00
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#
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# Keep this script formatted with autopep8 -i
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import argparse
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import json
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import random
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--map', type=str, required=True)
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parser.add_argument('--out', type=str, required=True)
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args = parser.parse_args()
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# Load the map and find all buildings
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residential_building_ids = []
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commercial_building_ids = []
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2020-10-14 19:28:02 +03:00
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with open(args.map, encoding='utf8') as f:
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2020-10-08 01:37:44 +03:00
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map = json.load(f)
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for b in map['buildings']:
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# These categories are inferred from OpenStreetMap tags
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if 'Residential' in b['bldg_type'] or 'ResidentialCommercial' in b['bldg_type']:
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residential_building_ids.append(b['id'])
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if 'Commercial' in b['bldg_type'] or 'ResidentialCommercial' in b['bldg_type']:
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commercial_building_ids.append(b['id'])
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# Randomly generate a few people who take just one trip
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scenario = {
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'scenario_name': 'monday',
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'people': []
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}
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for _ in range(100):
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src = random.choice(residential_building_ids)
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dst = random.choice(commercial_building_ids)
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scenario['people'].append({
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'origin': {
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'TripEndpoint': {
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'Bldg': src,
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}
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},
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'trips': [{
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'departure': 1.0,
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'destination': {
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'TripEndpoint': {
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'Bldg': dst,
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}
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},
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'mode': 'Bike'
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}]
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})
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with open(args.out, 'w') as f:
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f.write(json.dumps(scenario, indent=2))
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if __name__ == '__main__':
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main()
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