Instances for computational experiments in this projects are created from Neigbhourhood Improvement Areas (NIAs) at the City of Toronto. The Pedestrian Netowrk data (pednet.zip) can be obtained from the publicly available dataset created by the City of Toronto: https://github.com/gcc-dav-official-github/dav_cot_walkability/tree/master/data. Information on NIAs is publicly available from The City of Toronto’s Open Data Portal: https://www.toronto.ca/city-government/data-research-maps/neighbourhoods-communities/neighbourhood-profiles/nia-profiles/
To preprocess Pedestrian Network data:
python prepare_data.py
To preprocess data for each NIA (shortest path pairs, existing amenity instances, residential locations, and candidate allocation locations for each NIA):
python prepare_data.py --nia NIA_ID
To run optimization models:
python optimize.py MODEL_NAME NIA_ID --k_array k_{grocery}, k_{restaurant}, k_{school}
- Options for MODEL_NAME are:
- OptMultipleDepth: MILP, MultiChoice case
- OptMultiple: MILP, SingleChoice case
- OptMultipleDepthCP: CP, MultiChoice case
- OptMultipleCP: CP, SingleChoice case
- GreedyMultipleDepth: Greedy, MultiChoice case
- GreedyMultiple: Greedy, SingleChoice case
To get model comparison and empirical evaluation:
python results_summary.py
To visualize allocated amnenities:
python viz.py