Research map / Learning theory and algorithms / Combinatorial and search optimization
Walkability Optimization: Formulations, Algorithms, and a Case Study of Toronto
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MILP instead of CP | WalkOpt · Toronto NIAs, SingleChoice, Groups 1–4 | MRE (%): Group 1: 0.00 vs 0.00; Group 2: 0.00 vs 0.01; Group 3: 0.00 vs 0.04; Group 4: 0.00 vs 0.91 | Mixed |
| Use MILP instead of CP | WalkOpt · Toronto NIAs, MultiChoice, Groups 1–4 | MRE (%): Group 1: 0.43 -> 0.00; Group 2: 3.34 -> 0.00; Group 3: 12.41 -> 0.00; Group 4: 26.13 -> 0.05 | Helped |
| Use Greedy instead of CP | WalkOpt · Toronto NIAs, MultiChoice, Groups 2–4 | MRE (%): Group 2: 3.34 -> 0.51; Group 3: 12.41 -> 0.57; Group 4: 26.13 -> 0.20 | Helped |
| Use Greedy as the solution method | WalkOpt · Toronto NIAs, both scenarios, all groups | MRE (%): The MRE of Greedy is lower than 0.7% for all groups in both scenarios. | Helped |
| Use Greedy instead of MILP or CP | WalkOpt · Toronto NIAs, both scenarios | solving time | Helped |
| Include restaurant depth of choice | WalkOpt · WalkOpt objective, theoretical analysis | submodularity | Hurt |
| Allocate 3 additional amenities of each type | WalkOpt · 4 Toronto NIAs | WalkScore improvement: more than 50 points | Helped |
| Allocate k=3 additional amenities | WalkOpt · Residential locations across Toronto NIAs, MultiChoice | 75th-percentile walking time: to 10 minutes | Helped |
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