vix.ing · top · new · best · stats · spec

Walkability Optimization: Formulations, Algorithms, and a Case Study of Toronto

2022/12/10 by Weimin Huang, Elias B. Khalil, Huang, Weimin +1
Engineering · Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Optimization and Control (math.OC) #Smart Parking Systems Research #Transportation Planning and Optimization #Urban Transport and Accessibility

paper · pdf · doi:10.48550/arxiv.2212.05192

openalex publication_date 2022/12/10 · openalex created_date 2022/12/26 · openalex updated_date 2026/07/28

Abstract

The concept of walkable urban development has gained increased attention due to its public health, economic, and environmental sustainability benefits. Unfortunately, land zoning and historic under-investment have resulted in spatial inequality in walkability and social inequality among residents. We tackle the problem of Walkability Optimization through the lens of combinatorial optimization. The task is to select locations in which additional amenities (e.g., grocery stores, schools, restaurants) can be allocated to improve resident access via walking while taking into account existing amenities and providing multiple options (e.g., for restaurants). To this end, we derive Mixed-Integer Linear Programming (MILP) and Constraint Programming (CP) models. Moreover, we show that the problem's objective function is submodular in special cases, which motivates an efficient greedy heuristic. We conduct a case study on 31 underserved neighborhoods in the City of Toronto, Canada. MILP finds the best solutions in most scenarios but does not scale well with network size. The greedy algorithm scales well and finds near-optimal solutions. Our empirical evaluation shows that neighbourhoods with low walkability have a great potential for transformation into pedestrian-friendly neighbourhoods by strategically placing new amenities. Allocating 3 additional grocery stores, schools, and restaurants can improve the "WalkScore" by more than 50 points (on a scale of 100) for 4 neighbourhoods and reduce the walking distances to amenities for 75% of all residential locations to 10 minutes for all amenity types. Our code and paper appendix are available at https://github.com/khalil-research/walkability.

Related