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.
翻译:城市的步行发展概念因其公共卫生、经济和环境可持续性的好处而日益受到更多关注。不幸的是,土地分区和历史性投资不足导致居民之间在步行能力和社会不平等方面的空间不平等。我们通过组合优化的透镜处理步行能力优化问题。我们的任务是选择可以分配更多设施的地点(例如杂货店、学校、餐馆),以便通过步行改善居民的出入,同时考虑现有的便利条件并提供多种选择(例如餐馆)。为此,我们产生了混合- Interger线性方案(MILP)和 Constrain 程序(CP)模型。此外,我们的经验评估表明,在特殊情况下,问题的目标功能是次式的,这鼓励了高效的贪婪。我们对加拿大多伦多市的31个服务不足的街区进行了案例研究。 MILP在多数情况下找到最佳的解决方案,但与网络规模不相称。贪婪的算法尺度和近于最佳的解决方案。我们的经验评估显示,在低行车能力区/康规划(MILS)中,在50个特殊情况下,问题的目标功能是次要的亚模式功能,这能让所有行进/行道标准级的行进标准更适合城市的街道的街道, 将所有行进标准 将所有行道/行进标准级的行进标准 级的行进标准级标准 向更多的商店的行进标准级的行进标准 向更到更适合到更方便的行进标准。