To Redefine, Not Reinforce: A Spatial Decision Support System with Generative Design Model for Exploring Optimal Improvements to Existing Street Networks for Enhancing Equity of Accessibility

Abstract

Transport decision-making determines people’s level of accessibility and deeply influences an individual’s access to social and economic opportunities and the quality of life. Socially vulnerable populations are highly dependent on yet often more likely to have less access to transport services and experience lower accessibility. This creates and reinforces social and spatial inequalities by trapping people in disconnected neighborhoods and segregated areas that continue to be deprived of access to opportunities. This research aims to develop a Spatial Decision Support System (SDSS) to explore how re-purposing existing streets for walking and biking could influence the accessibility of vulnerable neighborhoods to support decision-making in enhancing equity of accessibility. In the SDSS, equity of accessibility is formulated as a generative design (GD) problem, named the Street Allocation Decision Problem (SADP), a single-objective optimization problem that searches for generated designs with the maximum weighted improvement in accessibility per unit of its cost. A GD model is built to solve SADP. Lastly, an operational framework is developed to guide prospective users in tuning and operating the SDSS for their specific context, problem, and objective. The SDSS is tested on a toy problem, a 0.09km2 area in The Hague, The Netherlands. The toy problem is small in scale, easy and fast to implement and useful for initial testing of the model. Preliminary results have demonstrated the feasibility of the model. This is the first and humble attempt at developing an SDSS for enhancing equity of accessibility with a GD model. However, there are shortcomings in methodology and result quality, which compromise the practicality of the model and the interpretability of results. Although, only a proof of concept at the moment, the SDSS is a valuable starting point due to its advantages, such as transparency, modularity, humane-ness, and flexibility. This SDSS is built to involve decision-makers in the design process, which could serve as a useful learning experience for questioning and understanding what is the problem, what is considered equitable, and what are possible solutions. The SDSS has the potential to facilitate learning during transport decision-making in experimental settings or as explorative aids in early design stages.Industrial Ecolog