Developing a transit desert interactive dashboard: Supervised modeling for forecasting transit deserts

• Published in: PloS one 2024-07, Vol.19 (7), p.e0306782
• Main Authors: Choi, Seung Jun, Jiao, Junfeng
• Format: Article
• Language: English
• Subjects: At risk populations; Biology and Life Sciences; Built Environment; Community; Community involvement; Community participation; Community planning; Computer and Information Sciences; Dashboards; Decision Trees; Demographics; Desert environments; Deserts; Design analysis; Design factors; Design improvements; Disadvantaged groups; Earth Sciences; Ecology and Environmental Sciences; Engineering and Technology; Environmental justice; Female; Food deserts; Forecasting - methods; Gender; Handicapped accessibility; Households; Humans; Identification; Impact analysis; Land use; Learning algorithms; Low income groups; Machine Learning; Male; Medicine and Health Sciences; Minority & ethnic groups; Models, Theoretical; Open spaces; Participatory planning; Planning; Population; Population density; Prediction models; Rankings; Real estate development; Regions; Social Sciences; Supervised learning; Supervised Machine Learning; Support Vector Machine; Support vector machines; Transportation; Urban environments; Women
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0306782

Transit deserts refer to regions with a gap in transit services, with the demand for transit exceeding the supply. This study goes beyond merely identifying transit deserts to suggest actionable solutions. Using a multi-class supervised machine learning framework, we analyzed factors leading to transit deserts, distinguishing demand by gender. Our focus was on peak-time periods. After assessing the Support Vector Machine, Decision Tree, Random Forest, and K-nearest Neighbor, we settled on the Random Forest method, supported by Diverse Counterfactual Explanation and SHapley Additive Explanation in our analysis. The ranking of feature importance in the trained Random Forest model revealed that factors such as density, design, distance to transit, diversity in the built environment, and sociodemographic characteristics significantly contribute to the classification of transit deserts. Diverse Counterfactual Explanation suggested that a reduction in population density and an increase in the proportion of green open spaces would likely facilitate the transformation of transit deserts into transit oases. SHapley Additive Explanation highlighted the differential impact of various features on each identified transit desert. Our analysis results indicate that identifying transit deserts can vary depending on whether the data is aggregated or separated by demographics. We found areas that have unique transit needs based on gender. The disparity in transit services was particularly pronounced for women. Our model pinpointed the core elements that define a transit desert. Broadly, to address transit deserts, strategies should prioritize the needs of disadvantaged groups and enhance the design and accessibility of transit in the built environment. Our research extends existing analyses of transit deserts by leveraging machine learning to develop a predictive model. We developed a machine learning-powered interactive dashboard. Integrating participatory planning approaches with the development of an interactive interface could enhance ongoing community engagement. Planning practices can evolve with AI in the loop.