干旱区生态环境知识资源中心(Arid): Developing a transit desert interactive dashboard: Supervised modeling for forecasting transit deserts

Arid

DOI	10.1371/journal.pone.0306782
	Developing a transit desert interactive dashboard: Supervised modeling for forecasting transit deserts
	Choi, Seung Jun; Jiao, Junfeng
通讯作者	Choi, SJ
来源期刊	PLOS ONE
ISSN	1932-6203
出版年	2024
卷号	19 期号:7
英文摘要	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.
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:001277539200013
WOS关键词	TRANSPORTATION EQUITY ; BUILT ENVIRONMENT ; PUBLIC-TRANSIT ; NORTHERN ; WOMEN
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405196
推荐引用方式 GB/T 7714	Choi, Seung Jun,Jiao, Junfeng. Developing a transit desert interactive dashboard: Supervised modeling for forecasting transit deserts[J],2024,19(7).
APA	Choi, Seung Jun,&Jiao, Junfeng.(2024).Developing a transit desert interactive dashboard: Supervised modeling for forecasting transit deserts.PLOS ONE,19(7).
MLA	Choi, Seung Jun,et al."Developing a transit desert interactive dashboard: Supervised modeling for forecasting transit deserts".PLOS ONE 19.7(2024).