Problem
Research questions and friction points this paper is trying to address.
smart-city mobility
transit desert
user-centered
Beyond the Desert Label: A Pathway Diagnostic for User-Centered Smart Mobility Service Design
🤖 AI Summary
本文提出一种基于路径的诊断方法，利用开放数据区分服务不匹配和基本服务缺失，以支持用户中心的智能出行服务设计。
📝 Abstract
Smart-city mobility platforms increasingly rely on spatial screening tools to identify neighborhoods where public transit fails dependent users, but a single transit desert label can mask very different user problems: localized mismatch between service and concentrated need, or basic absence of usable service. These call for different user-centered responses. This paper introduces a pathway-based, reproducible, data-driven diagnostic that distinguishes relative transit mismatch from minimum-service failure and reports the specific service attributes (frequency, span, weekend service, walking access, and destination accessibility) driving each classification. The workflow combines open data (GTFS, ACS, LEHD, Census, and OpenStreetMap), detects spatially coherent mismatch using Local Moran's I, and applies an equity-informed service-failure test that centers vulnerable users. Applied to Baltimore, Philadelphia, Nashville, and Dallas, the diagnostic shows that legacy-transit cities are dominated by localized mismatch, while auto-oriented cities show broader minimum-service failure, with distinct service-deficit profiles in each case. By making the mechanism behind an under-service label explicit, the tool supports more inclusive, user-centered smart-mobility planning across cities with different transit baselines.
Innovation
Methods, ideas, or system contributions that make the work stand out.
pathway-based diagnostic
data-driven
transit mismatch
minimum-service failure
vulnerable users
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