Transit deserts are areas where public transportation is inadequate despite evidence of travel demand, a condition that affects tens of millions of residents across the Americas. Planning for these areas is difficult because the usual demand signal is missing: ridership cannot be observed before service exists. To address that setting, we formulate risk-aware transit desert remediation as a partially observable Markov decision process with Conditional Value-at-Risk constraints for financial tail risk. The model uses demographic, land-use, and employment data to set a prior over latent demand, then updates that prior as new service deployments produce ridership observations. A myopic belief-aware planner is evaluated on 25 cities using a unified financial model for operating cost, capital expenditure, fare revenue, and net subsidy. After five years, the planner remediates a median of 53.6% of transit-desert tracts and improves on static optimization by 5.0 percentage points on average, with gains in 16 of 25 cities. Gains are largest at moderate budgets (+9.9 points at baseline) and persist under 50% prior-demand miscalibration, while population density and existing transit density are the strongest structural predictors of remediation cost ($R^2\!=\!0.41$ on per-tract cost)

翻译：[translated abstract in Chinese] 公交盲区（Transit deserts）指尽管存在出行需求证据但公共交通服务仍不充分的区域，该问题影响着美洲数千万居民。此类区域的规划具有挑战性，因为常规的需求信号缺失：在服务开通前无法观测到实际载客量。针对这一情境，我们将风险感知的公交盲区修复问题建模为部分可观测马尔可夫决策过程，并引入条件风险价值约束以控制财务尾部风险。该模型利用人口统计、土地利用和就业数据设定潜在需求的先验分布，随后随着新服务部署产生载客观测数据动态更新该先验。基于短视信念感知的规划器在25个城市中通过统一的财务模型（涵盖运营成本、资本支出、票价收入与净补贴）进行评估。五年后，该规划器在公交盲区区域中位数修复率达53.6%，相比静态优化方案平均提升5.0个百分点，并在25个城市中的16个实现收益提升。收益提升在中等预算下最为显著（基准值+9.9个百分点），且在先验需求偏差达50%时仍具稳健性；人口密度与现有公交密度是修复成本最显著的结构性预测因子（单区域成本$R^2\!=\!0.41$）。