AI 中文总结
本研究结合分层贝叶斯需求模型与空间耦合约束定价模型，利用SFpark数据校准弹性，所得CVaR策略表现优于基准规则，揭示了有效定价集的变化及相关权衡。
AI 中文摘要
针对以占用率为目标的路缘定价（包括旧金山的SFpark试点项目），现有方法常依赖未考虑空间替代或需求响应不确定性的局部阈值规则。本研究利用分层贝叶斯需求模型，从细分的SFpark数据中估算停车价格弹性，并将所得后验分布应用于空间耦合的约束定价模型。各区域间的部分池化处理给出弹性后验均值为-0.241，95%可信区间为[-0.325, -0.176]。定价问题被构建为含空间耦合、时段价格变动限制、邻区价格差距及软收入下限的约束非线性规划问题，采用Ipopt求解器求解。预期损失与CVaR（条件风险价值）目标通过联合后验抽样而非固定弹性进行评估。在保留的后验场景中，基于后验的CVaR策略的目标函数值低于SFpark阈值规则与历史费率的后验概率为1.00，低于文献校准的鲁棒策略的概率为0.85。ε约束前沿显示后验校准改变了有效定价集。来自菲尔莫尔、使命区与滨海区的60个区域窗口的结果，还报告了占用率区间表现、收入及空间差距间的相关权衡关系。
英文摘要
Occupancy-targeted curb pricing, including San Francisco's SFpark pilot, often relies on local threshold rules that do not account for spatial substitution or uncertainty in demand response. We estimate parking-price elasticities from disaggregated SFpark data using a hierarchical Bayesian demand model and use the resulting posterior distribution in a spatially coupled constrained pricing model. Partial pooling across districts gives a posterior mean elasticity of -0.241 with a 95% credible interval of [-0.325, -0.176]. The pricing problem is formulated as a constrained nonlinear program with spatial coupling, temporal price-change limits, neighbor price gaps, and a soft revenue floor, and is solved with Ipopt. Expected-loss and CVaR objectives are evaluated over joint posterior draws rather than a fixed elasticity. On held-out posterior scenarios, the posterior-based CVaR policy has a lower objective than the SFpark threshold rule and historical tariff with posterior probability 1.00, and than a literature-calibrated robust policy with probability 0.85. Epsilon-constraint frontiers show that posterior calibration changes the efficient pricing set. Results from 60 district-windows across Fillmore, Mission, and Marina also report the associated tradeoffs in occupancy-band performance, revenue, and spatial disparity.
CommentsPreprint. Submitted to the 3rd International Conference on Machine and Computing Technologies for Sustainable Development (MCT4SD), Manila, Philippines, 10-12 December 2026. Currently under peer review
