Due to transportation technologies having such heterogeneous impacts on different communities, there needs to be better tools to evaluate the deployment of emerging technologies with limited data. Microtransit is one such technology. We propose a novel framework based on existing methods to “upscale” the limited data available so that further decision-support analysis and forecast modeling can be achieved where none could prior. The framework involves expanding an initial day-to-day adjustment process to handle both first/last mile access trips and direct trips, updating a within-day microtransit simulator with a parametric design, and developing a synthetic scenario generation process. The framework is tested in a case study with data from Via for Salt Lake City, Austin, Cupertino, Sacramento, Columbus, and Jersey City showing an average 18% ridership error for the market equilibrium models. Data from four of those cities are upscaled to 326 synthetic scenarios to estimate forecast models for ridership and fleet vehicle-miles-traveled using Lasso regularization. While the models have root mean squared error (RMSE) values between 37-45% of the averages, using only four cities’ data alone would not produce any forecast model at all. The results show that variables with statistically significant positive impact on ridership and negative impact on vehicle-miles-traveled (VMT) include zones with more transit stations, higher employment, but lower “employment density $\times$ fixed fare”. The models are then used to identify two alternative portfolios with similar fleet VMT as the original four cities but are forecast to have up to 1.9 times the ridership.

由于交通技术对不同社区具有如此不同的影响，因此需要更好的工具来评估具有有限数据的新兴技术的部署。Microtransit 就是这样一种技术。我们提出了一个基于现有方法的新框架来“升级”可用的有限数据，以便可以在之前无法实现的情况下实现进一步的决策支持分析和预测建模。该框架涉及扩展初始的日常调整过程以处理第一英里/最后一英里的访问行程和直接行程，使用参数化设计更新日内微交通模拟器，以及开发综合场景生成过程。该框架在案例研究中进行了测试，数据来自盐湖城、奥斯汀、库比蒂诺、萨克拉门托、哥伦布、和泽西市显示市场均衡模型的平均乘客量误差为 18%。来自其中四个城市的数据被放大到 326 个综合场景，以使用 Lasso 正则化估计乘客量和车队车辆行驶里程的预测模型。虽然模型的均方根误差 (RMSE) 值在平均值的 37-45% 之间，但仅使用四个城市的数据根本无法生成任何预测模型。结果表明，在统计上对乘客量产生显着积极影响而对车辆行驶里程 (VMT) 产生负面影响的变量包括公交车站较多、就业率较高但“就业密度”较低的区域 来自其中四个城市的数据被放大到 326 个综合场景，以使用 Lasso 正则化估计乘客量和车队车辆行驶里程的预测模型。虽然模型的均方根误差 (RMSE) 值在平均值的 37-45% 之间，但仅使用四个城市的数据根本无法生成任何预测模型。结果表明，在统计上对乘客量产生显着积极影响而对车辆行驶里程 (VMT) 产生负面影响的变量包括公交车站较多、就业率较高但“就业密度”较低的区域 来自其中四个城市的数据被放大到 326 个综合场景，以使用 Lasso 正则化估计乘客量和车队车辆行驶里程的预测模型。虽然模型的均方根误差 (RMSE) 值在平均值的 37-45% 之间，但仅使用四个城市的数据根本无法生成任何预测模型。结果表明，在统计上对乘客量产生显着积极影响而对车辆行驶里程 (VMT) 产生负面影响的变量包括公交车站较多、就业率较高但“就业密度”较低的区域$\times$固定票价”。然后使用这些模型确定两个备选投资组合，其车队 VMT 与最初的四个城市相似，但预计乘客量将达到 1.9 倍。