We propose two neural network based and data-driven supply and demand models to analyze the efficiency, identify service gaps, and determine the significant predictors of demand, in the bus system for the Department of Public Transportation (HDPT) in Harrisonburg City, Virginia, which is the home to James Madison University (JMU). The supply and demand models, one temporal and one spatial, take many variables into account, including the demographic data surrounding the bus stops, the metrics that the HDPT reports to the federal government, and the drastic change in population between when JMU is on or off session. These direct and data-driven models to quantify supply and demand and identify service gaps can generalize to other cities' bus systems.

翻译：我们提出两种基于神经网络的数据驱动供需模型，用于分析弗吉尼亚州哈里森堡市（詹姆斯·麦迪逊大学所在地）公共交通局公交系统的效率、识别服务缺口并确定需求的关键预测因子。供需模型分为时间模型与空间模型两种，综合考虑多种变量，包括公交车站周边人口统计数据、公共交通局向联邦政府报告的指标，以及詹姆斯·麦迪逊大学在学期与假期期间的人口剧烈变化。这些直接且数据驱动的量化供需及识别服务缺口的模型，可推广至其他城市的公交系统。