论文标题
纽约市出租车区的乘车和出租车需求的时空分析
Spatiotemporal Analysis of Ridesourcing and Taxi Demand by Taxi zones in New York City
论文作者
论文摘要
对TNC的需求爆发极大地改变了运输景观，并急剧破坏了租用的车辆（VFH）市场，该市场过去多年来一直由出租车主导。自从Uber于2009年首次推出以来，Ridesing Companies已迅速渗透到市场。本文旨在根据纽约市出租车区层面的数据调查出租车和TNC使用中的时间和空间模式。具体而言，我们拟合合适的时间序列模型来估计时间模式。接下来，我们使用全球和本地Moran的I统计数据过滤出时间效应，并研究残差中的空间依赖性。我们讨论了在出租车区水平上的空间相关性与人口统计和土地利用效应之间的关系。通过多个线性回归（MLR）模型估算和消除这些效果，并重新计算Moran的I统计数据，使我们能够在考虑这些效果后研究空间依赖性。我们的分析表明，随着时间的流逝，纽约市出租车区域之间的空间相关性有趣的模式，表明骑乘用法的预测建模必须纳入时间和空间依赖性。
The burst of demand for TNCs has significantly changed the transportation landscape and dramatically disrupted the Vehicle for Hire (VFH) market that used to be dominated by taxicabs for many years. Since first being introduced by Uber in 2009, ridesourcing companies have rapidly penetrated the market. This paper aims to investigate temporal and spatial patterns in taxi and TNC usage based on data at the taxi zone level in New York City. Specifically, we fit suitable time series models to estimate the temporal patterns. Next, we filter out the temporal effects and investigate spatial dependence in the residuals using global and local Moran's I statistics. We discuss the relationship between the spatial correlations and the demographic and land use effects at the taxi zone level. Estimating and removing these effects via a multiple linear regression (MLR) model and recomputing the Moran's I statistics on the resulting residuals enables us to investigate spatial dependence after accounting for these effects. Our analysis indicates interesting patterns in spatial correlations between taxi zones in NYC and over time, indicating that predictive modeling of ridesourcing usage must incorporate both temporal and spatial dependence.
