一种基于移动终端位置数据的数据驱动出行方式份额估计框架
A Data-Driven Travel Mode Share Estimation Framework based on Mobile Device Location Data
摘要：
移动终端位置数据（MDLD）包含丰富的出行行为信息，为出行需求分析提供支持。与传统的旅行调查相比，MDLD对人口及其流动性的时空覆盖范围更大。但是，默认情况下不包括行程起点和目的地、旅行模式和行程目的等地面实况信息。必须估算这些重要属性，以最大限度地发挥数据的效用。本文旨在研究MDLD在总体水平上估计出行方式份额的能力。提出了一个数据驱动的框架，从MDLD中提取出行行为信息。该框架首先确定旅行结束与修改的时空密度为基础的空间聚类的应用程序与噪声（ST-DBSCAN）算法。然后针对每次出行提取三种类型的特征，使用机器学习模型来估算出行模式。一个标记的MDLD数据集与地面真相信息用于训练提出的模型，导致95%的准确率在识别行程结束和93%的准确率在输入五种出行模式（驱动器，铁路，公共汽车，自行车和步行）与随机森林（RF）分类。该框架被应用于两个大规模的MDLD数据集，分别覆盖巴尔的摩-华盛顿大都市区和美国。估计的行程距离，行程时间，行程率分布，和旅行方式的份额进行比较，对旅行调查在不同的地理位置。结果表明，该框架可以很容易地应用于不同的国家和大都市地区，以低成本，以研究多式联运的出行需求，了解流动性的趋势，并支持决策。
英文摘要：
Mobile device location data (MDLD) contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, MDLD has larger spatiotemporal coverage of population and its mobility. However, ground truth information such as trip origins and destinations, travel modes, and trip purposes are not included by default. Such important attributes must be imputed to maximize the usefulness of the data. This paper tends to study the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from the MDLD. The proposed framework first identifies trip ends with a modified Spatiotemporal Density-based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning models. A labeled MDLD dataset with ground truth information is used to train the proposed models, resulting in 95% accuracy in identifying trip ends and 93% accuracy in imputing five travel modes (drive, rail, bus, bike and walk) with a Random Forest (RF) classifier. The proposed framework is then applied to two large-scale MDLD datasets, covering the Baltimore-Washington metropolitan area and the United States, respectively. The estimated trip distance, trip time, trip rate distribution, and travel mode share are compared against travel surveys at different geographies. The results suggest that the proposed framework can be readily applied in different states and metropolitan regions with low cost in order to study multimodal travel demand, understand mobility trends, and support decision making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
