基于移动终端位置数据的多方式出行需求模式分析框架
A Data-Driven Analytical Framework of Estimating Multimodal Travel Demand Patterns using Mobile Device Location Data
摘要：
智能手机及其定位服务在为人们的日常生活带来诸多益处的同时，也产生了大量的移动终端位置数据，这些数据在帮助我们了解出行需求模式和制定未来交通规划方面具有巨大的潜力。虽然最近的研究已经分析了人类的出行行为，使用这些新的数据源，有限的研究已经做了提取多式联运出行需求模式。本文提出了一个数据驱动的分析框架，以弥补这一差距。为了能够使用被动收集的位置信息成功检测出行模式，我们进行了基于智能手机的GPS调查，以收集地面实况观测。然后开发了一种联合训练的单层模型和深度神经网络，用于出行方式归算。该模型集“宽”与“深”于一身，综合了两类模型的优点。该框架还纳入了多式联运网络，以评估出行路线与附近的铁路，地铁，公路和公共汽车线路的接近程度，从而提高插补精度。为了展示所介绍的框架在回答现实世界的规划需求的应用，一个单独的移动终端位置数据处理通过行程结束识别和属性生成，在一种方式，旅行模式插补可以直接应用。估计多式联运的出行需求模式，然后验证对典型的家庭出行调查在同一个华盛顿特区和巴尔的摩大都市区。
英文摘要：
While benefiting people's daily life in so many ways, smartphones and their location-based services are generating massive mobile device location data that has great potential to help us understand travel demand patterns and make transportation planning for the future. While recent studies have analyzed human travel behavior using such new data sources, limited research has been done to extract multimodal travel demand patterns out of them. This paper presents a data-driven analytical framework to bridge the gap. To be able to successfully detect travel modes using the passively collected location information, we conduct a smartphone-based GPS survey to collect ground truth observations. Then a jointly trained single-layer model and deep neural network for travel mode imputation is developed. Being "wide" and "deep" at the same time, this model combines the advantages of both types of models. The framework also incorporates the multimodal transportation network in order to evaluate the closeness of trip routes to the nearby rail, metro, highway and bus lines and therefore enhance the imputation accuracy. To showcase the applications of the introduced framework in answering real-world planning needs, a separate mobile device location data is processed through trip end identification and attribute generation, in a way that the travel mode imputation can be directly applied. The estimated multimodal travel demand patterns are then validated against typical household travel surveys in the same Washington D.C. and Baltimore Metropolitan Regions.
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。
