基于手机数据的城市交通大区OD分布估计 ——以旧金山市为例

在大数据时代,手机数据因普及率高、采集成本低等优点逐渐受到人们的重视.手机通信运营商在为手机用户提供通信服务的同时会记录每次服务的时空信息,因此利用手机数据可以挖掘出用户的位置信息,并应用于城市居民出行OD分布估计.提出以手机数据为基础对城市交通大区之间OD分布进行估计的方法.利用居民手机数据估计美国旧金山市各交通大区之间的出行分布,并结合居民出行调查数据对估计结果进行检验.结果显示基于手机数据的出行估计具有较高的可靠性.

Using Mobile Phone Data to Estimate Trip Distribution of Urban Mega Traffic Analysis Zones:A Case Study in San Francisco

When stepping into Big Data Era, mobile phone data attract more and more attentions by trans-portation domain because of its high penetration rate and low collecting cost. Mobile phone operators re-cord temporal and spatial information of users when providing services. This new data stream turns to be a promising resource to estimate trip distribution pattern due to the fact that each record is able to provide the space-time information. This paper proposes a method to estimate OD distribution among urban mega traffic analysis zones based on mobile phone data mining. A case study of San Francisco, US is selected to implement mobile phone data mining-based OD extraction by proposed method. The outcome is further statistically validated by household survey data. Results reveal that the proposed mobile phone data mining-based approach is able to generate a promising result.