基于手机传感器数据的出行特征提取方法

Cellular-based Data Extracting Method for Travel Characteristics

手机调查方法的已有研究较多集中于基于手机信令数据的宏观出行特征获取,而手机传感器数据在个体出行链微观出行特征提取方面具有优势。针对城市居民多采用组合交通方式出行的特征,研发智能手机应用软件,实现GPS数据(位置坐标与速度)、加速度计、服务基站、Wi Fi等传感器数据采集。运用小波分析、神经网络等数据挖掘技术分析不同交通方式出行数据差异,探索多种数据挖掘算法用于个体出行参数提取的可行性及效果。结合实际案例,总结应用手机传感器数据进行出行特征精细化提取的难点和技术关键。最后,探讨精细化个体出行数据在交通模型和理论优化方面的应用。

Existing research of cellular-based survey methods mainly focus on travel characteristics at macro level. It should be known that cellular probe data also have great advantages of extracting travel characteristics at micro level – individual travel chains. Considering majority of urban residents＇ multimodal travel patterns, a mobile app is developed to retrieve traveler disaggregated data, such as GPS（coordinates and speed）, accelerometer through base station and Wi-Fi connectivity. This paper analyzes the difference in data from various travel modes using wavelet analysis, neural network and other data mining techniques. The feasibility and multiple data mining algorithms used to extract individual travel parameters are discussed. Based on case studies, the paper summarizes difficulties and key technical points of using cellular probe data to extract accurate travel characteristics. Finally, the paper discusses the application of individual travel data in transportation modeling.