关键词: 城市交通, 通勤, 手机信令, 职住分布, 大数据, 交通调查

Abstract:

A methodology framework for training people’s residence and work place is presented to overcome defects in extracting people's activity pattern from mobile phone data like low precision positioning accuracy, irregular trajectory interval. First, each user’s daily trajectory is restructured to add the label of access time, departure time and delay time for each point. Spurious points are distinguished by given time and spatial threshold and then be clustered to filter out noise resulting from mobile service provider. Then, stable point for each time window is recognized by overlaying multi-days trajectory to get the most frequent location. User’s residence and workplace are determined by comparing stable points in workdays and holidays. Finally, commuting trips are constructed for each user between home and workplace. These trips are multiplied by expansion factors based on the street population. Analysis results of this methodology are in good agreement with the pattern revealed by the 2014 Census population and the 2014 Person Trip Survey in Chongqing, China.

Key words: urban traffic, commuting, mobile phone data, home and work place, big data, trip survey