Inferring individual daily activities from mobile phone traces: A Boston example

Abstract

Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer individuals’ activities from their mobile phone traces. Using Metro Boston as an example, we develop an activity detection model with travel diary surveys to reveal the common laws governing individuals’ activity participation, and apply the modeling results to mobile phone traces to extract the embedded activity information. The proposed approach enables us to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new “big data” such as mobile phone traces and traditional travel surveys to improve transportation planning and urban planning and management.