  Travel patterns can be impacted by abnormal events. Assessing the impacts has
important implications for relief operations and improving preparedness or
planning for future events. Conventionally, the assessment is done followed by
data collection from post-event surveys, which are economically costly,
suffering low-response rate, time-consuming and usually delayed for months (or
even years) after an event, leading to inefficient and unreliable assessment
and creating obstacles for relief organizations to reach people in need.
Penetration of smartphones and services enabled by them continuously generate
large amount of trajectory data (e.g., Call Records Data, App-based data),
containing trajectories of massive users. These trajectory data are passively
and timely collected and without additional cost and contain information of
travel patterns of the massive number of individuals in a region for a
prolonged time period (e.g., months to years). We propose a framework to
assessing the impacts on travel patterns using these data. Utilizing the
passively collected trajectory data, the proposed framework seeks to capturing
and understanding the full spectrum of travel pattern changes, which helps to
assess who, when and how people in a certain area were impacted. The proposed
framework is applied to a mobile phone trajectory dataset containing about
half-year trajectories of a million anonymous users to assess the impacts of
Hurricane Harvey (the second-costliest hurricane in US history). The results
are validated and show that the proposed framework can provide a comprehensive
assessment of impacts of Harvey on travel patterns, which could guide the
response to and the recovery from the impacts.
