Assessing Impacts of Abnormal Events on Travel Patterns Leveraging Passively Collected Trajectory Data

Abstract

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.