| Abstract |
The analysis of how city events causally affect human mobility is of critical importance. The city government will be thrilled to know how an impending event will influence mobility beforehand, so that they can either decide specifically when and where the event will be held (or not), or be more prepared for some possible circumstances such as crowd collapses and crushes. Previous research on human mobility mainly focuses on simple future prediction based on data correlation, yet the study on the underlying causal effect is woefully inadequate. Motivated by the recent tragedy, the Itaewon Halloween disaster, in this paper we try to explore the causal effects of city events on human mobility using counterfactual prediction. The main technical challenge here lies in capturing and debiasing the time-varying unobservable confounders (e.g., people’s willingness to go outdoors) that affect both the event organization and the number of event participants. Fortunately, the increasing sources of time-varying data offer the possibility to refactor such confounding effects from observation. To this end, we utilize multiple sources of observation data in New York City to construct a neural network-based causal framework, which automatically learns and balances the time-varying unobservable confounders representations and provides estimations for the ITE problem. © The Author(s) 2024. |
