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Crowd movement guidance has been a fascinating problem in various fields,such as easing traffic congestion in unusual events and evacuating people froman emergency-affected area. To grab the reins of crowds, there has beenconsiderable demand for a decision support system that can answer a typicalquestion: ``what will be the outcomes of each of the possible options in thecurrent situation. In this paper, we consider the problem of estimating theeffects of crowd movement guidance from past data. To cope with limited amountof available data biased by past decision-makers, we leverage two recenttechniques in deep representation learning for spatial data analysis and causalinference. We use a spatial convolutional operator to extract effective spatialfeatures of crowds from a small amount of data and use balanced representationlearning based on the integral probability metrics to mitigate the selectionbias and missing counterfactual outcomes. To evaluate the performance onestimating the treatment effects of possible guidance, we use a multi-agentsimulator to generate realistic data on evacuation scenarios in a crowdedtheater, since there are no available datasets recording outcomes of allpossible crowd movement guidance. The results of three experiments demonstratethat our proposed method reduces the estimation error by at most 56% fromstate-of-the-art methods.

Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference