Estimating Demand Shocks from Foot Traffic: A Big-Data Approach

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Authors

Marina Azzimonti · Federal Reserve Bank of Richmond

David Wiczer · Federal Reserve Bank of New York

Yang Xuan · Louisiana State University in Shreveport

Abstract

This study uses foot-traffic data and machine learning to separate unpredictable customer demand shocks from strategic business decisions at NYC establishments, revealing significant heterogeneity in shock persistence and trends that pooled analysis would miss.

Topics & Keywords

Traffic Prediction and Management Techniques Urban and Freight Transport Logistics Advanced Queuing Theory Analysis

Publication Details

Published in: Federal Reserve Bank of Richmond Working Papers

Volume 26, Issue 05, pp. 1-39

DOI: 10.21144/wp26-05

Field-Weighted Citation Impact: 0.00

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Estimating Demand Shocks from Foot Traffic: A Big-Data Approach

Authors

Abstract

Topics & Keywords

Publication Details