Smart Stores: A Scalable Foot Traffic Collection and Prediction System

An accurate foot traffic prediction system can help retail businesses, physical stores, and restaurants optimize their labor schedule and costs, and reduce food wastage. In this paper, we design a large scale data collection and prediction system for store foot traffic. Our data has been collected from wireless access points deployed at over 100 businesses across the United States for a period of more than one year. This data is centrally processed and analyzed to predict the foot traffic for the next 168 hours (a week). Our current predictor is based on Support Vector Regression (SVR). There are a few other predictors that we have found that are similar in accuracy to SVR. For our collected data the average foot traffic per hour is 35 per store. Our prediction result is on average within 22% of the actual result for a 168 hour (a week) period.