Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2022, 5(11); doi: 10.25236/AJCIS.2022.051108.

A Study on Prediction and Scheduler for Foot Traffic of University Canteens Based on Gray Prediction and BP Neural Network


Yan An1, Yang Yuqi1, Jiang Xinyang1, Mo Hanxiang2, Duan Boya3

Corresponding Author:
Duan Boya

1Faculty of Economics and Management, North China Electric Power University, Beijing, 102200, China

2Faculty of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102200, China

3Physical Education Department, North China Electric Power University, Beijing 102200, China


In the current context of epidemic prevention and control, the resumption of university education places greater emphasis on the prediction and regulation of foot traffic in public areas. Using the university canteens as an example, predicting the foot traffic during the dining period benefits the canteens staff's reasonable scheduling, reducing the potential virus transmission risk caused by the dense crowd, as well as providing time- sharing service for the distribution of the canteens foot traffic, reducing dining waste and practicing diligence and frugality, and helping to alleviate the crowded queue during the dining period. Based on field research on canteen patronage data, this paper combines gray theory, neural network technology research, and patronage information prediction research to adapt to the nonlinear characteristics of patronage, optimize the performance of the prediction model, and improve the accuracy of the prediction model. The results are used to determine patronage density and the number of canteen windows. This paper used factor analysis to build a refined foot traffic guidance and schedule according to each index and put it into the smart canteen website.


M/M/1 queuing model, foot traffic prediction, gray prediction, BP neural network, factor analysis

Cite This Paper

Yan An, Yang Yuqi, Jiang Xinyang, Mo Hanxiang, Duan Boya. A Study on Prediction and Scheduler for Foot Traffic of University Canteens Based on Gray Prediction and BP Neural Network. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 11: 50-56. https://doi.org/10.25236/AJCIS.2022.051108.


[1] Shuwei Wang, Ronggui Zhou, Yanyan Chen. Study on pedestrian flow forecasting method in Beijing transportation hub areas [C]. Annual meeting of the transportation research board; Transportation Research Board. 2014

[2] Liu, Yang. (2011) Intermodal Transit Hub: Improving the Transfer Environment at the Li Shui Bridge Transportation Node in Beijing [D]. Master’s Thesis, University of Washington,

[3] Tang Min An, Wang Xiao Ming, Yuan Shuang. Location Selection of Public Transit Transfer Hubs Based on Gray NN Model Improved by GA [C]. International Conference onuture Optical Materials and Circuit Design. 2013

[4] Zhenzhong Tian. Application of combined forecasting model based on improved grey neural networks in regional logistics demand [C]. International conference of logistics engineering and management; ICLEM 2010. 2011

[5] F ZHANG, J WU. Based on improved grey BP neural network of the regional logistics cost forecast [C]. Manufacturing Automation: Advanced Design and Manufacturing in Global Competition. 2004

[6] Fincannon, Tyler. (2017) Improving Regional Hydrology Forecasting for the North Central Texas Region Utilizing Conditional Ensemble Streamflow and Hydrometeorological Condition Predictions with Artificial Neural Network Modeling [D]. Master’s Thesis, the University of Texas at Arlington. 

[7] Zhe Gao, Xiaojiao Chen, Guannan Zhang. Fractional-order discrete grey models for China’s electricity consumption forecasting [C]. Chinese Control and Decision Conference. 2020

[8] Wenyan Guo, Xiaoliu Shen, Xinke Ma. Comparative Study of Grey Forecasting Model and ARMA Model on Beijing Electricity Consumption Forecasting [C]. International conference on mechatronics and automatic control systems . 2014

[9] Mammadova, Gulnara. (2010) Forecasting exchange rates using ARMA and neural network models [D]. Master’s Thesis, Western Illinois University.