Edge Computing Based on Convolutional Neural Network for Passenger Counting: A Case Study in Guadalajara, Mexico

One of the most common deficiencies in the public transport system is long waiting times. Currently, in the Guadalajara Metropolitan Area, Mexico, the frequencies of routes are fixed, making it impossible to satisfy a demand with a dynamic variation. An intelligent public transport system is required. The first step to solve this problem is knowing the number of users so that we can respond appropriately to each scenario. In this context, this work focuses on the design and implementation of an embedded system module for passenger counting that can be used to improves public transport service quality. This work presents three contributions. First, a design and experimental validation of the passenger counting system is presented to determine the number of users in an image and send this information to a server suitable for the public transportation system in Guadalajara, Mexico. Second, the generation of two new datasets is reported for training and testing the CSRNet algorithm with images of public transportation systems in Mexican cities. Finally, we make the hardware implementation of the passenger counting system in a Jetson Nano development board.