Abstract
For the development of intelligent transportation systems, if real-time information on the number of people on buses can be obtained, it will not only help transport operators to schedule buses but also improve the convenience for passengers to schedule their travel times accordingly. This study proposes a method for estimating the number of passengers on a bus. The method is based on deep learning to estimate passenger occupancy in different scenarios. Two deep learning methods are used to accomplish this: the first is a convolutional autoencoder, mainly used to extract features from crowds of passengers and to determine the number of people in a crowd; the second is the you only look once version 3 architecture, mainly for detecting the area in which head features are clearer on a bus. The results obtained by the two methods are summed to calculate the current passenger occupancy rate of the bus. To demonstrate the algorithmic performance, experiments for estimating the number of passengers at different bus times and bus stops were performed. The results indicate that the proposed system performs better than some existing methods.
Highlights
In recent years, with the rapid development of technologies such as sensing, communication, and management, improving the efficiency of traditional transportation systems through advanced technological applications is becoming more feasible
We propose the use of a deep learning object detection method and the establishment of a convolutional autoencoder (CAE) to extract the characteristics of passengers in crowded areas to evaluate the number of people on a bus
Passenger counting based on object detection: For areas where the head features of passengers are more visible, the deep learning object detection method is employed to calculate the number of passengers in the image
Summary
With the rapid development of technologies such as sensing, communication, and management, improving the efficiency of traditional transportation systems through advanced technological applications is becoming more feasible. To solve the problem in the evaluation phase of the model, the image is divided into 120 × 120 up to 480 × 480 different sizes for input; all the block images are sequentially extracted, and the output of the three columns is merged to output the multi-tasking result. We propose the use of a deep learning object detection method and the establishment of a convolutional autoencoder (CAE) to extract the characteristics of passengers in crowded areas to evaluate the number of people on a bus. The results of these two methods are summed into the total number of passengers on the bus.
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