# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 1 Introduction  
Urban area crowd flow prediction is a very challenging research work. The crowd flow in various places reflects the travel demands of city residents. The foundation for precise vehicle scheduling and public transportation system optimization, which helps to reduce urban traffic congestion and enhance urban traffic efficiency, is an accurate and effective prediction of the crowd flow in various areas. This is a requirement for fostering the collaborative development of intelligent transportation and smart cities. Traffic police departments can undertake prompt traffic diversion operations based on the accurate traffic flow prediction data to relieve congestion caused by an excessive amount of traffic flow. Companies such as taxies, online cars, and shared bikes can dispatch vehicles to guarantee the public's demand for vehicles [1]. The proper use of information regarding the prediction of crowd flow in urban areas can even prevent safety accidents. On 29 October 2022, a mass stampede occurred in Itaewon, Yongsan-gu, Seoul, South Korea, which caused major casualties due to insufficient prevention preparation, poor site management, and improper response handling. Therefore, it is significant and challenging to make accurate and timely prediction of crowd flow in future moments, which can scientifically help government departments to promptly deploy additional security personnel to crowded areas and to take measures such as temporary crowd control and evacuation in order to minimize the probability of trampling accidents caused by excessive crowd flow and avoid tragedies. The primary foundation for the crowd flow prediction is the correlation between origin-destination (OD) and the historical crowd inflow and outflow data for the area in order to forecast future crowd inflow and outflow and thus better alleviate the traffic congestion issue. Where the crowd flow of a region is the aggregated outcome of various positioning flows inside the region, and the OD flow denotes the travel volume between irregular regions. Figure 1a represents an example of OD flow, where inflow is the total number of people entering a region from other regions at a specific time, and outflow is the total number of people traveling from a region to other regions at a specified As shown in Figure 1b, the total OD flow in the central region is 9, with inflows and outflows of 5 and 4, respectively. For historical data on crowd flow, the spatio-temporal dependence is complicated; the crowd flow of an area depends on its surrounding area, i.e., spatial dependence; and at a specific time, it depends on the relationship of crowd flow in the time period before and after it, i.e., temporal dependence. Crowd flow prediction methods have been extensively studied. Traditional time-series prediction methods such as HA and ARIMA [2; 3] have difficulty in capturing both temporal correlation and spatial dependence in historical data. Numerous deep learning models have arisen in the field of crowd flow prediction as a result of deep learning's successful use in a number of fields. For instance, [PERSON] et al. [4] proposed ST-ResNet, predicting citywide crowd flows using deep spatio-temporal residual networks. [PERSON] et al. [5] proposed a deep multi-view spatio-temporal network (DMVST-Net) based on a convolutional network (CNN) and a long- and short-term memory module (LSTM) for taxi demand prediction, which uses a CNN module to extract spatial correlation and an LSTM module to extract temporal dependence. Although the CNN method can extract features of the input data well, it can only be used for Euclidean data, i.e., regular spatial networks, which are not realistic and cannot model feature correlation at the same time. [PERSON] et al. [6] proposed a new deep learning framework combining graph convolution and 1D convolution to address the spatio-temporal data prediction problem in the traffic domain on the basis of spatio-temporal graph convolution (STGCN). Based on the previous study, [PERSON] and [PERSON] et al.