# Dynamic Graph Representation Learning for Passenger Behavior Prediction  
[PERSON]  
1 CCSE Lab, Beihang University, Beijing 100191, China; [EMAIL_ADDRESS] (M.X. ); [EMAIL_ADDRESS] (T.Z. ); [EMAIL_ADDRESS] (B.D.) [PERSON]  
1 CCSE Lab, Beihang University, Beijing 100191, China; [EMAIL_ADDRESS] (M.X. ); [EMAIL_ADDRESS] (T.Z. ); [EMAIL_ADDRESS] (B.D.) [PERSON]  
2 School of Transportation Science and Engineering, Beihang University, Beijing 1001  
units to process information of different time lengths, while a PENN integrates different structural information using various models and then makes the final prediction. A long short-term memory neural network (LSTM_NN) [3] models the long-term patterns of passengers by applying LSTM and also explores the significant impact of weather on passenger flow. Additionally, many traditional methods [4; 5; 6] predict temporal features using an autoregressive integrated moving average model (ARIMA), analyze passenger flow with a support vector machine (SVM), or forecast short-term changes using a CNN or other methods. However, the current works overlook three important features of PP tasks:  
Firstly, passenger travel records are continuously growing. Current PP models typically only model the existing historical interaction sequences of passengers to make single-step predictions and evaluations for the next moment. In the real world, passenger travel records are continuously increasing, so prediction tasks should incorporate the latest interaction information in a timely manner, and predictions should be multi-step, with the model being promptly updated based on changes to travel records. Sequence-based solutions in PP models cannot adequately adapt to real-life applications. Secondly, passenger travel behavior encompasses both long-term and short-term characteristics. Human behavior exhibits periodicity, showing regular patterns over the long term but potentially undergoing abrupt changes in the short term. For example, a passenger might typically commute between home and work from Monday to Friday but stay home or visit a shopping center on weekends. Long-term periodic patterns reveal inherent characteristics of passengers, such as identifying them as workers based on their regular commute between work and home. Short-term variability, however, can be influenced by factors such as weather conditions and the passenger's mood. The existing methods often capture the long-term characteristics of passengers but struggle to effectively extract short-term variability features. Thirdly, passenger travel behavior involves complex interactions between passengers and stations. A passenger may have entered and exited multiple different stations before a certain time, and a station may have had multiple passengers enter and exit before that time. Therefore, the relationship between a passenger and a station is intricate and complex. Relying solely on statistical methods to determine the probability of a passenger interacting with a station at a given time can overlook the influence of passenger density at the station on the passenger's destination choice. To address these issues, a new method called **d**ynamic **c**raph representation learning for **p**assenger behavior **p**rediction (DyGPP) is proposed as shown in Figure 1. This method aims to track the dynamic travel behavior characteristics of passengers using continuous-time dynamic graphs (CTDG). In this dynamic graph, passengers and stations are represented as heterogeneous nodes, and the entire graph can be viewed as a bipartite graph. The goal is to predict the connection between specific passenger nodes and station nodes at particular times, indicating whether the passenger will interact with the corresponding station at a certain time. This novel design can adapt to the continuously growing interaction records between passengers and stations, while modeling passengers' long-term periodic features and short-term abrupt features using their historical interaction sequences.