Public Passenger Transport Logistics in the Context of Digital Transformation of Transportation Services Organisation Systems

Transport route specification models are used to analyse the need for combined passenger transportation on popular routes in a large urban agglomeration. The problem of managing the travel chains of passengers using public transport (PT) is revealed with the focus on the complexity of applying the principle of multimodality on the route network used by population due to the mismatch of the schemes of transport and users’ routes.The study of the logistics of passenger transportation with PT introduces the concept of «public transport user (PTU)» which has a variable status relative to the flows of people, pedestrians, passengers, and transport vehicles. The description of the registers of the main parameters of the routes under study serves to create their digital twins.To manage the travel chains of PTUs, identify related sections of transport routes, it is proposed to highlight within the passenger flow the currents of the same profile which include PTUs that have common transport behaviour.Models and algorithms of network proximity to transport infrastructure objects, visualisation of digital traces of PTUs and the results of comparing the used and the best route options according to the modelled parameters allow to identify behavioural profiles of PTUs, as well as regulators managing the travel chains.

Passenger Flow Prediction Using Smart Card Data from Connected Bus System Based on Interpretable XGBoost

Bus passenger flow prediction is a critical component of advanced transportation information system for public traffic management, control, and dispatch. With the development of artificial intelligence, many previous studies attempted to apply machine learning models to extract comprehensive correlations from transit networks to improve passenger flow prediction accuracy, given that the variety and volume of traffic data have been easily obtained. The passenger flow on a station is highly affected by various factors such as the previous time step, peak hours or nonpeak hours, and extracting the key features from the data is essential for a passenger flow prediction model. Although the neural networks, k -nearest neighbor, and some deep learning models have been adopted to mine the temporal correlations of the passenger flow data, the lack of interpretability of the influenced variables is still a big problem. Classical tree-based models can mine the correlations between variables and rank the importance of each variable. In this study, we presented a method to extract passenger flow of different routes on the station and implemented a XGBoost model to find the contributions of variables to the prediction of passenger flow. Comparing to benchmark models, the proposed model can reach state-of-the-art prediction accuracy and computational efficiency on the real-world dataset. Moreover, the XGBoost model can interpret the predicted results. It can be seen that period is the most important variable for the passenger flow prediction, and so the management of buses during peak hours should be improved.

Study on real-time prediction model of railway passenger flow based on big data technology

In order to solve the limitation of traditional offline forecasting application scenarios, the author uses a variety of big data open source frameworks and tools to combine with railway real-time data, and proposes a real-time prediction model of railway passenger flow. The model architecture is divided into four levels from bottom to top: data source layer, data transmission layer, prediction calculation layer and application layer. The main components of the model are data flow and prediction flow. Through message queue and ETL, the data process part realizes the synchronization of offline data and real-time data; through the big data technology frameworks such as Spark, Redis and Hive and the GBDT (Gradient Boosting Tree) algorithm, the prediction process partially realizes the real-time passenger flow of the train OD section prediction. The experimental results show that the model proposed by the author has certain practicability and accuracy both in performance and prediction accuracy.