Research on Using Deep Learning for Transport Demand Prediction

This research explores the application of deep learning techniques as a prominent Data Mining methodology for constructing a predictive model of transport demand. Beyond access to high-quality data, analysts face challenges in consolidating computational resources and integrating foreign software with domestic systems. Key aspects in model development involve utilizing existing algorithms for data analysis through a classifier object. The classifier is then trained on various datasets, including those generated and collected from road transport infrastructure devices. Simultaneously, patterns of respondents' movements along randomly selected routes or digitally designed paths are investigated. Predicting transport demand through probability functions for collecting and analyzing passenger transaction datasets is crucial for transport infrastructure and service planning. In modern multimodal transport systems, transport demand distribution between modes is the result of individual users' random choices from available alternatives. Deep data analysis in transport networks optimizes system efficiency and safety. Advanced data collection technologies (sensors, GPS, monitoring systems) provide extensive information for improved route planning, traffic forecasting, enhanced resource utilization, and innovative transport infrastructure management strategies. Data mining plays a pivotal role in predicting transport demand in behavioral models, enabling extraction of valuable insights from large passenger and traveler behavior datasets. Various data mining methods and models facilitate identification of hidden patterns, trends, and correlations in transport demand, ultimately enhancing forecasting accuracy and system optimization. By employing clustering analysis, researchers can identify passenger groups with similar preferences, facilitating targeted service strategies. Classification models enable prediction of transport service demands for different demographic groups or time periods. Associative rule analysis reveals connections between factors like ticket prices, schedules, and seasonal fluctuations, aiding in optimal pricing and resource management. Anomaly detection methods identify unusual transport demand patterns, allowing for swift responses to changes and risk minimization. In conclusion, data mining significantly contributes to transport demand prediction in behavioral models, offering more accurate and efficient solutions for managing transport systems and improving passenger service quality.