Predicting future urban health status is significant in terms of identifying urban diseases and
urban planning. Current studies have focused on using machine learning and deep learning …

The complexity of traffic scenarios, the spatial-temporal feature correlations pose higher
challenges for traffic prediction research. Traffic spatial-temporal model is an essential …

Taxi is one of the important means of transportation for people's daily travel activities, and it
is one of the important research objects of intelligent transportation system. Taxi demand …

The global outbreak of COVID-19 has had a substantial impact on various sectors
worldwide, including the economy, healthcare, entertainment, policy formulation, and …

Materials and Methods The research methodology involves implementing DRL techniques
for autonomous car trajectory management within the context of connected and autonomous …

In an economic market, participants usually need to conduct negotiations to complete
commercial trades. With the constant expansion of the economic scale, it has become much …

Highly accurate traffic flow prediction is essential for effectively managing traffic congestion,
providing real-time travel advice, and reducing travel costs. However, traditional traffic flow …

Traffic flow forecasting plays a crucial role in the construction of intelligent transportation.
The aims of this paper are to fully exploit the spatial correlation between nodes in a traffic …

频繁时序模式挖掘是指从时间序列数据中发现频繁出现的模式或规律的过程,
其目的是可以帮助理解时间序列数据中的重要特征, 例如周期性, 趋势和异常等 …

This paper provides a comprehensive exploration of urban travel demand forecasting and its
implications for intelligent transportation systems, emphasizing the crucial role of intelligent …