The present work aims to study how deep learning approaches solve the safety problems in
the interaction between autonomous vehicles and pedestrians. A Vehicle-Pedestrian …

In reality, the missing of some traffic data is inevitable due to some unexpected errors, which
not only affects traffic management but also hinders the development of traffic data research …

Traffic congestion is a vital factor hindering travel. As such, developing a reliable traffic
congestion early warning system is essential for providing traffic condition supervision and …

This paper introduces a novel image segmentation technique that is inspired by social
influence and opinion dynamics, establishing an association between social sciences and …

As for the increasing traffic pressure in urban cities, it is of great significance to analyze the
complex traffic system and grasp the recurrence characteristics of traffic state to better solve …

Accurately predicting short‐term congestions in ship traffic flow is important for water traffic
safety and intelligent shipping. We propose a method for predicting the traffic flow of ships by …

With the increasing popularity of cab services such as Didi and Uber, cities are faced with
the challenge of high carbon emissions and traffic congestion. Ride-sharing services, as a …

This work presents a novel approach to forecast streaming big time series based on nearest
similar patterns. This approach combines a clustering algorithm with a classifier and the …

Multivariate real-life time series data often contain missing values. These missing values
often affect subsequent prediction tasks. Traditional imputation methods generally consider …

Traffic congestion is one of the most important problems of urban traffic. Real time prediction
of urban traffic flow can provide data reference to congestion dredging and driving route …