With the rapid development of sensor technologies, time series data collected by multiple
and spatially distributed sensors have been widely used in different research fields …

Traffic speed prediction based on real-world traffic data is a classical problem in intelligent
transportation systems (ITS). Most existing traffic speed prediction models are proposed …

Spatiotemporal traffic data is increasingly important in transportation services with the
development of intelligent transportation system (ITS). However, due to various …

The quality of traffic services depends on the accuracy and completeness of the collected
traffic data. However, the existing traffic data imputation methods usually only rely on the …

High-quality traffic data is crucial for intelligent transportation system and its data-driven
applications. However, data missing is common in collecting real-world traffic datasets due …

We propose a kinematic wave-based Deep Convolutional Neural Network (Deep CNN) to
estimate high-resolution traffic speed fields from sparse probe vehicle trajectories. We …

Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent
transportation systems. Despite extensive research regarding traffic data imputation, there …

The purpose of this research is to create a simulated environment for teaching algorithms,
big data processing, and machine learning. The environment is similar to Google Maps, with …

Abstract This paper studies Deep Convolutional Neural Networks (DCNNs) for the accurate
estimation of space–time traffic speeds given sparse data on freeways. Several aspects are …

Assessing traffic states accurately is challenging due to the complex, high-dimensional, and
nonlinear nature of traffic systems. This study introduces the innovative High-Order …