Traffic state estimation (TSE) refers to the process of the inference of traffic state variables
(ie, flow, density, speed and other equivalent variables) on road segments using partially …

This paper focuses on the research on the state of the art for sensor fusion techniques,
applied to the sensors embedded in mobile devices, as a means to help identify the mobile …

Traffic data provide the basis for both research and applications in transportation control,
management, and evaluation, but real-world traffic data collected from loop detectors or …

Abstract Intelligent Transportation Systems (ITS) face the formidable challenge of large-
scale missing data, particularly in the imputation of traffic data. Existing studies have mainly …

Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent
transportation systems. Recent research has employed graph neural networks (GNNs) for …

Obtaining accurate information about current and near-term future traffic flows of all links in a
traffic network has a wide range of applications, including traffic forecasting, vehicle …

Traffic state estimation (TSE) bifurcates into two main categories, model-driven and data-
driven (eg, machine learning, ML) approaches, while each suffers from either deficient …

Missing and suspicious traffic data are inevitable due to detector and communication
malfunctions, which adversely affect the transportation management system (TMS). In this …

The missing data problem remains as a difficulty in a diverse variety of transportation
applications, eg traffic flow prediction and traffic pattern recognition. To solve this problem …

The missing data problem greatly affects traffic analysis. In this paper, we put forward a new
reliable method called probabilistic principal component analysis (PPCA) to impute the …