Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis

Article Engineering, Civil

Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation

Xinyu Chen et al.

Summary: This paper proposes a low-rank autoregressive tensor completion (LATC) framework by introducing temporal variation as a new regularization term, aiming to capture both global and local consistency in traffic data.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

Coupling Urban Road Travel Time and Traffic Status from Vehicle Trajectories by Gaussian Distribution

Liping Huang et al.

Summary: In this paper, the coherence between road travel time and traffic status is studied by utilizing a probability function. The experiments show that road travel time can be accurately estimated using the conditional Gaussian distribution. This research is of great importance for intelligent transportation applications.

2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) (2022)

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Article Computer Science, Artificial Intelligence

Missing data imputation for traffic congestion data based on joint matrix factorization

Xiaoyi Jia et al.

Summary: This paper proposes a novel Imputation Model for traffic Congestion data, CIM, based on joint matrix factorization to estimate missing congestion values. Experimental results show that modeling the periodicity, road similarity, and temporal coherence of congestion patterns simultaneously is effective.

KNOWLEDGE-BASED SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

IoT-based traffic prediction and traffic signal control system for smart city

S. Neelakandan et al.

Summary: This paper proposes an efficient IoT-based traffic prediction system and traffic signal control system using OWENN algorithm and Intel 80,286 microprocessor for a smart city, which goes through phases of data collection, feature extraction, classification, optimized traffic values, and traffic signal control. The OWENN algorithm achieves high accuracy and outperforms existing models in traffic prediction and control.

SOFT COMPUTING (2021)

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Proceedings Paper Computer Science, Theory & Methods

Imputation of Missing Traffic Flow Data Using Denoising Autoencoders

Boyuan Jiang et al.

Summary: In this study, Denoising Autoencoders were used to fill in missing traffic flow data, improving prediction accuracy and demonstrating the robustness of the models under various conditions.

12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS (2021)

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Review Computer Science, Information Systems

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Haitao Yuan et al.

Summary: This paper provides a comprehensive survey on traffic prediction, covering the topics from spatio-temporal data to intelligent transportation applications. It divides the research scope into four parts: spatio-temporal data, preprocessing, traffic prediction, and traffic application, discussing existing work and challenges in each area. The survey aims to help practitioners better understand traffic prediction problems and methods, encouraging them to address their intelligent transportation applications effectively.

DATA SCIENCE AND ENGINEERING (2021)

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Article Transportation Science & Technology