D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 55 Citations 10,680 299 World Ranking 1057 National Ranking 460

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial intelligence
  • Machine learning

His primary areas of investigation include Artificial intelligence, Data mining, Transport engineering, Traffic flow and Intelligent transportation system. Many of his research projects under Artificial intelligence are closely connected to Trial and error with Trial and error, tying the diverse disciplines of science together. His Artificial neural network research integrates issues from Reinforcement learning, Intelligent driver model and Time series.

The various areas that he examines in his Data mining study include CURE data clustering algorithm, Correlation clustering, Constrained clustering, Autoregressive integrated moving average and Fuzzy logic. In the field of Transport engineering, his study on Road traffic overlaps with subjects such as Urban area. His biological study spans a wide range of topics, including Algorithm, Estimation theory, Traffic generation model and Complex network.

His most cited work include:

  • Long short-term memory neural network for traffic speed prediction using remote microwave sensor data (727 citations)
  • Mining smart card data for transit riders’ travel patterns (324 citations)
  • Large-scale transportation network congestion evolution prediction using deep learning theory. (275 citations)

What are the main themes of his work throughout his whole career to date?

Yinhai Wang mainly focuses on Transport engineering, Simulation, Real-time computing, Artificial intelligence and Data mining. His research in Transport engineering intersects with topics in Truck and Geographic information system. His studies in Simulation integrate themes in fields like Queue, Detector and Traffic flow.

Yinhai Wang has included themes like Bluetooth and Data collection in his Real-time computing study. His Artificial intelligence research incorporates themes from Graph and Computer vision. His work deals with themes such as Intelligent transportation system and Missing data, Imputation, which intersect with Data mining.

He most often published in these fields:

  • Transport engineering (25.33%)
  • Simulation (15.57%)
  • Real-time computing (14.51%)

What were the highlights of his more recent work (between 2019-2021)?

  • Data mining (14.78%)
  • Transport engineering (25.33%)
  • Artificial intelligence (15.30%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Data mining, Transport engineering, Artificial intelligence, Real-time computing and Artificial neural network. His Data mining research includes themes of Structure, Missing data imputation, Missing data and Flow network. His work on Transit as part of general Transport engineering research is often related to Metropolitan area, Operational performance and Lead, thus linking different fields of science.

Yinhai Wang combines subjects such as Graph and Pattern recognition with his study of Artificial intelligence. He has researched Real-time computing in several fields, including Pedestrian, Traffic flow, Object detection, Bluetooth and Channel state information. His work carried out in the field of Artificial neural network brings together such families of science as Particle swarm optimization, Mathematical optimization, Road surface and Big data.

Between 2019 and 2021, his most popular works were:

  • Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (175 citations)
  • Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving (20 citations)
  • Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models (14 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Machine learning

Yinhai Wang focuses on Data mining, Artificial intelligence, Artificial neural network, Intelligent transportation system and Missing data. The concepts of his Data mining study are interwoven with issues in Backpropagation and Markov model. His Artificial intelligence research is multidisciplinary, relying on both Graph and Pattern recognition.

His Artificial neural network research is multidisciplinary, incorporating elements of Function, Particle swarm optimization, Key and Metaheuristic. His Intelligent transportation system study combines topics in areas such as Missing data imputation, Edge computing and Big data. Yinhai Wang works mostly in the field of Deep learning, limiting it down to topics relating to Traffic speed and, in certain cases, Real-time computing, as a part of the same area of interest.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

Xiaolei Ma;Zhimin Tao;Yinhai Wang;Haiyang Yu.
Transportation Research Part C-emerging Technologies (2015)

1040 Citations

Mining smart card data for transit riders’ travel patterns

Xiaolei Ma;Yao Jan Wu;Yinhai Wang;Feng Chen.
Transportation Research Part C-emerging Technologies (2013)

515 Citations

Large-scale transportation network congestion evolution prediction using deep learning theory.

Xiaolei Ma;Haiyang Yu;Yunpeng Wang;Yinhai Wang.
PLOS ONE (2015)

383 Citations

An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic

Jinjun Tang;Fang Liu;Yajie Zou;Weibin Zhang.
IEEE Transactions on Intelligent Transportation Systems (2017)

224 Citations

Estimating the risk of collisions between bicycles and motor vehicles at signalized intersections.

Yinhai Wang;Nancy L. Nihan.
Accident Analysis & Prevention (2004)

211 Citations

Uncovering urban human mobility from large scale taxi GPS data

Jinjun Tang;Jinjun Tang;Fang Liu;Yinhai Wang;Hua Wang.
Physica A-statistical Mechanics and Its Applications (2015)

198 Citations

Video-Based Vehicle Detection and Classification System for Real-Time Traffic Data Collection Using Uncalibrated Video Cameras

Guohui Zhang;Ryan Patrick Avery;Yinhai Wang.
Transportation Research Record (2007)

183 Citations

Accessibility impacts of China’s high-speed rail network

Jing Cao;Jing Cao;Xiaoyue Cathy Liu;Yinhai Wang;Qingquan Li;Qingquan Li.
Journal of Transport Geography (2013)

176 Citations

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Zhiyong Cui;Kristian Henrickson;Ruimin Ke;Yinhai Wang.
IEEE Transactions on Intelligent Transportation Systems (2020)

175 Citations

Extracting Roadway Background Image: Mode-Based Approach

Jianyang Zheng;Yinhai Wang;Nancy L Nihan;Mark E Hallenbeck.
Transportation Research Record (2006)

170 Citations

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