Peixiao Wang

Orcid: 0000-0002-1209-6340

According to our database1, Peixiao Wang authored at least 17 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A deep marked graph process model for citywide traffic congestion forecasting.
Comput. Aided Civ. Infrastructure Eng., April, 2024

Traffic condition estimation and data quality assessment for signalized road networks using massive vehicle trajectories.
J. Ambient Intell. Humaniz. Comput., January, 2024

Adding attention to the neural ordinary differential equation for spatio-temporal prediction.
Int. J. Geogr. Inf. Sci., January, 2024

2023
DeepIndoorCrowd: Predicting crowd flow in indoor shopping malls with an interpretable transformer network.
Trans. GIS, September, 2023

Urban traffic flow prediction: a dynamic temporal graph network considering missing values.
Int. J. Geogr. Inf. Sci., April, 2023

Forecasting Earthquake Magnitude and Epicenter by Incorporating Spatiotemporal Priors Into Deep Neural Networks.
IEEE Trans. Geosci. Remote. Sens., 2023

2022
A Hybrid Data-Driven Framework for Spatiotemporal Traffic Flow Data Imputation.
IEEE Internet Things J., 2022

A multi-view bidirectional spatiotemporal graph network for urban traffic flow imputation.
Int. J. Geogr. Inf. Sci., 2022

2021
Exploring the Spatiotemporal Characteristics of COVID-19 Infections among Healthcare Workers: A Multi-Scale Perspective.
ISPRS Int. J. Geo Inf., 2021

Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing Trajectories.
ISPRS Int. J. Geo Inf., 2021

Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges.
Int. J. Digit. Earth, 2021

Spatiotemporal Differences of COVID-19 Infection Among Healthcare Workers and Patients in China From January to March 2020.
IEEE Access, 2021

2020
Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data.
Sensors, 2020

Building an Open Resources Repository for COVID-19 Research.
Data Inf. Manag., 2020

Predicting Indoor Location based on a Hybrid Markov-LSTM Model.
Proceedings of the Web and Wireless Geographical Information Systems, 2020

2019
Indoor Location Prediction Method for Shopping Malls Based on Location Sequence Similarity.
ISPRS Int. J. Geo Inf., 2019

A Hybrid Markov and LSTM Model for Indoor Location Prediction.
IEEE Access, 2019


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