17 Papers
11 Citations
Weiya Chen is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Computer science & Train. The author has an hindex of 4, co-authored 4 publications.
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Papers
Building digital twins of existing highways using map data based on engineering expertise
TL;DR: In this paper , the authors presented an approach for creating the digital twin of a highway using map data, which consists of primary highway components, including horizontal alignment, vertical alignment, cross-section, lanes and central reserves.
User cohabitation in multi-stereoscopic immersive virtual environment for individual navigation tasks
Weiya Chen,Nicolas Ladeveze,Céline Clavel,Daniel Mestre,Patrick Bourdot +4 more
- 23 Mar 2015
TL;DR: This work proposes several alterations of the human joystick metaphor by introducing implicit adaptive control to allow safe individual navigation for multiple users in a Multi-stereoscopic immersive system and results highlight that the improved paradigm allows two users to navigate independently despite physical system limitations.
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Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving
TL;DR: In this paper , eye-tracking metrics (pupil diameter change, number of saccades, saccade duration, fixation duration, and 3D gaze entropy) were proven to be effective indicators for mental-workload level prediction in both visual and auditory multi-tasking situations.
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Long-term equilibrium relationship analysis and energy-saving measures of metro energy consumption and its influencing factors based on cointegration theory and an ARDL model
TL;DR: Wang et al. as mentioned in this paper analyzed the complex dynamic relationship between metro energy consumption and its influencing factors and provided a reference for metro energy conservation control, using the monthly energy consumption, passenger flow and operating distance statistical data for Wuhan Metro Line 2 from 2018 to 2019.
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Short-term urban rail transit passenger flow forecasting based on fusion model methods using univariate time series
Dung David Chuwang,Weiya Chen,Ming Zhong +2 more
TL;DR: This study proposes an ML-fusion strategy for short-term urban rail transit passenger flow forecasting, combining XGBoost, AdaBoost, and LightGBM models with dynamically predicted passenger flow to enhance accuracy and efficiency, achieving a mean absolute error of 1.54 and regression coefficient of 0.99.
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