1 citations · 1 across the 6 of their papers we have counts for
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Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
Xiannan Huang, Quan Yuan, Chao Yang
Accurately predicting short-term traffic demand is critical for intelligent transportation systems. While deep learning models achieve strong performance under stationary condition…
Individual Bus Trip Chain Prediction and Pattern Identification Considering Similarities
Xiannan Huang, Yixin Chen, Quan Yuan +1
Predicting future bus trip chains for an existing user is of great significance for operators of public transit systems. Existing methods always treat this task as a time-series pr…
Predicting Subway Passenger Flows under Incident Situation with Causality
Xiannan Huang, Shuhan Qiu, Quan Yuan +1
In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal conditions, with limited research addres…
Leveraging Intra-Period and Inter-Period Features for Enhanced Passenger Flow Prediction of Subway Stations
Xiannan Huang, Chao Yang, Quan Yuan
Accurate short-term passenger flow prediction of subway stations plays a vital role in enabling subway station personnel to proactively address changes in passenger volume. Despite…
Incorporating Long-term Data in Training Short-term Traffic Prediction Model
Xiannan Huang, Shuhan Qiu, Yan Cheng +2
Short-term traffic volume prediction is crucial for intelligent transportation system and there are many researches focusing on this field. However, most of these existing research…