6 papers
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…
A Two-Stage Trip Inference Model of Purposes and Socio-Economic Attributes of Regular Public Transit Users
Yitong Chen, Wentao Dong, Chengcheng Yu +2
Data-driven research is becoming a new paradigm in transportation, but the natural lack of individual socio-economic attributes in transportation data makes research such as activi…
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…