CausalTrans: Fusing Causal Inference and Large Language Models for Event-Driven Traffic Forecasting.
Developing a novel framework to integrate textual event data (e.g., accidents, construction) with LLMs and causal inference for causal-aware traffic flow prediction under disruptions, aiming to improve model robustness and interpretability.
Joint Design of SAV-Transit Systems for Tourist Cities
Explored integrated design of public transit and shared autonomous vehicle (SAV) systems for tourist cities, accounting for private tour trips, demonstrating up to 25% cost reduction in low-demand scenarios.
Traffic Prediction via Signal Decomposition and Attention-based BiLSTM
Developed a dual-stream BiLSTM model incorporating moving average, Fourier transform, and multi-head attention mechanisms to capture trend and seasonal components in traffic flow data by integrating signal decomposition with deep learning frameworks.
Behavioral Changes from EV Adoption: A Quasi-Experimental Study
Designed and executed a quasi-experimental study using mobile signaling data to assess causal effects of electric vehicle (EV) adoption on travel behavior, as well as the effects on other travel modes of mobility system.
LLM Analysis of Policy Impact on Cross-Border Spatial Sentiment
Bridged gaps in spatial sentiment quantification and causal inference by developing a pipeline integrating LLM-based sentiment analysis with interrupted time series (ITS) modeling to evaluate regional integration policy impacts.
Optimized Path Planning and Traffic Flow Distribution Model
Developed a model to mitigate navigation-induced traffic aggregation and user dissatisfaction, balancing user equilibrium with system optimization for enhanced network efficiency.
Travel Route Choice Preference Analysis Based on Phone Signaling Big Data
Employed machine learning models (SVM, XGBoost, Random Forest) to analyze factors influencing route choices, with emphasis on congestion impacts, enabling accurate monitoring of congestion states and preference features from mobile signaling data.
Synthesizing the Impact of Travel Behavior Interventions: A Meta-Analysis
Conducted a systematic review and meta-analysis to quantify the effectiveness and mechanism of travel behavior interventions, addressing fragmented knowledge and identifying key factors for public transport policy success.