111 citations · 427 across the 11 of their papers we have counts for
11 papers
MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation
Junlin He, Yihong Tang, Tong Nie +6
Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features. Existing paradigms, including deep generative models, LLM-ba…
Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning
Yuebing Liang, Shenhao Wang, Jiangbo Yu +3
Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To a…
Exploring Large Language Models for Human Mobility Prediction under Public Events
Yuebing Liang, Yichao Liu, Xiaohan Wang +1
Public events, such as concerts and sports games, can be major attractors for large crowds, leading to irregular surges in travel demand. Accurate human mobility prediction for pub…
RouteKG: A knowledge graph-based framework for route prediction on road networks
Yihong Tang, Zhan Zhao, Weipeng Deng +3
Short-term route prediction on road networks allows us to anticipate the future trajectories of road users, enabling various applications ranging from dynamic traffic control to pe…
Deep trip generation with graph neural networks for bike sharing system expansion
Yuebing Liang, Fangyi Ding, Guan Huang +1
Bike sharing is emerging globally as an active, convenient, and sustainable mode of transportation. To plan successful bike-sharing systems (BSSs), many cities start from a small-s…
Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networks
Yuebing Liang, Guan Huang, Zhan Zhao
For bike sharing systems, demand prediction is crucial to ensure the timely re-balancing of available bikes according to predicted demand. Existing methods for bike sharing demand…
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards
Zhan Zhao, Yuebing Liang
Route choice modeling is a fundamental task in transportation planning and demand forecasting. Classical methods generally adopt the discrete choice model (DCM) framework with line…
Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approach
Yuebing Liang, Guan Huang, Zhan Zhao
Bike sharing is an increasingly popular part of urban transportation systems. Accurate demand prediction is the key to support timely re-balancing and ensure service efficiency. Mo…
Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach
Yuebing Liang, Guan Huang, Zhan Zhao
Dynamic demand prediction is crucial for the efficient operation and management of urban transportation systems. Extensive research has been conducted on single-mode demand predict…
Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
Yuebing Liang, Zhan Zhao, Lijun Sun
Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems. Despite extensive research regarding traffic data imputation…
NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanisms
Yuebing Liang, Zhan Zhao
Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and loca…