2 citations · 3 across the 6 of their papers we have counts for
6 papers
Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information
Rei Tamaru, Pei Li, Bin Ran
Pedestrian trajectory prediction is essential for various applications in active traffic management, urban planning, traffic control, crowd management, and autonomous driving, aimi…
Analytical Optimized Traffic Flow Recovery for Large-scale Urban Transportation Network
Sicheng Fu, Haotian Shi, Shixiao Liang +2
The implementation of intelligent transportation systems (ITS) has enhanced data collection in urban transportation through advanced traffic sensing devices. However, the high cost…
Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction
Junwei You, Haotian Shi, Keshu Wu +4
Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS), enhancing road safety and traffic efficiency. While traditi…
The expressway network design problem for multiple urban subregions based on the macroscopic fundamental diagram
Yunran Di, Weihua Zhang, Haotian Shi +3
As urbanization advances, cities are expanding, leading to a more decentralized urban structure and longer average commuting durations. The construction of an urban expressway syst…
Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks
Keshu Wu, Yang Zhou, Haotian Shi +2
Predicting vehicle trajectories is crucial for ensuring automated vehicle operation efficiency and safety, particularly on congested multi-lane highways. In such dynamic environmen…
Developing a Conceptual Tribal Crash Safety Dashboard: Data-Driven Strategies for Identifying High-Risk Areas and Enhancing Tribal Safety Programs
Tianyi Chen, Haotian Shi, Steven T. Parker +3
Tribal lands in the United States have consistently exhibited higher crash rates and injury severities compared to other regions. To address this issue, effective data-driven safet…