Posters
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Documents
- Exploring Causality through Time Series Data in Urban Traffic Networks
- A Deep Reinforcement Learning Based Distributed Control Strategy for Connected Automated Vehicles in Mixed Traffic Platoon
- Distributed Coordinated Control of Mixed Expressway And Urban Road Networks: A Sensitivity-Based Approach
- Development of the Data Pipeline for a Connected Vehicle Corridor
- Snowflake Schema Based Data Warehouse for Analyzing Crash, Citation, and Warning Traffic Safety Records
- A Distributed Deep Reinforcement Learning Based Integrated Dynamic Bus Control System in a Connected Environment
- Connected Automated Vehicle Cooperative Control with a Deep Reinforcement Learning Approach in a Mixed Traffic Environment
- Data and System Architecture Improvements for Statewide Crash Mapping and Analysis
- Towards Ideal Hot Spot Analysis for Law Enforcement Safety Resource Allocation
- Work Zone Crash Occurrence Prediction based on Planning Stage Work Zone Configurations Using an Artificial Neural Network
- How to Collect Tribal Crash Data Properly? Experience from a New Wisconsin Crash Reporting System
Talks & Seminars
- [TRBAM 2023] Tianyi Chen, Haotian Shi, Steven T. Parker, Glenn Vorhes, David A. Noyce. Conceptual Development for a Generalized Tribal Crash Safety Dashboard. [slides]
- [TRBAM 2023] Ben Zhai, Dr. YanliWang, Dr. WenxuanWang, Dr. Bing Wu. Adaptive Control Strategy of Variable Speed Limit on Freeway Segments under Fog Conditions. [slides]
- [ITS WI Conference] Bin Ran, Yang Cheng, Steven T. Parker. Simulation of Connected Automated vehicle Highway Systems for Severe Weather Scenarios. [video]
- [ASCE Competition] Haotian Shi. Connected Automated Vehicle Cooperative Control with a Deep Reinforcement Learning Approach in a Mixed Traffic Environment. [video]
- [TRBAM 2022] Tianyi Chen, Haotian Shi, Glenn Vorhes, Steven T. Parker, David A. Noyce. Tribal Crash Reporting System Improvements in Wisconsin. [slides]
Last updated: January 16, 2022