2016 Traffic Signal Related Studies - Using Event-Based Traffic Data (under construction) from Maxview
Credit: Chengchuan An
2015~2018 Grant Rd Indirect left turn (ILT) Intersection: Before-After Studies
Grant Rd Probe Vehicle Data Collection (using Metropia)
Before Construction of Indirect Left Turn Lane (ILT)
After Construction of ILT (Data will be collected in 2018)
2013~2015
The Real Time Signal Timing and Traffic Information Project (Tucson Bluetooth Systems Deployment) is supported by the City of Tucson (and PAG). The original idea came from Dr. Larry Head. I was fortunate to get involved in this project as a PI. My Ph.D. students, Shu Yang and Ming Chen are the main developers of this project. Anthony Giang is our web application developer (before Ver 1.03). UA CEEM undergraduate students, Ryan Brown, Ahmad Fakhouri and Joel Amarillas also helped the project. (Installation locations) (data website - Partial data available for beta testing - Expected to be finished by August )
Bluetooth-based Travel Time Collection System
The system collects anonymous Bluetooth MAC addresses from nine detectors on Speedway in real time. By matching two identical MAC addresses collected from two locations, travel time can be calculated.
Bluetooth Data-based Heat Maps
The video shows the preliminary arterial performance results on Speedway based on the Bluetooth-based travel time data collected in February, 2014. We are still in the process of improving travel time estimation algorithm.
Tucson Bluetooth Project Demo Site (Beta Ver. 1.1) - Under Development
(Credit: Anthony Giang and Ming Chen). Updated Mar. 2015
Freeway Travel Time Estimation System - Phase 1
(using real time ITS sensor data)
Our lab built a freeway travel time estimation prototype system that helps Missouri Department of Transportation (MoDOT) analyze their freeway performance efficiently. The prototype system can extract speed/volume/occupancy data for any time periods/location/routes from the optimized freeway database in our lab. The video example shows how the system processes the real-time ITS traffic data collected from a 30 mile corridor for two consecutive days. Credit: Shu Yang (Ph.D. Student)
Real-time Freeway Speed Map Query using R
This online prototype application is developed by using R - a statistical package. This demo shows a real-time speed map query for 40-mile I-64 corridor (in St. Louis, MO) to visualize how the bottleneck is formed during a day. This prototype application shows various potential research ideas for freeway operations, e.g. bottleneck identification and congestion management.
Credit: Shu Yang (Ph.D. Student)
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Image Processing and Computer Vision Applications in Transportation
I really enjoy working on computer-vision-based systems. When I was at National Taiwan University (2002~2004), I collaborated with many smart and nice people. During my Ph.D. studies at Univ. of Washington (2006~2010), my brilliant colleague, Yegor Malinovskiy and I worked together on several computer-vision-based detection systems.
Computer Vision-based Driver Assistance System
Yao-Jan Wu, Feng-Li Lian, Chun-Po Huang, and Tang-Hsien Chang (2007). Image Processing Techniques for Lane-Related Information Extraction and Multi-Vehicle Detection in Intelligent Highway Vehicles, International Journal of Automotive Technology, Vol 8, No 4, pp. 513-520
Computer Vision-based Traffic Monitoring System
Yao-Jan Wu, Feng-Li Lian, and Tang-Hsien Chang (2006). Traffic Monitoring and Vehicle Tracking using Roadside Cameras, 2006 IEEE International Conference on Systems, Man, and Cybernetics, Taipei, Taiwan.
Pedtrack (Credit: Yegor Malinovskiy)
ST Map-based Vehicle Detection (Credit: Yegor Malinovskiy)
Yegor Malinovskiy, Yao-Jan Wu, and Yinhai Wang (2008). Video-Based Monitoring of Pedestrian Movements at Signalized Intersections, Transportation Research Record: Journal of the Transportation Research Board, 2073, pp 11-17.
Yegor Malinovskiy, Yao-Jan Wu, and Yinhai Wang (2009). Video-Based Vehicle Detection and Tracking Using Spatiotemporal Maps, Transportation Research Record: Journal of the Transportation Research Board, 2121, pp 81-89.
Online Traffic Network Data Analyses
I developed an Advance Traveler Information Systems (ATIS) called "Google-based Arterial Information (GATI) system" during 2006~2007. Then, I started to work with several colleagues to develop an online platform, called Digital Roadway Interactive Visualization and Evaluation Network (DRIVE Net). Xiaolei Ma and I have been working on this system since 2009. Please see more introduction here and see Conference Papers for more details.
GATI System (2006)
Yao-Jan Wu and Yinhai Wang (2009). An Interactive Web-based System for Urban Traffic Data Analysis, International Journal of Web Applications, Vol. 1, No 4, pp. 241-252.
DRIVE Net Ver. 1.0 (2010) Credit: Xiaolei Ma
Yao-Jan Wu, Shi An, Xiaolei Ma, and Yinhai Wang (2011). Development of a Web-based Arterial Network Analysis System for Real-time Decision Support, Transportation Research Record: Journal of the Transportation Research Board. Transportation Research Record: Journal of the Transportation Research Board, 2215, Pages 24-36.
Xiaolei Ma, Yao-Jan Wu, and Yinhai Wang (2011). DRIVE Net: An E-Science of Transportation Platform for Data Sharing, Visualization, Modeling, and Analysis, Transportation Research Record: Journal of the Transportation Research Board. Transportation Research Record: Journal of the Transportation Research Board, 2215, Pages 37-49
New website developed by the UW STAR Lab after I graduated: http://wsdot.uwdrive.net/
Real-time Arterial Traffic Collection System
My great UW colleague, Jon Corey, helped me a a lot on this project! Special thanks to the assistance from the City of Lynnwood, WA.