Browsing by Author "Lin, Wei"

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    Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT’s Traffic Management System
    (JTRP, 2022) Chien, Stanley; Christopher, Lauren; Chen, Yaobin; Qiu, Mei; Lin, Wei; Electrical and Computer Engineering, School of Engineering and Technology
    The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the INDOT Traffic Management Center have worked together to research and develop a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and the classification of vehicles involved in an incident. The goal was to develop a system and prepare for future implementation. The research team designed the new system, in­cluding the hardware and software components, the currently existing INDOT CCTV system, the database structure for traffic data extracted from the videos, and a user-friendly web-based server for identifying individual lanes on the highway and showing vehicle flowrates of each lane automatically. The preliminary prototype of some system components was implemented in the 2018–2019 JTRP projects, which provided the feasibility and structure of the automatic traffic status extraction from the video feeds. The 2019–2021 JTRP project focused on developing and improving many features’ functionality and computation speed to make the program run in real-time. The specific work in this 2021–2022 JTRP project is to improve the system further and implement it on INDOT’s premises. The system has the following features: vehicle-detection, road boundary detection, lane detection, vehicle count and flowrate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The research team has installed the system on one computer in INDOT for daily road traffic monitoring operations.
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    Lane-based Weaving Area Traffic Analysis Using Field Camera Video Data
    (2023-12) Lin, Wei; Tuceryan, Mihran; Chien, Stanley; Raje, Rajeev; Christopher, Lauren
    Vehicle weaving describes the lane-changing actions of vehicles, which is a critical aspect of traffic management and road design. This study focused on the weaving behavior of vehicles occurring between ramp merge and diverge areas. Weaving in these areas causes congestion and increases the risk of accidents, especially during heavy traffic. Redesigning such areas for enhanced safety requires a comprehensive analysis of the traffic conditions. Obtaining the weaving pattern is a challenge in the traffic industry. To address this challenge, we leveraged AI and image processing technology to develop algorithms for quantitative analysis of weaving using surveillance videos at the consecutive ramp merge and diverge areas. This approach can also determine the weaving patterns of passenger cars and trucks respectively. The experimental results captured the lane-based weaving behavior of around 30% of vehicles in the favorable areas. The captured weaving data is used as weaving data samples to derive an overall analysis of a weaving location. Remarkably, our approach can reduce the manual processing time for weaving analysis by more than 90%, making this highly practical for use.