(2024) Research In Progress: Using AI to Improve Work Zone Management and Safety, TPF-5(438), 2024. Transportation, Department of
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Abstract
Work zones are integral to infrastructure development and maintenance but can pose significant challenges related to traffic congestion, safety hazards, and delays. Traditional work zone management approaches often fall short in addressing the dynamic nature of these challenges. The research team, building on their prior work in automatic incident detection (AID) and dynamic message sign (DMS) optimization, intend to harness the power of connected vehicle data (CVD), artificial intelligence (AI) algorithms, and Industry 4.0 principles to develop a cutting-edge smart work zone management system. By leveraging CVD, the research team aims to enhance traffic incident detection accuracy and predictability. Incorporating an end-to-end cloud-based system, the team will capitalize on the scalability, flexibility, cost-efficiency, security, and data integration capabilities of Industry 4.0, ultimately creating a smart work zone that optimizes traffic flow, reduces congestion, and bolsters overall safety for both workers and road users with the wealth of insights provided by CVD through the following: Data-Driven Optimization: Harnessing real-time data from connected vehicles enables data-driven decision-making, optimizing routes, and enhancing operational efficiency Predictive Maintenance: By utilizing vehicle sensors, predictive maintenance strategies can be employed, minimizing downtime, reducing costs, and ensuring optimal fleet performance Smart Traffic Management: Enabling intelligent traffic management, optimized traffic signals, and congestion reduction The primary objectives of this research are as follows: To explore the potential opportunities, challenges, and limitations of using AI for work zone management To develop an AI-driven framework that integrates CVD augmented with other available data sources to enhance work zone management across the entire life cycle, from planning through operations
Item Type: | Departmental Report |
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Keywords: | Artificial intelligence; Behavior; Drivers; Work zones; automatic incident detection; connected vehicle data |
Subjects: | Transportation Transportation > Roads and highways Transportation > Traffic safety Transportation > Research Transportation > Design and Construction Transportation > Data and Information Technology |
ID Code: | 50207 |
Deposited By: | Iowa DOT Research |
Deposited On: | 20 Aug 2024 15:16 |
Last Modified: | 20 Aug 2024 15:16 |
URI: | https://publications.iowa.gov/id/eprint/50207 |