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Enhancing Real-Time Work Zone Lane Closures through Probe and OEM data

• Jul 10, 2024

Research Description

Research Idea Description

Transportation Systems Management and Operations (TSMO) has been increasingly harnessed by transportation sectors to optimize the performance of existing infrastructure and improve safety, mobility, and reliability to achieve Vision Zero. Despite the progress, there is a growing need to fuse various data sources and leverage cutting-edge analytics approaches including machine learning and artificial intelligence to enhance real-time decision-making and knowledge around workzone lane closures for use cases around public awareness, improved incident response capabilities, and improved situational awareness and management.

Research Objectives

The research aims to develop an integrated multimodal data analytics platform that synthesizes real-time data from various sources, including but not limited to, traffic operational measures, public transit, freight movements, and non-motorized transportation modes. This work and subsequent outputs will support real-time decision-making and improve the efficiency and effectiveness of incident response strategies.

Key Components:

 Data Integration Framework: Design and implement a robust framework that enables the seamless integration of data from diverse sources, such as vehicle probe data, OEM data, traffic sensors, transit schedules, and crowdsourced information (e.g. Waze, social media).  Scenario Building with Data Sets Real-Time Analytics Engine: Develop a data dictionary mapping sources of data to real time value. Analyze each data set for accuracy and value and then analyze fused data sets for value add to provide actionable information and insights.  Predictive Analytics and Machine Learning: Integrate predictive analytics and machine learning algorithms to forecast traffic conditions, identify potential incidents, and optimize response strategies.  Stakeholder Collaboration: Facilitate collaboration between various stakeholders, including state DOTs, transit agencies, OEMs, 3rd party data providers, emergency services, and private sector partners to ensure comprehensive data sharing.

Expected Outcomes:

 Enhanced situational awareness for transportation operators through real-time data visualization and analytics.  Improved knowledge (and knowledge dissemination) of workzone information.  Increased collaboration among stakeholders, fostering a unified approach to transportation management.  Evidence-based recommendation strategies to enhance workzone data.  Enhance understanding of the importance and role of the Workzone Data Exchange along with other methodologies of data analysis and dissemination for an Integrated Data Environment (IDE)

Potential Impact:

• Leveraging cutting-edge data analytics and real-time information. • Improving operational efficiency and safety. • Providing a scalable model that can be adopted by transportation agencies nationwide.

Additional Supporting Information

Submitted on behalf of the TRB Regional Transportation Systems Management and Operations Committee subcommittee on System Operations and Optimization.

Submitted by: Thomas H. Jacobs, co-Chair, RTSMO Research Subcommittee

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Submitted By

Thomas Jacobs

Center for Advanced Transportation Technology

301-405-7328

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