Real-Time Freeway Speed Prediction Based on Deep Learning in Connected And Autonomous Vehicles Environment
In the last few years, there has been a significant increase in the research of the connected autonomous vehicles (CAV) across the globe, perhaps due to an exponential increase in the popularity and usage of the artificial intelligence techniques in various applications. CAVs can greatly help traffic engineers manage the flow and mitigate traffic congestion on road networks by using the cooperative adaptive cruise control (CACC). For CAV to act more efficiently and improve mobility as well as alleviate traffic congestion, timely prediction of traffic flow is undoubtedly a critical component. A comprehensive review of the existing literature clearly suggests that research on CAVs has shifted from traditional optimization and statistical models to adaptive machine learning techniques. However, existing machine learning models may not be easily developed and directly applicable in this environment due to non-linear complex relationship between spatial and temporal data collected from the surroundings during the aforementioned adaptive decisions taken by the vehicles. In this project, the research team will develop a traffic prediction framework based on various deep learning models for CAVs and compared these models with respect to their applicability in modern smart transportation systems. This research will also establish the simulation environment for CAVs in mixed traffic scenarios with different market penetration rates of CAVs. The results of this study can greatly help traffic engineers and stakeholders better understand how CAV affect traffic flow and therefore improve its management and control.
Language
- English
Project
- Status: Completed
- Funding: $90006
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Contract Numbers:
69A3551747133
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Sponsor Organizations:
Center for Advanced Multimodal Mobility Solutions and Education
University of North Carolina, Charlotte
Charlotte, NC United States 28223Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of North Carolina - Charlotte
9201 University City Blvd
Charlotte, North Carolina United States 28223-0001 -
Project Managers:
Fan, Wei
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Performing Organizations:
Center for Advanced Multimodal Mobility Solutions and Education
University of North Carolina, Charlotte
Charlotte, NC United States 28223 -
Principal Investigators:
Fan, Wei
- Start Date: 20211001
- Expected Completion Date: 20220930
- Actual Completion Date: 20220930
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Connected vehicles; Forecasting; Highway capacity; Impacts; Market penetration; Methodology; Mobility; Traffic congestion; Traffic simulation
- Identifier Terms: Traffic Estimation and Prediction System (TrEPS)
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;
Filing Info
- Accession Number: 01784128
- Record Type: Research project
- Source Agency: Center for Advanced Multimodal Mobility Solutions and Education
- Contract Numbers: 69A3551747133
- Files: UTC, RIP
- Created Date: Oct 4 2021 10:56AM