Real-Time Freeway Speed Prediction Based on Deep Learning in Connected and Autonomous Vehicles Environment

Fan, Wei; Hua, Chengying

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Real-Time Freeway Speed Prediction Based on Deep Learning in Connected and Autonomous Vehicles Environment

2022-09-01
By Fan, Wei ; Hua, Chengying

File Language:

English

Details

Creators:

Fan, Wei ; Hua, Chengying
Corporate Creators:

University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education
Corporate Contributors:

United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
Subject/TRT Terms:
Publication/ Report Number:

CAMMSE-UNCC-2022-UTC-Project-02
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education
Abstract:

The main objective of this research project is to enhance the accuracy of traffic speed prediction in ITS. The objectives of this project include: (1) Conducting a comprehensive review of traffic prediction techniques for CAVs. (2) Identifying a potential freeway segment and collecting the features of the selected scenario. (3) Developing and applying a logically intelligent car following model that can describe CAVs on the highway mainline. (4) Predicting the average traffic speed in the CAVs environment on the freeway, and comparing the performance of emerging deep learning technology with the existing car following models.
Format:

PDF
Funding:

69A3551747133
Download URL:

https://rosap.ntl.bts.gov/view/dot/64516/dot_64516_DS1.pdf
File Type:

[PDF - 1.51 MB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; Autonomous Vehicles ; University Transportation Centers
Main Document Checksum:

urn:sha-512:42089f8764554ef3711e9c74baafe8c57bd5c8be2a263804a9ef78693f67e654f872947ca60ea4d69ed57721e33376b692593840b704900cf3cf4adaa313835f

File Language:

English

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