Prediction of Traffic Mobility Based on Historical Data and Machine Learning Approaches

Deng, Yong; Chen, Chuang; Shi, Xianming; University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education; Washington State University. Department of Civil and Environmental Engineering

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Prediction of Traffic Mobility Based on Historical Data and Machine Learning Approaches

2022-08-01
By Deng, Yong ; Chen, Chuang ; Shi, Xianming

[PDF-1.75 MB]

English

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Details:

Creators:

Deng, Yong ; Chen, Chuang ; Shi, Xianming

Deng, Yong ; Chen, Chuang ; Shi, Xianming Less -
Corporate Creators:

University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education ; Washington State University. Department of Civil and Environmental Engineering

University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education ; Washington State University. Department of Civil and Environmental Engineering Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

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

[+]

Case Studies Climate Data Mining Highway Safety Machine Learning Mobility Predictive Models Winter Maintenance
Publication/ Report Number:

CAMMSE-UNCC-2022-UTC-Project-15
Resource Type:

Tech Report
Geographical Coverage:

Washington ; United States

Washington ; United States Less -
Edition:

Final Report
Corporate Publisher:

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

The goal of this project is to develop predictive models for traffic mobility using ML approaches. The focus is placed on two essential components - traffic speed and traffic volume. To this end, this project addresses the following objectives: (1) identifying appropriate WSDOT highway segments for this modeling study and collecting the relevant historical data related to traffic mobility; (2) developing ML models suitable for predicting the traffic mobility, from model type selection to model validation; (3) comparing different ML models and traditional models in terms of accuracy and stability; and (4) selecting desirable models according to their prediction performance for future studies.
Format:

PDF
Funding:

69A3551747133
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:050e296483a85efb59687a70cbbfb74af8162be335e536d3d31345ed3940281f
Download URL:

https://rosap.ntl.bts.gov/view/dot/64522/dot_64522_DS1.pdf
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Prediction of Traffic Mobility Based on Historical Data and Machine Learning Approaches

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