"A Gat-Bilstma Model for Weather-Aware Prediction of Traffic Speed" by Bikis Muhammed
 

Masters Theses

Abstract

This thesis presented a method for incorporating the effect of weather conditions in the prediction of the average speed of vehicular traffic for each segment of a road network. The proposed approach utilized two different deep learning methods: graph attention networks and bidirectional long short-term memory with attention layers. The accuracy of predictions is increased by considering the real-world driving distance between road segments, in contrast to the Haversine distance used in several existing prediction methods. Categorization of input data as weekend or weekday further increased the prediction accuracy. The proposed approach was validated using two data sets published by the California Department of Transportation, PeMSD4 and PeMSD7. One year of traffic data was supplemented with weather data and used to predict the average traffic speed of each road segment for up to 60 minutes into the future. The method was shown to maintain accuracy over multiple time horizons, scale well with respect to the number of road segments and outperform existing prediction methods in prediction accuracy.

Advisor(s)

Sedigh, Sahra

Committee Member(s)

Hurson, A. R.; Gamage, Lasanthi; Ricardo, Ricardo

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

2024

Pagination

ix, 42 pages

Note about bibliography

Includes_bibliographical_references_(pages )

Rights

©2024 Bikis Muhammed , All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12449

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