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
Recommended Citation
Muhammed, Bikis, "A Gat-Bilstma Model for Weather-Aware Prediction of Traffic Speed" (2024). Masters Theses. 8220.
https://scholarsmine.mst.edu/masters_theses/8220