Work

Autonomous Vehicle Traffic Rate Prediction in Dense Networks: A Deep Learning Approach

Year: 2024

Type: conference-paper

Abstract: Autonomous Vehicles (AVs) are expected to transform transportation, although their effective implementation depends on precise and prompt traffic forecasting. To tackle this difficulty, we examine the utilization of deep learning models, including Bidirectional Long Short-Term Memory (BiLSTM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), for predicting traffic flow in congested autonomous vehicle networks. These approaches are adept at capturing the intricate temporal interdependence and spatial correlations of traffic data. Our findings demonstrate that BiLSTM consistently outperforms other models, even under variable traffic volumes and conditions. This research propels the development of intelligent transportation systems and improves the optimization of autonomous vehicle operations.

Source: International Conference on Future Networks & Distributed Systems (ICFNDS)

DOI: 10.1145/3726122.3726141

Authors: Ali R. Abdellah, Ammar Muthanna, Andrey Koucheryavy, Aziza Usmanova

Institutions: Al-Azhar University, Saint-Petersburg State University of Telecommunications, Tashkent State University of Economics

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

OpenAlex ID: W4411936877

FWCI: 4.446

Cites: 31

Cited by: 4

Related to: 10