SWMLP: Shared Weight Multilayer Perceptron for Car Trajectory Speed Prediction using Road Topographical Features

Although traffic is one of the massively collected data, it is often only available for specific regions. One concern is that, although there are studies that give good results for these data, the data from these regions may not be sufficiently representative to describe all the traffic patterns in...

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Bibliographic Details
Published in:arXiv.org 2023-10
Main Authors: Sarah Almeida Carneiro, Chierchia, Giovanni, Charléty, Jean, Chataignon, Aurélie, Najman, Laurent
Format: Article
Language:English
Subjects:
Data collection
Multilayer perceptrons
Qualitative analysis
Regression analysis
Roads
Traffic analysis
Traffic speed
Online Access:Get full text
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Summary:Although traffic is one of the massively collected data, it is often only available for specific regions. One concern is that, although there are studies that give good results for these data, the data from these regions may not be sufficiently representative to describe all the traffic patterns in the rest of the world. In quest of addressing this concern, we propose a speed prediction method that is independent of large historical speed data. To predict a vehicle's speed, we use the trajectory road topographical features to fit a Shared Weight Multilayer Perceptron learning model. Our results show significant improvement, both qualitative and quantitative, over standard regression analysis. Moreover, the proposed framework sheds new light on the way to design new approaches for traffic analysis.
ISSN:2331-8422
DOI:10.48550/arxiv.2310.02282