Abstract
AbstractEfficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data.
Funder
Javna Agencija za Raziskovalno Dejavnost RS
HORIZON EUROPE Framework Programme
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Transportation,Automotive Engineering
Cited by
1 articles.
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1. A Comparison of ML Models for Predicting Congestion in Urban Cities;International Journal of Intelligent Transportation Systems Research;2024-02-05