Models for forecasting the traffic flow within the city of Ljubljana

Efficient 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...

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Bibliographic Details
Published in:European transport research review 2023-12, Vol.15 (1), p.30-20, Article 30
Main Authors: Petelin, Gašper, Hribar, Rok, Papa, Gregor
Format: Article
Language:English
Subjects:
Accuracy
Automotive Engineering
Civil Engineering
Data collection
Datasets
Engineering
Holidays & special occasions
Machine learning
Meteorological data
Model comparison
Original Paper
Real time
Regional/Spatial Science
Roads & highways
Time-series forecasting
Traffic congestion
Traffic engineering
Traffic flow
Traffic management
Traffic modeling
Traffic-count data set
Transportation
Travel time
Trends
Urban areas
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Summary:Efficient 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.
ISSN:1866-8887
1867-0717
1866-8887
DOI:10.1186/s12544-023-00600-6