Models for forecasting the traffic flow within the city of Ljubljana

European Transport Research Review

Original Paper

Gregor Papa¹  Rok Hribar¹  Gašper Petelin¹ 

[1] Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, Ljubljana, Slovenia;Jožef Stefan International Postgraduate School, Jamova cesta 39, Ljubljana, Slovenia;

关键词: Traffic modeling;    Time-series forecasting;    Traffic-count data set;    Machine learning;    Model comparison;   

DOI  :  10.1186/s12544-023-00600-6

 received in 2023-01-03, accepted in 2023-08-28,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© European Conference of Transport Research Institutes 2023

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