Models for forecasting the traffic flow within the city of Ljubljana

Gašper Petelin (Author), Rok Hribar (Author), Gregor Papa (Author)

Abstract

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.

Keywords

modeliranje prometa;napovedovanje časovnih vrst;modeliranje količine prometa;strojno učenje;primerjava modelov;traffic modeling;time-series forecasting;traffic-count data set;machine learning;model comparison;

Data

Language:	English
Year of publishing:	2023
Typology:	1.01 - Original Scientific Article
Organization:	IJS - Jožef Stefan Institute
Publisher:	Springer Nature
UDC:	656
COBISS:	163745027
ISSN:	1866-8887
Views:	6
Downloads:	0
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	Promet;
Source comment:	Nasl. z nasl. zaslona; Opis vira z dne 8. 9. 2023;
Pages:	str. 1-20
Issue:	ǂVol. ǂ15, article no. 30
Chronology:	2023
DOI:	10.1186/s12544-023-00600-6
ID:	20005343