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1.
Models for forecasting the traffic flow within the city of Ljubljana
Gašper Petelin, Rok Hribar, Gregor Papa, 2023, izvirni znanstveni članek

Povzetek: 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.
Ključne besede: traffic modeling, time-series forecasting, traffic-count data set, machine learning, model comparison
Objavljeno v DiRROS: 28.09.2023; Ogledov: 299; Prenosov: 128
.pdf Celotno besedilo (5,05 MB)
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2.
Modelling seasonal dynamics of secondary growth in R
Jernej Jevšenak, Jožica Gričar, Sergio Rossi, Peter Prislan, 2022, izvirni znanstveni članek

Povzetek: The monitoring of seasonal radial growth of woody plants addresses the ultimate question of when, how and why trees grow. Assessing the growth dynamics is important to quantify the effect of environmental drivers and understand how woody species will deal with the ongoing climatic changes. One of the crucial steps in the analyses of seasonal radial growth is to model the dynamics of xylem and phloem formation based on increment measurements on samples taken at relatively short intervals during the growing season. The most common approach is the use of the Gompertz equation, while other approaches, such as general additive models (GAMs) and generalised linear models (GLMs), have also been tested in recent years. For the first time, we explored artificial neural networks with Bayesian regularisation algorithm (BRNNs) and show that this method is easy to use, resistant to overfitting, tends to yield s-shaped curves and is therefore suitable for deriving temporal dynamics of secondary tree growth. We propose two data processing algorithms that allow more flexible fits. The main result of our work is the XPSgrowth() function implemented in the radial Tree Growth (rTG) R package, that can be used to evaluate and compare three modelling approaches: BRNN, GAM and the Gompertz function. The newly developed function, tested on intra-seasonal xylem and phloem formation data, has potential applications in many ecological and environmental disciplines where growth is expressed as a function of time. Different approaches were evaluated in terms of prediction error, while fitted curves were visually compared to derive their main characteristics. Our results suggest that there is no single best fitting method, therefore we recommend testing different fitting methods and selection of the optimal one.
Ključne besede: artificial neural networks, cambium, generalized additive model, Gompertz function, growing season, intra-annual time series
Objavljeno v DiRROS: 21.07.2022; Ogledov: 464; Prenosov: 312
.pdf Celotno besedilo (1,26 MB)
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