Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints

Abstract

Traffic speed prediction for a selected road segment from a short-term and long-term perspective is among the fundamental issues of intelligent transportation systems (ITS). During the course of the past two decades, many artefacts (e.g., models) have been designed dealing with traffic speed prediction. However, no satisfactory solution has been found for the issue of a long-term prediction for days and weeks using the vast spatial and temporal data. This article aims to introduce a long-term traffic speed prediction ensemble model using country-scale historic traffic data from 37,002 km of roads, which constitutes 66% of all roads in the Czech Republic. The designed model comprises three submodels and combines parametric and nonparametric approaches in order to acquire a good-quality prediction that can enrich available real-time traffic information. Furthermore, the model is set into a conceptual design which expects its usage for the improvement of navigation through waypoints (e.g., delivery service, goods distribution, police patrol) and the estimated arrival time. The model validation is carried out using the same network of roads, and the model predicts traffic speed in the period of 1 week. According to the performed validation of average speed prediction at a given hour, it can be stated that the designed model achieves good results, with mean absolute error of 4.67 km/h. The achieved results indicate that the designed solution can effectively predict the long-term speed information using large-scale spatial and temporal data, and that this solution is suitable for use in ITS.

Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods

“…Simunek et al proposed a long-term traffic speed prediction ensemble model [18] using country-scale historic traffic data from Czech Republic roads. Their model combined parametric and nonparametric approaches in order to acquire a good-quality prediction that can enrich available real-time traffic information.…”

“…Table 2 summarizes the information about the related work documented in terms of the dataset used, the processing techniques that were used and the learning process objective. This analysis allows to perceive different forecasting objective [13,15,21,22], works that did not sufficiently detail the dataset used or the conditions of use [17,19] or were developed for very different road models [14,18], so our option was to test several deep learning methods in order to evaluate them in terms of accuracy and efficiency.…”

Route and Travel Time Optimization for Delivery and Utility Services: An Industrial Viewpoint

“…Uvedený modul je využit při predikci dopravního provozu na jednotlivých úsecích v mapě. Za tímto účelem byl vytvořen model pro dlouhodobou předpověď rychlosti provozu pro lepší navigaci přes průjezdní body, který byl představen v článku (Šimůnek & Smutný, 2021). Data pro tento model zahrnují údaje o dopravním provozu a údaje o počasí 4 v České republice z 20 504 silničních úsecích (37 002 km silnic).…”

Dataset: Mobility Patterns of a Coastal Area Using Traffic Classification Radars

“…Vacationers and residents have different travel patterns, and their impact on the overall traffic differs from day to day and it varies depending on the time of year. Although there is a vast amount of work on data analysis and traffic forecasting studies published, namely, [4][5][6][7][8][9][10][11][12][13], datasets are not in the public domain. Publicly available dataset repositories such as OpenDataMonitor, Kaggle, and MDPI Data, allow one to find several datasets related with traffic accidents, but none representing highway traffic, especially Portuguese traffic, behavior.…”