Rising fuel prices can affect driver behavior and thus the number of accidents, which is a key road safety issue. The aim of this paper was to assess and quantify the relationship between fuel prices (FP) and the number of road accidents in Europe. Content analysis of statistics from the countries was used to collect data, which were examined using Ramsey resets and Poisson distributions and then processed using negative binomial regression (NB), cluster analysis and visualization using contour plots. The results show that in Germany and Poland there is a statistically significant low negative correlation between fuel price and the number of traffic accidents, while in the Czech Republic and Denmark the relationship is weaker and statistically insignificant. In Iceland, no significant correlation was found. The contribution of this paper is to provide important insights that can be used in the development of transport policies and regulations to improve road safety. The main limitations include the difficulty of data collection, as many countries do not publish detailed statistics, and the low number of accidents in Iceland, which makes it impossible to perform a robust analysis for this country and may cause generalization of the results.

In the era of digital disruption, the imperative development of broadband services is evident. The emergence of 5G technology represents the latest stride in commercial broadband, offering data speeds poised to drive significant societal advancement. The midst of responding to this transformative phenomenon. This pursuit unveils a landscape replete with opportunities and challenges, particularly regarding how 5G’s potential benefits can drive the government towards equitable distribution, ensuring accessibility for all. Simultaneously, there exists a legal hurdle to ensure this vision’s fruition. From a legal perspective, perceived as infrastructure for transformation, the law must seamlessly adapt to and promptly address technological progress. Utilizing normative juridical methods and analytical techniques via literature review, this research endeavors to outline the advantages of 5G and scrutinize Indonesia’s latest telecommunications regulations and policies, alongside corresponding investments. The study ultimately aims to provide a juridical analysis of 5G implementation within Indonesia’s legal framework.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This study examines the spatial distribution and structure of traffic offences in the Northern Great Plain region. The research is unique in that it examines a specific area through the lens of geography. The research shows and demonstrates that the research area of crime and transport geography is much broader than previous researches has shown. At the beginning of the study, the authors clarified the conceptual framework, as the terms “violation” and “offence” are often confused even in technical materials. The research shows which routes are the most frequently used by road hauliers in the regions under study and what type of checks have been carried out on these routes by the Transport Authorities of the Government Offices. The type of administrative penalty detected and the nationality breakdown of the infringements are described. The study typifies the infringements involving administrative fines by nationality category.

This study critically examines the implications of international transport corridor projects for Central Asian countries, focusing on the Western-backed Transport Corridor Europe-Caucasus-Asia (TRACECA), the Chinese initiative “One Belt—One Road”, and the International North-South Transport Corridor (INSTC) supported by the Russian Federation, India, and Iran. The analysis underscores the risks associated with Western projects, highlighting a need for a more explicit commitment to substantial infrastructure investments and persistent contradictions among key investors and beneficiaries. While the Chinese initiative presents significant benefits such as transit participation, infrastructure development, and economic investments, it also carries risks, notably an increased debt burden and potential monopolization by Chinese corporations. The study emphasizes that Central Asian countries, though indirect beneficiaries of INSTC, may not be directly involved due to geographical constraints. Study findings advocate for Central Asian nations to balance foreign investments, promote economic integration, and safeguard political and economic sovereignty. The study underscores the region’s wealth of natural and human resources, emphasizing the potential for increased demand for goods and services with improved living standards, strategically positioning these countries in the evolving global economic landscape.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.