Résultats de recherche pour « Internet traffic patterns »

Traffic lights play an important role in efficient traffic management, especially in crowded cities. Optimizing traffic helps to reduce crowding, save time, and ensure the smooth flow of traffic. Metaheuristic algorithms have a proven ability to optimize smart traffic management systems. This paper investigates the effectiveness of two metaheuristic algorithms: particle swarm optimization (PSO) and grey wolf optimization (GWO). In addition, we posit a hybrid PSO-GWO method of optimizing traffic light control using IoT-enabled data from sensors. In this study, we aimed to enhance the movement of traffic, minimize delays, and improve overall traffic precision. Our results demonstrate that the hybrid PSO-GWO method outperforms individual PSO and GWO algorithms, achieving superior traffic movement precision (0.925173), greater delay reduction (0.994543), and higher throughput improvement (0.89912) than standalone methods. PSO excels in reducing wait times (0.7934), while GWO shows reasonable performance across a range of metrics. The hybrid approach leverages the power of both PSO and GWO algorithms, proving to be the most effective solution for smart traffic management. This research highlights using hybrid optimization techniques and IoT (Internet of Things) in developing traffic control systems.

This paper examines how often GDPR-relevant dark patterns appear in cookie-consent banners and how often users receive a choice that is free and informed. We use an adapted version of CCrawler to crawl 99 high-traffic websites accessed from Denmark, detect consent interfaces, and extract first-layer choice pathways and settings-layer elements for compliance-risk coding. We then assess detected banners against four GDPR-informed interface-level criteria: first-layer refusal availability, absence of pre-ticked or default-enabled options, relative accept/reject prominence, and non-obstructive presentation. These operational criteria are observable interface proxies rather than final legal determinations of GDPR compliance. Cookie banners were detected on 90 of 99 sites; the nine non-detections are reported separately and are not treated as evidence of non-compliance. The results indicate a structural imbalance: acceptance is usually available on the first layer, while refusal is often placed behind additional steps or settings dialogues. A subset of reachable settings interfaces also contained pre-ticked options. To place these observations in the European context, we compare the two directly comparable first-layer choice indicators with a recent 31-country study. Acceptance availability is close to that study’s Denmark-specific estimate, whereas first-layer rejection in our high-traffic Denmark-accessed sample lies between its Denmark-specific estimate and its 31-country aggregate. These cross-study comparisons are descriptive and are not treated as matched statistical estimates because the sampling frames and measurement procedures differ. The findings provide Denmark-specific, time-bounded evidence that known consent-interface asymmetries remain visible on high-traffic websites, together with a reproducible workflow for future audits.

The significant advancements in intelligent transportation systems (ITS) have contributed to the increased development in traffic modeling. These advancements include prediction and simulation models that are used to simulate and predict traffic behaviors on highway roads and urban networks. These models are capable of precise modeling of the current traffic status and accurate predictions of the future status based on varying traffic conditions. However, selecting the appropriate traffic model for a specific environmental setting is challenging and expensive due to the different requirements that need to be considered, such as accuracy, performance, and efficiency. In this research, we present a comprehensive literature review of the research related to traffic prediction and simulation models. We start by highlighting the challenges in the long-term and short-term prediction of traffic modeling. Then, we review the most common nonparametric prediction models. Lastly, we look into the existing literature on traffic simulation tools and traffic simulation algorithms. We summarize the available traffic models, define the required parameters, and discuss the limitations of each model. We hope that this survey serves as a useful resource for traffic management engineers, researchers, and practitioners in this domain.