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Traffic accidents pose a major concern in many countries, including Indonesia, causing considerable losses, injuries, and fatalities each year. Properly classifying the severity of these incidents is essential for author...

Model-based reinforcement learning (RL) is anticipated to exhibit higher sample efficiency than model-free RL by utilizing a virtual environment model. However, obtaining sufficiently accurate representations of environm...

Real-time vehicle prediction is crucial in autonomous driving technology, as it allows adjustments to be made in advance to the driver or the vehicle, enabling them to take smoother driving actions to avoid potential col...

Parameter calibration is essential for ensuring the accuracy of microscopic traffic simulations. The expected speed is a critical parameter that characterizes behaviors of vehicles in most simulation models, which is inf...

Urban air pollution from traffic emissions poses a significant risk to students residing in student communal housing (SCH) near major roadways. This study investigates the impact of peak hour traffic on ambient air quali...

Understanding traffic scenes under diverse and challenging conditions is critical for intelligent transportation systems (ITSs). Existing methods primarily focus on ideal scenarios and often lack the ability to perform f...

In this work, we quantified the effects of harvest traffic having different tyre ground pressures and axle loads on soil properties and maize (Zea Mays L.) yields. The treatments consisted of a control plot with no traff...

Subjects / keywords: NEWELL, G. F; THE MIT PRESS; 388 N545t

Subjects / keywords: Psicología; Psicología Social

Subjects / keywords: Psicología; Psicología Social

Illicit arms trafficking in Ukraine has been a significant issue since the Cold War, with leftover Soviet stockpiles providing lucrative smuggling routes. With over US$100 billion in aid being provided by the West, a siz...

Abstract Traffic flow prediction is a fundamental building block for Intelligent Transportation Systems (ITS), enabling proactive congestion mitigation, adaptive traffic signal control, and reliable route guidance. Howev...

Urban air mobility (UAM) represents a transformative approach to alleviating ground-level congestion by transitioning from two-dimension (2D) to three-dimension (3D) transportation systems. Envisioned as a safe, sustaina...

An effective method of traffic state classification is crucial for managing urban traffic congestion. Existing methods usually assume a given number of state categories, which is not flexible if real applications are req...

Microscopic traffic flow models and simulations are crucial for capturing vehicle interactions and analyzing traffic. They can provide critical insights for transport planning, management, and operation through scenario ...

Automated Dependent Surveillance Broadcast (ADS-B) provides position and state information about aircraft and is becoming an essential component in the global air traffic management system. ADS-B transponders broadcast t...

Abstract Modern broadband access networks increasingly carry heterogeneous, delay-sensitive traffic generated by cloud-assisted applications, real-time communication services, and short-form multimedia exchanges. These w...

Abstract Autonomous driving systems critically rely on the precise detection of distant, small traffic signs to ensure safe and efficient navigation. Nonetheless, existing detection algorithms are confronted with several...

Traffic signs are part of road equipment that is very important for motorists because they can provide direction while on the highway, and if there is damage, repair or replacement must be carried out immediately because...

The dynamic range limitation is intrinsic to conventional RGB cameras, which reduces global contrast and causes the loss of high-frequency details such as textures and edges in complex, dynamic traffic environments (e.g....

Accurate and efficient traffic speed prediction is crucial for improving roaDongguand safety and efficiency. With the emerging deep learning and extensive traffic data, data-driven methods are widely adopted to achieve t...

Accurate, efficient, and rapid traffic forecasting is essential for intelligent transportation systems and plays a pivotal role in urban traffic planning, management, and control. While existing spatiotemporal transforme...

Connected and automated vehicles (CAVs) are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling. While numerous studies have proposed optimal modeling and cont...

Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, their black-box nature presents challenges for interpretability and usability, particularly whe...