AIMC Topic: Transportation

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Toward a conceptual model to improve the user experience of a sustainable and secure intelligent transport system.

Acta psychologica
The rapid advancement of automotive technologies has spurred the development of innovative applications within intelligent transportation systems (ITS), aimed at enhancing safety, efficiency and sustainability. These applications, such as advanced dr...

Uncovering soil heavy metal pollution hotspots and influencing mechanisms through machine learning and spatial analysis.

Environmental pollution (Barking, Essex : 1987)
Soil heavy metal (HM) pollution is a significant and widespread environmental issue in China, highlighting the need to quantify influencing factors and identify priority concern areas for effective prevention and management. Based on published litera...

Subway opening enables urban green development: Evidence from difference-in-differences and double dual machine learning methods.

Journal of environmental management
Urban rail transport is an important part of transport infrastructure, which is significant in empowering green-oriented urban development. However, few studies in the existing literature explore the effect of subway opening on urban green developmen...

Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation.

Sensors (Basel, Switzerland)
Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and...

Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks.

Poultry science
Dead on arrival (DOA) refers to animals, particularly poultry, that die during the pre-slaughter phase. Elevated rates of DOA frequently signify substandard welfare conditions and might stem from multiple causes, resulting in diminished productivity ...

SE-MAConvLSTM: A deep learning framework for short-term traffic flow prediction combining Squeeze-and-Excitation Network and Multi-Attention Convolutional LSTM Network.

PloS one
Traffic flow prediction is an important part of transportation management and planning. For example, accurate demand prediction of taxis and online car-hailing can reduce the waste of resources caused by empty cars. The prediction of public bicycle f...

Enhancing urban flow prediction via mutual reinforcement with multi-scale regional information.

Neural networks : the official journal of the International Neural Network Society
Intelligent Transportation Systems (ITS) are essential for modern urban development, with urban flow prediction being a key component. Accurate flow prediction optimizes routes and resource allocation, benefiting residents, businesses, and the enviro...

The hazard analysis of passenger-cargo ferries: a revised risk matrix model based on fuzzy best-worst method.

Environmental science and pollution research international
Improving hazards in maritime transport is essential to maintain the reliability and sustainability of the industry, ensure safety and security, and support global trade and economic growth. This paper is aimed at analyzing the hazards of passenger-c...

A navigational risk evaluation of ferry transport: Continuous risk management matrix based on fuzzy Best-Worst Method.

PloS one
Ferry transport has witnessed numerous fatal accidents due to unsafe navigation; thus, it is of paramount importance to mitigate risks and enhance safety measures in ferry navigation. This paper aims to evaluate the navigational risk of ferry transpo...