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Predictability and interpretability of hybrid link-level crash frequency models for urban arterials compared to cluster-based and general negative binomial regression models.

International journal of injury control and safety promotion
Machine learning (ML) techniques have higher prediction accuracy compared to conventional statistical methods for crash frequency modelling. However, their black-box nature limits the interpretability. The objective of this research is to combine bot...

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong.

International journal of environmental research and public health
With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some lo...

Scaling Laws in City Growth: Setting Limitations with Self-Organizing Maps.

PloS one
Do scaling relations always provide the means to anticipate the relationships between the size of cities, costs of maintenance, and the socio-economic benefits resulting from their growth? Scaling laws are considered a universal principle that descri...

Increasing Intelligence in Inter-Vehicle Communications to Reduce Traffic Congestions: Experiments in Urban and Highway Environments.

PloS one
Intelligent Transportation Systems (ITS) rely on Inter-Vehicle Communication (IVC) to streamline the operation of vehicles by managing vehicle traffic, assisting drivers with safety and sharing information, as well as providing appropriate services f...

Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

Environmental monitoring and assessment
This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passin...

Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network.

Computational intelligence and neuroscience
The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parame...

Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor.

Bioresource technology
The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, veg...

How to improve WEEE management? Novel approach in mobile collection with application of artificial intelligence.

Waste management (New York, N.Y.)
In global demand of improvement of electrical and electronic waste management systems, stakeholders look for effective collection systems that generate minimal costs. In this study we propose a novel model for application in mobile collection schemes...

Uncovering Hospitalists' Information Needs from Outside Healthcare Facilities in the Context of Health Information Exchange Using Association Rule Learning.

Applied clinical informatics
BACKGROUND: Important barriers to health information exchange (HIE) adoption are clinical workflow disruptions and troubles with the system interface. Prior research suggests that HIE interfaces providing faster access to useful information may stimu...

Feature Selection and Pedestrian Detection Based on Sparse Representation.

PloS one
Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. ...