12,105 research outputs found
Predicting parking space availability based on heterogeneous data using Machine Learning techniques
Abstract. These days, smart cities are focused on improving their services and bringing quality to everyday life, leveraging modern ICT technologies. For this reason, the data from connected IoT devices, environmental sensors, economic platforms, social networking sites, governance systems, and others can be gathered for achieving such goals. The rapid increase in the number of vehicles in major cities of the world has made mobility in urban areas difficult, due to traffic congestion and parking availability issues. Finding a suitable parking space is often influenced by various factors such as weather conditions, traffic flows, and geographical information (markets, hospitals, parks, and others). In this study, a predictive analysis has been performed to estimate the availability of parking spaces using heterogeneous data from Cork County, Ireland. However, accumulating, processing, and analysing the produced data from heterogeneous sources is itself a challenge, due to their diverse nature and different acquisition frequencies. Therefore, a data lake has been proposed in this study to collect, process, analyse, and visualize data from disparate sources. In addition, the proposed platform is used for predicting the available parking spaces using the collected data from heterogeneous sources. The study includes proposed design and implementation details of data lake as well as the developed parking space availability prediction model using machine learning techniques
Open City Data Pipeline
Statistical data about cities, regions and at country level is collected for various purposes and from various institutions. Yet, while
access to high quality and recent such data is crucial both for decision makers as well as for the public, all to often such collections of
data remain isolated and not re-usable, let alone properly integrated. In this paper we present the Open City Data Pipeline, a focused
attempt to collect, integrate, and enrich statistical data collected at city level worldwide, and republish this data in a reusable manner
as Linked Data. The main feature of the Open City Data Pipeline are: (i) we integrate and cleanse data from several sources in a
modular and extensible, always up-to-date fashion; (ii) we use both Machine Learning techniques as well as ontological reasoning
over equational background knowledge to enrich the data by imputing missing values, (iii) we assess the estimated accuracy of such
imputations per indicator. Additionally, (iv) we make the integrated and enriched data available both in a we browser interface and as
machine-readable Linked Data, using standard vocabularies such as QB and PROV, and linking to e.g. DBpedia.
Lastly, in an exhaustive evaluation of our approach, we compare our enrichment and cleansing techniques to a preliminary version
of the Open City Data Pipeline presented at ISWC2015: firstly, we demonstrate that the combination of equational knowledge and
standard machine learning techniques significantly helps to improve the quality of our missing value imputations; secondly, we
arguable show that the more data we integrate, the more reliable our predictions become. Hence, over time, the Open City Data
Pipeline shall provide a sustainable effort to serve Linked Data about cities in increasing quality.Series: Working Papers on Information Systems, Information Business and Operation
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
As machine learning systems move from computer-science laboratories into the
open world, their accountability becomes a high priority problem.
Accountability requires deep understanding of system behavior and its failures.
Current evaluation methods such as single-score error metrics and confusion
matrices provide aggregate views of system performance that hide important
shortcomings. Understanding details about failures is important for identifying
pathways for refinement, communicating the reliability of systems in different
settings, and for specifying appropriate human oversight and engagement.
Characterization of failures and shortcomings is particularly complex for
systems composed of multiple machine learned components. For such systems,
existing evaluation methods have limited expressiveness in describing and
explaining the relationship among input content, the internal states of system
components, and final output quality. We present Pandora, a set of hybrid
human-machine methods and tools for describing and explaining system failures.
Pandora leverages both human and system-generated observations to summarize
conditions of system malfunction with respect to the input content and system
architecture. We share results of a case study with a machine learning pipeline
for image captioning that show how detailed performance views can be beneficial
for analysis and debugging
Deep Perspective Transformation Based Vehicle Localization on Bird's Eye View
An accurate understanding of a self-driving vehicle's surrounding environment
is crucial for its navigation system. To enhance the effectiveness of existing
algorithms and facilitate further research, it is essential to provide
comprehensive data to the routing system. Traditional approaches rely on
installing multiple sensors to simulate the environment, leading to high costs
and complexity. In this paper, we propose an alternative solution by generating
a top-down representation of the scene, enabling the extraction of distances
and directions of other cars relative to the ego vehicle. We introduce a new
synthesized dataset that offers extensive information about the ego vehicle and
its environment in each frame, providing valuable resources for similar
downstream tasks. Additionally, we present an architecture that transforms
perspective view RGB images into bird's-eye-view maps with segmented
surrounding vehicles. This approach offers an efficient and cost-effective
method for capturing crucial environmental information for self-driving cars.
Code and dataset are available at
https://github.com/IPM-HPC/Perspective-BEV-Transformer.Comment: 7 pages, 2 figure
Interactive Spatiotemporal Analysis of Oil Spills Using Comap in North Dakota
The aim of the study is to analyze the oil spill pattern from various types of incidents and contaminants to determine the extent that incident data can be used as a baseline to prevent hazardous material releases and improve response activities at a state level. This study addresses the importance of collecting and sharing oil spill incidents as well as analytics using the data. Temporal, spatial and spatiotemporal analysis techniques are employed for the oil-spill related environmental incidents observed in the state of North Dakota, United States of America, from 2000 to 2014, as a result of the oil boom. Specifically, spatiotemporal methods are used to examine how the patterns of environmental incidents in North Dakota, which vary with the time of day, the day, the month, and the season. Results indicate that there were critical spatial and time variations in the distribution of environmental incidents. Application of spatiotemporal interaction visualization techniques, called comap has the potential to help planners and decision makers formulate policy to mitigate the risks associated with environmental incidents, improve safety, and allocate resources
Six Pillars of Effective Dropout Prevention and Recovery: An Assessment of Current State Policy and How to Improve it
This report identifies six model policy elements that frame a sound legislative strategy for dropout prevention and recovery, and it assesses the extent to which recent state policy aligns with these model elements. Overall, 36 states and the District of Columbia have enacted new dropout legislation since 2002. While some states have moved toward adopting comprehensive dropout prevention and recovery policies, nearly all of them have a long way to go. Nearly one-third of the nation—14 states—have enacted no new laws aimed at increasing graduation rates in the past eight years
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