On the Use of Bayesian Networks for Real-Time Urban Traffic Measurements: a Case Study with Low-Cost Devices

Climate change and sea-level rise are increasingly leading to higher and prolonged high tides, which, in combination with the growing intensity of rainfall and storm surges, and insufficient drainage infrastructure, result in frequent recurrent flooding in coastal cities. There is a pressing need to understand the occurrence of roadway flooding incidents in order to enact appropriate mitigation measures. Agency data for roadway flooding events are scarce and resource-intensive to collect. Crowdsourced data can provide a low-cost alternative for mapping roadway flood incidents in real time; however, the reliability is questionable. This research demonstrates a framework for asserting trustworthiness on crowdsourced flood incident data in a case study of Norfolk, Virginia. Publicly available (but spatially limited) flood incident data from the city in combination with different environmental and topographical factors are used to create a logistic regression model to predict the probability of roadway flooding at any location on the roadway network. The prediction accuracy of the model was found to be 90.5%. When applying this model to crowdsourced Waze flood incident data, 71.7% of the reports were predicted to be trustworthy. This study demonstrates the potential for using Waze incident report data for roadway flooding detection, providing a framework for cities to identify trustworthy reports in real time to enable rapid situation assessment and mitigation to reduce incident impact.

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Combining VR Visualization and Sonification for Immersive Exploration of Urban Noise Standards

Multimodal Technologies and Interaction ◽

10.3390/mti3020034 ◽

2019 ◽

Vol 3 (2) ◽

pp. 34 ◽

Cited By ~ 3

Author(s):

Markus Berger ◽

Ralf Bill

Keyword(s):

Real Time ◽

Sound Propagation ◽

Road Traffic ◽

Expert Knowledge ◽

Low Cost ◽

Traffic Noise ◽

Urban Traffic ◽

Road Traffic Noise ◽

Common Noise ◽

Noise Assessment

Urban traffic noise situations are usually visualized as conventional 2D maps or 3D scenes. These representations are indispensable tools to inform decision makers and citizens about issues of health, safety, and quality of life but require expert knowledge in order to be properly understood and put into context. The subjectivity of how we perceive noise as well as the inaccuracies in common noise calculation standards are rarely represented. We present a virtual reality application that seeks to offer an audiovisual glimpse into the background workings of one of these standards, by employing a multisensory, immersive analytics approach that allows users to interactively explore and listen to an approximate rendering of the data in the same environment that the noise simulation occurs in. In order for this approach to be useful, it should manage complicated noise level calculations in a real time environment and run on commodity low-cost VR hardware. In a prototypical implementation, we utilized simple VR interactions common to current mobile VR headsets and combined them with techniques from data visualization and sonification to allow users to explore road traffic noise in an immersive real-time urban environment. The noise levels were calculated over CityGML LoD2 building geometries, in accordance with Common Noise Assessment Methods in Europe (CNOSSOS-EU) sound propagation methods.

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Real-time reliability evaluation methodology based on dynamic Bayesian networks: A case study of a subsea pipe ram BOP system

ISA Transactions ◽

10.1016/j.isatra.2015.06.011 ◽

2015 ◽

Vol 58 ◽

pp. 595-604 ◽

Cited By ~ 42

Author(s):

Baoping Cai ◽

Yonghong Liu ◽

Yunpeng Ma ◽

Zengkai Liu ◽

Yuming Zhou ◽

...

Keyword(s):

Bayesian Networks ◽

Real Time ◽

Reliability Evaluation ◽

Dynamic Bayesian Networks ◽

Evaluation Methodology

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A new method for real-time evaluation of urban traffic congestion: a case study in Xi’an, China

Geocarto International ◽

10.1080/10106049.2018.1552325 ◽

2019 ◽

Vol 35 (10) ◽

pp. 1033-1048 ◽

Cited By ~ 4

Author(s):

Chaode Yan ◽

Xiaobing Wei ◽

Xiao Liu ◽

Zhiguo Liu ◽

Jinxi Guo ◽

...

Keyword(s):

Real Time ◽

Traffic Congestion ◽

Urban Traffic ◽

New Method ◽

Time Evaluation

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Wireless-Integrated Embedded Real-Time Control: A Case Study in Adopting Resources for Development of a Low-Cost Interdisciplinary Laboratory Project

10.18260/1-2--18530 ◽

2020 ◽

Author(s):

Paul Flikkema ◽

Kenji Yamamoto ◽

Carol Haden ◽

Jeff Frolik ◽

Tom Weller

Keyword(s):

Real Time ◽

Low Cost ◽

Real Time Control ◽

Time Control

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COMBINING BAYESIAN NETWORKS AND ROUGH SETS: FURTHER STEP TOWARDS REASONING ABOUT UNCERTAINTY

International Journal of Computing ◽

10.47839/ijc.7.3.518 ◽

2014 ◽

pp. 6-14

Author(s):

Janusz Zalewski ◽

Sławomir T. Wierzchoń ◽

Henry L. Pfister

Keyword(s):

Bayesian Networks ◽

Real Time ◽

Rough Sets ◽

Probability Distributions ◽

Bayesian Belief Networks ◽

Critical Systems ◽

Safety Critical ◽

Safety Critical Systems ◽

Number Of Authors

This paper discusses a combination of Bayesian belief networks and rough sets for reasoning about uncertainty. The motivation for this work is the problem with assessment of properties of software used in real-time safety-critical systems. A number of authors applied Bayesian networks for this purpose, however, their approach suffers from problems related to calculating the conditional probability distributions, when there is scarcity of experimental data. The current authors propose enhancing this method by using rough sets, which do not require knowledge of probability distributions and thus are helpful in making preliminary evaluations, especially in real-time decision making. The combination of Bayesian network and rough sets tools, Netica and Rosetta, respectively, is used to demonstrate the applicability of this method in a case study of the Australian Navy exercise.

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A Microprocessor-Based Monitoring and Control System via Internet in Case Study of Pet Care

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.772 ◽

2011 ◽

Vol 268-270 ◽

pp. 772-780 ◽

Cited By ~ 2

Author(s):

Hsiung Cheng Lin ◽

Liang Yih Liu ◽

Kuo Hung Pai

Keyword(s):

Real Time ◽

Interface Design ◽

Low Cost ◽

Graphical Interface ◽

Well Performance ◽

Monitoring And Control ◽

Long Distance ◽

Industry Applications ◽

And Control

Since the past years, the microprocessor (8051) has been still playing an indispensable role as a controller in industry applications because of fast executing process, low-cost, small size and low power consumption, etc. It, however, usually lacks of long distance transmission, graphical interface and vision. On the other hand, VB is now a very popular software package for graphical interface design due to easy exploring and low price. Combining both superiorities as above, this paper develops a remote visional microprocessor-based monitoring and control platform using VB graphical interface. The nearby PC (server) can collect real-time sensing signals from the 8051 through RS232 and transmit it to remote PCs (client) for on line monitoring mechanism via Internet. Also, the client can send the control signals to the server and thus control the 8051. The real-time case study for feeding care in the Pet House is provided to verify its well performance and remote Web-based capability in term of fast, simple and robust performance.

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An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening

Sensors ◽

10.3390/s21010050 ◽

2020 ◽

Vol 21 (1) ◽

pp. 50

Author(s):

Steve H. L. Liang ◽

Sara Saeedi ◽

Soroush Ojagh ◽

Sepehr Honarparvar ◽

Sina Kiaei ◽

...

Keyword(s):

Real Time ◽

Situational Awareness ◽

Low Cost ◽

Heterogeneous Systems ◽

Geospatial Data ◽

Sensor Data ◽

Contact Tracing ◽

Time Data ◽

Sensor Web

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

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