Supervisory Decision-Making

2002 ◽  
Vol 46 (3) ◽  
pp. 318-322
Author(s):  
Dwight P. Miller ◽  
Jack Schryver ◽  
Daniel R. Tufano
Keyword(s):  
Decision Making ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Decision Aids ◽  
Organization Design ◽  
Transportation Systems ◽  
Design Guidelines ◽  
Intelligent Software ◽  
Trust In Automation ◽  
Dynamic Decisions

Supervisory Decision-Making (SDM) refers to human supervision of several semi-autonomous (nonhuman) systems in a collaborative manner to accomplish a goal. This study defined SDM and distinguished it from traditional supervisory control and decision-making. An examination of diverse literature in organization design, biology, robotics, innovation diffusion, and trust in automation, yielded no directly applicable or comprehensive models. Field observations were made of large-scale war-games, where operators interacted with semi/autonomous sensors and defense-management systems. Four cognitive models were subsequently developed describing 1) adaptive partnering with automation, 2) technology adoption, 3) trust in automation, and 4) dealing with advice from decision aids. The latter quantitatively models individual, dynamic decisions to accept or reject recommendations made by automated battlespace advisors. The anticipated benefits of this work include more effective human-robot coordination, communication, the identification of experiments, and ultimately design guidelines for robotics, intelligent software agents, intelligent transportation systems, and space exploration.

scholarly journals GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research

2021 ◽  
Vol 13 (4) ◽  
pp. 544
Author(s):  
Guohao Zhang ◽  
Bing Xu ◽  
Hoi-Fung Ng ◽  
Li-Ta Hsu
Keyword(s):  
Urban Area ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Systems ◽  
Accurate Localization ◽  
Gnss Receivers ◽  
Multipath Signal ◽  
Scale Test ◽  
Error Behavior

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research

2020 ◽  
Author(s):  
Guohao Zhang ◽  
Bing Xu ◽  
Hoi-Fung Ng ◽  
Li-Ta Hsu
Keyword(s):  
Urban Area ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Systems ◽  
Accurate Localization ◽  
Multipath Signal ◽  
Multi Agent ◽  
Scale Test ◽  
Error Behavior

Accurate localization of road agents is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques are recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiment requiring numbers of devices is hard to be conducted, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, carrier-phase, 〖C/N〗_0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

New Approach to Transportation Planning for the 21st Century

2005 ◽  
Vol 1931 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Joseph M. Sussman ◽  
Sgouris P. Sgouridis ◽  
John L. Ward
Keyword(s):  
Intelligent Transportation Systems ◽  
Transportation Planning ◽  
Large Scale ◽  
New Technologies ◽  
Open Systems ◽  
Regional Scale ◽  
Transportation Systems ◽  
Cultural Perspectives ◽  
Institutional Issues ◽  
Operations And Maintenance

The complex large-scale integrated open systems (CLIOS) process is an overarching mechanism for considering systems, especially those exhibiting nested complexity, in which both the physical and the institutional aspects are complex. A special case of the CLIOS process is regional strategic transportation planning. New technologies, such as intelligent transportation systems, allow consideration of the planning, management, operations, and maintenance of transportation systems at the regional scale. Although the technological issues in advancing to this scale have proved tractable, the institutional issues concerned with deploying these systems across political jurisdictions with different measures of performance and different cultural perspectives has proved quite difficult. This paper explores processes for studying these institutional questions and integrating a number of concepts into the process: technological change, sustainability, real options, supply chain management, the various transport modes, and social, political, and economic factors. The paper serves as a case study of tailoring the CLIOS process into a process specifically designed to systematically address particular issues, in this case, regional strategic transportation planning.

Clustering, Connectivity, and Monitoring Challenges in Vehicular Ad hoc Networks

2010 ◽  
pp. 257-281
Author(s):  
Ameneh Daeinabi ◽  
Akbar Ghaffarpour Rahbar
Keyword(s):  
Ad Hoc Networks ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Clustering Algorithms ◽  
Transportation Systems ◽  
High Rate ◽  
Efficient Resource ◽  
Hoc Networks

Vehicular Ad Hoc Networks (VANETs) are appropriate networks that can be applied for intelligent transportation systems. Three important challenges in VANETs are studied in this chapter. The first challenge is to defend against attackers. Because of the lack of a coordination unit in a VANET, vehicles should cooperate together and monitor each other in order to enhance security performance of the VANET. As the second challenge in VANETs, scalability is a critical issue for a network designer. Clustering is one solution for the scalability problem and is vital for efficient resource consumption and load balancing in large scale VANETs. On the other hand, due to the high-rate topology changes and high variability in vehicles density, transmission range of a vehicle is an important issue for forwarding and receiving messages. In this chapter, we study the clustering algorithms, the solutions appropriate to increase connectivity, and the algorithms that can detect attackers in a VANET.

Proactive Decision Making for ITS Communication

2020 ◽  
pp. 197-226 ◽  
Author(s):  
Rodrigo Silva ◽  
Christophe Couturier ◽  
Thierry Ernst ◽  
Jean-Marie Bonnin
Keyword(s):  
Decision Making ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Network Resources ◽  
Communication Architecture ◽  
Cooperative Vehicles ◽  
Near Future ◽  
Cooperative Intelligent Transportation Systems ◽  
Exchange Data

Demand from different actors for extended connectivity where vehicles can exchange data with other vehicles, roadside infrastructure, and traffic control centers have pushed vehicle manufacturers to invest in embedded solutions, which paves the way towards cooperative intelligent transportation systems (C-ITS). Cooperative vehicles enable the development of an ecosystem of services around them. Due to the heterogeneousness of such services and their specific requirements, as well as the need for network resources optimization for ubiquitous connectivity, it is necessary to combine existing wireless technologies, providing applications with a communication architecture that hides such underlying access technologies specificities. Due to vehicles' high velocity, their connectivity context can change frequently. In such scenario, it is necessary to take into account the short-term prevision about network environment; enabling vehicles proactively manage their communications. This chapter discusses about the use of near future information to proactive decision-making process.

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