Failure restoration for location server with user movement learning and prediction

1999 ◽  
pp. 399-406
Author(s):  
Chan Yeol Park ◽  
Joon-Min Gil ◽  
Youn-Hee Han ◽  
Chong-Sun Hwang
Keyword(s):  
User Movement ◽  
Failure Restoration

scholarly journals A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 177-191
Author(s):  
Theodoros Anagnostopoulos
Keyword(s):  
Recommendation System ◽  
Smart Cities ◽  
Urban Pollution ◽  
Real Data ◽  
Movement Behavior ◽  
Daily Movement ◽  
Passenger Transportation ◽  
Ride Sharing ◽  
Mobility Behavior ◽  
User Movement

Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs.

An Evaluation of User Movement Data

2018 ◽  
pp. 729-735 ◽  
Author(s):  
Janelle Mason ◽  
Christopher Kelley ◽  
Bisoye Olaleye ◽  
Albert Esterline ◽  
Kaushik Roy
Keyword(s):  
Movement Data ◽  
User Movement

Contemporary mental health services

2019 ◽  
pp. 79-110
Author(s):  
Ian Cummins
Keyword(s):  
Mental Health ◽  
Mental Health Services ◽  
Health Services ◽  
Service User ◽  
Health It ◽  
User Movement ◽  
User Perspectives

This chapter begins with a discussion of the development of the service user movement within mental health. It emphasises the importance of service user perspectives before going on to examine a range of contemporary concerns within services.

Post-Disruption Performance Recovery to Enhance Resilience of Interconnected Network Systems

2019 ◽  
Author(s):  
Jiaxin Wu ◽  
Pingfeng Wang
Keyword(s):  
Distribution System ◽  
Disruption Management ◽  
Network Systems ◽  
Analysis Process ◽  
Recovery Strategies ◽  
Performance Recovery ◽  
Different Types ◽  
Failure Restoration ◽  
Different Characteristics ◽  
Disruption Recovery

Abstract Mitigating the effect of potential disruptive events at the operating phase of an engineered system therefore improving the system’s failure resilience is an importance yet challenging task in system operation. For complex networked system, different stakeholders complicate the analysis process by introducing different characteristics, such as different types of material flow, storage, response time, and flexibility. With different types of systems, the resilience can be improved by enhancing the failure restoration capability of the systems with appropriate performance recovery strategies. These methods include but not limit to, rerouting paths, optimal repair sequence and distributed resource centers. Considering different characteristics of disruptive events, effective recovery strategies for the failure restoration must be selected correspondingly. However, the challenge is to develop a generally applicable framework to optimally coordinate different recovery strategies and thus lead to desirable failure restoration performances. This paper presents a post-disruption recovery decision-making framework for networked systems, to help decision-makers optimize recovery strategies, in which the overall recovery task is formulated as an optimization problem to achieve maximum resilience. A case study of an electricity distribution system is used to demonstrate the feasibility of the developed framework and the comparison of several recovery strategies for disruption management.

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