Reverse $k$ Nearest Neighbor Search over Trajectories

Wang, Sheng; Bao, Zhifeng; Culpepper, J. Shane; Sellis, Timos; Cong, Gao

doi:10.1109/tkde.2017.2776268

Cited by 39 publications

(36 citation statements)

References 36 publications

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“…The detailed pseudo codes, the source code, data sets, and the scripts to run experiments and draw figures. In LA trajectory, CPU cost is reduce sequentially for proposed methodology as compare than CPU cost of RkNNT [1]. In a similar way, CPU cost is reduce sequentially for proposed methodology as compare than CPU cost of RkNNT [1] for given NYC-Route trajectory.…”

Section: Results and Analysismentioning

confidence: 99%

“…In LA trajectory, CPU cost is reduce sequentially for proposed methodology as compare than CPU cost of RkNNT [1]. In a similar way, CPU cost is reduce sequentially for proposed methodology as compare than CPU cost of RkNNT [1] for given NYC-Route trajectory. In LA trajectory, route distance is take more for proposed methodology as compare than route distance of RkNNT [1].…”

Section: Results and Analysismentioning

confidence: 99%

“…In a similar way, CPU cost is reduce sequentially for proposed methodology as compare than CPU cost of RkNNT [1] for given NYC-Route trajectory. In LA trajectory, route distance is take more for proposed methodology as compare than route distance of RkNNT [1]. In a similar way, route distance takes more sequentially for proposed methodology as compare than route distance of RkNNT [1] for given NYC-Route trajectory.…”

Section: Results and Analysismentioning

confidence: 99%

“…In LA trajectory, route distance is take more for proposed methodology as compare than route distance of RkNNT [1]. In a similar way, route distance takes more sequentially for proposed methodology as compare than route distance of RkNNT [1] for given NYC-Route trajectory. …”

Section: Results and Analysismentioning

confidence: 99%

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Route Searching using Modified k-Nearest Neighbor with Hill Climbing over Trajectories

Gupta¹,

Yadav²

2018

IJARCSSE

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Abstract-Optimal planning for public transportation is one of the keys to sustainable development and better quality of life in urban areas. Based on mobility patterns, propose a localized transportation mode choice model, with which we can dynamically predict the bus travel demand for different bus routing. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. It also leverages the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips. GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Ola, Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. Now, study a new kind of query -Modified k-Nearest Neighbor Search with Hill Climbing (MkNNHC), which can be used for route planning and capacity estimation. Given a set of existing routes D R , a set of passenger transitions D T , and a query route Q, an MkNNHC query returns all transitions that take Q as one of its k nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering an RkNNT query. Then introduce a filter refinement framework for processing MkNNHC queries using the proposed indexes. Experiments on real datasets demonstrate the efficiency and scalability of our approaches.Keywords: GPS, Hill Climbing, Routing Optimization, MkNNHC, Trajectories, Efficiency & Scalability. I. INTRODUCTIONMore and more people live in metropolitan areas or big cities due in large part to rapid development of urbanization. One major side effect is increasing traffic congestion due to limited space, and consequently unnecessary energy consumption during traffic congestion. Public transportation (e.g., bus, subway) not only saves fuel and reduces congestion, but also offers a safe, affordable, and convenient way to travel. According to American Public Transportation Association1, Americans living in areas served by public transportation save 865 million hours of travel time and 450 million gallons of fuel annually in congestion reduction alone. Households that are likely to use public transportation on a given day save more than $9,700 every year. Better public transportation planning can significantly help to foster a more sustainable development and improve quality of life. Traditional public transportation planning methods have relied on human surveys to understand people's mobility patterns and their choice among different transportation modes. Despite the substantial time and c...

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

See 2 more Smart Citations

Route Searching using Modified k-Nearest Neighbor with Hill Climbing over Trajectories

Gupta¹,

Yadav²

2018

IJARCSSE

View full text Add to dashboard Cite

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“…In order to overcome these difficulties, the kernel-based machine learning technique of [27]- [30] is applied for modeling the authentication problem in this paper. Although the family of parametric learning methods has become mature in the literature [31]- [35], as exemplified by the linear regression methods of [31] and the polynomial regression methods of [32], [33], these parametric techniques usually rely on the assumption of knowing the distribution of samples [36] and the decision tree based solutions [37]. However, these two nonparametric methods have a limited ability to deal with challenges C2-C4.…”

Section: B Challenges For Physical Layer Authenticationmentioning

confidence: 99%

Learning-Aided Physical Layer Authentication as an Intelligent Process

Fang

Wang

Hanzo

2019

IEEE Trans. Commun.

135

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Performance of the existing physical layer authentication schemes could be severely affected by the imperfect estimates and variations of the communication link attributes used. The commonly adopted static hypothesis testing for physical layer authentication faces significant challenges in time-varying communication channels due to the changing propagation and interference conditions, which are typically unknown at the design stage. To circumvent this impediment, we propose an adaptive physical layer authentication scheme based on machine-learning as an intelligent process to learn and utilize the complex and time-varying environment, and hence to improve the reliability and robustness of physical layer authentication. Explicitly, a physical layer attribute fusion model based on a kernel machine is designed for dealing with multiple attributes without requiring the knowledge of their statistical properties. By modeling the physical layer authentication as a linear system, the proposed technique directly reduces the authentication scope from a combined N -dimensional feature space to a singledimensional (scalar) space, hence leading to reduced authentication complexity. By formulating the learning (training) objective of the physical layer authentication as a convex problem, an adaptive algorithm based on kernel least-mean-square is then proposed as an intelligent process to learn and track the variations of multiple attributes, and therefore to enhance the authentication performance.Both the convergence and the authentication performance of the proposed intelligent authentication process are theoretically analyzed. Our simulations demonstrate that our solution significantly improves the authentication performance in time-varying environments.

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Access to Hydroxy‐Functionalized Polypropylene through Coordination Polymerization

Zhong

Wang

et al. 2020

Angewandte Chemie

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Preparation of polyethylenes containing hydroxy groups has been already industrialized through radical copolymerization under harsh conditions followed by alcoholysis. By contrast, hydroxy‐functionalized polypropylene has proven a rather challenging goal in polymer science. Propylene can't be polymerized through a radical mechanism, and its coordination copolymerization with polar monomers is frustrated by catalyst poisoning. Herein, we report a new strategy to reach this target. The coordination polymerization of allenes by rare‐earth‐metal precursors affords pure 1,2‐regulated polyallenes, which are facilely transformed into poly(allyl alcohol) analogues by subsequent hydroboration/oxidation. Strikingly, the copolymerization of allenes and propylene gives unprecedented hydroxy‐functionalized polypropylene after post‐polymerization modification. Mechanistic elucidation by DFT simulation suggests kinetic rather than thermodynamic control.

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Reverse $k$ Nearest Neighbor Search over Trajectories

Cited by 39 publications

References 36 publications

Route Searching using Modified k-Nearest Neighbor with Hill Climbing over Trajectories

Route Searching using Modified k-Nearest Neighbor with Hill Climbing over Trajectories

Learning-Aided Physical Layer Authentication as an Intelligent Process

Access to Hydroxy‐Functionalized Polypropylene through Coordination Polymerization

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