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k-nearest neighbors on road networks

Tenindra Abeywickrama, Muhammad Aamir Cheema, David Taniar
2016 Proceedings of the VLDB Endowment  
A k nearest neighbor (kNN) query on road networks retrieves the k closest points of interest (POIs) by their network distances from a given location.  ...  While Euclidean distance has been used as a heuristic to search for the closest POIs by their road network distance, its efficacy has not been thoroughly investigated.  ...  Now, IER retrieves the next nearest Euclidean neighbor p.  ... 
doi:10.14778/2904121.2904125 fatcat:5qrmasyryvf5dhi7paivyrtmtm

k-Nearest Neighbors on Road Networks: A Journey in Experimentation and In-Memory Implementation [article]

Tenindra Abeywickrama, Muhammad Aamir Cheema, David Taniar
2016 arXiv   pre-print
A k nearest neighbor (kNN) query on road networks retrieves the k closest points of interest (POIs) by their network distances from a given location.  ...  While Euclidean distance has been used as a heuristic to search for the closest POIs by their road network distance, its efficacy has not been thoroughly investigated.  ...  The Voronoi-based Network Nearest Neighbor (VN 3 ) [19] computes the network equivalent of a Voronoi diagram for a given object set to partition all road network vertices based on its nearest neighbor  ... 
arXiv:1601.01549v2 fatcat:kj5llfoomnahnlgm2zsjeb2yzi

A Hybrid Network/Grid Model of Urban Morphogenesis and Optimization [article]

Juste Raimbault, Arnaud Banos, René Doursat
2016 arXiv   pre-print
It consists of a cellular automata grid coupled to a dynamic network topology.  ...  These variables are here: d 1 , the density, i.e. the average δ around a cell (i, j) in a circular neighborhood of radius ρ; d 2 , the Euclidean distance of a cell to the nearest road; d 3 the network-distance  ...  Another evolving structure is laid out on top of the lattice: a Euclidean network G(t) = (V (t), E(t)) whose vertices V are a finite subset of the world and edges E (its agents) represent roads.  ... 
arXiv:1612.08552v1 fatcat:kmiupie2jrfandvrzwwpqo5lya

Multi-Agent Routing Value Iteration Network [article]

Quinlan Sykora, Mengye Ren, Raquel Urtasun
2020 arXiv   pre-print
In contrast, we propose a graph neural network based model that is able to perform multi-agent routing based on learned value iteration in a sparsely connected graph with dynamically changing traffic conditions  ...  We also show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.  ...  perform the following iterative update through an LSTM with an attention module across neighboring nodes: X (k+1) = X (k) + LSTM(Att(X (k) , A); H (k) ), (4) for t = 1 . . .  ... 
arXiv:2007.05096v2 fatcat:jxcs7osdjnc2lopuxfotk2jqha

LiDAR-based Panoptic Segmentation via Dynamic Shifting Network [article]

Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, Ziwei Liu
2020 arXiv   pre-print
As one of the first endeavors towards this new challenging task, we propose the Dynamic Shifting Network (DS-Net), which serves as an effective panoptic segmentation framework in the point cloud realm.  ...  Notably, we achieve 1st place on the public leaderboard of SemanticKITTI, outperforming 2nd place by 2.6% in terms of the PQ metric.  ...  Dynamic Shifting Point Clustering Revisit.  ... 
arXiv:2011.11964v2 fatcat:cpoofyegezf5xib6ybbi2xtuga

Multi-target tracking and data association on road networks using unmanned aerial vehicles

Brett E. Barkley, Derek A. Paley
2017 2017 IEEE Aerospace Conference  
The road network is formed into a graph with nodes that indicate the target likelihood ratio (before detection) and position probability (after detection).  ...  A cooperative search and track algorithm for surveilling multiple road vehicles is presented for fixed-wing Unmanned Air Vehicles (UAVs) with a finite field of view.  ...  of the target distribution on the road network.  ... 
doi:10.1109/aero.2017.7943751 fatcat:vz3q5otdvzhfhcjn3343ggpo74

Processing spatial skyline queries in both vector spaces and spatial network databases

Mehdi Sharifzadeh, Cyrus Shahabi, Leyla Kazemi
2009 ACM Transactions on Database Systems  
Furthermore, their output computed based on Euclidean distance is a good approximation of the spatial skyline in network space.  ...  Considering Euclidean distance, we propose two algorithms, B 2 S 2 and VS 2 , for static query points and one algorithm, VCS 2 , for streaming Q whose points change location over time (e.g., are mobile  ...  Their proposed algorithms rely on existing nearest neighbor and range query approaches to find a candidate set.  ... 
doi:10.1145/1567274.1567276 fatcat:tadrwirlcbdazofz26t5oei25e

Batching and Matching for Food Delivery in Dynamic Road Networks [article]

Manas Joshi, Arshdeep Singh, Sayan Ranu, Amitabha Bagchi, Priyank Karia, Puneet Kala
2020 arXiv   pre-print
Extensive experiments on food-delivery data from large metropolitan cities establish that FoodMatch is substantially better than baseline strategies on a number of metrics, while being efficient enough  ...  To mitigate this computational bottleneck, we develop an algorithm called FoodMatch, which maps the vehicle assignment problem to that of minimum weight perfect matching on a bipartite graph.  ...  In reality, the delivery time is dependent on the road network distance.  ... 
arXiv:2008.12905v1 fatcat:5fudfefstffebpjh76wvax23b4

Beyond Millisecond Latency $k$ NN Search on Commodity Machine

Bailong Liao, Leong Hou U, Man Lung Yiu, Zhiguo Gong
2015 IEEE Transactions on Knowledge and Data Engineering  
The k nearest neighbor (kNN) search on road networks is an important function in web mapping services.  ...  Experimental results show that our solutions offer very low query latency (0.1ms) and require only small index sizes, even for 10-million-node networks.  ...  PRELIMINARIES Problem Definition and Settings In this work, we focus on the k nearest neighbor (kNN) search in a road network.  ... 
doi:10.1109/tkde.2015.2426702 fatcat:326vjx5jxfdvze34nh4joxpt4q

A Taxonomyof Intelligent Algorithms used for Solving Traveling Salesman Problem (TSP) in the Year 2019

Haroon Altarawneh
2021 Zenodo  
This research considered as road map for more research in this filed.  ...  Travel salesman problem (TSP) considered as one of the most important complex optimization problem.  ...  Alipour&Razavi [119] developed a novel local heuristic search ,based on nearest insertion into the convex hull construction heuristic NICH-LS to solve Euclidean TSP. Chen, Zhixiang, et al.  ... 
doi:10.5281/zenodo.4785093 fatcat:s62cji4vvfdwdnrolrrbb75t5m

An Evolutionary Game-Based Mechanism for Routing P2P Network Flow among Selfish Peers

Fang Zuo, Wei Zhang
2014 Journal of Networks  
The DAIM model can provide richness of nature-inspired adaptation algorithms on a complex distributed computing environment.  ...  Networking devices are increasing in complexity among various services and platforms, from different vendors. The network complexity is required experts' operators.  ...  In this respect, the method based on graph may also be local minimum number or more computational cost. Nearest neighbors' selection is a key step in nonlinear manifold learning.  ... 
doi:10.4304/jnw.9.01.10-17 fatcat:tbmafdamk5am7a6ba26gsxzydq

Scalable shortest paths browsing on land surface

Songhua Xing, Cyrus Shahabi
2010 Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '10  
Observation 1: Given a terrain T and two vertex sets A and B, the degree of sharing common edges of the shortest surface paths between A and B depends on the roughness of T: the rougher T is, the more  ...  RELATED WORK k Nearest Neighbor Query on Surface The surface kNN query, which returns the k nearest neighbors based on the surface distance, is introduced very recently.  ...  vertex v in a SPT must be one of the neighbors of the source s; 3) surface paths oracle does not store the approximate distances as the road network paths oracle does.  ... 
doi:10.1145/1869790.1869806 dblp:conf/gis/XingS10 fatcat:r4jg5opf4jbllld3mffw2ky744

Welcome Message from the General Chair

2006 International Conference on Dependable Systems and Networks (DSN'06)  
The international conference series on Very Large Data Bases (VLDB) was launched in 1975 in Framingham MA, about 20 miles from Boston.  ...  More than that, Toronto is a well-run, safe city that visitors enjoy visiting and revisiting. Welcome to VLDB'04 and Toronto. We hope that you enjoy both the technical programme and the city!!  ...  the data points that have q as one of their k nearest neighbors.  ... 
doi:10.1109/dsn.2006.75 dblp:conf/dsn/X06 fatcat:k4duddvbk5glboxkqxkkfsh4p4

Integrated planning of electric vehicles routing and charging stations location considering transportation networks and power distribution systems

Andrés Arias, Juan D. Sanchez, Mauricio Granada
2018 International Journal of Industrial Engineering Computations  
Furthermore, optimal siting of EVCSs does not depend exclusively on the transportation network requirements, because those installations imply large consumptions of electricity.  ...  Therefore, the effect of the charging stations on the power distribution networks has to be taken into account, in order to avoid congestion or additional costs associated with energy losses.  ...  Based on a shared nearest neighbor clustering algorithm and queuing theory, the authors in (Dong et al., 2016) have developed a planning method, which is decomposed into three parts: a spatial-temporal  ... 
doi:10.5267/j.ijiec.2017.10.002 fatcat:apd2ydvk3jfi3fu5q7faj5txba

Multi-track Map Matching [article]

Adel Javanmard, Maya Haridasan, Li Zhang
2012 arXiv   pre-print
We study algorithms for matching user tracks, consisting of time-ordered location points, to paths in the road network.  ...  Previous work has focused on the scenario where the location data is linearly ordered and consists of fairly dense and regular samples.  ...  We assume that the user traverses a path Γ on the road network with some bounded velocity.  ... 
arXiv:1209.2759v1 fatcat:fmesjupg6fhezjxfmrbxhs4k6m
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