A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

Nearest neighbor (NN) and range (RN) queries are basic query types in spatial databases. In this study, we refer to collections of NN and RN queries as spatial proximity (SP) queries. At peak times, location-based services (LBS) need to quickly process SP queries that arrive simultaneously. Timely processing can be achieved by increasing the number of LBS servers; however, this also increases service costs. Existing solutions evaluate SP queries sequentially; thus, such solutions involve unnecessary distance calculations. This study proposes a unified batch algorithm (UBA) that can effectively process SP queries in dynamic spatial networks. With the proposed UBA, the distance between two points is indicated by the travel time on the shortest path connecting them. The shortest travel time changes frequently depending on traffic conditions. The goal of the proposed UBA is to avoid unnecessary distance calculations for nearby SP queries. Thus, the UBA clusters nearby SP queries and exploits shared distance calculations for query clusters. Extensive evaluations using real-world roadmaps demonstrated the superiority and scalability of UBA compared with state-of-the-art sequential solutions.

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Efficient Processing of Movingk-Range Nearest Neighbor Queries in Directed and Dynamic Spatial Networks

Mobile Information Systems ◽

10.1155/2016/2406142 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Hyung-Ju Cho ◽

Rize Jin

Keyword(s):

Location Privacy ◽

Nearest Neighbor ◽

Real Life ◽

Location Based Services ◽

Spatial Networks ◽

Weight Changes ◽

Server Side ◽

Query Region ◽

Efficient Processing ◽

Nearest Neighbor Queries

Ak-range nearest neighbor (kRNN) query in a spatial network finds thekclosest objects to each point in the query region. The essential nature of thekRNN query is significant in location-based services (LBSs), where location-aware queries with query regions such askRNN queries are frequently used because of the issue of location privacy and the imprecision of the associated positioning techniques. Existing studies focus on reducing computation costs at the server side while processingkRNN queries. They also consider snapshot queries that are evaluated once and terminated, as opposed to moving queries that require constant updating of their results. However, little attention has been paid to evaluating movingkRNN queries in directed and dynamic spatial networks where every edge is directed and its weight changes in accordance with the traffic conditions. In this paper, we propose an efficient algorithm called MORAN that evaluates movingk-range nearest neighbor (MkRNN) queries in directed and dynamic spatial networks. The results of a simulation conducted using real-life roadmaps indicate that MORAN is more effective than a competitive method based on a shared execution approach.

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STATE-OF-THE-ART, CHALLENGES: PRIVACY PROVISIONING IN TTP LOCATION BASED SERVICES SYSTEMS

International Journal of Advanced Research in Computer Science ◽

10.26483/ijarcs.v10i2.6396 ◽

2019 ◽

Vol 10 (2) ◽

pp. 68-75 ◽

Cited By ~ 1

Author(s):

Muhammad Usman Ashraf ◽

Keyword(s):

State Of The Art ◽

Location Based Services

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Equity Effects of Congestion Charges: An Exploratory Analysis with MATSim

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2670-10 ◽

2017 ◽

Vol 2670 (1) ◽

pp. 75-82 ◽

Cited By ~ 2

Author(s):

Lucas Meyer de Freitas ◽

Oliver Schuemperlin ◽

Milos Balac ◽

Francesco Ciari

Keyword(s):

Travel Time ◽

Large Scale ◽

Agent Based ◽

Promising Tool ◽

Travel Time Savings ◽

Time Savings ◽

Time Changes ◽

High Level ◽

Congestion Charging ◽

Equity Effects

This paper shows an application of the multiagent, activity-based transport simulation MATSim to evaluate equity effects of a congestion charging scheme. A cordon pricing scheme was set up for a scenario of the city of Zurich, Switzerland, to conduct such an analysis. Equity is one of the most important barriers toward the implementation of a congestion charging system. After the challenges posed by equity evaluations are examined, it is shown that agent-based simulations with heterogeneous values of time allow for an increased level of detail in such evaluations. Such detail is achieved through a high level of disaggregation and with a 24-h simulation period. An important difference from traditional large-scale models is the low degree of correlation between travel time savings and welfare change. While traditional equity analysis is based on travel time savings, MATSim shows that choice dimensions not included in traditional models, such as departure time changes, can also play an important role in equity effects. The analysis of the results in light of evidence from the literature shows that agent-based models are a promising tool to conduct more complete equity evaluations not only of congestion charges but also of transport policies in general.

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Chi-Squared Distance Metric Learning for Histogram Data

Mathematical Problems in Engineering ◽

10.1155/2015/352849 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Wei Yang ◽

Luhui Xu ◽

Xiaopan Chen ◽

Fengbin Zheng ◽

Yang Liu

Keyword(s):

Nearest Neighbor ◽

State Of The Art ◽

Metric Learning ◽

Nearest Neighbors ◽

Distance Metric Learning ◽

Distance Metric ◽

Projected Gradient Method ◽

Proper Distance ◽

Chi Squared ◽

Real World Datasets

Learning a proper distance metric for histogram data plays a crucial role in many computer vision tasks. The chi-squared distance is a nonlinear metric and is widely used to compare histograms. In this paper, we show how to learn a general form of chi-squared distance based on the nearest neighbor model. In our method, the margin of sample is first defined with respect to the nearest hits (nearest neighbors from the same class) and the nearest misses (nearest neighbors from the different classes), and then the simplex-preserving linear transformation is trained by maximizing the margin while minimizing the distance between each sample and its nearest hits. With the iterative projected gradient method for optimization, we naturally introduce thel2,1norm regularization into the proposed method for sparse metric learning. Comparative studies with the state-of-the-art approaches on five real-world datasets verify the effectiveness of the proposed method.

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IMPROVEMENT OF THE PERFORMANCE OF FINGERPRINT VERIFICATION USING A COMBINATORIAL APPROACH

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237218500199 ◽

2018 ◽

Vol 30 (03) ◽

pp. 1850019

Author(s):

Fatemeh Alimardani ◽

Reza Boostani

Keyword(s):

Nearest Neighbor ◽

State Of The Art ◽

Recognition Rate ◽

Acceptance Rate ◽

Feature Reduction ◽

Support Vector ◽

Fingerprint Verification ◽

Linear Discriminant ◽

Comparative Results ◽

Verification Systems

Fingerprint verification systems have attracted much attention in secure organizations; however, conventional methods still suffer from unconvincing recognition rate for noisy fingerprint images. To design a robust verification system, in this paper, wavelet and contourlet transforms (CTS) were suggested as efficient feature extraction techniques to elicit a coverall set of descriptive features to characterize fingerprint images. Contourlet coefficients capture the smooth contours of fingerprints while wavelet coefficients reveal its rough details. Due to the high dimensionality of the elicited features, across group variance (AGV), greedy overall relevancy (GOR) and Davis–Bouldin fast feature reduction (DB-FFR) methods were adopted to remove the redundant features. These features were applied to three different classifiers including Boosting Direct Linear Discriminant Analysis (BDLDA), Support Vector Machine (SVM) and Modified Nearest Neighbor (MNN). The proposed method along with state-of-the-art methods were evaluated, over the FVC2004 dataset, in terms of genuine acceptance rate (GAR), false acceptance rate (FAR) and equal error rate (EER). The features selected by AGV were the most significant ones and provided 95.12% GAR. Applying the selected features, by the GOR method, to the modified nearest neighbor, resulted in average EER of [Formula: see text]%, which outperformed the compared methods. The comparative results imply the statistical superiority ([Formula: see text]) of the proposed approach compared to the counterparts.

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Road Short-Term Travel Time Prediction Method Based on Flow Spatial Distribution and the Relations

Mathematical Problems in Engineering ◽

10.1155/2016/7626875 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Mingjun Deng ◽

Shiru Qu

Keyword(s):

Time Series ◽

Spatial Distribution ◽

Travel Time ◽

Nonparametric Regression ◽

Nearest Neighbor ◽

Nearest Neighbor Search ◽

Short Term ◽

Combination Model ◽

The Road ◽

Neighbor Search

There are many short-term road travel time forecasting studies based on time series, but indeed, road travel time not only relies on the historical travel time series, but also depends on the road and its adjacent sections history flow. However, few studies have considered that. This paper is based on the correlation of flow spatial distribution and the road travel time series, applying nearest neighbor and nonparametric regression method to build a forecasting model. In aspect of spatial nearest neighbor search, three different space distances are defined. In addition, two forecasting functions are introduced: one combines the forecasting value by mean weight and the other uses the reciprocal of nearest neighbors distance as combined weight. Three different distances are applied in nearest neighbor search, which apply to the two forecasting functions. For travel time series, the nearest neighbor and nonparametric regression are applied too. Then minimizing forecast error variance is utilized as an objective to establish the combination model. The empirical results show that the combination model can improve the forecast performance obviously. Besides, the experimental results of the evaluation for the computational complexity show that the proposed method can satisfy the real-time requirement.

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Multi-itinerary optimization as cloud service

Communications of the ACM ◽

10.1145/3485626 ◽

2021 ◽

Vol 64 (11) ◽

pp. 121-129

Author(s):

Alexandru Cristian ◽

Luke Marshall ◽

Mihai Negrea ◽

Flavius Stoichescu ◽

Peiwei Cao ◽

...

Keyword(s):

Travel Time ◽

Time Windows ◽

Algorithm Design ◽

Cloud Service ◽

Multiple Agents ◽

Solution Quality ◽

Traffic Conditions ◽

Time Distance ◽

And Performance ◽

Vehicle Capacity

In this paper, we describe multi-itinerary optimization (MIO)---a novel Bing Maps service that automates the process of building itineraries for multiple agents while optimizing their routes to minimize travel time or distance. MIO can be used by organizations with a fleet of vehicles and drivers, mobile salesforce, or a team of personnel in the field, to maximize workforce efficiency. It supports a variety of constraints, such as service time windows, duration, priority, pickup and delivery dependencies, and vehicle capacity. MIO also considers traffic conditions between locations, resulting in algorithmic challenges at multiple levels (e.g., calculating time-dependent travel-time distance matrices at scale and scheduling services for multiple agents). To support an end-to-end cloud service with turnaround times of a few seconds, our algorithm design targets a sweet spot between accuracy and performance. Toward that end, we build a scalable approach based on the ALNS metaheuristic. Our experiments show that accounting for traffic significantly improves solution quality: MIO finds efficient routes that avoid late arrivals, whereas traffic-agnostic approaches result in a 15% increase in the combined travel time and the lateness of an arrival. Furthermore, our approach generates itineraries with substantially higher quality than a cutting-edge heuristic (LKH), with faster running times for large instances.

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