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

2018 ◽  
Vol 8 (5) ◽  
pp. 84
Author(s):  
Vineet Kumar Gupta ◽  
Sriram Yadav
Keyword(s):  
Public Transportation ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Travel Demand ◽  
Choice Model ◽  
Nearest Neighbor Search ◽  
Hill Climbing ◽  
K Nearest Neighbor ◽  
Real World Data ◽  
Bus Routing

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 DR, a set of passenger transitions DT, 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.

scholarly journals K Nearest Neighbor Search in Navigation Systems

2005 ◽  
Vol 1 (3) ◽  
pp. 207-224 ◽  
Author(s):  
Maytham Safar
Keyword(s):  
Nearest Neighbor ◽  
Response Times ◽  
Shortest Paths ◽  
Network Connectivity ◽  
Nearest Neighbor Search ◽  
Navigation Systems ◽  
Large Network ◽  
K Nearest Neighbor ◽  
Real World Data ◽  
Spatial Network Databases

A frequent type of query in a car navigation system is to find theknearest neighbors (kNN) of a given query object (e.g., car) using the actual road network maps. With road networks (spatial networks), the distances between objects depend on their network connectivity and it is computationally expensive to compute the distances (e.g., shortest paths) between objects. In this paper, we propose a novel approach to efficiently and accurately evaluatekNN queries in a mobile information system that uses spatial network databases. The approach uses first order Voronoi diagram and Dijkstra's algorithm. This approach is based on partitioning a large network to small Voronoi regions, and then pre-computing distances across the regions. By performing across the network computation for only the border points of the neighboring regions, we avoid global pre-computation between every object-pair. Our empirical experiments with real-world data sets show that our proposed solution outperforms approaches that are based on on-line distance computation by up to one order of magnitude. In addition, our approach has better response times than approaches that are based on pre-computation.

scholarly journals Estimasi Tujuan Penumpang Menggunakan Predictive Model dengan Data Smart Card

2019 ◽  
Vol 2 (2) ◽  
pp. 99-110
Author(s):  
Ekky Alam ◽  
Inkreswari Hardini ◽  
Goklas Panjaitan ◽  
Sita Rosida
Keyword(s):  
Predictive Model ◽  
Public Transportation ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Performance Indicator ◽  
K Nearest Neighbor ◽  
Fundamental Information ◽  
And Performance ◽  
Public Transportation Planning

Bus Rapid Transit (BRT) is one of the main choices of public transportation that supports mobility of Jakarta community. As one of the main choices of public transportation, BRT should provide good service and always improve its performance. Needs for moving or mobility will cause a problem if the moving itself is heading at the same area and at the same time. That will cause some problems which are often faced in urban areas such as traffic and delay. To overcome those problems there needs to be a strategy to build good public transportation planning, besides need to know individual travel patterns to overcome problems and improve BRT service. In case to realize those plans needs to be built origin-destination (O-D) matrix. O-D matrix is a matrix that each cell is an amount of trip from the source(row) to the destination (column). O-D matrix is beneficial for analysis, design and public transportation management. O-D matrix also provides useful information like amount of trip between 2 different locations, that can be utilized as fundamental information for decision making for three levels of strategic management (long term planning), tactic (service adjustment and network development), and operational (scheduling, passenger statistic, and performance indicator). To build O-D matrix is required a predictive model that can be measured to predict passenger destination. The predictive model will be build using classification algorithms such as Decision Tree and K-Nearest Neighbor (KNN).

Hydroponic Nutrient Control System Based on Internet of Things

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Herman Herman ◽  
Demi Adidrana ◽  
Nico Surantha ◽  
Suharjito Suharjito
Keyword(s):  
Urban Areas ◽  
Human Population ◽  
Mineral Water ◽  
Nearest Neighbor ◽  
Total Dissolved Solids ◽  
K Nearest Neighbor ◽  
Turn On ◽  
Nutrient Film Technique ◽  
Planting Method

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it is k = 5.

scholarly journals Two-Pass K Nearest Neighbor Search for Feature Tracking

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 72939-72951
Author(s):  
Mingwei Cao ◽  
Wei Jia ◽  
Zhihan Lv ◽  
Wenjun Xie ◽  
Liping Zheng ◽  
...  
Keyword(s):  
Feature Tracking ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
K Nearest Neighbor ◽  
Neighbor Search ◽  
K Nearest Neighbor Search

A Biologically Inspired Saliency Priority Extraction Using Bayesian Framework

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20
Author(s):  
Jila Hosseinkhani ◽  
Chris Joslin
Keyword(s):  
Nearest Neighbor ◽  
Saliency Detection ◽  
Color Space ◽  
Bayesian Framework ◽  
Nearest Neighbor Search ◽  
Video Frame ◽  
K Nearest Neighbor ◽  
Feature Maps ◽  
Ranking System ◽  
New Feature

In this article, the authors used saliency detection for video streaming problem to be able to transmit regions of video frames in a ranked manner based on their importance. The authors designed an empirically-based study to investigate bottom-up features to achieve a ranking system stating the saliency priority. We introduced a gradual saliency detection model using a Bayesian framework for static scenes under conditions that we had no cognitive bias. To extract color saliency, we used a new feature contrast in Lab color space as well as a k-nearest neighbor search based on k-d tree search technique to assign a ranking system into different colors according to our empirical study. To find the salient textured regions we employed contrast-based Gabor energy features and then we added a new feature as intensity variance map. We merged different feature maps and classified saliency maps using a Naive Bayesian Network to prioritize the saliency across a frame. The main goal of this work is to create the ability to assign a saliency priority for the entirety of a video frame rather than simply extracting a salient area which is widely performed.

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