Effects of Signalization at Rural Intersections Considering the Elderly Driving Population

2019 ◽  
Vol 2673 (2) ◽  
pp. 743-757 ◽  
Author(s):  
Lishengsa Yue ◽  
Mohamed Abdel-Aty ◽  
Jaeyoung Lee ◽  
Ahmed Farid
Keyword(s):  
Nearest Neighbor ◽  
Linear Models ◽  
Clustering Algorithms ◽  
The Elderly ◽  
K Nearest Neighbor ◽  
Cross Sectional ◽  
Crash Data ◽  
Elderly Drivers ◽  
Elderly Driver ◽  
Crash Modification Factors

The main objective of this study is to quantify the safety impacts of signalization at Florida’s rural three-leg and four-leg stop-controlled intersections by estimating crash modification factors. The intersections are those in which stop signs are provided for the minor approaches or all-way stop-controlled intersections. The crash modification factors (CMF) are estimated using the cross-sectional method. Generalized linear models (GLM) and multivariate adaptive regression spline models (MARS) are employed with four years of Florida crash data. The K-nearest neighbor (KNN) and K-means clustering algorithms are implemented to identify the comparison sites which are sites having similar characteristics as those of the converted intersections. Furthermore, the quasi-induced exposure method is used to evaluate separately the safety effects of signalization for elderly and non-elderly drivers. According to the results, signalization contributes to an increase in property damage only (PDO) and rear-end crashes. In addition, elderly drivers are more at risk of being involved in such crashes than non-elderly drivers. In particular, at rural four-leg two-way stop-controlled intersections, signalization decreases crash severity, and a greater percentage of the decrease is observed for the elderly drivers than non-elderly especially when the intersection has a high level of major-road average annual daily traffic (AADT) and elderly driver proportion. This study also demonstrates that the MARS model shows a better model fit than the GLM model due to its strength in capturing nonlinear relationships and interaction effects among variables. This study’s findings have implications for both practitioners and researchers.

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

An improved OPTICS clustering algorithm for discovering clusters with uneven densities

2021 ◽  
Vol 25 (6) ◽  
pp. 1453-1471
Author(s):  
Chunhua Tang ◽  
Han Wang ◽  
Zhiwen Wang ◽  
Xiangkun Zeng ◽  
Huaran Yan ◽  
...  
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Substantial Improvement ◽  
Experimental Results ◽  
High Time ◽  
Parameter Setting ◽  
K Nearest Neighbor ◽  
Density Based Clustering

Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.

scholarly journals A novel content based image retrieval system using K-means/KNN with feature extraction

2012 ◽  
Vol 9 (4) ◽  
pp. 1645-1661 ◽  
Author(s):  
Ray-I Chang ◽  
Shu-Yu Lin ◽  
Jan-Ming Ho ◽  
Chi-Wen Fann ◽  
Yu-Chun Wang
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Color Analysis ◽  
Image Retrieval System ◽  
First Time ◽  
System Designs

Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.

A Walking-Assistant Robot Controlled by Tactile-Slip Sensation

2014 ◽  
Vol 494-495 ◽  
pp. 1133-1136 ◽  
Author(s):  
Xiao Juan Wei ◽  
Xiao Dong Zhang
Keyword(s):  
Control System ◽  
Nearest Neighbor ◽  
The Elderly ◽  
Feature Representation ◽  
K Nearest Neighbor ◽  
Human Interface ◽  
Physical Support ◽  
Robot Perception ◽  
Perception System ◽  
Slip Sensor

A walking-assistant robot guided by the intention and power-driven is presented, its purpose is to provide physical support and walking assistance for the elderly to meet their needs of walking autonomy, friendliness, and maintaining the ability of walking and taking care of themselves. Tactile and slip sensor is selected as the human interface to perceive the users walking intent, and the sensor is also used to detect the user's slip trend. And the paper researches the feature representation and extraction method of tactile and slip signal for driving control pattern recognition. An improved classification and identification method combining K-means in clustering and K-nearest neighbor algorithm in classification is proposed. The paper introduces the overall design schemes of tactile and slip drive control system of walking-assistant robot, perception system, motion control system. Finally the feasibility and effectiveness of the entire system are verified by experiment.

A SCALABLE CLUSTERING METHOD FOR CATEGORICAL SEQUENCE DATA

2005 ◽  
Vol 02 (02) ◽  
pp. 167-180
Author(s):  
SEUNG-JOON OH ◽  
JAE-YEARN KIM
Keyword(s):  
Nearest Neighbor ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
K Nearest Neighbor ◽  
Clustering Method ◽  
Scalable Clustering ◽  
Log Files ◽  
Web Access ◽  
Better Than

Clustering of sequences is relatively less explored but it is becoming increasingly important in data mining applications such as web usage mining and bioinformatics. The web user segmentation problem uses web access log files to partition a set of users into clusters such that users within one cluster are more similar to one another than to the users in other clusters. Similarly, grouping protein sequences that share a similar structure can help to identify sequences with similar functions. However, few clustering algorithms consider sequentiality. In this paper, we study how to cluster sequence datasets. Due to the high computational complexity of hierarchical clustering algorithms for clustering large datasets, a new clustering method is required. Therefore, we propose a new scalable clustering method using sampling and a k-nearest-neighbor method. Using a splice dataset and a synthetic dataset, we show that the quality of clusters generated by our proposed approach is better than that of clusters produced by traditional algorithms.

Safety Effects of Turning Movement Restrictions at Stop-Controlled Intersections

2018 ◽  
Vol 2672 (21) ◽  
pp. 10-19
Author(s):  
Thanh Q. Le ◽  
Frank Gross ◽  
Tim Harmon
Keyword(s):  
Confidence Level ◽  
Cost Effective ◽  
Cross Sectional ◽  
Benefit Cost Analysis ◽  
Crash Data ◽  
Crash Modification Factors ◽  
Design Speed ◽  
Benefit Cost ◽  
Crash Types ◽  
Safety Benefit

This study evaluates the safety effectiveness of physical right-in-right-out (RIRO) operations compared with full turning movements at stop-controlled intersections. Geometric, traffic, and crash data from California were obtained for urban, three-legged, stop-controlled intersections with full movement and RIRO operations, as well as the downstream four-legged, stop-controlled or signalized intersections with full movement. A cross-sectional analysis provided estimates of the effects of turning movement restrictions while controlling for other differences between sites with RIRO and full movement. The aggregate results indicate reductions in total, all intersection-related, and fatal and injury intersection-related crashes at intersections with RIRO operations compared with full movement, with estimated crash modification factors of 0.55, 0.32, and 0.20, respectively. The reductions are statistically significant at the 95% confidence level for all crash types. Based on the disaggregate results, it does not appear that RIRO operations have different effects for different levels of traffic, design speed, or number of lanes. The analysis also examined the potential for crash migration from intersections where RIRO is implemented to the downstream intersection when determining the net benefits. The results indicate potential crash increases at downstream intersections, but many of the increases are not statistically significant at the 90% confidence level. Although the safety benefit-cost analysis suggests the strategy can be cost effective in reducing crashes at stop-controlled intersections, there is a need to analyze potential costs and benefits on a case-by-case basis with site-specific values.

Safety Analysis Considering the Impactof Travel Time Reliability on Elderly Drivers

2017 ◽  
Author(s):  
Emmanuel Kidando ◽  
Ren Moses ◽  
Yassir Abdelrazig ◽  
Eren Erman
Keyword(s):  
Travel Time ◽  
The Elderly ◽  
Safety Analysis ◽  
Travel Time Reliability ◽  
Crash Frequency ◽  
Elderly Drivers ◽  
Elderly Driver ◽  
Unit Increase ◽  
Risk Ratios

The main goal of this research was to evaluate how travel time reliability (TTR) might be associated with crashes involving elderly drivers, defined as those age 65 and above. Several TTR metrics were used to estimate their influence on elderly crash frequency and severity of the crash on freeways and arterial highways. The results suggest that TTR is statistically significant in affecting both elderly crash frequency and the severity of a crash involving an elderly driver. In particular, the analysis of risk ratios illustrates that a one-unit increase in the probability of congestion reduces the likelihood of the elderly severe crash by 22%.

scholarly journals Abuse in Colombian elderly and its association with socioeconomic conditions and functionality

2019 ◽  
pp. 77-88 ◽  
Author(s):  
Carmen Lucia Curcio ◽  
Claudia Payán Villamizar ◽  
Abelardo Jiménez ◽  
Fernando Gómez
Keyword(s):  
Elderly People ◽  
Living Arrangements ◽  
Urban Areas ◽  
Daily Living ◽  
Linear Models ◽  
Functional Dependence ◽  
The Elderly ◽  
Cross Sectional ◽  
Lower Socioeconomic ◽  
Type Of Abuse

Abstract Objective: To describe the presence of abuse in elderly people in Colombia and its association with socio-demographic and functional conditions. Methods: Cross-sectional and descriptive research. Data were taken from the SABE Colombia Survey, a population study, with a national representative sample of 23,694 adults aged over 60 years. Presence and type of abuse by partners or family members, members were investigated. Generalized linear models with Poisson link function were used to estimate the causes of the prevalence of abuse by area of residence, region, age, sex, dependence on activities of daily living and living arrangements. Results: 15.1% of the elderly in Colombia reported some type of abuse, and over 50% reported more than one form of abuse. Abuse proportion is greater in people who are aged 60-69, in women, people with lower levels of education, people who belong to lower socioeconomic status, people who live alone, people who live with children, and people in urban areas. The most frequent abuse form is psychological, followed by neglect and physical abuse. Dependence on basic and instrumental daily living activities increases the probabilities of suffering abuse. Conclusions: Home is a risky place for the elderly people, especially for those with functional dependence, those who belong to low socioeconomic strata and women. Results should encourage debate among researchers, professionals and decision makers on public policy about necessary actions and means to change violent family dynamics in homes with elderly people.

Semi-Supervised Point Prototype Clustering

1998 ◽  
Vol 12 (05) ◽  
pp. 625-643 ◽  
Author(s):  
Amine M. Bensaid ◽  
James C. Bezdek
Keyword(s):  
Nearest Neighbor ◽  
Structural Information ◽  
Clustering Algorithms ◽  
Unlabeled Data ◽  
Training Data ◽  
Clustering Methods ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Rule ◽  
Small Set ◽  
Supervise Classification

This paper describes a class of models we call semi-supervised clustering. Algorithms in this category are clustering methods that use information possessed by labeled training data Xd⊂ ℜp as well as structural information that resides in the unlabeled data Xu⊂ ℜp. The labels are used in conjunction with the unlabeled data to help clustering algorithms partition Xu ⊂ ℜp which then terminate without the capability to label other points in ℜp. This is very different from supervised learning, wherein the training data subsequently endow a classifier with the ability to label every point in ℜp. The methodology is applicable in domains such as image segmentation, where users may have a small set of labeled data, and can use it to semi-supervise classification of the remaining pixels in a single image. The model can be used with many different point prototype clustering algorithms. We illustrate how to attach it to a particular algorithm (fuzzy c-means). Then we give two numerical examples to show that it overcomes the failure of many point prototype clustering schemes when confronted with data that possess overlapping and/or non uniformly distributed clusters. Finally, the new method compares favorably to the fully supervised k nearest neighbor rule when applied to the Iris data.

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