New color road segmentation method

1992 ◽  
Author(s):  
Ke Liu ◽  
Yanxing Liu ◽  
Jingyu Yang ◽  
Yong-Qing Cheng ◽  
Nian-Chun Gu
Keyword(s):  
Segmentation Method ◽  
Road Segmentation

scholarly journals Comparison of Different Road Segmentation Methods

2019 ◽  
Vol 31 (2) ◽  
pp. 163-172
Author(s):  
Maen Qaseem Ghadi ◽  
Árpád Török
Keyword(s):  
Road Safety ◽  
Road Network ◽  
Highway Safety ◽  
Segmentation Method ◽  
Constant Length ◽  
Highway Safety Manual ◽  
Segmentation Methods ◽  
Average Annual Daily Traffic ◽  
Clustering Approach ◽  
Road Segmentation

In road safety, the process of organizing road infrastructurenetwork data into homogenous entities is called segmentation.Segmenting a road network is considered thefirst and most important step in developing a safety performancefunction (SPF). This article aims to study the benefitof a newly developed network segmentation method which is based on the generation of accident groups applying K-means clustering approach. K-means algorithm has been used to identify the structure of homogeneous accident groups. According to the main assumption of the proposed clustering method, the risk of accidents is strongly influenced by the spatial interdependence and traffic attributes of the accidents. The performance of K-means clustering was compared with four other segmentation methods applying constant average annual daily traffic segments, constant length segments, related curvature characteristics and a multivariable method suggested by the Highway Safety Manual (HSM). The SPF was used to evaluate the performance of the five segmentation methods in predicting accident frequency. K-means clustering-based segmentation method has been proved to be more flexible and accurate than the other models in identifying homogeneous infrastructure segments with similar safety characteristics.

More interesting regions: an efficient road segmentation method based on vanishing point

2019 ◽  
Vol 28 (01) ◽  
pp. 1
Author(s):  
Yao Wang ◽  
Fangfa Fu ◽  
Fengchang Lai ◽  
Weizhe Xu ◽  
Jinxiang Wang
Keyword(s):  
Vanishing Point ◽  
Segmentation Method ◽  
Road Segmentation

A road segmentation method based on the deep auto-encoder with supervised learning

2018 ◽  
Vol 68 ◽  
pp. 381-388 ◽  
Author(s):  
Xiaona Song ◽  
Ting Rui ◽  
Sai Zhang ◽  
Jianchao Fei ◽  
Xinqing Wang
Keyword(s):  
Supervised Learning ◽  
Segmentation Method ◽  
Road Segmentation

scholarly journals Evaluation of Traffic Performance in Basuki Rahmat Street, Palu City by Using Road Segmentation Method

2021 ◽  
Vol 23 (1) ◽  
pp. 9-18
Author(s):  
Arief Setiawan ◽  
Nopkrialtur Bakti ◽  
Jurair Patunrangi
Keyword(s):  
Evaluation Method ◽  
Segmentation Method ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
The Road ◽  
Traffic Performance ◽  
Traffic Generation ◽  
Road Segmentation ◽  
National Road ◽  
Electronic Center

Abstract : Basuki Rahmat Street is a National Road with a primary-arterial-function based on the Regional Master Plan of Palu City 2010-2030. The area is focused as a trade and service area which attracts visitors to have economic and social activities. Therefore, each section of the road has other traffic performance. The research was conducted in three road segments: Grand Hero Supermarket, Samsung store, and BCA Bank, and Star Kitchen Electronic Center based on traffic generation of business activity. The study aims to understand Basuki Rahmat street's performance and its activities in each segment. A survey was conducted periodically (early, middle, and end of the month) by taking a workday and a holiday for each period, from 09.00-22.00 WITA. The evaluation method was based on the Indonesian Highway Capacity Manual (IHCM) 1997 urban area on peak hour. The result showed that the Road Segmentation Method (RMS) of Basuki Rahmat street has 0.37 degrees of Saturation (DS) and was included in a B level of service. The differences between DS-RMS and DS on each segment were 0.04. The road segmentation method should be considered to assess the traffic performance of the road link.

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