Robust vocabulary recognition clustering model using an average estimator least mean square filter in noisy environments

Ahn, Chang‐Beom; Oh, Sang-Yeob

doi:10.1007/s00779-013-0732-5

Cited by 3 publications

(1 citation statement)

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“…Clustering is a fundamental approach to organize data into distinct groups for finding intrinsic hidden patterns of the data. It can be applied on various fields such as image processing, pattern recognition, protein analysis, micro array analysis, social networks etc. Of course, it also includes finding hotspots.…”

Section: Related Workmentioning

confidence: 99%

Relieve the congestion by shuttle bus in rush hours using aggregation clustering algorithm on group travel pattern

Wang

Zhong-heng

et al. 2018

Concurrency and Computation

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Summary The travel demand is of vital importance for transport planning. Especially in rush hours, the commuters aggregate in certain areas, resulting in traffic jams, which hinder the normal operation of the urban traffic. The key issue to solve the problem is to find out the demand of the passengers and accordingly arrange the transit resource more reasonably. This paper proposes a framework that provides a shuttle bus solution to satisfy the travel demand of the gathered passengers in rush hours according to their travel history obtained by smartcard data. Firstly, an aggregation algorithm basing on Clustering by fast search and find of density peaks (CFSFDP) is presented to highlight the area, which consists of adjacent bus stations with high passenger flow, addressed as spark region. Secondly, group travel pattern (GTP) is put forward to describe the travel trend on a city‐wide scale, which reveals the common travel demand of the commuters. Lastly, an algorithm named Variable Visibility Path Optimization Algorithm based on ant colony algorithm is proposed to make schedule solution of shuttle bus according to GTP basing on the historical running information of the bus. The experiment basing on the bus passenger flow data collected by AFCS in Aug 2014 from Beijing shows that our method helps to ease the traffic aggregation effectively and practically and offer reference to the bus scheduling issue.

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Section: Related Workmentioning

confidence: 99%