Development of a representative driving cycle for urban buses based on the K-means cluster method

Peng, Yuhui; Yuan, Zhong; Huibao, Yang

doi:10.1007/s10586-017-1673-y

Cited by 44 publications

(23 citation statements)

References 14 publications

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“…where P 0 -position of the zero point, P 1 ,P 2 -position of the intermediate points, be possible to compare the research results. That is why many researches in separate cities and regions in order to determine a representative driving cycle were conducted [13][14][15]. A driving cycle is determined as a numeric series of speeds and a scheme of movement is reproduced by a number of parameters and operation characteristics [16].…”

Section: Research Resultsmentioning

confidence: 99%

Use of the Bezier Curves for a Vehicles Driving Cycles' Modeling

Dembitskyi¹

2021

Komunikácie

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While performing the research of the vehicles operating characteristics in real operating conditions, a question of creation or correction of a movement driving cycle always appears. An absence of a mathematical model rather complicates the research process and makes a quick correction of movement conditions impossible. To solve the given problem, it was proposed to model a driving cycle using the Bezier curves. As a result of this research, it is stated that the most advantageous is to use the Bezier curves of the second and third degrees. During the analysis of results of driving cycles modeling and their comparison to the standardized and real movement cycles, a satisfactory coincidence of results was obtained. The conducted thematic research confirms the previous results, directed to creation of a universal dynamic model of a vehicle's driving cycle.

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Section: Research Resultsmentioning

confidence: 99%

Use of the Bezier Curves for a Vehicles Driving Cycles' Modeling

Dembitskyi¹

2021

Komunikácie

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“…The k-means algorithm has been widely used in cluster computing in the existing literature [3,4,19,24,43]. The k-means clustering algorithm uses the Euclidean distance between a sample and cluster center as a criterion for similarity determination, and it has the advantages of efficiency and simplicity [19,43]. The Euclidean distance is calculated as shown in (7).…”

Section: Clustering Based On the K-means And Svmmentioning

confidence: 99%

“…The driving cycle is constructed by connecting the most representative driving segments according to predetermined rules until the driving cycle duration is reached. Generally, the duration of the general international standard driving cycles and real-world representative driving cycles is between 600 s and 1800 s. Therefore, according to the Hong Kong, Colombo, and Sydney driving cycles, the duration of the Xi'an EV urban driving cycle in this paper is set to 1200 s [17,33,36,43]. Then, the duration of each driving segment class in the constructed Xi'an EV urban driving cycle is determined based on experimental data.…”

Section: Driving Cycle Constructionmentioning

confidence: 99%

Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm

Zhao

et al. 2018

Journal of Advanced Transportation

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This paper proposes a scientific and systematic methodology for the development of a representative electric vehicle (EV) urban driving cycle. The methodology mainly includes three tasks: test route selection and data collection, data processing, and driving cycle construction. A test route is designed according to the overall topological structure of the urban roads and traffic flow survey results. The driving pattern data are collected using a hybrid method of on-board measurement method and chase car method. Principal component analysis (PCA) is used to reduce the dimensionality of the characteristic parameters. The driving segments are classified using a hybrid k-means and support vector machine (SVM) clustering algorithm. Scientific assessment criteria are studied to select the most representative driving cycle from multiple candidate driving cycles. Finally, the characteristic parameters of the Xi’an EV urban driving cycle, international standard driving cycles, and other city driving cycles are compared and analyzed. The results indicate that the Xi’an EV urban driving cycle reflects more aggressive driving characteristics than the other cycles.

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“…This method involves a simple and time-efficient distribution of the microtrips according to their average speed in predefined speed intervals. Other studies preferred classifying the microtrips using clustering algorithms as k-means ( 11, 14–16 ) and k-medoids ( 17 ), combined with different distance measurements.…”

Section: Introductionmentioning

confidence: 99%

“…In other studies, a principal component analysis (PCA) was conducted to consider more characteristics of the microtrips. Yuhui et al ( 15 ) and Peng et al ( 11 ) characterized the microtrips with approximately 15 parameters linked to speed, acceleration, and driving mode proportions. They applied the PCA on the 15 characteristic parameters and measured the distance between microtrips in their three or four new dimensions.…”

Section: Introductionmentioning

confidence: 99%

Comparing Driving Cycle Development Methods Based on Markov Chains

Roy

Morency

2020

Transportation Research Record: Journal of the Transportation R

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The transportation sector is a major contributor to greenhouse gas (GHG) emissions, accounting for 14% of global emissions in 2010 according to the United States Environmental Protection Agency. In Quebec, this share amounts to 43%, of which 80% is caused by road transport according to the MinistÉre de l’Environnement et de la Lutte contre les changements climatiques of QuÅbec. It is therefore essential to support the actions taken to reduce GHGs emissions from this sector and to quantify the impact of these actions. To do so, accurate and reliable emission models are needed. Driving cycles are defined as speed profiles over time and they are a key element of emission models. They represent driving behaviors specific to various road types in each region. The most widely used method to construct driving cycles is based on Markov chains and consists of concatenating small sections of speed profiles, called microtrips, following a transition matrix. Two of the main steps involved in the development of driving cycles are microtrip segmentation and microtrip classification. In this study, several combinations of segmentation and clustering methods are compared to generate the most reliable driving cycle. Results show that segmentation of microtrips with a fixed distance of 250 m and clustering of the microtrips by applying a principal component analysis on many key parameters related to their speed and acceleration provide the most accurate driving cycles.

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Development of a representative driving cycle for urban buses based on the K-means cluster method

Cited by 44 publications

References 14 publications

Use of the Bezier Curves for a Vehicles Driving Cycles' Modeling

Use of the Bezier Curves for a Vehicles Driving Cycles' Modeling

Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm

Comparing Driving Cycle Development Methods Based on Markov Chains

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