Three-phase classification of an uninterrupted traffic flow: a <i>k</i>-means clustering study

Esfahani, Reihaneh Kouhi; Shahbazi, Farhad; Akbarzadeh, Meisam

doi:10.1080/21680566.2018.1447409

Cited by 21 publications

(16 citation statements)

References 29 publications

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“…K-means clustering algorithm is a machine learning algorithm for clustering [8][9][10], which takes the Euclidean distance as the similarity measure and the sum of square error criterion function as the clustering criterion function. The specific steps are as follows:…”

Section: Methodsmentioning

confidence: 99%

Study on Severity and Influencing Factors of Injury at Intersections

Wang¹,

Zhang²,

Feng³

2020

Proceedings of the 5th International Symposium on Social Science (ISSS 2019)

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Intersections are the places where traffic accidents occur frequently. As the hub of urban transportation, ensuring the efficient and safe operation of vehicles has become a problem that needs to be solved. The research on the mechanism of the severity of traffic accident injury at intersections can provide an important basis for traffic managers or autonomous vehicles to make driving decisions under dangerous conditions. The K-means clustering is used to analyze the traffic accident data, and the vehicle-road-environment is taken as the internal cause variable to characterize the severity of the collision injury, and the clustering results are used to classify the severity of the injury. The severity of injury can be divided into mild injury, moderate injury and severe injury. The clustering results based on the severity of the injury show that the four most significant factors are the type of intersection, the type of collision vehicles, the relative speed and the signal control of the intersection. The results of the study put forward policy suggestions for the planning and management of urban intersections from many angles.

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Section: Methodsmentioning

confidence: 99%

Study on Severity and Influencing Factors of Injury at Intersections

Wang¹,

Zhang²,

Feng³

2020

Proceedings of the 5th International Symposium on Social Science (ISSS 2019)

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“…K-means clustering method has a good effect on data partition, and is widely used in traffic data division [28]. The core idea is to update the discrete variable factor data to each clustering center iteratively, while the distance is used as the similarity index.…”

Section: K-means Clustering Methodsmentioning

confidence: 99%

Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations

Jing

et al. 2019

Sustainability

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The improvement of accuracy of short-term passenger flow prediction plays a key role in the efficient and sustainable development of metro operation. The primary objective of this study is to explore the factors that influence prediction accuracy from time granularity and station class. An important aim of the study was also in presenting the proposition of change in a forecasting method. Passenger flow data from 87 Metro stations in Xi’an was collected and analyzed. A framework of short-term passenger flow based on the Empirical Mode Decomposition-Support Vector Regression (EMD-SVR) was proposed to predict passenger flow for different types of stations. Also, the relationship between the generation of passenger flow prediction error and passenger flow data was investigated. First, the metro network was classified into four categories by using eight clustering factors based on the characteristics of inbound passenger flow. Second, Pearson correlation coefficient was utilized to explore the time interval and time granularity for short-term passenger flow prediction. Third, the EMD-SVR was used to predict the passenger flow in the optimal time interval for each station. Results showed that the proposed approach has a significant improvement compared to the traditional passenger flow forecast approach. Lookback Volatility (LVB) was applied to reflect the fluctuation difference of passenger flow data, and the linear fitting of prediction error was conducted. The goodness-of-fit (R2) was found to be 0.768, indicating a good fitting of the data. Furthermore, it revealed that there are obvious differences in the prediction error of the four kinds of stations.

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“…Many studies on transportation based on time series models have been reported. Esfahani et al investigated a speed time series of vehicles on a section of a highway in the city of Isfahan, Iran [29]. Wang et al developed a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data [30].…”

Section: Study On Time Series Clusteringmentioning

confidence: 99%

Study on Clustering of Free-Floating Bike-Sharing Parking Time Series in Beijing Subway Stations

Bian²,

Rong

et al. 2019

Sustainability

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In recent years, the free-floating bike-sharing (FFBS) system has become a significant mode of travel to satisfy urban residents’ travel demands. However, with the rapid development of FFBS, many problems have arisen, among which the parking problem is the most prominent. To solve the FFBS parking problem around urban subways, firstly, the time series of FFBS parking pattern and subway station classification in Beijing were constructed based on parking intensity, showing a significant spatial distribution of subway stations with different intensity levels. Second, a hierarchical clustering method based on dynamic time warping (DTW) was proposed to cluster the FFBS parking time series. Subway stations in Beijing were grouped into 11 clusters, and the clustering purity reached 0.939, which achieved the expected effect. Then, the peak and off-peak period features of time series were extracted to discuss the clustering results. Finally, a two-level early-warning index for monitoring FFBS was constructed, which took the real-time parking quantity and land use capacity of FFBS into consideration. And FFBS parking management strategies for different early-warning indices were put forward. It is very important for the sustainable development of FFBS and cities.

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Three-phase classification of an uninterrupted traffic flow: a k-means clustering study

Cited by 21 publications

References 29 publications

Study on Severity and Influencing Factors of Injury at Intersections

Study on Severity and Influencing Factors of Injury at Intersections

Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations

Study on Clustering of Free-Floating Bike-Sharing Parking Time Series in Beijing Subway Stations

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