Interval data‐based
            <i>k</i>
            ‐means clustering method for traffic state identification at urban intersections

Rao, Wenming; Xia, Jingxin; Lyu, Weitao; Lu, Zhenbo

doi:10.1049/iet-its.2018.5379

Cited by 24 publications

(10 citation statements)

References 19 publications

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“…As shown in Figure 6, the accuracy of the improved fast peak clustering algorithm is notably higher than those of the original fast peak clustering algorithm and the classical k-means [32,33] and DBSCAN [34] algorithms for data from both the Spring Festival and the fourth week of February, which demonstrates that the improved fast peak clustering algorithm has a higher validity and accuracy than the others. It also indicates that the algorithm results are highly consistent with the actual traffic situations.…”

Section: E Feasibility and Accuracy Validation Of The Improvedmentioning

confidence: 96%

Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Pei

Sun

Han

et al. 2021

Mathematical Problems in Engineering

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Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to process the original data. Next, to avoid the shortcomings of the excessive subjectivity of the original algorithm, an improved fast peak clustering algorithm is proposed. Finally, the improved algorithm is applied to highway traffic condition analysis and abnormal event mining to obtain more accurate and intuitive clustering results. Compared with two classical algorithms, namely, the k-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms, as well as the unimproved original fast peak clustering algorithm, the proposed algorithm is faster and more accurate and can reveal the complex relationships among massive data more efficiently. During the process of reforming the toll system, the algorithm can automatically and more efficiently analyze massive toll data and detect abnormal events, thereby providing a theoretical basis and data support for the operation monitoring and maintenance of highways.

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Section: E Feasibility and Accuracy Validation Of The Improvedmentioning

confidence: 96%

Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Pei

Sun

Han

et al. 2021

Mathematical Problems in Engineering

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“…Each regime describes a homogeneous traffic condition. According to the fundamental diagram in traffic flow theory and a series of relevant studies [37][38][39][40][41], five distinct regimes could be defined in practice, as shown in Figure 2. The first two regimes, Regime 1 and Regime 2, describe two kinds of free-flow traffic conditions, while the last two regimes, Regime 4 and Regime 5, represent two categories of congested traffic conditions.…”

Section: ) a State Transition Probability Distributionmentioning

confidence: 99%

Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning

Xia

Wang³

et al. 2020

Applied Sciences

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Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures.

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“…To identify the commuting vehicles, a clustering technique was utilized to analyze temporal-spatial features extracted from ALPR data. Many clustering algorithms and strategies, such as K-means [24,31], DBSCAN [32], GMM (Gaussian Mixture Model) [33], nested clustering [34], online agglomerative clustering [35], hierarchical clustering [36], and other algorithms [37,38] had been proposed in the past decades. Hierarchical clustering, as a typical unsupervised machine learning algorithm, has been applied to a wide spectrum of transportation researches.…”

Section: Commuting Vehicles Identification Using Ward's Hierarchical mentioning

confidence: 99%

Commuting Pattern Recognition Using a Systematic Cluster Framework

et al. 2020

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Identifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters' route feature is not fully considered or not accurately characterized. In this study, a systematic framework considering the route feature for commuting pattern recognition was developed for urban road networks. Three modules are included in the proposed framework. These modules were proposed based on automatic license plate recognition (ALPR) data. First, the temporal and spatial features of individual vehicles were extracted based on the trips detected by ALPR sensors, then a hierarchical clustering technique was applied to classify the detected vehicles and the ratio of commuting trips was derived. Based on the ratio of commuting trips, the temporal and spatial commuting patterns were investigated, respectively. The proposed method was finally implemented in a ring expressway of Kunshan, China. The results showed that the method can accurately extract the commuting patterns. Further investigations revealed the dynamic temporal-spatial features of commuting patterns. The findings of this study demonstrate the effectiveness of the proposed method in mining commuting patterns at urban traffic networks.

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Interval data‐based k ‐means clustering method for traffic state identification at urban intersections

Cited by 24 publications

References 19 publications

Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning

Commuting Pattern Recognition Using a Systematic Cluster Framework

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