Systematic clustering method to identify and characterise spatiotemporal congestion on freeway corridors

Ou, Jishun; Yang, Shu; Wu, Yao; An, Chengchuan; Xia, Jingxin

doi:10.1049/iet-its.2017.0355

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…In general, the understanding of human resources can be examined from a macro scale (Dartey-Baah et al, 2020;Jang et al, 2020;Kögel & Wolbring, 2020;Martdianty et al, 2020;Sangwan et al, 2020;A. Valenti & Horner, 2020), where human resources are defined as "The overall potential of labour contained in a country (Daanaa et al, 2020;Ou et al, 2018;Reiner et al, 2019;Subramanien & Joseph, 2019;M. Valenti et al, 2019;Volodko et al, 2020).…”

Section: Human Resource Managementmentioning

confidence: 99%

Discipline and Work Environment Affect Employee Productivity: Evidence From Indonesia

Prasetyo¹,

Aliyyah²,

Rusdiyanto³

et al. 2021

Preprint

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Objective: This paper aims to test and evaluate the Effect of Discipline And Work Environment on Employee Productivity of state-owned public bodies.Design/methodology/approach: This paper uses a quantitative approach using a survey approach. The survey is a study conducted on the employee population of state-owned public agency companies, samples taken from the employee population of state-owned public agency companies to find events related to discipline variables and work environments that can affect employee productivity variables, to analise the influence between discipline variables and the work environment on employee productivity variables using a statistic regression approach. This method is used to explain the influence of discipline variables and work environment on employee productivity variables. This approach is simply to provide a description and test the influence between discipline variables and the work environment on employee productivity variables that can be known how much the influence of discipline variables and work environment on employee productivity variables.Findings: The findings of this study explain that discipline has an influence on the productivity of employees of publicly owned companies, the work environment has an influence on the productivity of employees of publicly owned companies, while together discipline and work environment have an influence on the productivity of employees of publicly owned companies.Practical Implications: The results of the study are recommended for employees to improve the effectiveness and efficiency of the performance of state-owned public bodies.Originality: Previous research conducted to test the influence of discipline and work environment on the productivity of employees of manufacturing companies listed on the Indonesia Stock Exchange, the findings concluded that discipline and work environment have an influence on the work productivity of employees of manufacturing companies listed on the Indonesia Stock Exchange. This research object of research on publicly owned companies owned by the state.

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Section: Human Resource Managementmentioning

confidence: 99%

Discipline and Work Environment Affect Employee Productivity: Evidence From Indonesia

Prasetyo¹,

Aliyyah²,

Rusdiyanto³

et al. 2021

Preprint

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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

Self Cite

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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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“…Among them, intelligent transportation system (ITS) has become the most popular and effective one. By effectively and efficiently collecting, processing, and disseminating traffic data, ITS helps traffic researchers and practitioners make reasonable and reliable decisions, and has achieved great success during the past decade [1][2][3][4][5].…”

Section: Introductionmentioning

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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Systematic clustering method to identify and characterise spatiotemporal congestion on freeway corridors

Cited by 12 publications

References 21 publications

Discipline and Work Environment Affect Employee Productivity: Evidence From Indonesia

Discipline and Work Environment Affect Employee Productivity: Evidence From Indonesia

Commuting Pattern Recognition Using a Systematic Cluster Framework

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

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