Commuting Pattern Recognition Using a Systematic Cluster Framework

Hong, Rongrong; Rao, Wenming; Zhou, De Qun; An, Chengchuan; Lu, Zhenbo; Xia, Jingxin

doi:10.3390/su12051764

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…The urban transportation system should meet the requirements of various social groups and be fair and comprehensive (Park et al, 2018). Furthermore, urban travel requires higher levels of sustainable urban transport (Hong et al, 2020). Thus, population mobility and circular migration from the suburban Metropolitan Mamminasata urban area to the core city of Makassar are inefficient and ineffective.…”

Section: Resultsmentioning

confidence: 99%

Population mobility and sustainable development in the Mamminasata Metropolitan South Sulawesi, Indonesia

Surya¹,

Rasyidi²,

Abubakar³

et al. 2021

hgeo

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Economic growth and urban agglomeration have triggered an increase in the size and mobility of the Metropolitan Mamminasata urban population. This study aims to analyse spatial interactions working as a determinant of connectivity of the transportation system and the growth of suburban areas toward smart and sustainable cities in the Mamminasata Metropolitan urban system and the effects that spatial expansion, spatial integration, urban agglomeration, spatial use, and the transportation system have on population mobility. The research method used is a sequential explanatory design that combines quantitative and qualitative research methods. The results show that the coefficients of determination are as follows: spatial expansion to population mobility – 4.90%, spatial integration to population mobility – 2.99%, spatial interaction to population mobility – 4.87%, urban agglomeration to population mobility – 2.09%, space use to population mobility – 2.64%, and transportation system to population mobility – 5.15%. The results of this study will assist in the formulation of development policies, management of the urban transportation system, and allocation of space utilisation going forward.

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Section: Resultsmentioning

confidence: 99%

Population mobility and sustainable development in the Mamminasata Metropolitan South Sulawesi, Indonesia

Surya¹,

Rasyidi²,

Abubakar³

et al. 2021

hgeo

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“…For instance, by extracting flow information from ANPR data, [45] devised a 3D CNN approach to reveal high-dimensional correlations between the local vehicle patterns and OD flows. In addition, some scholars have used ANPR data as benchmarks to comparatively assess the performance of other data collection methods in terms of travel time estimation [46] and combined them with other techniques, such as clustering methods, to identify commuting patterns [47]. In general, ANPR data are considered very useful and informative in transportation studies and have been widely applied in many research topics.…”

Section: Anpr Systems and Anpr Datamentioning

confidence: 99%

Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using Graph Learning

Tang

Wei

et al. 2023

IEEE Systems Journal

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The optimization of systemic redundancy by minimizing the sensor quantity can improve the efficiency of sensor networks and save costs. However, from the perspective of risk management, this redundancy reduction can also bring a significant loss in the overall network resilience because the less the systemic redundancy is, the fewer backup components in the network when shocks hit and, therefore, the less overall resilience. In this paper, we investigate this intractable dilemma and attempt to pinpoint the tradeoff point for a city-scale automatic number plate recognition (ANPR) system in Cambridge, UK. By developing a two-stage graph deep learning (GDL) model, we first optimize the layout of the ANPR system to reduce redundancy and find its efficiency profile. Next, we study what effects this redundancy reduction can bring to the overall resilience, as the overall observability drops with the reduction in the number of sensors and find an optimal balance. The results show that our approach can effectively optimize the system's redundancy by using only 47% of the original sensors to reconstruct the full picture with a mean absolute error (MAE) of only 11.18 and a root mean square error (RMSE) of 19.49; most importantly, the overall system resilience is maintained at 70% in the meantime. This paper provides an alternative perspective for dealing with the well-known 'efficiency-resilience' dilemma and offers new evidence to enable better decision and policy making for city managers and planners in local authorities.

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“…K-means algorithm is the most common partitioning method [20]. Another technique is to classify data into groups using DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which is a density-based clustering algorithm [21]. Some existing clustering methods can balance accuracy and efficiency, but cluster quality is not so good that researchers usually combine other algorithms or use optimization strategies to improve the clustering method [20].…”

Section: Introductionmentioning

confidence: 99%

Fraud Detection Call Detail Record Using Machine Learning in Telecommunications Company

Jabbar¹,

Suharjito²

2020

Adv. sci. technol. eng. syst. j.

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Fraud calls have a serious impact on telecommunications operator revenues. Fraud detection is very important because service providers can feel a significant loss of income. We conducted a fraud research case study on one of the operators that experienced fraud in 2009 and 2018. Call Detail Record (CDR) containing records of customer conversations such as source and destination number, call start time, duration of calls at the operator can be a source of information to use in fraud detection. The method used in this study uses machine learning with unsupervised learning techniques which are quite popular methods used in fraud detection. The purpose of this study is to propose an effective method that can be applied to detect fraud on the CDR. Variables used include caller number, number dialled, duration, fee and destination city of the dataset totalling 11,418 rows from record periods 01 to 31 May 2018. In analyzing our CDR using the K-Means and DBSCAN algorithms, we then evaluate the results to calculate accuracy by comparing to actual fraud data. Based on evaluations using confusion matrix on actual CDR fraud, we obtained the K-Means algorithm to show a better accuracy value to model fraud on telecommunications CDR compared to DBSCAN.

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Commuting Pattern Recognition Using a Systematic Cluster Framework

Cited by 7 publications

References 37 publications

Population mobility and sustainable development in the Mamminasata Metropolitan South Sulawesi, Indonesia

Population mobility and sustainable development in the Mamminasata Metropolitan South Sulawesi, Indonesia

Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using Graph Learning

Fraud Detection Call Detail Record Using Machine Learning in Telecommunications Company

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