Research on Urban Road Traffic Congestion Charging Based on Sustainable Development

Sun, Ye

doi:10.1016/j.phpro.2012.02.231

Cited by 50 publications

(25 citation statements)

References 2 publications

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“…Metode ini membantu pemangku kebijakan dalam melihat singkat bagaimana kondisi kemacetan dan efektifitas kebijakan yang telah dilakukan. Apabila dibandingkan dengan metode MKJI (Koloway, 2009) (Novalia, Sulistiyorini, & Putra, 2016) (Astuti, 2015) dalam menganalisa kemacetan ruas jalan yang membutuhkan waktu dan biaya yang besar, dengan metode analisa model google traffic akan membantu menilai kemacetan suatu area dengan efektif meskipun tidak seakurat metode MKJI ataupun (Suresh, 2018) (Wang, 2018) (Ye, 2012) (Zhang, 2008) (Mallinckrodt, 2010).…”

Section: Tabel 4 Perbandingan Kebijakan Dan Hasil Traffic Counting Munclassified

Google Traffic sebagai Masukan Kebijakan Transportasi Perkotaan (Studi Kasus: Kota Bandar Lampung)

2020

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Congestion in urban areas is vulnerable because of the impact of the growth of activities and an increasingly specialized and complex economy. For this reason, it is necessary to formulate a good urban transportation policy to accommodate increased urban transportation needs. Google Traffic is a feature found on Google Maps to see the level of congestion in an area. Google Traffic can detect an area with red, yellow or green indications through the principle of Real Time Data using data from the Global Positioning System (GPS). This study wanted to see how the use of the Google Traffic Feature as input to urban transportation policies by looking at existing urban transportation policies, analyzing the movement system model on Google Traffic, and analyzing the development of Google Traffic model policies, which would be useful for the development of Urban Transportation Policies, especially Bandar Lampung City as a case study.

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Section: Tabel 4 Perbandingan Kebijakan Dan Hasil Traffic Counting Munclassified

Google Traffic sebagai Masukan Kebijakan Transportasi Perkotaan (Studi Kasus: Kota Bandar Lampung)

2020

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“…LITERATURE REVIEW Sustainable development of the economy of large cities, along with other factors, is associated with the effective organization of transport services. The population growth (including through migration), improve his well-being leads to an increase in units of private vehicles and growth in all types of freight and passenger traffic [2]. The congestion of the transport system of large cities creates obstacles for forecasting the timing of transport services.…”

Section: IImentioning

confidence: 99%

Digitalization of Transport Services as a Way to Improve the Environmental Situation of Large Cities

Бардасова¹,

Utkina²

2019

Proceedings of the First International Volga Region Conference on Economics, Humanities and Sports (FICEHS 2019)

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The Relevance of the study is due to the fact that the orderly movement of traffic flows, reducing downtime and congestion, due to the digitalization of transport services, will ensure their implementation on time and reduce the burden on the environment. The aim of the study is to develop an empirical model that links the results of flow detection and their evaluation by satellite intelligent systems to optimize motion. The research methods were analysis, comparison, identification of contradictions, synthesis based on the study of scientific information; observation of practical implementation of detection and optimization of transport flows; modeling of prospects for digitalization of this sphere. The novelty of this study is the theoretical justification of the possibilities of digitalization of transport services for dynamic traffic management, while improving the environmental situation in large cities. The proposed empirical model of digitalization of transport services sphere has practical value. it provides the possibility of integration of data from motion detectors and intelligent systems with subsequent modeling of flows and issuance of corrective actions.

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“…Beberapa penelitian terkait kemacetan sudah dilakukan diantaranya oleh Ye (2012) Tabel 1 menyajikan Basis Spline dan functional data regression yang digunakan untuk membuat functional data pada data kepadatan. Setelah functional data terbentuk, dibuat model peramalan dengan ARMA (p,q).…”

Section: Metode Analisisunclassified

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Anggorowati¹,

Monika²

2019

asks

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Model lalu lintas mikroskopik merupakan kombinasi model Principal Component Analysis (PCA) dengan timeseries ARMA (Autoregressive Moving Average). Pemodelan lalu lintas mikroskopik dapat digunakan dalam solusi permasalahan prediksi kemacetan lalu lintas di Indonesia. Penelitian ini bertujuan untuk mengaplikasikan functional ARMA (p,q) processes dan model berbasis vektor pada functional PCA guna memprediksi kemacetan lalu lintas pada jalan tol. Data yang digunakan merupakan high dimensional data yang diambil dari Road Traffic Microwave System (RTMS) Jasa Marga pada ruas Jalan Tol Cikampek. Variabel yang digunakan untuk memprediksi kemacetan adalah kecepatan, arus, dan kepadatan dengan rentang waktu jam. Dari hasil functional data analysis, dilakukan pemodelan ARMA dengan hasil model terbaik adalah ARMA (1,1).

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Research on Urban Road Traffic Congestion Charging Based on Sustainable Development

Cited by 50 publications

References 2 publications

Google Traffic sebagai Masukan Kebijakan Transportasi Perkotaan (Studi Kasus: Kota Bandar Lampung)

Google Traffic sebagai Masukan Kebijakan Transportasi Perkotaan (Studi Kasus: Kota Bandar Lampung)

Digitalization of Transport Services as a Way to Improve the Environmental Situation of Large Cities

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