SZTS: A Novel Big Data Transportation System Benchmark Suite

Xiong, Wen; Yu, Zhibin; Eeckhout, Lieven; Bei, Zhengdong; Zhang, Fan; Xu, Cheng‐Zhong

doi:10.1109/icpp.2015.91

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…Faktor lain yang mempengaruhi kemacetan lalu lintas pada suatu rute adalah kemacetan jalan di sekitarnya. Seperti penelitian yang dilakukan oleh ahn et al, hubungan antar jalan diekstraksi menggunakan model 3D Markov [17], atau dengan memvisualisasikan dan menyoroti dampak lalu lintas mempengaruhi jalan yang berdekatan [16], visualisasi kemacetan mencerminkan penyebaran arus lalu lintas [18], deteksi kemacetan lalu lintas berdasarkan pada irisan antar simpang menunjukkan adanya hubungan antar jalur [19], kemacetan penambangan antar ruas jalan [20] mencatat hubungan antara satu jalan dengan jalan lainnya. Berdasarkan faktor-faktor yang mempengaruhi kemacetan lalu lintas di atas.…”

Section: Pendahuluanunclassified

Prediksi Perambatan Arus Lalu Lintas Berdasarkan Korelasi Tertinggi Antar Jalan

Priambodo

Jumaryadi²,

Salamah³

2022

Jur. Ris. Kom.

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Over the past few years, many algorithms for traffic flow predictions have been proposed to predict traffic flow. Time series models and neural network models have been widely implemented to predict traffic flow and traffic congestion based on traffic data, vehicle speed, weather, accidents, and special days. However, most of previous studies are used to predict traffic flow, not applied to predict the propagation of traffic flow. Traffic flow propagation is an interesting study. Using this finding the driver can avoid neighboring roads affected by road congestion. We propose the correlation method to find the relationship between the road. To evaluate the relationship between the road, we display the correlation results in the map. The visualization show that the correlation of traffic when congestion occurs shows better in showing the relationship between the road than the correlation at all times

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