Cost Effective Road Traffic Prediction Model using Apache Spark

Prathilothamai, M.; Lakshmi, A. M. Sree; Viswanthan, Dilna

doi:10.17485/ijst/2016/v9i17/87334

Cited by 12 publications

(1 citation statement)

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“…The system model was tested on different road segments during peak hours, and the HDM showed a promising prediction result with an average accuracy of 86%. Prathilothamai, Lakshmi and Viswanthan (2016) adopted the Apache Hadoop and Apache Spark framework for increasing the accuracy of prediction using an advanced data processing technique. The data was collected offline using an Ultrasonic and Passive Infrared sensor during peak time and off-peak time.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of a Real-Time Adaptive Prediction Algorithm for Traffic Congestion

Nadeem

Fowdur

2018

JICT

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Traffic congestion is a major factor to consider in the development of a sustainable urban road network. In the past, several mechanisms have been developed to predict congestion, but few have considered an adaptive real-time congestion prediction. This paper proposes two congestion prediction approaches are created. The approaches choose between five different prediction algorithms using the Root Mean Square Error model selection criterion. The implementation consisted of a Global Positioning System based transmitter connected to an Arduino board with a Global System for Mobile/General Packet Radio Service shield that relays the vehicle's position to a cloud server. A control station then accesses the vehicle's position in real-time, computes its speed. Based on the calculated speed, it estimates the congestion level and it applies the prediction algorithms to the congestion level to predict the congestion for future time intervals. The performance of the prediction algorithms was analysed, and it was observed that the proposed schemes provide the best prediction results with a lower Mean Square Error than all other prediction algorithms when compared with the actual traffic congestion states.

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Section: Introductionmentioning

confidence: 99%