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Effective management of daily road traffic is a huge challenge for traffic personnel. Urban traffic management has come a long way from manual control to artificial intelligence techniques. Still real-time adaptive traffic control is an unfulfilled dream due to lack of low cost and easy to install traffic sensor with real-time communication capability. With increasing number of on-board Bluetooth devices in new generation automobiles, these devices can act as sensors to convey the traffic information indirectly. This paper presents the efficacy of road-side Bluetooth scanners for traffic data collection and big-data analytics to process the collected data to extract traffic parameters. Extracted information and analysis are presented through visualizations and tables. All data analytics and visualizations are carried out off-line in R Studio environment. Reliability aspects of the collected and processed data are also investigated. Higher speed of traffic in one direction owing to the geometry of the road is also established through data analysis. Increased penetration of smart phones and fitness bands in day to day use is also established through the device type of the data collected. The results of this work can be used for regular data collection compared to the traditional road surveys carried out annually or bi-annually. It is also found that compared to previous studies published in the literature, the device penetration rate and sample size found in this study are quite high and very encouraging. This is a novel work in literature, which would be quite useful for effective road traffic management in future.


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Efficacy of Bluetooth-Based Data Collection for Road Traffic Analysis and Visualization Using Big Data Analytics

Show Author's information Ashish Rajeshwar Kulkarni1( )Narendra Kumar1K. Ramachandra Rao2
Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India
Department of Civil Engineering, Indian Institute of Technology Delhi, Delhi 110016, India

Abstract

Effective management of daily road traffic is a huge challenge for traffic personnel. Urban traffic management has come a long way from manual control to artificial intelligence techniques. Still real-time adaptive traffic control is an unfulfilled dream due to lack of low cost and easy to install traffic sensor with real-time communication capability. With increasing number of on-board Bluetooth devices in new generation automobiles, these devices can act as sensors to convey the traffic information indirectly. This paper presents the efficacy of road-side Bluetooth scanners for traffic data collection and big-data analytics to process the collected data to extract traffic parameters. Extracted information and analysis are presented through visualizations and tables. All data analytics and visualizations are carried out off-line in R Studio environment. Reliability aspects of the collected and processed data are also investigated. Higher speed of traffic in one direction owing to the geometry of the road is also established through data analysis. Increased penetration of smart phones and fitness bands in day to day use is also established through the device type of the data collected. The results of this work can be used for regular data collection compared to the traditional road surveys carried out annually or bi-annually. It is also found that compared to previous studies published in the literature, the device penetration rate and sample size found in this study are quite high and very encouraging. This is a novel work in literature, which would be quite useful for effective road traffic management in future.

Keywords:

Bluetooth scanners, big data, visualization, speed, sensors
Received: 15 May 2022 Revised: 02 October 2022 Accepted: 12 October 2022 Published: 26 January 2023 Issue date: June 2023
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Publication history

Received: 15 May 2022
Revised: 02 October 2022
Accepted: 12 October 2022
Published: 26 January 2023
Issue date: June 2023

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© The author(s) 2023.

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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