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A Privacy-Preserving Urban Traffic Estimation System

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Transportation Analytics in the Era of Big Data

Part of the book series: Complex Networks and Dynamic Systems ((CNDS,volume 4))

Abstract

This chapter describes a novel traffic monitoring system based on data generated by Inertial Measurement Units (IMUs) in conjunction with short range Bluetooth or WiFi readers. The IMUs are used to estimate the vehicle path along the transportation network, detect traffic stops and go waves, classify traffic-related events, and possibly monitor the condition of the roadway. We introduce a trajectory estimation method for estimating vehicle paths from IMU data and Bluetooth reader position data only. Using this method, we show that the state of traffic on an urban network can be estimated locally by solving a set of independent traffic estimation problems with unknown boundary conditions. This set of independent solutions are then regularized using a consensus-type algorithm to estimate the unknown boundary conditions during the process. This system allows one to estimate the state of traffic over an urban network, while maintaining the privacy of the users, unlike current systems.

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Notes

  1. 1.

    The use of the readers in their usual configuration (re-identification of MAC addresses) requires the transmission of MAC addresses within nodes, and has potential for privacy intrusion.

  2. 2.

    In the present chapter, the GPS data is only used for validation.

  3. 3.

    This assumption is not restrictive in practice, since the definition of the boundaries of each cluster can be adjusted to meet this requirement.

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Acknowledgements

The authors would like to thank the Texas Department of Transportation for supporting this research under project 0-6838, Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas.

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Authors and Affiliations

  1. University of Texas, Austin, TX, USA

    Tian Lei, Alexander Minbaev & Christian G. Claudel

  2. Chang’an University, Xi’an, China

    Tian Lei

Authors
  1. Tian Lei
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  2. Alexander Minbaev
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  3. Christian G. Claudel
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Corresponding author

Correspondence to Christian G. Claudel .

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Editors and Affiliations

  1. School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA

    Satish V. Ukkusuri

  2. Tongji University, Shanghai, China

    Chao Yang

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Lei, T., Minbaev, A., Claudel, C.G. (2019). A Privacy-Preserving Urban Traffic Estimation System. In: Ukkusuri, S., Yang, C. (eds) Transportation Analytics in the Era of Big Data. Complex Networks and Dynamic Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-75862-6_4

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