Obtaining Long Trajectory Data of Disordered Traffic Using a Swarm of Unmanned Aerial Vehicles

23 Pages Posted: 5 Sep 2024

See all articles by Shashank Rajput

Shashank Rajput

Indian Institute of Technology Kanpur

Shivachethan Venkateshappa

affiliation not provided to SSRN

Venkatesan Kanagaraj

Indian Institute of Technology (IIT), Kanpur

Gowri Asaithambi

Indian Institute of Technology (IIT), Tirupati

Martin Treiber

affiliation not provided to SSRN

Abstract

For the development of algorithms and models for driver behaviour at microscopic levels, trajectory data is needed.  Access to trajectory datasets under disordered traffic conditions (wide mix of vehicle types with the absence of lane discipline) is very limited. Particularly, existing datasets only cover shorter sections limiting the understanding of driving behaviour. This paper presents the design and preliminary results of a first-of-its-kind experiment to create a long trajectory dataset for an urban arterial road (Chennai city, India) under disordered traffic conditions using a swarm of six Unmanned Aerial Vehicles (UAVs). The steps followed to obtain the detailed trajectory data from UAV traffic videos are 1) Georegistration, 2) Image Stabilization and Stitching, 3) Data Annotation, 4) Vehicle Detection and Classification, and 5) Vehicle Tracking. Finally, to remove the noises and disturbances from the trajectory dataset, a Symmetric Exponential Moving Average (sEMA) filter technique was applied to smooth the positions of the vehicles and the first and second derivative of the positions, i.e., speeds and accelerations were obtained using the central difference method. The trajectory dataset was used to analyze the driver's lateral position preferences. The results highlight the unique behavior of different categories of vehicles under disordered traffic conditions. This dataset is an order of magnitude larger than existing datasets allowing, for the first time, to calibrate and validate car-following and lane-changing models as well as fully two-dimensional models in disordered traffic.

Keywords: Trajectory Data, Unmanned Aerial Vehicles, Deep learning, Vehicle Detection and Tracking, Lateral Distribution of Vehicles, Disordered Traffic

Suggested Citation

Rajput, Shashank and Venkateshappa, Shivachethan and Kanagaraj, Venkatesan and Asaithambi, Gowri and Treiber, Martin, Obtaining Long Trajectory Data of Disordered Traffic Using a Swarm of Unmanned Aerial Vehicles. Available at SSRN: https://ssrn.com/abstract=4947305 or http://dx.doi.org/10.2139/ssrn.4947305

Shashank Rajput

Indian Institute of Technology Kanpur ( email )

Kalyanpur
Kanpur
Kanpur, IN 208016
India

Shivachethan Venkateshappa

affiliation not provided to SSRN ( email )

No Address Available

Venkatesan Kanagaraj

Indian Institute of Technology (IIT), Kanpur ( email )

IIT Kanpur
kalyanpur
Kanpur, 208016
India

Gowri Asaithambi (Contact Author)

Indian Institute of Technology (IIT), Tirupati ( email )

Tirupati - 517506
Andhra Pradesh
India

Martin Treiber

affiliation not provided to SSRN ( email )

No Address Available

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