NGSIM US-101 Dataset Smoothing

Description

NGSIM US-101 Dataset Smothing provides a less-noisy and smoothed version of the well known trajectory NGSIM US-101 dataset using Savitzky-Golay Filter. The Smoothing is done as a two step process which is composed of: first, smoothing the X and Y values, then recomputing velocities and acceleration with respect to the smoothed X, Y values.

Table of Content

1. The NGSIM US-101 Dataset
2. Smoothing approach
3. How to use
4. References

The NGSIM US-101 Dataset

The NGSIM US 101 datatset has been the ultimate open source dataset for trajectory prediction for researchers since its release in 2005. Many Researchers including [1-3] have pointed out the presence of noise in the dataset, mainly due to the fact that it has been automatically extracted from video recordings of 8 cameras mounted over buildings overlooking the Hollywood Freeway, in Los Angeles, California, also known as southbound US 101. The software used for the extraction of the NGSIM US-101 dataset is called NG-VIDEO software. In addition, the NGSIM documentation explicitly says:

no accuracy assessment has been performed for the data set

[We do] not make any claims regarding data completeness. There may be gaps in the data provided

We found that when plotting acceleration against time, there is a hard acceleration and deceleration in only few seconds, which is unrealistic. The figure dipicts the hard acceleration and deceleration for vehicle 2 across the time line of its trajectory between 7:00 am and 8:35 am. In addition, we found that 8.99% of the dataset has an unrealistic acceeration above 3 m/s².

For more details on the NGSIM dataset, here are Some useful links

• Website the official dataset webpage
• Fact sheet about the dataset
• NGSIM project webpage

Smoothing Approach:

There are three main approaches that can be used to smooth the dataset, which are:

1. Smooth all four variables i.e. X, Y, velocities and accelerations
2. Smooth X, and Y, then differentiate velocities and acceleration
3. Smooth X and Y, then differentiate velocities, smooth velocities, differentiate acceleration, and finally smooth acceleration

We rely on the second approach and we use the Savitzky-Golay filter implemented in the python scipy.signal library.

NOTE. we apply the filter on the Local_X and Local_Y and not the Global_X and Global_Y values since they are based on the California state plane coordinate system.

How To Use

There are two main ways you can make use of this repository:

-- The first way is for anyone who isn't interested in the technical details, but only wants to use the smoothed dataset. The smoothed dataset can be downloaded from the smoothed dataset folder.

-- The second way is for programmers, who maybe interested in the behind the scene code built in python 3.6 to smooth the data.

Follow the read me file in each corresponding folder for more details.

References

[1] Montanino, Marcello, and Vincenzo Punzo. "Making NGSIM data usable for studies on traffic flow theory: Multistep method for vehicle trajectory reconstruction." Transportation Research Record 2390.1 (2013): 99-111.

[2] Thiemann, Christian, Martin Treiber, and Arne Kesting. "Estimating acceleration and lane-changing dynamics from next generation simulation trajectory data." Transportation Research Record 2088.1 (2008): 90-101.

[3] Altché, Florent, and Arnaud de La Fortelle. "An LSTM network for highway trajectory prediction." 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017.