1. Team： Chenge Li，Kun Xie, Shaurya Agarwal, Jiaxu Zhou(GRA)
Tutor Advisor： Kaan Ozbay, Greg Dobler
Email: Jiaxu Zhou(GRA): jz2308@nyu.edu
Background & Problem
Definition
• Each year, there are about 1.24 million road
traffic deaths worldwide
• In 2012, there were 30,800 fatal crashes
resulting in 33,561 fatalities
• NYC Safety Issues and Vision Zero Plan
Goals: Advance data-driven traffic
analytics to enhance Global Resilience
Objectives:
• Propose a novel approach for examining
traffic safety performance at intersections
• Quantify traffic conflicts using developed
“surrogate” safety measures
• Develop automatic data acquisition,
analysis and modeling approaches based
on computer vision techniques
GRA works
1.Manaul Tracking the Trajectory of
Vehicles in each Intersection.
2.Write python program to filter the vehicle
trajectories which is misjudged by the
program.
3.Write python program to do basic
statistic analysis of vehicle
4.Train prediction model on pedestrian
counting
Method & Results
• Identify Conflict Risk from Trajectory Data
• Extract Trajectories from Videos
• Safety Assessment & Monitoring
Potential Impact
• A new approach to identify /assess
/address traffic safety issues in a
significantly more efficient and faster
way than existing techniques (Several
months rather than several years)
• Allows the development of robust
statistical models to support the
selection and testing of effective safety
countermeasures
• Proposed approach is transferrable and
can be applied by transportation agency
in different states and countries
• Improved safety performance of the
transportation system ultimately will
lead to the significant reduction in the
number of crashes, which in turn will
benefit general public as well as private
companies such as AIG
Future Study
• Simulation calibration to account for the
safety
• Object classification using deep
learning
• Citywide traffic monitoring and risk
identification: Hotspot Map
General Approach
Time to Collision (TTC): The
time required for two
vehicles to collide if they
continue at their present
speed and on the same path.
Post-Encroachment Time
(PET):
The time difference between
the arrival of two vehicles at
the potential conflict point.
“Surrogate” Safety Measures: Indicators that describe the
scenarios in which a vehicle would collide with another vehicle if
they did not change their current intentions.
Fig. 1: Original video
recording
Fig. 2: Extract feature
points using Kanade-
Lucas-Tomasi (KLT)
Feature Tracker
Fig. 3: Group feature
points using Dirchlet
process mixture algorithm
Fig. 4: Convert coordinates
to relative distances
[Pre-processing] [Using Robust PCA as background subtraction]
Noisy/ short/ twisted raw trajectory segments will cause some
information loss.
Smoothing the trajectory helps apply further algorithm.
Before Smoothing After Smoothing
[Trajectory smoothing]
Jay & Fulton (9:00-16:00, Feb 4th )Correlation between
conflict number and vehicle number is 0.94.
Jay & Johnson (9:00-16:00, Feb 4th )Correlation between
conflict number and vehicle number is 0.91.
Jay & Fulton
Correlation
between conflict
number and
crash count is
0.65
Jay & Johnson
Correlation
between conflict
number and
crash count is
0.47
Traffic Cameras in NYC
(Source: NYCDOT website)
Citywide Hotspot Identification
In 2012:
30,800 fatal
crashes, 33,561
fatalities
Urban Areas:
46% of total fatal
crashes
Data:
Video from CUSP: video in CUSP to record
the intersection vehicle situation of Jay &
Johnson St. and Jay & Fulton St.
High quality, high resolution ratio
Video from DOT: some of the videos at each
intersections on the street of NYC
Low quality, low resolution ratio
1
1
( ) ( )
( )
( ) ( )
i i i
i
i i
X t X t L
TTC t
V t V t


 


where X is the position of
the vehicle at time t; V is the
speed of the vehicle at time
t; and L is the vehicle length
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