OPTIMIZING TIME SPENT IN ROAD TRAFFIC VIA THE USE OF SMART TRAFFIC LIGHTS.

1. SUMMARY
This research uses the Yolov4 algorithm for
detecting vehicles from live images from CCTV
cameras installed at traffic intersection points.
It then utilizes the DeepSort Algorithm to
associate the detected objects in all frames of the
video.
The model above introduces smartness/
intelligence to the traditional traffic light system. It
will help reduce the time spent in traffic jams and
intersection points because the decision is based
on a density function which will always have it’s
parameters being recorded in real time.
The summarized process of the research is as
shown below.
KEY OBJECTIVES
1. To be able to detect the
vehicles on the Ghanaian
road and implement a
count using computer
vision for lane intensity
calculations at traffic light
intersection areas.
2. To communicate the
densities of each lane for
a smart decision by the
traffic lights to determine
which lane is to be given
priority.
NB. The Research is grouped
into two phases based on the
objectives of the Study
OBJECT DETECTION
To be able to detect the vehicles and
their classes, we utilized the
architecture of the Yolov4 Neural
Network Model which is a pretrained
Computer Vision Algorithm.
This algorithm was used due to it’s
efficiency in addressing issues like
occlusion and it’s capability to detect
multiple objects in a single frame.
INTRODUCTION
Traffic lights, although they are
useful artifacts in ensuring the flow of
vehicles and pedestrians in Ghana,
tend to be a cause of delay due to
it’s unintelligible manner of operation.
Most road offenses range from basic
traffic infractions like jumping red
lights, etc. (Amoh, 2021). Travel time
studies in urban areas show that 12–
55% of commute travel time is due to
delays induced by signalized
intersections (Levinson. 1998). This
problem can be approached to a
significant degree if the decision of
traffic lights is not just automatic but
smart such based on the current
nature of the traffic.
Place of Case Study: The KNUST Agriculture Junction.
METHODS
OPTIMIZING TIME SPENT IN ROAD TRAFFIC VIA
THE USE OF SMART TRAFFIC LIGHTS.
Authors: Stephen Maranatha Asiedu, Joseph Sedem Setsoafia.
Supervisors: Dr. Peter Amoako Yirenkyi, Jeremiah Ishaya.
For the purpose of this study, the
Agriculture Junction at the Kwame
Nkrumah University of Science and
Technology was used as a Case Study.
The primary data for this research was
live feed from CCTV Camera’s installed
at the Agriculture Junction. It is
strategically positioned to help give a
good information of vehicles that are in
the traffic.
A screenshot of the Yolov4 Algorithm detecting vehicles at the Paa Joe Roundabout
at KNUST
OBJECT TRACKING
After the objects are detected, we need to
keep track of them across all the frames.
The DeepSort which is the SORT (Simple
Online Real-Time Tracking) with a Deep
Association Metric was utilized for this
purpose. It applies the concept of Deep
Learning, Kalman Filter to track
detections(Kalman, 1960), Hungarian
Algorithm for associating objects in
different frames and the Mahalanobis
distance for Association metrics.
DeepSort was preferred because it runs in
real time, able to track through longer
periods of Occlusion and thus reduces the
number of identity switches (Wojke et al,
2017).
THE TRAFFIC LIGHT CONTROL
For the purpose of this study, we shall
communicate with the traffic light via Arduino
boards since a majority of traffic lights are either
made of Arduino or Raspberry Pi.
REFERENCES
1. Amoh K. Emmanuel. 2021. Over 2000 offences
captured by Ghana Police’s new traffic monitoring
cameras. 3news.com. Accessed September 16,2022.
<https://3news.com/over-2000-offences-captured-by-
ghana-polices-new-traffic-monitoring-cameras/>
2. David M Levinson. 1998. Speed and delay on
signalized arterials. Journal of Transportation
Engineering 124, 3 (1998), 258–263.
3. Kalman, R.E., 1960. A new approach to linear filtering
and prediction problems. Journal of Basic
Engineering. Transactions of the American Society of
Mechanical Engineers 82, Series D, 35–45.
4. N. Wojke, A. Bewley, and D. Paulus, “Simple online
and realtime tracking with a deep association metric,”
CoRR, vol. abs/1703.07402, 2017. [Online]. Available:
http://arxiv.org/abs/1703.07402
A Project by Stephen Asiedu Maranatha and Joseph Sedem Setsoafia of the
KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY,
Department of Mathematics.