Postgraduate Student, Department of Civil Engineering, Bayero University Kano

1. BAYERO UNIVERSITY KANO
DEPARTMENT OF CIVIL ENGINEERING
ADVANCE TRAFFIC ENGINEERING
CIV8331
Review on Microscopic Model Using Artificial Intelligent
GHALI MUSA
SPS/20/MCE/00051
ghalicv@gmail.com
COURSE LECTURER
PROF. HASHIM M. ALHASSAN

2. Microscopic models
Microscopic models describe the longitudinal (car-following) and lateral (lane-
changing) behaviour of individual vehicles. Most microscopic models are car-
following models: they describe the movement of each vehicle based on the
behavior (movements) of the vehicle(s) in front of it
Microscopic traffic models simulate single vehicle-driver units. The dynamic
variables of the models represent microscopic properties like the position and
velocity of single vehicles. These models can be divided into two categories: cell
automata models, which are discrete in time and space, and continuous models,
which are continuous in time

3. Artificial intelligence
 Artificial intelligence is the simulation of human intelligence processes by
machines, especially computer systems. Specific applications of AI include
expert systems, natural language processing and speech recognition and
machine vision

4. Review
The transportation industry has undergone multiple changes and revolutions over the
last few hundred years and we’re now at the stage where major breakthroughs are
being achieved in the form of Artificial Intelligence in transportation.
Whether via self-driving cars for more reliability, road condition monitoring for
improved safety, or traffic flow analysis for more efficiency, AI is catching the eye of
transportation bosses around the world.
Indeed, many in the transportation sector have already identified the awesome
potential of AI, with the global market forecast to reach $3,870,000,000 by 2026.
Such spending can help companies leverage advanced technologies like computer
vision and machine learning to shape the future of transportation so that passenger
safety increases, road accidents are lessened, and traffic congestion is reduced.
Deep learning and machine learning in transportation can also help to create “smart
cities,” such as we’ve seen in Glasgow, where the technology monitors vehicle dwell
times, parking violations and traffic density trends.

5.  Research has shown that the production of self-driving cars is expected to
reach 800,000 units worldwide between the years 2023-2030. Despite
concerns around the technology and its ability to safeguard passengers from
harm, KPMG has predicted the adoption of self-driving vehicle technology
could reduce the frequency of accidents by approximately 90%.

6. Aims
To review microscopic mode using artificial intelligence

7. Transpiration system is consider as AI used if it comprises of
1. Self-driving Vehicles
2. Traffic Detection (and Traffic Signs)
3. Pedestrian Detection
4. Traffic Flow Analysis
5. Computer Vision-Powered Parking Management
6. Road Condition Monitoring
7. Automatic Traffic Incident Detection
8. Automated License Plate Recognition
9. Driver Monitoring

8. Self-driving Vehicles
The concept of self-driving vehicles is nothing new. General Motors introduced it
back in 1939.
But it’s only in our current age of AI transportation that companies are able to
use computer vision techniques like object detection to create intelligent
systems that decode and make sense of visual data to essentially allow a vehicle
to drive itself.
The algorithm is fed huge swathes of relevant data, before being trained to
detect specific objects and then take the correct actions, such as braking,
turning, speeding up, slowing down, and need to recognise other vehicles on the
road, road signs, traffic lights, lane markings, pedestrians and more.
To collect and use data, autonomous vehicles use cameras and sensors. To train
the model and to make it reliable, it needs to be consistently fed masses of
data.

9. Naturally, there still remain some challenges.
An algorithm needs to get access to those huge swathes of relevant data, while
situational conditions like bad weather and uneven terrain can also pose a
problem. Other issues include poor lighting and the possibility of a self-driving
car coming across an unidentified object while out on the road.
Indeed, Mckinsey has predicted that self-driving trucks will reduce operating
costs by some 45%. Environmental impact will also be greatly reduced

10. Traffic Detection (and Traffic Signs)
It’s a game with tragic consequences, too, with over 50% of those deaths
accounted for by passengers or drivers who didn’t run the red light.
The problem is that the traffic light system itself might be perfect, but the
humans behind the wheel aren’t always perfect. Mistakes happen, sometimes
drivers run a red light—and accidents occur.
The solution to this terrible problem can be found in autonomous vehicles that,
alongside smart cities, can prevent those deaths.
Indeed, automakers are putting the traffic signal issue at the front and centre of
their self-driving cars capabilities.

11. An AI-based system can be trained to recognise lights—green, amber and red—via
computer vision models that are trained in a wide range of scenarios, such as
poor light conditions, inclement weather and occlusions.
As such, a self-driving car’s cameras first spot a traffic signal, before the image is
analysed, processed—and, if it turns out that the light is red, the car puts the
brakes on.
Naturally, there are issues here. When a camera is scanning what’s in front of it,
it may spot other lights—such as a billboard or a streetlamp. Yes, a traffic light
is different to a streetlamp in that it has three lights, but an image analyser
capability still needs to be so good that it can spot the traffic signal instantly and
not be fooled by other lights.

12. Pedestrian Detection
How cool would it be if a computer system could automatically spot and identify
pedestrians in images and videos?
Pedestrian detection is actually a key problem in Computer Vision and Pattern
Recognition, because pedestrians can be super unpredictable in the context of
road traffic. They’re so unpredictable that they pose one of the greatest threats
to the success of self-driving cars.
The key is to properly distinguish a human from another object, as well as
understand what a pedestrian is planning to do next, will they cross the road?
To begin the task of identifying and visualising pedestrians, computer vision
systems use bounding boxes.

13. Traffic Flow Analysis
The flow of traffic impacts a country’s economy for the better or worse, and it
also impacts road safety. Traffic congestion costs money and time, it causes
stress to the drivers and passengers, and it also contributes to global warming.
With better traffic flow, a country’s economy can grow better, and the safety of
its road user’s is improved immeasurably.
With this in mind, it’s no surprise that Artificial Intelligence is now paving the
way for better traffic flow analysis using machine learning and computer vision.
AI can help to reduce bottlenecks and eradicate choke-points that are otherwise
clogging up our roadsand our economy.

14. Road objects detection for traffic flow
analysis
The algorithms are able to track and count freeway traffic
with accuracy, as well as analyse traffic density in urban
settings, such as on the freeway and at intersections. This
helps towns and cities to understand what’s going on so that
they can design more efficient traffic management systems,
while at the same time improving road safety.
CCTV cameras can spot dangerous events and other
anomalies, as well as provide insights into peak hours,
choke-points and bottlenecks. It can also quantify and track
changes over a period of time so as to allow the
measurement of traffic congestion. As a result, urban traffic
and emissions can be greatly reduced by town planners.

15. The researchers wanted to improve the process of counting traffic volume, which
itself has been a complex task, based as it is on the CCTV system. The issue has
always been the involvement of too many vehicle movements. If the researchers
could implement distinguished regions tracking so as to monitor the different
movements of vehicles, they could improve the counting process.
The results?
The experiment results are promising, with the model achieving an accuracy for
different movements between 80 and 98%, all with just a single view of the
camera.

16. Computer Vision-Powered Parking
Management
The parking spot issue is so prevalent. How does computer vision work for parking
management?
Sensors are installed to monitor the parking lot for any empty spaces. Whenever a
vehicle is parked in a space, the sensor is able to calculate the distance to its
underpart.
But because a sensor can’t scan license plates, cameras, parking meters and
computer vision need to get involved.
Cameras are thus installed that use computer vision to identify spots with no meters.
Using automatic number-plate technology, they spot vehicles that are parked, as well
as measure the amount of time they are parked for.
Computer vision can then use data to update in real-time the inventory of all empty
and available spaces. Drivers can then access the map on their mobile device to check
out all the available parking spots. This saves huge amounts of time, and is especially
useful in overcrowded parking lots,

17. Road Condition Monitoring
Road condition monitoring has largely been left in the hands of citizens, whose
“task” is to raise awareness of damaged roads to their local councils.
Now, computer vision in AI transportation can detect defection successfully, as
well as assess the surrounding infrastructure by looking for changes in the asphalt
and concrete.
Computer vision algorithms are able to identify potholes, as well as show exactly
how much road damage there is so that the relevant authorities can take action
and improve road maintenance.
The algorithms work by collecting image data, before processing it to create
automatic crack detection and classification systems. These will then foster
targeted rehabilitation and preventative maintenance and that is free from
human involvement.

18. Automatic Traffic Incident Detection
Traffic incident detection is one of the most heavily researched areas of
ITPs (Intelligent Transportation Systems) and AI transportation in general.
Using computers, and combining sensors with computer vision to constantly
monitor all cameras, it looks out for incidents, queues and unusual traffic
conditions.
Urban road networks are kitted out with CCTV cameras and multiple
detectors. Together, they offer the foundations for automated,
uninterrupted monitoring.
Powered by computer vision, the detectors are able to provide a constant
data flow that assists TMC’s with their traffic operations.
Control centre operators are alerted whenever there is an anomaly in the
traffic conditions, and they are able to respond as soon as possible to any
incidents that the AI-driven systems have detected.

19. Automated License Plate Recognition
Automated license plate recognition involves the use of
computer vision camera systems attached to highway
overpasses and street poles to capture a license plate
number, as well as the location, date and time.
Once the image has been captured, the data is then fed into
a central server.
However, automated license plate recognition can also be
used to spot travel patterns and it’s used extensively in high
monitoring, parking management and toll management.
Driver Monitoring

20. Personal responsibility has to come into it, but as those damning statistics show,
it’s not enough. Human error will not be eradicated by asking drivers to drive
more carefully.
Computer vision has now been added to car cabins for the purpose of better,
safer driver monitoring. The technology, which uses face detection and head
pose estimation to look out for things like drowsiness and emotional recognition,
can prevent thousands of crashes and deaths each year.

21. Conclusion
 Artificial intelligence is changing the transport sector. It is applied in multiple
transport domains, from helping cars, trains, ships and airplanes to function
autonomously to making traffic flows smoother. Further making our lives
easier can help make all transport modes more intelligent, secure, more
hygienic, and more efficient. Artificial intelligence-led autonomous transport
could help reduce the human errors involved in many traffic accidents.