This document provides a review of using microscopic traffic models with artificial intelligence. It begins with an introduction to artificial intelligence and outlines several topics that will be covered. It then discusses microscopic traffic models, including self-driving car following models that can be applied to artificial intelligence. Applications of these models with AI are presented, along with the advantages and disadvantages. Current research state and future research directions are reviewed, followed by a conclusion that recommends building real-world data repositories and benchmarking platforms to evaluate modeling approaches. References are provided at the end.

1. BAYERO UNIVERSITY KANO
FACULTY OF ENGINEERING
DEPARTMENT OF CIVIL ENGINEERING
CIV8331: Advanced Traffic Engineering
ASSIGNMENT
ON
Review on Microscopic Traffic Models Using Artificials Intelligence
By
Harisu Muhammad Muhammad
SPS/20/MCE/00053
SUPERVISED BY

2. Outlines
Introduction Of Artificial Intelligence.
Review of Microscopic Traffic models for Artificial Intelligence.
Self-driving car following Models
Applications of Microscopic Traffic Models in Artificial Intelligence
Advantage of Microscopic Traffic Models in Artificial Intelligence
Disadvantages of Microscopic Traffic Models in Artificial Intelligence

3. Outlines cont……………..
Current state of Research
Future Research
Conclusion
Recommendation
 References

4. Introduction Of Artificial Intellegence.
• Artificial Intelligence (AI) are widely expected to bring a profound
revolution in transportation systems.
• Although the timeline and the means of introducing (AI) on a massive
scale are unknown, many automotive and IT companies already work
on self-driving cars (or software/hardware/services for them), with
plenty of trials and pilot projects on testing self-driving cars and
Connected and automated vehicles (CAVs) Similarly, standards for
communication between vehicles (V2V - vehicle-to-vehicle
communication, and V2I - vehicle-to-infrastructure) are being
developed.

5. Review of Microscopic Traffic models for Artificial Intelligence.
• Microscopic models describe each vehicle's behavior and interactions in the
traffic system, making more detailed modeling for each movement of the
vehicle . For this reason, microscopic models can be applied with a much
higher level of detail.(Halim et al., 2016).
• In microscopic traffic models describe the dynamics of traffic flow at the
level of each individual Self driving Car., Self driving Car are represented
as separate agents, whose motion is governed by specific rules.
• Those agents may be in interaction, which also has an impact on their
behavior. (Gora et al., 2020)
• Those agents may be in interaction, which also has an impact on their behavior

6. Review Cont………….
There are many well established microscopic models for conventional
vehicles, such as Gipps model, Wiedemann model, NagelSchreckenberg
model or Intelligent Driver Model.
Since introduction of automation and communication between vehicles
may significantly change vehicles’ behavior on a microscopic level, it is
clear that a need for new microscopic models including CAVs emerged
and many new microscopic models have recently appeared, along with
studies using such models (Gora et al., 2020)
It is believed that autonomous vehicles will replace conventional human
drive vehicles in the next decades due to the emerging autonomous
driving technology, which will definitely bring a massive transformation
in the road transport sector.

7. Self-driving car following Models
• Gipp's model is based directly on self driving car functions and
expectancy for vehicles in a stream of traffic and examine the
longitudinal movement of each vehicle in front.
• Predictive model is a statistical technique using machine learning and
data mining where Self driving Car Used to predict and forecast likely
future outcomes with the aid of historical and existing data.
• Artificial intelligence models are the tools and algorithms used to
train computers to process and analyze data – just as humans do. the
model incorporates the data gathered from these travels and can give
more accurate route information by recognizing changes in traffic
flow.

9. Applications of Microscopic Traffic Models in Artificial Intelligence
• Predictive Models the rapid development of intelligent transport
systems (ITS) has increased the need to propose advanced methods
to Predict traffic information. These methods play an important role
in the success of ITS subsystems such as advanced traveler
information systems, advanced traffic management systems,
advanced public transportation systems, and commercial vehicle
operations.(Abduljabbar et al., 2019)
• Artificial intelligence (AI) applications are utilized to simulate human
intelligence for either solving a problem or making a decision.AI
provides the advantages of permanency, reliability, and cost-
effectiveness while also addressing uncertainty and speed in either
solving a problem or reaching a decision. (Chowdhury & Sadek, 2012)

11. Advantage of Microscopic Traffic Models in Artificial Intelligence
• AVs have the following obvious advantages: smaller gap acceptance,
shorter headway, no reaction time in front of the signal system,
maintenance of a constant desired speed, and stable acceleration and
deceleration.
• AI is deemed to be a good fit for transportation systems to overcome
the challenges of an increasing travel demand, CO2 emissions, safety
concerns, and environmental degradation.(Abduljabbar et al., 2019)
• The future vision for intelligent urban mobility is smarter decision-
making based on real-time information, and network optimization by
efficient use of infrastructure.

12. Disadvantages of Microscopic Traffic Models in Artificial Intelligence
• Data protection issues. The first problem that arises, is in
that, being connected all the time with the whole
environment, it can become a cyber-problem of data
protection. Even the correct handling of road networks can be
compromised.
• High cost of implementation. Autonomous vehicle
infrastructure revolves around 5G network coverage, which is
still expensive, so it may take government’s considerable
time to invest in sufficient infrastructure for optimal
performance of autonomous vehicles.

13. Current state of Research
• Development of an AI-based for an efficient transportation system is
very complicated, due to the creation of a mechanical intelligence
along with the proper understanding the human-based information.
• The research and development of autonomous driving systems have
been developing rapidly. Still, the industry and the governments have
not yet reached a clear consensus on how to conduct safety testing
and reliable proving in the real world. Because dangerous traffic
scenes are difficult to exhaust, there are technical bottlenecks in
scene-based actual vehicle testing methods.

14. Future Research
• Future research will be directed towards enhancing predictive
operations using more than two features and more than one hidden
layer for the structure of the model. Furthermore, it is estimated that
if 30% of vehicles were self-driving vehicles by 2030, then the
congestion cost will be decreased from 38 $ billion to around 26 $
billion in Australian cities.(Abduljabbar et al., 2019)
• AIs techniques can be used to find an optimum and fastest route for
the convenient of road users and delivery service purposes. One
European company has managed to detect real-time truck
performance and driver behavior by analyzing information from
sensors on the roads.

15. Conclusion
• The review also focused on a number of application areas
which are expected to have more influence in future cities
including autonomous vehicles, Microscopic models of CAVs
showed a large amount of approaches. Although some models
are applied more often, there are no universal methods and it
is difficult to compare different models and draw proper
conclusions regarding the outcomes.
• so it is not obvious which models may eventually become the
most accurate and useful

16. Recommendation
• Building a repository of real-world data (e.g., trajectories) for
Artificial Intelligences, establishing standards for building,
calibrating and validating traffic models of CAV using real-
world data, building scenarios (e.g., standard road networks)
• to conduct experiments for CAVs, developing a generic,
open, software-agnostic benchmarking platform for the
evaluation of alternative modelling approaches and
conducting further metaresearch to build a database of
models and research works with information about their
assumptions, inputs, outcomes, scope of applicability, in
order to ensure comparability and reproducibility of results.

17. References
• Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019).
Applications of artificial intelligence in transport: An overview.
Sustainability, 11(1), 189.
• Chowdhury, M., & Sadek, A. W. (2012). Advantages and limitations of
artificial intelligence. Artificial Intelligence Applications to Critical
Transportation Issues, 6(3), 360–375.
• Gora, P., Katrakazas, C., Drabicki, A., Islam, F., & Ostaszewski, P. (2020).
Microscopic traffic simulation models for connected and automated
vehicles (CAVs) – state-of-the-art. Procedia Computer Science, 170, 474–
481. https://doi.org/10.1016/j.procs.2020.03.091
• HUANG, S., & SADEK, A. W. (2012). Artificial intelligence and microscopic
traffic simulation models. Artificial Intelligence Applications to Critical
Transportation Issues, 65.

18. .
Thank you For Listening