Review of Microscopic Traffic Model Using Artificial Intelligence Slides.pptx
Review of Microscopic
Traffic Model Using
BAYERO UNIVERSITY KANO
DEPATMENT OF CIVIL ENGINEERING
FACULTY ENGINEERING TECHNOLOGY
REVIEW OF MICROSCOPIC TRAFFIC MODEL USING ARTIFICIAL INTELLIGENCE
PWAVIRON KENEDY GAMBIYE
Today, the problem of cities urban transportation is becoming something
we have to face in our daily life. Typical traffic simulation models can be
classified as either microscopic, mesoscopic, or macroscopic.
Microscopic models predict the state of individual vehicles; typical
measures are individual vehicle speeds and locations.
Definitions of microscopic simulation was assorted, but in general
microscopic simulation can be determined as an effort to develop a driver
behaviour and vehicle models in order to produce a more realistic
Artificial intelligence (AI) is the ability of a computer or a robot
controlled by a computer to do tasks that are usually done by humans
because they require human intelligence and discernment. AI refers to
methods and approaches that mimic biologically intelligent behaviour in
order to solve problems that so far have been difficult to solve by
classical mathematics (Sadek, 2007).
Background of Microscopic Traffic Model
Using Artificial Intelligence
Microscopic traffic flow modelling specialized on the minute aspects of
traffic stream like vehicle-to-vehicle interaction and individual vehicle
Traffic simulation is the mathematical modelling of traffic dynamics with
the use of computer software and application to support the planning,
operation, and development of transportation systems
Microscopic simulation is a model that describes the behaviour and
interactions of each driver in a traffic system, which is made more detailed
modelling for each movement of the vehicle.
Microscopic traffic models describe the details of traffic flow and the
interaction taking place within it. Microscopic traffic models simulate
single vehicle-driver units.
Advantages of Microscopic Model Using
1. It can track a single vehicle on the road,
2. it can reflect the interaction between vehicles and also predict traffic
performance indicators such as vehicle travel time, delay and emission while
avoiding the impact on actual road traffic;
3. Through the microscopic model using artificial intelligence, the impact of a
specific parameter on traffic can be reflected; through the animation interface
of the simulator,
4. With microscopic model one can intuitively visualize the changes in road traffic,
and provide a good platform for understanding the traffic operation status under
different traffic demands.
Disadvantages of Microscopic Model
Using Artificial Intelligence
1. High cost the ability to create a machine that can simulate
human intelligence is high.
2. Increase rate of unemployment and make humans lazy.
3. It does not improve with experience and lack creativity
4. Cannot replace human
Review Of Microscopic Traffic Model
Using Artificial Intelligence
Microscopic traffic models describe the
details of traffic flow and the interaction
taking place within it.
Studies found several models which are
related to this study.
Simulation Based on Intelligent
Kesting (2008) developed an existing IDM using
the new constant acceleration heuristic.To know
the effect of influenced vehicle using ACC.
Schinder (2010) Modeled traffic to explores the
interaction between subsystem(driver, vehicle and
infrastructure).To find an appropriate model use as
a foundation for ADAS
Modeling Based Microscopic Car Following
And Lane Changing
DAS (2009) The research trying to develop
a car following model for narrow roads
using automata cell approach.
LU (2013) This research try to modeled the
decision making of drivers using parameters
desired using parameters desired safety
margin(DSM) theory of homeostasis
Modeling Driver Behavior
SONG (2000) His research tried to model driving
behavior model of the existing simulation
tools.The contributions is to build a database of
knowledge formation’s driver and the development
of cognitive processes modeling when the driver
was doing driving activities
Other Reviews on Various Models
Olayede et al (2020), in their
research “Modelling of Urban
Traffic System Using Artificial
Intelligence” tying to solve the
increasing traffic congestion in
recent years created a new more
efficient control solutions.
Raghuwanshi, Salunke, Hou and Hulume (2014),
carried out a study on “Development of a
Microscopic Artificially Intelligent Traffic Model for
Simulation” evaluated numerous traffic simulation
models for supporting next-generation ITS research
applications. The survey justified the need for the
design and development of a microscopic Artificially
Intelligent Traffic Model (AITM) intended for
civilian ground vehicle research applications. The
research concludes that while traffic simulation
models allow for capturing dynamics of full-scale
traffic networks, they often lack behavioural realism.
Leal, Almeida & Ribeiro (2019) on the topic Calibrating
Traffic Microscopic Simulation Model Parameters Using
an Evolutionary approach” which aims at using
Microscopic Simulation models to provide traffic
management solutions. The paper presents a genetic
algorithm-based microscopic simulation model to
calibrate the parameters of AIMSUN simulator to a
network of intersections in Belo Horizonte city, Brazil.
Results obtained showed that calibration process is
essential in the use of microscopic simulation models to
define and predict traffic managements strategies
In conclusion the objective of microscopic
traffic model using artificial intelligence is to
presents a real traffic situation in to dynamic
model. Based on the literature that has been
discussed ,there is no generic model that can be
used to represent all traffic conditions
Conditions. Infrastructure ,Traffic management
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