This presentation was done to fulfil the course requirement for the pursuit of my M. ENG on the course title: Advanced traffic engineering Course code : (CIV 8331). Course Lecturer : ENGR. PROF H. M. AlHASSAN

1. 1
REVIEW OF MICROSCOPIC TRAFFIC MODEL USING ARTIFICIAL
INTELLIGENCE
Etah, Boniface Eneji (SPS/20/MCE/00020)
Department of Civil Engineering, Bayero University Kano
Course lecturer: Engr. Prof. H. M. Alhassan.

2. INTRODUCTION
 When the description of the motion of each individual vehicle is considered,
it is called microscopic traffic simulation.
 Artificial intelligence (AI) research has been advanced with the aim of
reducing human problem-solving behavior in difficult real-world scenario.
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 The studies of traffic flow reveal the relationship between individual traffic
participants and the resulting traffic flow dynamics.

3. 3
AIMS AND OBJECTIVES
To DRAW get the relationships between the artificial intelligence
and traffic flow theory.
To generate general assumptions in Avs on traffic flows studies
To identify shortcomings of the current AV-involved traffic flow
models

4. 4
LITERATURE
REVIEW
According to a review by (Yu et al, 2021),
presented a systematically and comprehensively
survey reviews “the existing automated vehicle
AV-involved traffic flow models” with different
levels of details, and examines the connection
among the design of AV-based driving strategies,.
The pros and cons of the present models and
approaches.

5. MICROSCOPIC MODELS ON HUMAN VEHICLE AND AUTOMATED
VEHICLE
The decisions levels of HV and AV models are different. Usually, the time
resolution level of AV control models could be even smaller and should
be within [0.1 s, 0.5 s]; while the time resolution level of microscopic
flow models (such as car-following and lane-changing models) is within
[0.5 s, 1 s]. it’s possible the major traffic flow dynamics could be captured
with some detailed control actions intensified and accumulated. (Haiyang
et al, 2021).
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6. FIG. 1: An illustration of different level-of-detail settings for the input of the decision models for
AVs (Wang et al., 2021).
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7. Desired measure models
Safe distance or collision avoidance models
Optimal velocity models
Adaptive Cruise control (ACC)
cooperative Adaptive cruise Control model (CACC).
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MODELS APPLICATION S ON AV

8. 𝑎𝑖 𝑡 = 𝑎𝑚𝑎𝑥 1 −
𝑣𝑖 𝑡
𝑣0
ծ
−
𝑠∗ 𝑣𝑖(𝑡 ,∆𝑣𝑖−1,𝑖(𝑡))
𝑣0
2
(1)
where 𝑎𝑚𝑎𝑥 is the maximum acceleration/deceleration of a vehicle and
𝑣0 is the desired speed; δ is the free acceleration exponent, which is
usually set to be 4 for simplicity; 𝑠𝑖(𝑡 = ∆𝑣𝑖−1,𝑖(𝑡) – Li-1 is the
spacing between the front edge of the subject vehicle i to the rear end of
the leading vehicle (i - 1), where Li- 1 is the length of the leading vehicle
(i - 1) and ∆𝑣𝑖−1,𝑖(𝑡) is the position difference between vehicles i and (i -
1) at time 𝑣𝑖 𝑡 is the speed of the subject vehicle i at time t.
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DESIRED MEASURE MODELS

9. The desired spacing (denoted by s*) to the leading vehicle is defined as
𝑠∗ 𝑣𝑖(𝑡 , ∆𝑣𝑖−1,𝑖 𝑡 = 𝑠0 + 𝑚𝑎𝑥 𝑇0𝑣𝑖 𝑡 +
𝑣𝑖(𝑡 ∆𝑣𝑖−1,𝑖(𝑡))
2 𝑎𝑏
(2)
where 𝑠0 is the minimum net distance in congested traffic; 𝑇0 is a constant
desired (safety) time gap of the leading vehicle (i - 1).
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10. SAFETY DISTANCE OR COLLISION AVOIDANCE MODELS
, This equation indicates that two vehicles will not collide even in the
worst scenario, i.e., the leading vehicle brakes by at most bmax,brake until
a full stop; the following vehicle accelerates by at most amax,accel during
the response time τ, and then brakes by at least amin,brake until a full stop.
(Aycin and Benekohal, 1999).
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𝑑𝑚𝑖𝑛 𝑡 = 𝑣𝑖 𝑡 𝜏 +
𝑎𝑚𝑎𝑥,𝑎𝑐𝑐𝑒𝑙𝑝2
2
+
𝑣𝑖 𝑡 +𝑎𝑚𝑎𝑥,𝑎𝑐𝑐𝑒𝑙𝑝
2
2𝑎𝑚𝑖𝑛,𝑏𝑟𝑎𝑘𝑒
−
𝑣2
𝑖−1(𝑡)
2𝑏𝑚𝑎𝑥,𝑏𝑟𝑎𝑘𝑒
+
(3)

11. Zheng et al. (2018) introduced the consideration of AV-related driving safety into the
classical cellular automaton model. With the safety concerns, the following four gap
distances between two successive vehicles are defined:
 the safe gap distance for the subject vehicle to accelerate at a relatively larger value;
 the safe gap distance of the subject vehicle to accelerate normally;
 the safe gap distance for the subject vehicle to keep the current velocity;
 the safe gap distance for the subject vehicle to brake normally.
Source: (Yu et al, 2021)
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Fig 2 illustration of the virtual
mapping technique.

12. CURRENT RESEARCH:
Unfortunately, the behaviors of AV prototypes that are currently
available on the market or under development are largely unknown since
the information related to the flaws and deficiencies of those AV
prototypes are treated as confidential business information. Meanwhile,
AV technologies are still rapidly evolving, and the capabilities of AV
prototypes are constantly improving.
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13. FUTURE RESEARCH:
It can be seen from the existing studies that those car-following
strategies reduce the shock of human drivers when they need to share
roads with AVs. However, the most existing safety distance models for
AVs only addressed the minimum required car-following distance for
AVs. The dynamic features of car-following behavior, in particular
when the gap is larger than this minimum distance, need to be further
studied and described.
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14. CONCLUSION:
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Since the dawn of traffic flow studies, With the advent of AVs, the
coupling between traffic flow modeling and vehicle studies is becoming
further unavoidable. There is so much for researchers in traffic flow
studies and researchers in vehicle studies to learn and benefit from each
other. On the one hand, the former can learn from the latter to accurately
understand mechanical features and controller design of AVs and
pinpoint the difference between HVs and AVs, so as to use this vital
knowledge to better analyze, model and predict the impact of AVs on
traffic flow dynamics, and ultimately develop effective strategies to
maximize the benefits of AVs on road safety, traffic efficiency, fuel
consumption, etc.

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