1
Application of ANFIS in Transportation Problems
Ali Gholami,
PhD Student
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Weekly Seminar Nov 13, 2014
2
Fuzzy
Sets
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
3
How to present a fuzzy set in a computer
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
4
Fuzzy rules
If
x is A
Then y is B
For example:
Rule 1:
Rule 2:
IF
Green time is > 30 sec
IF
Green time is < 10 sec
AND
It is during peak-hour
THEN “traffic_volume” is low
THEN “traffic_volume” is high
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
5
Fuzzy inference



A process of mapping from a given input to an output,
using the theory of fuzzy sets
Mamdani-style inference
Sugeno-style inference
Fuzzification
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Rule
evaluation
Aggregation of
the rule
outputs
Defuzzification
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
6
Fuzzy
inference:
Mamdanistyle
inference
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
7
Fuzzy
inference:
Sugenostyle
inference
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
8
Neural network
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012


Summation
Defuzzification
ANFIS
Normalisation
Rules
Fuzzification
Inputs
9
Adaptive Neuro-Fuzzy Inference System
A fuzzy model with a systematic approach
to generate fuzzy rules using neural
network
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
10
Case Study: Incentive
Mean Absolute Percentage
Error
80
60
40
20
0
Thru Left Thru Left Thru Left Thru Left
NB
SB
EB WB
Kietzke and Moana,
Reno, NV
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
SB
WB
NB
EB
Sparks and Prater, Sparks, NV
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
11
Research Question
Can turning
volumes be
estimated based on
signal information
without using loop
detector data?
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
12
Methodology
Modeling
the
intersection
in VISSIM
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Developing a
code in COM
interface
(VISSIM tool)
with different
parameters
Making a
prediction
model for
the data
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
13
Data Analysis
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
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Weekly Seminar
Nov 13,
2014
March 27, 1012
14
Modeling
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
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Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
15
R
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
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Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
16
Accuracy

𝑀𝐴𝑃𝐸 % =
𝑛 𝐷𝑖 −𝐵𝑖
𝑖=1 𝐵
𝑖
𝑛
MAPE: Mean Absolute Percentage Error
Di: the detector data value
Bi: the reference (base) data value
n: the total number of intervals
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
17
Case Study Intersections
9th
St. and Sierra St.
8th Street and Center Street
N McCarran Blvd and Clear Acre Ln
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
18
Results
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
19
Conclusion
-
• Needs additional effort for learning the
ANFIS concept
-
• Needs more data records to have better
training and data preparation
+
• More flexibility for modeling complex
data sets
+
• More accuracy especially when data pattern
is not clear
+
• Ready toolbox in MATLAB
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012
20
Thank you for not being like these!
Any comment or question?
Center for Advanced Transportation
Education and Research
University of Nevada, Reno
Project Panel
Meeting
Weekly Seminar
Nov 13,
2014
March 27, 1012