Road Traffic Decision Support System

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Road Traffic Decision Support System
Using Fuzzy Logic
K AlMejalli, K Dahal and A Hossain
March 2006
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Outline

• Brief Introduction
• Proposed Solution.
• Case Study.
• Conclusion and future work.

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Brief Introduction

• Laws and policies depend on a large number of
  factors.
• Traffic decisions are based so much on human
  experience.

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Proposed Solution
Decision Support Model
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Decision Support Model
First Step Identifying typical decision and the
influencing factors and their relation on
decision making process.
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Decision Support Model
Second Step (Fuzzifying fuzzy variables) Each
of those variables have been fuzzified with five
linguistic variables.
Range (10000 100000)
The Triangle membership for the Traffic Violation
factor
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Decision Support Model
Third Step (Building the rule-based structure)
Selective rules from the Riyadh Traffic
rule-based structure
Last Step (Centroid Method ) Applying an
appropriate defuzzification method in order to
obtain a crisp value (decision priority) for
each decision.
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Case Study
The Summer Vacation Period in Riyadh

• Characteristics
• The number of vehicles decreases.
• A decrease in the number of traffic violation.
• The number of traffic policemen on duty records a
  slight decrease.
• There is an obvious increase in the number of
  traffic accidents.

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Case Studies
The following table illustrates the influencing
factors of the system according to the Riyadh
Traffic information for the period (131 August
2003)
The following table shows how the system analyzed
the traffic decision based on the values of these
factors.
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Conclusion and future work

• Defining and analysing all factors that may
  influence the road traffic decision-making
  process.

• Building a Knowledge-based DSS

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Thank you very much