faculty of engineering, universiti malaysia sabah, malaysia ams … · 2014-09-29 · 4.0...

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1.0 Introduction

Modelling, Simulation & Computing Laboratory (mscLab)Faculty of Engineering, Universiti Malaysia Sabah, Malaysia

AMS 2014Asia Modelling Symposium 2014

Kuala Lumpur, Malaysia, 25 September 2014

• What is traffic congestion?• Physical – impedance vehicles impose on each other• Relative – user perception of system performance

• What are the causes of traffic congestion?• Land-use patterns• Employments patterns• Income levels car ownership trends• Regional economic dynamics

• Negative impacts of traffic congestion?• Air pollution• Economic loss

• Why need to reduce the traffic congestion?• Reduce in traffic congestion will improve 10% to key employment, retail, education and population centres within a region increases regional productivity by 1% (Hartgen and Fields, 2009)

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1.0 Introduction




• Solution to Traffic congestion?• Increase the capacity of current network

• more and wider roads• HARD – availability of lands (valuable for development)

– long time to construct

•Traffic Signalization Control• Passive – Road signs board• Active – Traffic conductor (police)• – Traffic Light Systems

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• To improve the fluency of the traffic flow within a trafficnetwork via Q-learning algorithm for distributing optimumtraffic signalization to each intersection

a. To model and simulate the traffic network systemb. To formulate and compute the Q-learning algorithm

(microscopic control)

2.0 Objective




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3.0 Methodology (Behavior of Incoming Traffic Flow)




• Total number of arrivingvehicles

• is the proportionalconstant for the traffic flowfrom link i to link j• is the traffic flow fromlink i of intersection k

• Total arriving vehicles from INT Bto INT A

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3.0 Methodology (Framework of Q-Learning )




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3.0 Methodology (Formulation of Q-Learning )

• Q-Learning

State iAgent

S N E W

State i +1Agent

S N E WAction

b

Reward Function

• State i•QL agent choseaction from list

• Action chosen willlead QL agent toanother state i+1

• Reward Function• Evaluate theaction chosen• Store theexperience valuein QL agent




N N

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4.0 Simulations (Traffic Network Topology)




Case I:Under-saturated INT A and Saturated INT B

5.0 km

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4.0 Simulations (Traffic Network Topology)




Case II:Ramp release its traffic flow into INT A

5.0 km

2.5 km

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5.0 Results and Discussions (CS2)




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6.0 Conclusions




• The vehicles queuing condition at traffic intersections can beimproved by the Q-learning Traffic Signalization (QLTS )

• Due to the learning behavior, QLTS changes the green signalduration according to the traffic flow environment atintersections particularly during congestion and traffic flowdisturbance

• The vehicles passed through the intersections with the QLTSare 2.9 % – 19.0 % more than the Fixed Traffic Signalization(FTS) management

faculty of engineering, universiti malaysia sabah, malaysia ams … · 2014-09-29 · 4.0...

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