AUTOMATIC TRAFFIC LIGHT

CONTROL BASED ON ROAD

CONGESTION

EXPERT SYSTEM

-Kartik Shenoy-

Motivation

Problems caused by traffic congestion:

• Missed opportunities, loss of time for commuters

• Lost worker productivity, trade opportunities, delivery delays, increased costs for employers

• Trouble to Traffic Police in coordinating and directing the traffic

Solutions possible:

• Improve road infrastructure

• Create new transport facilities

• Use technology to manage this congestion

Problem Definition

• Use video feed and loop detectors for managing traffic across multiple

intersections by controlling the traffic signals at these intersections.

• Aim:

• Minimize traffic congestion

• Maximize traffic flow

• Prevent traffic jams

• Reduce load on traffic police for handling traffic

Modules

Image Courtesy: Google Images

User Interface

• Humans drive cars (and follow traffic rules)

• Traffic signals are the main user interface for this expert system

• These indicate the user what to do next

• Knowledge Engineers may use server computer for changing fuzzy rules

Knowledge Base

• KB consists of

• rule based knowledge for deciding which signals to change and for what time to keep

it that way depending on inputs

• case specific knowledge as input to system

• Rules are stored as if <antecedent clauses> then <consequent clauses> rules

• Basic traffic rules are also stored

Example Rules used by [1]

• Rule: 1 if 3.0 < Interarrival_time then Singal_Type = ‘‘1’’

• Rule: 2 if 1.7 < Interarrival_time <= 3.0 then Singal_Type = ‘‘2’’

• Rule: 3 if 0 5 < Ineterarrival_time <= 1.7 then Singal_Type = ‘‘3’’

• Rule: 4 if Interarrival_time = ‘‘Exception’’ then Singal_Type = ‘‘4’’

• Rule: 5 if Singal_Type = ‘‘1’’ then Red_light_duration =65 and Green_light_duration = 95

• Rule: 6 if Singal_Type = ‘‘2’’ then Red_light_duration= 65 and Green_light_duration = 110

• Rule: 7 if Singal_Type = ‘‘3’’ then Red_light_duration =65 and Green_light_duration = 125

• Rule: 8 if Singal_Type = ‘‘4’’ then Red_light_duration =‘‘Manual’’ and Green_light_duration =

‘Manual’’

Case Specific Knowledge Acquisition

• Loop Detector or Video Detector or RFID[1] for finding NVWQ (No of Vehicles Waiting for Queue) [2]

• Video Feed for detecting accidents

• From this data at various intersections calculate maximum flow, inter arrival time, inter departure time, average car speed

• Here the system gathers the information automatically and humans don’t need to voluntarily provide data

Image Courtesy: Google Images

Image Courtesy: [1]

Inference Engine

• Case Specific KB (CSKB) acquisition –Receive data from loop detectors, video feeds and calculate inter arrival, departure times and NVWQ

• Fuzzy Controller[2] uses CSKB and temporal information (past flow) across various intersections to decide signal times and sequences across intersections

Image Courtesy: [2]

Image Courtesy: [4]

Image Courtesy: [6]

Image Courtesy: [1]

Simulation Model as used by [4]

• The agent receives at (given) time intervals the information on the current state of traffic (data collection).

• The agent receives information on other adjoining signalised intersections from other ITSA's (data collection).

• The agent has an accurate model of the controlled intersection and knows the traffic rules (analysis).

• The agent knows the recent trends (analysis/interpretation of data).

• The agent should be able to calculate the next cycle mathematically correct (analysis/decision).

• The agent should be able to actuate the next cycle and operate the signals accordingly (control).

• The agent should be able to detect and handle current traffic problems by itself (analysis/decision and control/action) and should inform other agents of the nature, severity and possible cause of the problem, if necessary (data distribution).

• The agent passes information on to other adjoining agents (data distribution).

Conclusion

• The accuracy of NVWQ estimation using the fuzzy neural networks

approaches is more than 90% [2]

Currently Used At

• Isolated Intersections Automatic Traffic Signal Control

• MOVA (UK)

• LHOVRA (Sweden)

LHOVRA[4]

• L: Freight Traffic - Detector 300m away

• H: Priority For Main Road - Detector 200m away

• O: Accident Reduction by Dilemma - Detector 140m away

• V: Variable Yellow Light - Yellow light retained if traffic continues to flow

• R: Red-light negative protection by prolonged evacuation time

• A: All Red function

References

[1] W. Wen, “A dynamic and automatic traffic light control expert system for

solving the road congestion problem”

[2] L. Conglin, W. Wu, IEEE Member, Tan Yuejin, “Traffic Variable Estimation and Traffic Signal Control Based on Soft Computation”, 2004

[3] K. W. Lim, G. C. Kim, “Knowledge-Based Expert System in Traffic Signal Control Systems”

[4] D. A. Roozemond, “Using Intelligent Agents For Urban Traffic Control Systems”

[5] https://nl.wikipedia.org/wiki/LHOVRA, Sweden

[6] A. Zaied, W. Othman, “Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait”

Thank You