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Expert System - Automated Traffic Light Control Based on Road Congestion

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This provides a summary of the aforementioned Expert System as referred from few reference papers cited at the end. It describes the summary of the modules of this expert system and the technique used behind them.

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Expert System - Automated Traffic Light Control Based on Road Congestion

  1. 1. AUTOMATIC TRAFFIC LIGHT CONTROL BASED ON ROAD CONGESTION EXPERT SYSTEM -Kartik Shenoy-
  2. 2. 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
  3. 3. 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
  4. 4. Modules Image Courtesy: Google Images
  5. 5. 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
  6. 6. 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
  7. 7. 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’’
  8. 8. 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
  9. 9. Image Courtesy: [1]
  10. 10. 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]
  11. 11. Image Courtesy: [4]
  12. 12. Image Courtesy: [6]
  13. 13. Image Courtesy: [1]
  14. 14. 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).
  15. 15. Conclusion • The accuracy of NVWQ estimation using the fuzzy neural networks approaches is more than 90% [2]
  16. 16. Currently Used At • Isolated Intersections Automatic Traffic Signal Control • MOVA (UK) • LHOVRA (Sweden)
  17. 17. 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
  18. 18. 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”
  19. 19. Thank You

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