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Fine Tuning of Traffic in Our Cities with Smart Panels: The Quito City Case Study

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In this article we work towards the desired future smart city in which IT and knowledge will hopefully provide a highly livable environment for citizens. To this end, we test a new concept based on intelligent LED panels (the Yellow Swarm) to guide drivers when moving through urban streets so as to finally get rid of traffic jams and protect the environment. This is a minimally invasive, low cost idea for the city that needs advanced simulations with real data coupled with new algorithms which perform well. Our proposal is to use evolutionary computation in the Yellow Swarm, which will finally help alleviate the traffic congestion, improve travel times, and decrease gas emissions, all at the same time and for a real case like the city of Quito (Ecuador).

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Fine Tuning of Traffic in Our Cities with Smart Panels: The Quito City Case Study

  1. 1. FINE TUNING OF TRAFFIC IN OUR CITIES WITH SMART PANELS: THE QUITO CITY CASE STUDY Daniel H. Stolfi1 dhstolfi@lcc.uma.es Rolando Armas2 rolandoarmas@gmail.com Enrique Alba1 eat@lcc.uma.es Hernan Aguirre2 ahernan@shinshu-u.ac.jp Kiyoshi Tanaka2 ktanaka@shinshu-u.ac.jp 1Departamento de Lenguajes y Ciencias de la Computaci´on, University of Malaga 2Faculty of Engineering, Shinshu University Genetic and Evolutionary Computation Conference GECCO 2016 Denver, Colorado, USA July 2016 D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 1 / 19
  2. 2. Introduction Our Proposal Experimentation Conclusions and Future Work CONTENTS 1 INTRODUCTION 2 OUR PROPOSAL 3 EXPERIMENTATION 4 CONCLUSIONS AND FUTURE WORK D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 2 / 19
  3. 3. INTRODUCTION: QUITO CITY
  4. 4. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM The Yellow Swarm, consists of: Several LED panels Installed in the city Suggest potential detours to drivers Our Evolutionary Algorithm Evaluates the training scenarios Calculates the configuration of the panels D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 4 / 19
  5. 5. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM The Yellow Swarm, consists of: Several LED panels Installed in the city Suggest potential detours to drivers Our Evolutionary Algorithm Evaluates the training scenarios Calculates the configuration of the panels D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 4 / 19
  6. 6. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM The Yellow Swarm, consists of: Several LED panels Installed in the city Suggest potential detours to drivers Our Evolutionary Algorithm Evaluates the training scenarios Calculates the configuration of the panels D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 4 / 19
  7. 7. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM Yellow Swarm offers: Cheap and easy to install Rerouting vehicles according to an optimal strategy Prevention of traffic jams Reduction of travel times Reduction of fuel consumption and gas emissions D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 5 / 19
  8. 8. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM Yellow Swarm offers: Cheap and easy to install Rerouting vehicles according to an optimal strategy Prevention of traffic jams Reduction of travel times Reduction of fuel consumption and gas emissions D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 5 / 19
  9. 9. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM Yellow Swarm offers: Cheap and easy to install Rerouting vehicles according to an optimal strategy Prevention of traffic jams Reduction of travel times Reduction of fuel consumption and gas emissions D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 5 / 19
  10. 10. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM Yellow Swarm offers: Cheap and easy to install Rerouting vehicles according to an optimal strategy Prevention of traffic jams Reduction of travel times Reduction of fuel consumption and gas emissions D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 5 / 19
  11. 11. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM Yellow Swarm offers: Cheap and easy to install Rerouting vehicles according to an optimal strategy Prevention of traffic jams Reduction of travel times Reduction of fuel consumption and gas emissions D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 5 / 19
  12. 12. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM Yellow Swarm offers: Cheap and easy to install Rerouting vehicles according to an optimal strategy Prevention of traffic jams Reduction of travel times Reduction of fuel consumption and gas emissions D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 5 / 19
  13. 13. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM ARCHITECTURE Offline:The EA calculates the system configuration (time slots) Online:The LED panels suggest possible detours to drivers D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 6 / 19
  14. 14. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM ARCHITECTURE Offline:The EA calculates the system configuration (time slots) Online:The LED panels suggest possible detours to drivers D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 6 / 19
  15. 15. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm YELLOW SWARM ARCHITECTURE Offline:The EA calculates the system configuration (time slots) Online:The LED panels suggest possible detours to drivers D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 6 / 19
  16. 16. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm LED PANELS They are made of LEDs (Light-Emitting Diode) They show the different detour options. Straight on Turn left Turn right Each option is visible during a time slot calculated by the Evolutionary Algorithm. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 7 / 19
  17. 17. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm LED PANELS They are made of LEDs (Light-Emitting Diode) They show the different detour options. Straight on Turn left Turn right Each option is visible during a time slot calculated by the Evolutionary Algorithm. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 7 / 19
  18. 18. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm LED PANELS They are made of LEDs (Light-Emitting Diode) They show the different detour options. Straight on Turn left Turn right Each option is visible during a time slot calculated by the Evolutionary Algorithm. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 7 / 19
  19. 19. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm LED PANELS They are made of LEDs (Light-Emitting Diode) They show the different detour options. Straight on Turn left Turn right Each option is visible during a time slot calculated by the Evolutionary Algorithm. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 7 / 19
  20. 20. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm LED PANELS They are made of LEDs (Light-Emitting Diode) They show the different detour options. Straight on Turn left Turn right Each option is visible during a time slot calculated by the Evolutionary Algorithm. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 7 / 19
  21. 21. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm LED PANELS They are made of LEDs (Light-Emitting Diode) They show the different detour options. Straight on Turn left Turn right Each option is visible during a time slot calculated by the Evolutionary Algorithm. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 7 / 19
  22. 22. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm CASE STUDY We have worked with the map of Quito imported from OpenStreetMap: 1 Download the map from OpenStreetMap 2 Clean the irrelevant elements by using JOSM 3 Import the city model into SUMO by using NETCONVERT 4 Generate the vehicle flows by using ACTIVITYGEN D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 8 / 19
  23. 23. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm CHARACTERISTICS OF CASE STUDIES DMQ Center: 14 districts - 5x8 Km2 - 560.000 inhabitants Simulation Time: 24 hours - 245.000 journeys - Scenarios: 4 training + 30 testing D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 9 / 19
  24. 24. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm LOCALIZATION OF THE PANELS D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 10 / 19
  25. 25. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm EVOLUTIONARY ALGORITHM (10+2)-EA It evaluates the individuals by using the traffic simulator SUMO The decisions (detours) made by the drivers are implemented by using TraCI As a result it produces the configuration of the panels D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 11 / 19
  26. 26. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm EVOLUTIONARY ALGORITHM (10+2)-EA It evaluates the individuals by using the traffic simulator SUMO The decisions (detours) made by the drivers are implemented by using TraCI As a result it produces the configuration of the panels D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 11 / 19
  27. 27. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm REPRESENTATION The solution vector contain 10 groups of values representing the time slots for the panels, 24 (6x2 + 4x3) values in total. Time values are kept in the range of 0 – 300 seconds The search space is huge! We need to use a metaheuristic in order to solve this problem D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 12 / 19
  28. 28. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm REPRESENTATION The solution vector contain 10 groups of values representing the time slots for the panels, 24 (6x2 + 4x3) values in total. Time values are kept in the range of 0 – 300 seconds The search space is huge! We need to use a metaheuristic in order to solve this problem D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 12 / 19
  29. 29. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm REPRESENTATION The solution vector contain 10 groups of values representing the time slots for the panels, 24 (6x2 + 4x3) values in total. Time values are kept in the range of 0 – 300 seconds The search space is huge! We need to use a metaheuristic in order to solve this problem D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 12 / 19
  30. 30. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Study Evolutionary Algorithm EVALUATION FUNCTION It is the ratio of the number of vehicles that reach their destinations during the interval given by 1 λ λ k=1 (nvYS)k (nvQ)k (1) nvYS: Number of vehicles that reach their destinations with Yellow Swarm nvQ: Number of vehicles that reach their destinations without Yellow Swarm λ: Number of training scenarios. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 13 / 19
  31. 31. Introduction Our Proposal Experimentation Conclusions and Future Work Optimization Results INTERVAL STUDY We set a threshold of 2000 vehicles to detect when traffic jams are likely to happen. TABLE: Optimization sub-intervals. Sub-interval Begin (h) End (h) Duration (m) 25% 8:30 9:45 75 50% 8:30 11:00 150 75% 8:30 12:15 225 100% 8:30 13:30 300 We divided the morning time interval into four sub-intervals to be optimized. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 14 / 19
  32. 32. Introduction Our Proposal Experimentation Conclusions and Future Work Optimization Results OPTIMIZATION PROCESS TABLE: Fitness values obtained from the four optimization process and statistical tests. Sub-interval Fitness Friedman Wilcoxon Average StdDev Test p-value 25% 1.023 0.15% 2.00 0.00 50% 1.043 0.27% 3.10 0.00 75% 1.053 0.44% 3.90 — 100% 1.019 0.10% 1.00 0.00 D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 15 / 19
  33. 33. Introduction Our Proposal Experimentation Conclusions and Future Work Optimization Results IMPROVEMENTS TABLE: Improvements achieved in the traffic of Quito city during an entire day when using Yellow Swarm just for 225 minutes (75% of the morning peak hours). Average StdDev Minimum Maximum Travel Time 11.9% 0.4% 5.3% 28.4% CO2 5.3% 0.4% 2.4% 13.2% Fuel 5.3% 0.4% 2.4% 13.2% Distance -1.2% -0.1% -1.3% -1.0% D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 16 / 19
  34. 34. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions CONCLUSIONS Conclusions We have modeled the real traffic demand of Quito. We have reduced travel times, greenhouse gas emissions, and fuel consumption. We have achieved average reductions up to 11.9% in travel times, 5.3% in CO2 emissions, and 5.3% in fuel consumption. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 17 / 19
  35. 35. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions CONCLUSIONS Conclusions We have modeled the real traffic demand of Quito. We have reduced travel times, greenhouse gas emissions, and fuel consumption. We have achieved average reductions up to 11.9% in travel times, 5.3% in CO2 emissions, and 5.3% in fuel consumption. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 17 / 19
  36. 36. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions FUTURE WORK Future work: We plan to address the optimization of the afternoon traffic. We need to study the scalability of Yellow Swarm. We want to combine Yellow Swarm with other techniques such as traffic light cycle optimization. We want to study the Yellow Swarm under other mobility scenarios. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 18 / 19
  37. 37. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions FUTURE WORK Future work: We plan to address the optimization of the afternoon traffic. We need to study the scalability of Yellow Swarm. We want to combine Yellow Swarm with other techniques such as traffic light cycle optimization. We want to study the Yellow Swarm under other mobility scenarios. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 18 / 19
  38. 38. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions QUESTIONS Fine Tuning of Traffic in our Cities with Smart Panels: The Quito City Case Study http://neo.lcc.uma.es http://www.shinshu-u.ac.jp http://danielstolfi.com Acknowledgements: This research is partially funded by the Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports. University of Malaga. International Campus of Excellence Andalucia TECH. Rolando Armas gratefully acknowledges the support of National Secretariat of Higher Education, Science, Technology and Innovation of Ecuador. D. H. Stolfi, R. Armas, E. Alba, H. Aguirre & K. Tanaka Tuning of Traffic in our Cities with Smart Panels 19 / 19

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