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Computing New Optimized Routes for GPS Navigators Using Evolutionary Algorithms

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GPS navigators are now present in most vehicles and smartphones. The usual goal of these navigators is to take the user in less time or distance to a destination. However, the global use of navigators in a given city could lead to traffic jams as they have a highly biased preference for some streets. From a general point of view, spreading the traffic throughout the city could be a way of preventing jams and making a better use of public resources. We propose a way of calculating alternative routes to be assigned by these devices in order to foster a better use of the streets. Our experimentation involves maps from OpenStreetMap, real road traffic, and the microsimulator SUMO. We contribute to reducing travel times, greenhouse gas emissions, and fuel consumption. To analyze the sociological aspect of any innovation, we analyze the penetration (acceptance) rate which shows that our proposal is competitive even when just 10% of the drivers are using it.

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Computing New Optimized Routes for GPS Navigators Using Evolutionary Algorithms

  1. 1. COMPUTING NEW OPTIMIZED ROUTES FOR GPS NAVIGATORS USING EVOLUTIONARY ALGORITHMS Daniel H. Stolfi dhstolfi@lcc.uma.es Enrique Alba eat@lcc.uma.es Departamento de Lenguajes y Ciencias de la Computación University of Malaga Genetic and Evolutionary Computation Conference GECCO 2017 Berlin, Germany July 2017
  2. 2. CONTENTS 1 INTRODUCTION 2 OUR PROPOSAL 3 RESULTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 1 / 17
  3. 3. CONTENTS 1 INTRODUCTION 2 OUR PROPOSAL 3 RESULTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 1 / 17
  4. 4. CONTENTS 1 INTRODUCTION 2 OUR PROPOSAL 3 RESULTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 1 / 17
  5. 5. CONTENTS 1 INTRODUCTION 2 OUR PROPOSAL 3 RESULTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 1 / 17
  6. 6. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators INTRODUCTION Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
  7. 7. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets The number of traffic jams is rising Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
  8. 8. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets The number of traffic jams is rising Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
  9. 9. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets The number of traffic jams is rising Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
  10. 10. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets The number of traffic jams is rising Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
  11. 11. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets The number of traffic jams is rising Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
  12. 12. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators GPS NAVIGATORS Fixed routes Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
  13. 13. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators GPS NAVIGATORS Fixed routes Shortest vs. fastest routes Avenues, main streets, . . . Everyone is taking the same (fast?) route Some of them use traffic data Internet? Expensive? Developing world? Traffic jams Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
  14. 14. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators GPS NAVIGATORS Fixed routes Shortest vs. fastest routes Avenues, main streets, . . . Everyone is taking the same (fast?) route Some of them use traffic data Internet? Expensive? Developing world? Traffic jams Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
  15. 15. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators GPS NAVIGATORS Fixed routes Shortest vs. fastest routes Avenues, main streets, . . . Everyone is taking the same (fast?) route Some of them use traffic data Internet? Expensive? Developing world? Traffic jams Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
  16. 16. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators GPS NAVIGATORS Fixed routes Shortest vs. fastest routes Avenues, main streets, . . . Everyone is taking the same (fast?) route Some of them use traffic data Internet? Expensive? Developing world? Traffic jams Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
  17. 17. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators GPS NAVIGATORS Fixed routes Shortest vs. fastest routes Avenues, main streets, . . . Everyone is taking the same (fast?) route Some of them use traffic data Internet? Expensive? Developing world? Traffic jams Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
  18. 18. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators GPS NAVIGATORS Fixed routes Shortest vs. fastest routes Avenues, main streets, . . . Everyone is taking the same (fast?) route Some of them use traffic data Internet? Expensive? Developing world? Traffic jams Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
  19. 19. Introduction Our Proposal Results Conclusions & Future Work Road Traffic GPS Navigators GPS NAVIGATORS Fixed routes Shortest vs. fastest routes Avenues, main streets, . . . Everyone is taking the same (fast?) route Some of them use traffic data Internet? Expensive? Developing world? Traffic jams Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
  20. 20. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm ALTERNATIVE ROUTES FOR GPS NAVIGATORS Alternative routes to prevent traffic jams For vehicles driving throughout the city Reduce travel times Reduce greenhouse gas emissions Reduce fuel consumption Save money Improve health and quality of life Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 4 / 17
  21. 21. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm ALTERNATIVE ROUTES FOR GPS NAVIGATORS Alternative routes to prevent traffic jams For vehicles driving throughout the city Reduce travel times Reduce greenhouse gas emissions Reduce fuel consumption Save money Improve health and quality of life Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 4 / 17
  22. 22. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm ALTERNATIVE ROUTES FOR GPS NAVIGATORS Alternative routes to prevent traffic jams For vehicles driving throughout the city Reduce travel times Reduce greenhouse gas emissions Reduce fuel consumption Save money Improve health and quality of life Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 4 / 17
  23. 23. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm ALTERNATIVE ROUTES FOR GPS NAVIGATORS Alternative routes to prevent traffic jams For vehicles driving throughout the city Reduce travel times Reduce greenhouse gas emissions Reduce fuel consumption Save money Improve health and quality of life Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 4 / 17
  24. 24. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm CALCULATING ALTERNATIVE ROUTES Based on the Dynamic User Equilibrium (DUE) Different probabilities for each route Strategies: Dijkstra DUE.r DUE.rp DUE.ea Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
  25. 25. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm CALCULATING ALTERNATIVE ROUTES Based on the Dynamic User Equilibrium (DUE) Different probabilities for each route Strategies: Dijkstra DUE.r DUE.rp DUE.ea Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
  26. 26. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm CALCULATING ALTERNATIVE ROUTES Based on the Dynamic User Equilibrium (DUE) Different probabilities for each route Strategies: Dijkstra DUE.r DUE.rp DUE.ea Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
  27. 27. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm CALCULATING ALTERNATIVE ROUTES Based on the Dynamic User Equilibrium (DUE) Different probabilities for each route Strategies: Dijkstra DUE.r DUE.rp DUE.ea Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
  28. 28. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm CALCULATING ALTERNATIVE ROUTES Based on the Dynamic User Equilibrium (DUE) Different probabilities for each route Strategies: Dijkstra DUE.r DUE.rp DUE.ea Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
  29. 29. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm CALCULATING ALTERNATIVE ROUTES Based on the Dynamic User Equilibrium (DUE) Different probabilities for each route Strategies: Dijkstra DUE.r DUE.rp DUE.ea Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
  30. 30. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm MALAGA CITY CENTER Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 6 / 17
  31. 31. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm MALAGA CITY CENTER Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 6 / 17
  32. 32. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm MALAGA CITY CENTER OpenStreetMap Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 6 / 17
  33. 33. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm MALAGA CITY CENTER OpenStreetMap SUMO Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 6 / 17
  34. 34. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm BUILDING MALAGA 1 Download the map from OpenStreetMap 2 Clean the irrelevant elements using JOSM 3 Import the city model using NETCONVERT 4 Define its routes using DUAROUTER and the Flow Generator Algorithm (FGA) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 7 / 17
  35. 35. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm BUILDING MALAGA 1 Download the map from OpenStreetMap 2 Clean the irrelevant elements using JOSM 3 Import the city model using NETCONVERT 4 Define its routes using DUAROUTER and the Flow Generator Algorithm (FGA) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 7 / 17
  36. 36. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm BUILDING MALAGA 1 Download the map from OpenStreetMap 2 Clean the irrelevant elements using JOSM 3 Import the city model using NETCONVERT 4 Define its routes using DUAROUTER and the Flow Generator Algorithm (FGA) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 7 / 17
  37. 37. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm BUILDING MALAGA 1 Download the map from OpenStreetMap 2 Clean the irrelevant elements using JOSM 3 Import the city model using NETCONVERT 4 Define its routes using DUAROUTER and the Flow Generator Algorithm (FGA) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 7 / 17
  38. 38. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm BUILDING MALAGA 1 Download the map from OpenStreetMap 2 Clean the irrelevant elements using JOSM 3 Import the city model using NETCONVERT 4 Define its routes using DUAROUTER and the Flow Generator Algorithm (FGA) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 7 / 17
  39. 39. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm CHARACTERISTICS OF THE CASE STUDY 3 km2 58 traffic lights 107 routes More than 4800 vehicles per hour Flows calculated using the Flow Generator Algorithm1 12 traffic measurement points Working days, Saturdays, and Sundays 1 Daniel H Stolfi and Enrique Alba. “An Evolutionary Algorithm to Generate Real Urban Traffic Flows”. In: Advances in Artificial Intelligence. Vol. 9422. Lecture Notes in Computer Science. Springer International Publishing, 2015, pp. 332–343. Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 8 / 17
  40. 40. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm CHARACTERISTICS OF THE CASE STUDY 3 km2 58 traffic lights 107 routes More than 4800 vehicles per hour Flows calculated using the Flow Generator Algorithm1 12 traffic measurement points Working days, Saturdays, and Sundays 1 Daniel H Stolfi and Enrique Alba. “An Evolutionary Algorithm to Generate Real Urban Traffic Flows”. In: Advances in Artificial Intelligence. Vol. 9422. Lecture Notes in Computer Science. Springer International Publishing, 2015, pp. 332–343. Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 8 / 17
  41. 41. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm CHARACTERISTICS OF THE CASE STUDY 3 km2 58 traffic lights 107 routes More than 4800 vehicles per hour Flows calculated using the Flow Generator Algorithm1 12 traffic measurement points Working days, Saturdays, and Sundays 1 Daniel H Stolfi and Enrique Alba. “An Evolutionary Algorithm to Generate Real Urban Traffic Flows”. In: Advances in Artificial Intelligence. Vol. 9422. Lecture Notes in Computer Science. Springer International Publishing, 2015, pp. 332–343. Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 8 / 17
  42. 42. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm EVOLUTIONARY ALGORITHM (10+2)-EA Individuals are evaluated using SUMO Detours are implemented by using TraCI Calculates the probability of each route (DUE.ea) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 9 / 17
  43. 43. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm EVOLUTIONARY ALGORITHM (10+2)-EA Individuals are evaluated using SUMO Detours are implemented by using TraCI Calculates the probability of each route (DUE.ea) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 9 / 17
  44. 44. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm REPRESENTATION Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 10 / 17
  45. 45. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm REPRESENTATION 121 probability values Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 10 / 17
  46. 46. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm REPRESENTATION 121 probability values Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 10 / 17
  47. 47. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm REPRESENTATION 121 probability values Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 10 / 17
  48. 48. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm EVALUATION Fitness Function Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 11 / 17
  49. 49. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm EVALUATION Fitness Function Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 11 / 17
  50. 50. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm EVALUATION Fitness Function F = α N N i=1 travel timei We are minimizing travel times, so the lower the better Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 11 / 17
  51. 51. Introduction Our Proposal Results Conclusions & Future Work Alternative Routes Case Study: Malaga Evolutionary Algorithm EVALUATION Fitness Function F = α N N i=1 travel timei We are minimizing travel times, so the lower the better Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 11 / 17
  52. 52. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate RESULTS TABLE: Results and statistical analysis. Scenario Strategy # Veh. TT CO CO2 HC PM NO Fuel Dist. Friedman Wilcoxon (s) (mg) (mg) (mg) (mg) (mg) (l) (m) Rank p-value Working Days Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00 Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00 DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01 DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09 DUE.ea 4883 288.5 1374.9 299418.3 77.4 19.6 518.1 119.4 1922.3 2.90 — Saturdays Malaga 3961 344.1 1547.7 323919.4 87.1 20.9 557.0 129.1 2004.9 3.18 0.00 Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00 DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00 DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00 DUE.ea 3961 291.7 1363.9 305130.4 77.9 20.0 528.1 121.7 2011.0 2.84 — Sundays Malaga 3679 279.6 1292.4 291131.9 74.0 19.1 503.9 116.1 1933.3 3.09 0.00 Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05 DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02 DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03 DUE.ea 3679 271.1 1232.5 284807.0 71.6 18.6 492.9 113.5 1940.3 2.92 — Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 12 / 17
  53. 53. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate RESULTS TABLE: Results and statistical analysis. Scenario Strategy # Veh. TT CO CO2 HC PM NO Fuel Dist. Friedman Wilcoxon (s) (mg) (mg) (mg) (mg) (mg) (l) (m) Rank p-value Working Days Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00 Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00 DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01 DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09 DUE.ea 4883 288.5 1374.9 299418.3 77.4 19.6 518.1 119.4 1922.3 2.90 — Saturdays Malaga 3961 344.1 1547.7 323919.4 87.1 20.9 557.0 129.1 2004.9 3.18 0.00 Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00 DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00 DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00 DUE.ea 3961 291.7 1363.9 305130.4 77.9 20.0 528.1 121.7 2011.0 2.84 — Sundays Malaga 3679 279.6 1292.4 291131.9 74.0 19.1 503.9 116.1 1933.3 3.09 0.00 Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05 DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02 DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03 DUE.ea 3679 271.1 1232.5 284807.0 71.6 18.6 492.9 113.5 1940.3 2.92 — DUE.ea Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 12 / 17
  54. 54. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate RESULTS TABLE: Results and statistical analysis. Scenario Strategy # Veh. TT CO CO2 HC PM NO Fuel Dist. Friedman Wilcoxon (s) (mg) (mg) (mg) (mg) (mg) (l) (m) Rank p-value Working Days Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00 Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00 DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01 DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09 DUE.ea 4883 288.5 1374.9 299418.3 77.4 19.6 518.1 119.4 1922.3 2.90 — Saturdays Malaga 3961 344.1 1547.7 323919.4 87.1 20.9 557.0 129.1 2004.9 3.18 0.00 Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00 DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00 DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00 DUE.ea 3961 291.7 1363.9 305130.4 77.9 20.0 528.1 121.7 2011.0 2.84 — Sundays Malaga 3679 279.6 1292.4 291131.9 74.0 19.1 503.9 116.1 1933.3 3.09 0.00 Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05 DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02 DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03 DUE.ea 3679 271.1 1232.5 284807.0 71.6 18.6 492.9 113.5 1940.3 2.92 — Shorter Travel Times Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 12 / 17
  55. 55. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate RESULTS TABLE: Results and statistical analysis. Scenario Strategy # Veh. TT CO CO2 HC PM NO Fuel Dist. Friedman Wilcoxon (s) (mg) (mg) (mg) (mg) (mg) (l) (m) Rank p-value Working Days Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00 Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00 DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01 DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09 DUE.ea 4883 288.5 1374.9 299418.3 77.4 19.6 518.1 119.4 1922.3 2.90 — Saturdays Malaga 3961 344.1 1547.7 323919.4 87.1 20.9 557.0 129.1 2004.9 3.18 0.00 Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00 DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00 DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00 DUE.ea 3961 291.7 1363.9 305130.4 77.9 20.0 528.1 121.7 2011.0 2.84 — Sundays Malaga 3679 279.6 1292.4 291131.9 74.0 19.1 503.9 116.1 1933.3 3.09 0.00 Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05 DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02 DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03 DUE.ea 3679 271.1 1232.5 284807.0 71.6 18.6 492.9 113.5 1940.3 2.92 — Reduction of Greenhouse Gas Emissions Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 12 / 17
  56. 56. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate RESULTS TABLE: Results and statistical analysis. Scenario Strategy # Veh. TT CO CO2 HC PM NO Fuel Dist. Friedman Wilcoxon (s) (mg) (mg) (mg) (mg) (mg) (l) (m) Rank p-value Working Days Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00 Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00 DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01 DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09 DUE.ea 4883 288.5 1374.9 299418.3 77.4 19.6 518.1 119.4 1922.3 2.90 — Saturdays Malaga 3961 344.1 1547.7 323919.4 87.1 20.9 557.0 129.1 2004.9 3.18 0.00 Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00 DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00 DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00 DUE.ea 3961 291.7 1363.9 305130.4 77.9 20.0 528.1 121.7 2011.0 2.84 — Sundays Malaga 3679 279.6 1292.4 291131.9 74.0 19.1 503.9 116.1 1933.3 3.09 0.00 Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05 DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02 DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03 DUE.ea 3679 271.1 1232.5 284807.0 71.6 18.6 492.9 113.5 1940.3 2.92 — Reduction of Fuel Consumption Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 12 / 17
  57. 57. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate RESULTS TABLE: Results and statistical analysis. Scenario Strategy # Veh. TT CO CO2 HC PM NO Fuel Dist. Friedman Wilcoxon (s) (mg) (mg) (mg) (mg) (mg) (l) (m) Rank p-value Working Days Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00 Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00 DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01 DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09 DUE.ea 4883 288.5 1374.9 299418.3 77.4 19.6 518.1 119.4 1922.3 2.90 — Saturdays Malaga 3961 344.1 1547.7 323919.4 87.1 20.9 557.0 129.1 2004.9 3.18 0.00 Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00 DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00 DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00 DUE.ea 3961 291.7 1363.9 305130.4 77.9 20.0 528.1 121.7 2011.0 2.84 — Sundays Malaga 3679 279.6 1292.4 291131.9 74.0 19.1 503.9 116.1 1933.3 3.09 0.00 Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05 DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02 DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03 DUE.ea 3679 271.1 1232.5 284807.0 71.6 18.6 492.9 113.5 1940.3 2.92 — Results are statistically significant Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 12 / 17
  58. 58. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate PREVENTING TRAFFIC JAMS Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 13 / 17
  59. 59. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate BETTER ROUTES Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 14 / 17
  60. 60. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate PENETRATION RATE What if not all drivers are using our proposal? Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 15 / 17
  61. 61. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate PENETRATION RATE What if not all drivers are using our proposal? Penetration Rate Study Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 15 / 17
  62. 62. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate PENETRATION RATE What if not all drivers are using our proposal? Penetration Rate Study Working Days Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 15 / 17
  63. 63. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate PENETRATION RATE What if not all drivers are using our proposal? Penetration Rate Study Working Days Saturdays Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 15 / 17
  64. 64. Introduction Our Proposal Results Conclusions & Future Work Improvements Examples Penetration Rate PENETRATION RATE What if not all drivers are using our proposal? Penetration Rate Study Working Days Saturdays Sundays Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 15 / 17
  65. 65. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work CONCLUSIONS Alternative routes for GPS navigators Based on the Dynamic User Equilibrium Suggested according to probabilities (DUE.ea) Scenarios based on real road traffic data (FGA) DUE.ea achieved: Shorter travel times (up to 18%) Less greenhouse gas emissions (up to 14%) Fuel saving (up to 7.5%) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 16 / 17
  66. 66. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work CONCLUSIONS Alternative routes for GPS navigators Based on the Dynamic User Equilibrium Suggested according to probabilities (DUE.ea) Scenarios based on real road traffic data (FGA) DUE.ea achieved: Shorter travel times (up to 18%) Less greenhouse gas emissions (up to 14%) Fuel saving (up to 7.5%) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 16 / 17
  67. 67. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work CONCLUSIONS Alternative routes for GPS navigators Based on the Dynamic User Equilibrium Suggested according to probabilities (DUE.ea) Scenarios based on real road traffic data (FGA) DUE.ea achieved: Shorter travel times (up to 18%) Less greenhouse gas emissions (up to 14%) Fuel saving (up to 7.5%) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 16 / 17
  68. 68. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work CONCLUSIONS Alternative routes for GPS navigators Based on the Dynamic User Equilibrium Suggested according to probabilities (DUE.ea) Scenarios based on real road traffic data (FGA) DUE.ea achieved: Shorter travel times (up to 18%) Less greenhouse gas emissions (up to 14%) Fuel saving (up to 7.5%) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 16 / 17
  69. 69. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work CONCLUSIONS Alternative routes for GPS navigators Based on the Dynamic User Equilibrium Suggested according to probabilities (DUE.ea) Scenarios based on real road traffic data (FGA) DUE.ea achieved: Shorter travel times (up to 18%) Less greenhouse gas emissions (up to 14%) Fuel saving (up to 7.5%) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 16 / 17
  70. 70. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work CONCLUSIONS Alternative routes for GPS navigators Based on the Dynamic User Equilibrium Suggested according to probabilities (DUE.ea) Scenarios based on real road traffic data (FGA) DUE.ea achieved: Shorter travel times (up to 18%) Less greenhouse gas emissions (up to 14%) Fuel saving (up to 7.5%) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 16 / 17
  71. 71. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work CONCLUSIONS Alternative routes for GPS navigators Based on the Dynamic User Equilibrium Suggested according to probabilities (DUE.ea) Scenarios based on real road traffic data (FGA) DUE.ea achieved: Shorter travel times (up to 18%) Less greenhouse gas emissions (up to 14%) Fuel saving (up to 7.5%) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 16 / 17
  72. 72. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work CONCLUSIONS Alternative routes for GPS navigators Based on the Dynamic User Equilibrium Suggested according to probabilities (DUE.ea) Scenarios based on real road traffic data (FGA) DUE.ea achieved: Shorter travel times (up to 18%) Less greenhouse gas emissions (up to 14%) Fuel saving (up to 7.5%) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 16 / 17
  73. 73. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work CONCLUSIONS Alternative routes for GPS navigators Based on the Dynamic User Equilibrium Suggested according to probabilities (DUE.ea) Scenarios based on real road traffic data (FGA) DUE.ea achieved: Shorter travel times (up to 18%) Less greenhouse gas emissions (up to 14%) Fuel saving (up to 7.5%) Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 16 / 17
  74. 74. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work FUTURE WORK Extend our analysis to other/bigger areas Optimization of the entire city by districts/neighborhoods Address the simulation of thousands of vehicles Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 17 / 17
  75. 75. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work FUTURE WORK Extend our analysis to other/bigger areas Optimization of the entire city by districts/neighborhoods Address the simulation of thousands of vehicles Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 17 / 17
  76. 76. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work FUTURE WORK Extend our analysis to other/bigger areas Optimization of the entire city by districts/neighborhoods Address the simulation of thousands of vehicles Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 17 / 17
  77. 77. Introduction Our Proposal Results Conclusions & Future Work Conclusions Future Work FUTURE WORK Extend our analysis to other/bigger areas Optimization of the entire city by districts/neighborhoods Address the simulation of thousands of vehicles Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 17 / 17
  78. 78. QUESTIONS Computing New Optimized Routes for GPS Navigators Using Evolutionary Algorithms Questions? Daniel H. Stolfi Enrique Alba dhstolfi@lcc.uma.es eat@lcc.uma.es http://danielstolfi.com http://neo.lcc.uma.es Acknowledgements: This research has been partially funded by Spanish MINECO project TIN2014-57341-R (moveON). 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.

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