Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel Case

2. SMART MOBILITY POLICIES WITH EVOLUTIONARY ALGORITHMS: THE ADAPTING INFO PANEL CASE 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 2015 Madrid, Spain July 2015 Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 1 / 19

3. Introduction Our Proposal Experimentation Conclusions and Future Work CONTENTS 1 INTRODUCTION 2 OUR PROPOSAL 3 EXPERIMENTATION 4 CONCLUSIONS AND FUTURE WORK Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 2 / 19

7. Introduction Our Proposal Experimentation Conclusions and Future Work Introduction INTRODUCTION Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 3 / 19

8. Introduction Our Proposal Experimentation Conclusions and Future Work Introduction INTRODUCTION Nowadays most of people are living or thinking about moving from the countryside to cities. . . As a result: 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 Smart Mobility Policies with EAs: Panels 3 / 19

13. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies Evolutionary Algorithm YELLOW SWARM Our proposal, called 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 Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 4 / 19

16. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies Evolutionary Algorithm YELLOW SWARM Yellow Swarm offers: A system that is cheap and easy to install Rerouting vehicles according to an optimal strategy Prevention of trafﬁc jams Reduction of travel times Less greenhouse gas emissions Reduction of fuel consumption It minimizes the drivers’ distractions Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 5 / 19

24. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies Evolutionary Algorithm YELLOW SWARM ARCHITECTURE Offline:The EA calculates the system configuration (time slots) Online:The LED panels suggest possible detours to drivers Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 6 / 19

27. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies 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. Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 7 / 19

33. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies Evolutionary Algorithm CASE STUDIES We have worked with maps imported from OpenStreetMap 1 Firstly, we have downloaded the map from OpenStreetMap 2 Secondly, we have cleaned the irrelevant elements by using JOSM 3 Thirdly, We have defined the vehicle flows (experts’ solution) by using DUAROUTER 4 Finally, we have imported the city model into SUMO by using NETCONVERT Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 8 / 19

38. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies Evolutionary Algorithm CHARACTERISTICS OF CASE STUDIES Malaga MalagaTT Madrid MadridTT Area (Km2) 10.7 10.3 Trafﬁc lights 515 942 LED panels 8 4 Vehicles 4500 4840 Routes 365 134 1641 574 These routes are called the experts’ solution from SUMO Analysis Time: 2 hours Scenarios: 8 training + 200 testing Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 9 / 19

41. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies Evolutionary Algorithm LOCALIZATION OF THE PANELS Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 10 / 19

42. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies Evolutionary Algorithm LOCALIZATION OF THE PANELS Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 10 / 19

43. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies 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 Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 11 / 19

44. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies 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 Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 11 / 19

45. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies Evolutionary Algorithm REPRESENTATION The solution vector contain the P pairs of values representing the time slots for the panels Time values are kept in the range of 30 – 300 seconds This is about 8.4 ∗ 1013 combinations (Malaga, P = 8) The evaluation of each conﬁguration lasts about 1 minute We need to use a metaheuristic in order to solve this problem Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 12 / 19

48. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies Evolutionary Algorithm EVALUATION FUNCTION F = α1(N − n) + α2 1 n n i=1 travel timei (1) N: Total number of vehicles n: Number of vehicles leaving the city during the analysis time α1 y α2: Normalize the ﬁtness value We are minimizing travel times, so the lower the better Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 13 / 19

49. Introduction Our Proposal Experimentation Conclusions and Future Work Yellow Swarm Architecture Case Studies Evolutionary Algorithm EVALUATION FUNCTION F = α1(N − n) + α2 1 n n i=1 travel timei (1) N: Total number of vehicles n: Number of vehicles leaving the city during the analysis time α1 y α2: Normalize the ﬁtness value We are minimizing travel times, so the lower the better Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 13 / 19

50. Introduction Our Proposal Experimentation Conclusions and Future Work Optimization Results Penetration Rate OPTIMIZATION PROCESS TABLE: Results of the optimization of both case studies when optimizing four scenarios Metrics Malaga Madrid Experts’ Yellow Swarm Improvement Experts’ Yellow Swarm Improvement Travel Time (s) 1903.2 1562.1 17,9% 1374.7 1318.5 4.1% CO (mg) 15744.6 13829.2 12.1% 12144.2 11705.8 3.6% CO2 (mg) 1418052.7 1332355.0 6.0% 1165631.8 1148906.4 1.4% HC (mg) 2360.3 2103.4 10.9% 1828.7 1779.4 2.7% PM (mg) 224.9 207.7 7.6% 172.4 171.4 0.6% NO (mg) 8904.6 8483.0 4.7% 7188.5 7158.3 0.4% Fuel (ml) 562.6 529.0 6.0% 463.1 456.5 1.4% Distance (m) 3451.3 3457.2 -0.2% 3096.3 3099.8 -0.1% The distances traveled are slightly longer as we are suggesting routes that are not part of the shortest path Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 14 / 19

51. Introduction Our Proposal Experimentation Conclusions and Future Work Optimization Results Penetration Rate OPTIMIZATION PROCESS TABLE: Results of the optimization of both case studies when optimizing four scenarios Metrics Malaga Madrid Experts’ Yellow Swarm Improvement Experts’ Yellow Swarm Improvement Travel Time (s) 1903.2 1562.1 17,9% 1374.7 1318.5 4.1% CO (mg) 15744.6 13829.2 12.1% 12144.2 11705.8 3.6% CO2 (mg) 1418052.7 1332355.0 6.0% 1165631.8 1148906.4 1.4% HC (mg) 2360.3 2103.4 10.9% 1828.7 1779.4 2.7% PM (mg) 224.9 207.7 7.6% 172.4 171.4 0.6% NO (mg) 8904.6 8483.0 4.7% 7188.5 7158.3 0.4% Fuel (ml) 562.6 529.0 6.0% 463.1 456.5 1.4% Distance (m) 3451.3 3457.2 -0.2% 3096.3 3099.8 -0.1% The distances traveled are slightly longer as we are suggesting routes that are not part of the shortest path Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 14 / 19

52. Introduction Our Proposal Experimentation Conclusions and Future Work Optimization Results Penetration Rate IMPROVEMENTS Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 15 / 19

53. Introduction Our Proposal Experimentation Conclusions and Future Work Optimization Results Penetration Rate PENETRATION RATE Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 16 / 19

54. Introduction Our Proposal Experimentation Conclusions and Future Work Optimization Results Penetration Rate PENETRATION RATE Malaga Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 16 / 19

55. Introduction Our Proposal Experimentation Conclusions and Future Work Optimization Results Penetration Rate PENETRATION RATE Malaga Madrid Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 16 / 19

56. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions CONCLUSIONS Conclusions By using Yellow Swarm we have reduced travel times, greenhouse gas emissions, and fuel consumption We have achieved average reductions up to 32% in travel times, 25% in gas emissions, and 16% in fuel consumption We have improved all the metrics, even when only 10% of vehicles are following the instructions of Yellow Swarm Although Madrid allowed us to include more vehicles in the study, it also was more difﬁcult to optimize Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 17 / 19

57. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions CONCLUSIONS Conclusions By using Yellow Swarm we have reduced travel times, greenhouse gas emissions, and fuel consumption We have achieved average reductions up to 32% in travel times, 25% in gas emissions, and 16% in fuel consumption We have improved all the metrics, even when only 10% of vehicles are following the instructions of Yellow Swarm Although Madrid allowed us to include more vehicles in the study, it also was more difﬁcult to optimize Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 17 / 19

58. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions FUTURE WORK Future work: We are testing different strategies to optimally place LED panels throughout the city We are also looking at possible complex operators for the EA which take into account deeper relationships existing between the panels We want to improve our results, especially in the harder scenarios, and extend our study to the entire city Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 18 / 19

59. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions FUTURE WORK Future work: We are testing different strategies to optimally place LED panels throughout the city We are also looking at possible complex operators for the EA which take into account deeper relationships existing between the panels We want to improve our results, especially in the harder scenarios, and extend our study to the entire city Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 18 / 19

60. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions QUESTIONS Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel Case Questions? http://neo.lcc.uma.es http://danielstolfi.com Acknowledgements: This research has been partially funded by project number 8.06/5.47.4142, Universidad de Málaga UMA/FEDER FC14-TIC36, Spanish MINECO project TIN2014-57341-R, project maxCT of the ”Programa Operativo FEDER de Andalucía 2014-2020“. 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. Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19

62. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions RESULTS TABLE: Improvements achieved in the average vehicles’ travel times, gas emissions, fuel consumption, and distance traveled in the four case studies. Travel Time CO CO2 HC PM NO Fuel Distance Malaga Average 50 Scenarios 13.4% 10.3% 5.0% 9.5% 7.6% 4.9% 4.9% -0.9% Best Scenario 18.4% 12.9% 7.4% 11.8% 10.6% 7.2% 7.4% -0.6% % Scenarios Improved 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 8.0% MalagaTT Average 50 Scenarios 22.2% 17.9% 9.8% 16.2% 13.1% 9.0% 9.6% -2.6% Best Scenario 32.3% 25.3% 16.5% 23.3% 22.9% 16.6% 16.4% -1.1% % Scenarios Improved 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 2.0% Madrid Average 50 Scenarios 2.1% 1.5% 0.8% 1.3% 1.1% 0.7% 0.8% -0.5% Best Scenario 8.1% 10.1% 3.2% 8.9% 3.7% 2.5% 3.2% 0.5% % Scenarios Improved 72.0% 66.0% 68.0% 68.0% 60.0% 62.0% 68.0% 34.0% MadridTT Average 50 Scenarios 2.3% 1.7% 0.8% 1.6% 1.4% 0.8% 0.8% -0.4% Best Scenario 9.1% 7.5% 3.8% 6.4% 3.9% 2.9% 3.8% -0.2% % Scenarios Improved 74.0% 70.0% 64.0% 70.0% 68.0% 68.0% 64.0% 16.0% Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19

63. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions SYSTEM SCALABILITY Malaga Madrid Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19

64. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions TRAFFIC DENSITY Malaga Madrid Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19

65. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions SCENARIOS OPTIMIZED Malaga Madrid Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19

66. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions RECOMBINATION OPERATOR Uniform Crossover Daniel H. Stolﬁ & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19

67. Introduction Our Proposal Experimentation Conclusions and Future Work Conclusions Future Work Questions MUTATION OPERATOR We have developed a specific mutation operator: First, a panel is selected to be modified Second, one of the time values is increased τ1 seconds Finally, the other time value is decremented en τ2 seconds Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19

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