Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel Case
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Transcript of Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel Case
SMART MOBILITY POLICIES WITH
EVOLUTIONARY ALGORITHMS: THE ADAPTING
INFO PANEL CASE
Daniel H. [email protected]
Enrique [email protected]
Departamento de Lenguajes y Ciencias de la ComputaciónUniversity of Malaga
Genetic and Evolutionary Computation ConferenceGECCO 2015
Madrid, SpainJuly 2015
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 1 / 19
IntroductionOur Proposal
ExperimentationConclusions 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
IntroductionOur Proposal
ExperimentationConclusions 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
IntroductionOur Proposal
ExperimentationConclusions 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
IntroductionOur Proposal
ExperimentationConclusions 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Introduction
INTRODUCTION
Nowadays most of people are living or thinking about movingfrom 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Introduction
INTRODUCTION
Nowadays most of people are living or thinking about movingfrom 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Introduction
INTRODUCTION
Nowadays most of people are living or thinking about movingfrom 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Introduction
INTRODUCTION
Nowadays most of people are living or thinking about movingfrom 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Introduction
INTRODUCTION
Nowadays most of people are living or thinking about movingfrom 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Introduction
INTRODUCTION
Nowadays most of people are living or thinking about movingfrom 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Our proposal, called Yellow Swarm, consists of:
Several LED panelsI Installed in the cityI Suggest potential detours to drivers
Our Evolutionary AlgorithmI Evaluates the training scenariosI Calculates the configuration of the panels
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 4 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Our proposal, called Yellow Swarm, consists of:
Several LED panelsI Installed in the cityI Suggest potential detours to drivers
Our Evolutionary AlgorithmI Evaluates the training scenariosI Calculates the configuration of the panels
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 4 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Our proposal, called Yellow Swarm, consists of:
Several LED panelsI Installed in the cityI Suggest potential detours to drivers
Our Evolutionary AlgorithmI Evaluates the training scenariosI Calculates the configuration of the panels
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 4 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Yellow Swarm offers:A system that is cheap and easy to installRerouting vehicles according to an optimal strategyPrevention of traffic jamsReduction of travel timesLess greenhouse gas emissionsReduction of fuel consumptionIt minimizes the drivers’ distractions
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 5 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Yellow Swarm offers:A system that is cheap and easy to installRerouting vehicles according to an optimal strategyPrevention of traffic jamsReduction of travel timesLess greenhouse gas emissionsReduction of fuel consumptionIt minimizes the drivers’ distractions
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 5 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Yellow Swarm offers:A system that is cheap and easy to installRerouting vehicles according to an optimal strategyPrevention of traffic jamsReduction of travel timesLess greenhouse gas emissionsReduction of fuel consumptionIt minimizes the drivers’ distractions
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 5 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Yellow Swarm offers:A system that is cheap and easy to installRerouting vehicles according to an optimal strategyPrevention of traffic jamsReduction of travel timesLess greenhouse gas emissionsReduction of fuel consumptionIt minimizes the drivers’ distractions
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 5 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Yellow Swarm offers:A system that is cheap and easy to installRerouting vehicles according to an optimal strategyPrevention of traffic jamsReduction of travel timesLess greenhouse gas emissionsReduction of fuel consumptionIt minimizes the drivers’ distractions
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 5 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Yellow Swarm offers:A system that is cheap and easy to installRerouting vehicles according to an optimal strategyPrevention of traffic jamsReduction of travel timesLess greenhouse gas emissionsReduction of fuel consumptionIt minimizes the drivers’ distractions
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 5 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Yellow Swarm offers:A system that is cheap and easy to installRerouting vehicles according to an optimal strategyPrevention of traffic jamsReduction of travel timesLess greenhouse gas emissionsReduction of fuel consumptionIt minimizes the drivers’ distractions
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 5 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
YELLOW SWARM
Yellow Swarm offers:A system that is cheap and easy to installRerouting vehicles according to an optimal strategyPrevention of traffic jamsReduction of travel timesLess greenhouse gas emissionsReduction of fuel consumptionIt minimizes the drivers’ distractions
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 5 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
LED PANELS
They are made of LEDs (Light-Emitting Diode)They show the different detour options.
I Straight onI Turn leftI Turn right
Each option is visible during a time slotcalculated by the Evolutionary Algorithm.
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 7 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
LED PANELS
They are made of LEDs (Light-Emitting Diode)They show the different detour options.
I Straight onI Turn leftI Turn right
Each option is visible during a time slotcalculated by the Evolutionary Algorithm.
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 7 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
LED PANELS
They are made of LEDs (Light-Emitting Diode)They show the different detour options.
I Straight onI Turn leftI Turn right
Each option is visible during a time slotcalculated by the Evolutionary Algorithm.
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 7 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
LED PANELS
They are made of LEDs (Light-Emitting Diode)They show the different detour options.
I Straight onI Turn leftI Turn right
Each option is visible during a time slotcalculated by the Evolutionary Algorithm.
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 7 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
LED PANELS
They are made of LEDs (Light-Emitting Diode)They show the different detour options.
I Straight onI Turn leftI Turn right
Each option is visible during a time slotcalculated by the Evolutionary Algorithm.
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 7 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
LED PANELS
They are made of LEDs (Light-Emitting Diode)They show the different detour options.
I Straight onI Turn leftI Turn right
Each option is visible during a time slotcalculated by the Evolutionary Algorithm.
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 7 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
CASE STUDIES
We have worked with maps imported from OpenStreetMap1 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 usingDUAROUTER
4 Finally, we have imported the city model into SUMO by usingNETCONVERT
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 8 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
CASE STUDIES
We have worked with maps imported from OpenStreetMap1 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 usingDUAROUTER
4 Finally, we have imported the city model into SUMO by usingNETCONVERT
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 8 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
CASE STUDIES
We have worked with maps imported from OpenStreetMap1 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 usingDUAROUTER
4 Finally, we have imported the city model into SUMO by usingNETCONVERT
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 8 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
CASE STUDIES
We have worked with maps imported from OpenStreetMap1 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 usingDUAROUTER
4 Finally, we have imported the city model into SUMO by usingNETCONVERT
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 8 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
CASE STUDIES
We have worked with maps imported from OpenStreetMap1 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 usingDUAROUTER
4 Finally, we have imported the city model into SUMO by usingNETCONVERT
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 8 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
CHARACTERISTICS OF CASE STUDIES
Malaga MalagaTT Madrid MadridTT
Area (Km2) 10.7 10.3Traffic lights 515 942LED panels 8 4Vehicles 4500 4840Routes 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
CHARACTERISTICS OF CASE STUDIES
Malaga MalagaTT Madrid MadridTT
Area (Km2) 10.7 10.3Traffic lights 515 942LED panels 8 4Vehicles 4500 4840Routes 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
CHARACTERISTICS OF CASE STUDIES
Malaga MalagaTT Madrid MadridTT
Area (Km2) 10.7 10.3Traffic lights 515 942LED panels 8 4Vehicles 4500 4840Routes 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
LOCALIZATION OF THE PANELS
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 10 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
LOCALIZATION OF THE PANELS
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 10 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
EVOLUTIONARY ALGORITHM
(10+2)-EA
It evaluates the individuals by using the trafficsimulator SUMO
The decisions (detours) made by the drivers areimplemented by using TraCI
As a result it produces the configuration of thepanels
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 11 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
EVOLUTIONARY ALGORITHM
(10+2)-EA
It evaluates the individuals by using the trafficsimulator SUMO
The decisions (detours) made by the drivers areimplemented by using TraCI
As a result it produces the configuration of thepanels
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 11 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
REPRESENTATION
The solution vector contain the P pairs of values representingthe 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
REPRESENTATION
The solution vector contain the P pairs of values representingthe 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
REPRESENTATION
The solution vector contain the P pairs of values representingthe 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
EVALUATION FUNCTION
F = α1(N − n) + α21n
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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
Yellow SwarmArchitectureCase StudiesEvolutionary Algorithm
EVALUATION FUNCTION
F = α1(N − n) + α21n
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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
OptimizationResultsPenetration Rate
OPTIMIZATION PROCESS
TABLE: Results of the optimization of both case studies when optimizing four scenarios
MetricsMalaga Madrid
Experts’ Yellow Swarm Improvement Experts’ Yellow Swarm ImprovementTravel 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 longeras 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
OptimizationResultsPenetration Rate
OPTIMIZATION PROCESS
TABLE: Results of the optimization of both case studies when optimizing four scenarios
MetricsMalaga Madrid
Experts’ Yellow Swarm Improvement Experts’ Yellow Swarm ImprovementTravel 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 longeras 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
OptimizationResultsPenetration Rate
IMPROVEMENTS
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 15 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
OptimizationResultsPenetration Rate
PENETRATION RATE
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 16 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
OptimizationResultsPenetration Rate
PENETRATION RATE
Malaga
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 16 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
OptimizationResultsPenetration Rate
PENETRATION RATE
Malaga Madrid
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 16 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
CONCLUSIONS
ConclusionsBy using Yellow Swarm we have reduced travel times, greenhouse gasemissions, and fuel consumption
We have achieved average reductions up to 32% in travel times, 25% ingas emissions, and 16% in fuel consumption
We have improved all the metrics, even when only 10% of vehicles arefollowing the instructions of Yellow Swarm
Although Madrid allowed us to include more vehicles in the study, it alsowas more difficult to optimize
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 17 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
CONCLUSIONS
ConclusionsBy using Yellow Swarm we have reduced travel times, greenhouse gasemissions, and fuel consumption
We have achieved average reductions up to 32% in travel times, 25% ingas emissions, and 16% in fuel consumption
We have improved all the metrics, even when only 10% of vehicles arefollowing the instructions of Yellow Swarm
Although Madrid allowed us to include more vehicles in the study, it alsowas more difficult to optimize
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 17 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
FUTURE WORK
Future work:We are testing different strategies to optimally place LED panelsthroughout the city
We are also looking at possible complex operators for the EA whichtake into account deeper relationships existing between the panels
We want to improve our results, especially in the harder scenarios, andextend our study to the entire city
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 18 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
FUTURE WORK
Future work:We are testing different strategies to optimally place LED panelsthroughout the city
We are also looking at possible complex operators for the EA whichtake into account deeper relationships existing between the panels
We want to improve our results, especially in the harder scenarios, andextend our study to the entire city
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 18 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
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/FEDERFC14-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 ofMalaga. International Campus of Excellence Andalucia TECH.
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
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
MalagaAverage 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%
MadridAverage 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
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
SYSTEM SCALABILITY
Malaga Madrid
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
TRAFFIC DENSITY
Malaga Madrid
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
SCENARIOS OPTIMIZED
Malaga Madrid
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
RECOMBINATION OPERATOR
Uniform Crossover
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19
IntroductionOur Proposal
ExperimentationConclusions and Future Work
ConclusionsFuture WorkQuestions
MUTATION OPERATOR
We have developed a specific mutation operator:
First, a panel is selected to be modifiedSecond, one of the time values is increased τ1 secondsFinally, the other time value is decremented en τ2 seconds
Daniel H. Stolfi & Enrique Alba Smart Mobility Policies with EAs: Panels 19 / 19