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COMPUTING NEW OPTIMIZED ROUTES
FOR GPS NAVIGATORS
USING EVOLUTIONARY ALGORITHMS
Daniel H. [email protected]
Enrique [email protected]
Departamento de Lenguajes y Ciencias de la ComputaciónUniversity of Malaga
Genetic and Evolutionary Computation ConferenceGECCO 2017
Berlin, GermanyJuly 2017
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
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
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
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
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streetsThe number of traffic jams is risingTons of greenhouse gases are emitted to theatmosphereThe citizens’ quality of life is decreasing
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streetsThe number of traffic jams is risingTons of greenhouse gases are emitted to theatmosphereThe citizens’ quality of life is decreasing
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streetsThe number of traffic jams is risingTons of greenhouse gases are emitted to theatmosphereThe citizens’ quality of life is decreasing
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streetsThe number of traffic jams is risingTons of greenhouse gases are emitted to theatmosphereThe citizens’ quality of life is decreasing
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streetsThe number of traffic jams is risingTons of greenhouse gases are emitted to theatmosphereThe citizens’ quality of life is decreasing
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streetsThe number of traffic jams is risingTons of greenhouse gases are emitted to theatmosphereThe citizens’ quality of life is decreasing
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 2 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
GPS NAVIGATORS
Fixed routesShortest vs. fastest routesAvenues, main streets, . . .Everyone is taking the same (fast?) routeSome of them use traffic dataInternet? Expensive? Developing world?Traffic jams
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
GPS NAVIGATORS
Fixed routesShortest vs. fastest routesAvenues, main streets, . . .Everyone is taking the same (fast?) routeSome of them use traffic dataInternet? Expensive? Developing world?Traffic jams
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
GPS NAVIGATORS
Fixed routesShortest vs. fastest routesAvenues, main streets, . . .Everyone is taking the same (fast?) routeSome of them use traffic dataInternet? Expensive? Developing world?Traffic jams
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
GPS NAVIGATORS
Fixed routesShortest vs. fastest routesAvenues, main streets, . . .Everyone is taking the same (fast?) routeSome of them use traffic dataInternet? Expensive? Developing world?Traffic jams
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
GPS NAVIGATORS
Fixed routesShortest vs. fastest routesAvenues, main streets, . . .Everyone is taking the same (fast?) routeSome of them use traffic dataInternet? Expensive? Developing world?Traffic jams
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
GPS NAVIGATORS
Fixed routesShortest vs. fastest routesAvenues, main streets, . . .Everyone is taking the same (fast?) routeSome of them use traffic dataInternet? Expensive? Developing world?Traffic jams
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
GPS NAVIGATORS
Fixed routesShortest vs. fastest routesAvenues, main streets, . . .Everyone is taking the same (fast?) routeSome of them use traffic dataInternet? Expensive? Developing world?Traffic jams
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Road TrafficGPS Navigators
GPS NAVIGATORS
Fixed routesShortest vs. fastest routesAvenues, main streets, . . .Everyone is taking the same (fast?) routeSome of them use traffic dataInternet? Expensive? Developing world?Traffic jams
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 3 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
ALTERNATIVE ROUTES FOR GPS NAVIGATORS
Alternative routes to prevent traffic jamsFor vehicles driving throughout the cityReduce travel timesReduce greenhouse gas emissionsReduce fuel consumptionSave moneyImprove health andquality of life
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 4 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
ALTERNATIVE ROUTES FOR GPS NAVIGATORS
Alternative routes to prevent traffic jamsFor vehicles driving throughout the cityReduce travel timesReduce greenhouse gas emissionsReduce fuel consumptionSave moneyImprove health andquality of life
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 4 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
ALTERNATIVE ROUTES FOR GPS NAVIGATORS
Alternative routes to prevent traffic jamsFor vehicles driving throughout the cityReduce travel timesReduce greenhouse gas emissionsReduce fuel consumptionSave moneyImprove health andquality of life
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 4 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
ALTERNATIVE ROUTES FOR GPS NAVIGATORS
Alternative routes to prevent traffic jamsFor vehicles driving throughout the cityReduce travel timesReduce greenhouse gas emissionsReduce fuel consumptionSave moneyImprove health andquality of life
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 4 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
CALCULATING ALTERNATIVE ROUTES
Based on the Dynamic User Equilibrium (DUE)Different probabilities for each route
Strategies:I DijkstraI DUE.rI DUE.rpI DUE.ea
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
CALCULATING ALTERNATIVE ROUTES
Based on the Dynamic User Equilibrium (DUE)Different probabilities for each route
Strategies:I DijkstraI DUE.rI DUE.rpI DUE.ea
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
CALCULATING ALTERNATIVE ROUTES
Based on the Dynamic User Equilibrium (DUE)Different probabilities for each route
Strategies:I DijkstraI DUE.rI DUE.rpI DUE.ea
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
CALCULATING ALTERNATIVE ROUTES
Based on the Dynamic User Equilibrium (DUE)Different probabilities for each route
Strategies:I DijkstraI DUE.rI DUE.rpI DUE.ea
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
CALCULATING ALTERNATIVE ROUTES
Based on the Dynamic User Equilibrium (DUE)Different probabilities for each route
Strategies:I DijkstraI DUE.rI DUE.rpI DUE.ea
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
CALCULATING ALTERNATIVE ROUTES
Based on the Dynamic User Equilibrium (DUE)Different probabilities for each route
Strategies:I DijkstraI DUE.rI DUE.rpI DUE.ea
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 5 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
MALAGA CITY CENTER
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 6 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
MALAGA CITY CENTER
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 6 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
MALAGA CITY CENTER
OpenStreetMap
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 6 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
MALAGA CITY CENTER
OpenStreetMap SUMO
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 6 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
BUILDING MALAGA
1 Download the map from OpenStreetMap2 Clean the irrelevant elements using JOSM3 Import the city model using NETCONVERT4 Define its routes using DUAROUTER and the Flow
Generator Algorithm (FGA)
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 7 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
BUILDING MALAGA
1 Download the map from OpenStreetMap2 Clean the irrelevant elements using JOSM3 Import the city model using NETCONVERT4 Define its routes using DUAROUTER and the Flow
Generator Algorithm (FGA)
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 7 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
BUILDING MALAGA
1 Download the map from OpenStreetMap2 Clean the irrelevant elements using JOSM3 Import the city model using NETCONVERT4 Define its routes using DUAROUTER and the Flow
Generator Algorithm (FGA)
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 7 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
BUILDING MALAGA
1 Download the map from OpenStreetMap2 Clean the irrelevant elements using JOSM3 Import the city model using NETCONVERT4 Define its routes using DUAROUTER and the Flow
Generator Algorithm (FGA)
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 7 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
BUILDING MALAGA
1 Download the map from OpenStreetMap2 Clean the irrelevant elements using JOSM3 Import the city model using NETCONVERT4 Define its routes using DUAROUTER and the Flow
Generator Algorithm (FGA)
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 7 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
CHARACTERISTICS OF THE CASE STUDY
3 km2
58 traffic lights107 routesMore than 4800 vehicles per hourFlows calculated using the Flow Generator Algorithm1
12 traffic measurement pointsWorking days, Saturdays, and Sundays
1Daniel 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
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
CHARACTERISTICS OF THE CASE STUDY
3 km2
58 traffic lights107 routesMore than 4800 vehicles per hourFlows calculated using the Flow Generator Algorithm1
12 traffic measurement pointsWorking days, Saturdays, and Sundays
1Daniel 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
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
CHARACTERISTICS OF THE CASE STUDY
3 km2
58 traffic lights107 routesMore than 4800 vehicles per hourFlows calculated using the Flow Generator Algorithm1
12 traffic measurement pointsWorking days, Saturdays, and Sundays
1Daniel 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
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
EVOLUTIONARY ALGORITHM
(10+2)-EAIndividuals are evaluated using SUMODetours are implemented by using TraCICalculates the probability of each route (DUE.ea)
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 9 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
EVOLUTIONARY ALGORITHM
(10+2)-EAIndividuals are evaluated using SUMODetours are implemented by using TraCICalculates the probability of each route (DUE.ea)
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 9 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
REPRESENTATION
121 probability values
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 10 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
REPRESENTATION
121 probability values
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 10 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
REPRESENTATION
121 probability values
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 10 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
REPRESENTATION
121 probability values
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 10 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
EVALUATION
Fitness Function
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 11 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary Algorithm
EVALUATION
Fitness Function
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 11 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary 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
IntroductionOur Proposal
ResultsConclusions & Future Work
Alternative RoutesCase Study: MalagaEvolutionary 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration Rate
RESULTSTABLE: 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
WorkingDays
Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09DUE.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 0.00Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00DUE.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 0.00Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03DUE.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
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration Rate
RESULTSTABLE: 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
WorkingDays
Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09DUE.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 0.00Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00DUE.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 0.00Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03DUE.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
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration Rate
RESULTSTABLE: 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
WorkingDays
Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09DUE.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 0.00Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00DUE.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 0.00Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03DUE.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
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration Rate
RESULTSTABLE: 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
WorkingDays
Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09DUE.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 0.00Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00DUE.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 0.00Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03DUE.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
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration Rate
RESULTSTABLE: 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
WorkingDays
Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09DUE.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 0.00Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00DUE.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 0.00Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03DUE.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
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration Rate
RESULTSTABLE: 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
WorkingDays
Malaga 4883 351.6 1591.9 322840.7 88.6 20.7 554.1 128.7 1926.6 3.20 0.00Dijkstra 4883 297.3 1424.7 304507.5 79.6 19.9 526.7 121.4 1917.4 3.00 0.00DUE.r 4883 294.5 1401.5 302745.6 78.8 19.8 523.5 120.7 1924.6 2.98 0.01DUR.rp 4883 292.7 1390.5 301328.7 78.3 19.7 521.0 120.1 1924.1 2.93 0.09DUE.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 0.00Dijkstra 3961 324.7 1481.6 316290.6 83.6 20.5 545.3 126.1 2000.2 3.06 0.00DUE.r 3961 303.8 1399.7 309326.1 80.0 20.2 534.2 123.3 2008.0 2.95 0.00DUR.rp 3961 314.0 1421.3 310741.4 81.2 20.2 535.6 123.9 2003.5 2.97 0.00DUE.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 0.00Dijkstra 3679 275.7 1269.0 287901.5 72.9 18.9 498.4 114.8 1928.6 2.99 0.05DUE.r 3679 275.8 1261.6 288565.0 72.9 18.9 499.4 115.0 1945.0 3.04 0.02DUR.rp 3679 273.6 1248.3 286268.5 72.3 18.7 495.4 114.1 1937.9 2.96 0.03DUE.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
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration Rate
PREVENTING TRAFFIC JAMS
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 13 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration Rate
BETTER ROUTES
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 14 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration Rate
PENETRATION RATE
What if not all drivers areusing our proposal?
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 15 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ImprovementsExamplesPenetration 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
CONCLUSIONS
Alternative routes for GPS navigatorsBased on the Dynamic User EquilibriumSuggested 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
CONCLUSIONS
Alternative routes for GPS navigatorsBased on the Dynamic User EquilibriumSuggested 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
CONCLUSIONS
Alternative routes for GPS navigatorsBased on the Dynamic User EquilibriumSuggested 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
CONCLUSIONS
Alternative routes for GPS navigatorsBased on the Dynamic User EquilibriumSuggested 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
CONCLUSIONS
Alternative routes for GPS navigatorsBased on the Dynamic User EquilibriumSuggested 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
CONCLUSIONS
Alternative routes for GPS navigatorsBased on the Dynamic User EquilibriumSuggested 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
CONCLUSIONS
Alternative routes for GPS navigatorsBased on the Dynamic User EquilibriumSuggested 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
CONCLUSIONS
Alternative routes for GPS navigatorsBased on the Dynamic User EquilibriumSuggested 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
CONCLUSIONS
Alternative routes for GPS navigatorsBased on the Dynamic User EquilibriumSuggested 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
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
FUTURE WORK
Extend our analysis to other/bigger areasOptimization of the entire city by districts/neighborhoodsAddress the simulation of thousands of vehicles
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 17 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
FUTURE WORK
Extend our analysis to other/bigger areasOptimization of the entire city by districts/neighborhoodsAddress the simulation of thousands of vehicles
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 17 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
FUTURE WORK
Extend our analysis to other/bigger areasOptimization of the entire city by districts/neighborhoodsAddress the simulation of thousands of vehicles
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 17 / 17
IntroductionOur Proposal
ResultsConclusions & Future Work
ConclusionsFuture Work
FUTURE WORK
Extend our analysis to other/bigger areasOptimization of the entire city by districts/neighborhoodsAddress the simulation of thousands of vehicles
Daniel H. Stolfi & Enrique Alba Computing New Optimized Routes for GPS. . . 17 / 17
QUESTIONS
Computing New Optimized Routes for GPS NavigatorsUsing Evolutionary Algorithms
Questions?
Daniel H. Stolfi Enrique [email protected] [email protected]
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 issupported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports. University of Malaga. InternationalCampus of Excellence Andalucia TECH.
