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

CONTENTS

1 INTRODUCTION

2 OUR PROPOSAL

3 EXPERIMENTATION

CONTENTS

1 INTRODUCTION

2 OUR PROPOSAL

3 EXPERIMENTATION

CONTENTS

1 INTRODUCTION

2 OUR PROPOSAL

3 EXPERIMENTATION

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

Introduction

INTRODUCTION

As a result:

Introduction

INTRODUCTION

As a result:

Introduction

INTRODUCTION

As a result:

Introduction

INTRODUCTION

As a result:

Introduction

INTRODUCTION

As a result:

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

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

YELLOW SWARM

YELLOW SWARM ARCHITECTURE

Offline:The EA calculates the system configuration (time slots)

Online:The LED panels suggest possible detours to drivers

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.

LED PANELS

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

CASE STUDIES

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

LOCALIZATION OF THE PANELS

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

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

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

REPRESENTATION

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

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

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

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

IMPROVEMENTS

PENETRATION RATE

Malaga

PENETRATION RATE

Malaga Madrid

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

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

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

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

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.

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%

SYSTEM SCALABILITY

Malaga Madrid

TRAFFIC DENSITY

Malaga Madrid

SCENARIOS OPTIMIZED

Malaga Madrid

RECOMBINATION OPERATOR

Uniform Crossover

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

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

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