Eco-friendly Reduction of Travel Times in European Smart Cities (GECCO'14)

ECO-FRIENDLY REDUCTION OF TRAVEL TIMES

IN EUROPEAN SMART CITIES

Daniel H. [email protected]

Enrique [email protected]

Departamento de Lenguajes y Ciencias de la ComputaciónUniversity of Malaga

Genetic and Evolutionary Computation Conference

July 2014

Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 1 / 20

[email protected]

[email protected]

IntroductionProposal

ExperimentsConclusions

CONTENTS

1 INTRODUCTION

2 PROPOSAL

3 EXPERIMENTS

4 CONCLUSIONS




INTRODUCTION

Nowadays there is a higher amount of vehicles in streets

The number of traffic jams is increasing

Tons of air pollutants are emitted to the atmosphere

The inhabitants’ health and quality of life is decreasing




Red SwarmArchitectureCase StudiesEvolutionary Algorithm

RED SWARM

Our proposal, Red Swarm, consists of:A few spots distributed throughout the city

I Installed at traffic lightsI Linked to vehicles by using Wi-Fi

Our Evolutionary Algorithm

Our Rerouting AlgorithmSeveral User Terminal Units

I They visualize the alternatives routessuggested

I They could be smartphones, tablets, orOn Board Units





RED SWARM

Red Swarm offers:Personalized information for each vehicle (online, distributed)Prevention of traffic jamsReduction of greenhouse gas emissionsSensing of the city’s state





RED SWARM ARCHITECTURE

Configuration:Spot’s configuration is calculated by the Evolutionary Algorithm (offline)

Deployment and Use:Spots suggest new alternative routes to vehicles (online)





RED SWARM SPOT

Connects with vehicles and suggests alternative routes

Runs an instance of the Rerouting Algorithm

S1 and S2 are the Input Streets where vehicles arrive the junction

An output street is selected according to the probability value calculatedby our EA.





REROUTING EXAMPLE





SCENARIO BUILDING

We work with real maps imported from OpenStreetMapWe clean the irrelevant elements by using JOSM

We define the vehicle flows (experts’ solution) by using DUAROUTER

We import the city model into SUMO by using NETCONVERT





CASE STUDIES (I)

MalagaI 2.5 Km2

I 262 traffic lightsI 10 Red Swarm spotsI 1200 vehiclesI 169 routes

StockholmI 2.9 Km2






CASE STUDIES (II)

BerlinI 7 Km2


ParisI 5.6 Km2






SYSTEM CONFIGURATION

If a vehicle which is driving to Destination 2 enters by Street 1in the coverage area of a red swarm spot, a new route will besuggested by the Rerouting Algorithm according to theprobability values stored in the system configuration.





STATUS VECTOR

It represents the configuration of the N streets which are inputto a junction controlled by a red swarm spot. There are Mchunks of probabilities values in each street block in order tohold different configurations depending on the vehicles’ finaldestination.





EVOLUTIONARY ALGORITHM

The result of the algorithm is the configuration for allthe spots

The configuration is calculated in the offline stage.

(10+2)-EA

Evaluates individuals by using the SUMO trafficsimulator

The rerouting made by the Rerouting Algorithm isimplemented in SUMO by TraCI.





FITNESS FUNCTION

F = α1(Θ − n) +

+ α21n

n∑i=1

COi + α31n

n∑i=1

CO2i + α41n

n∑i=1

HCi +

+ α51n

n∑i=1

PMi + α61n

n∑i=1

NOi + α71n

n∑i=1

Fueli (1)

Θ: Total amount of vehicles

n: Vehicles that end their itinerary during the period analyzed

α1 to α7: Normalize each variable

The lower, the better




Results50 ScenariosGraphs

AVERAGE AND BEST IMPROVEMENTS

We have reduced the CO, CO2, HC, PM, and NO emissions

We have also reduced travel times and fuel consumption

Case Study T .Time CO CO2 HC PM NO FuelMalaga 5.5% 4.1% -1.5% 3.0% 0.9% -1.8% -1.6%

Average Stockholm 14.2% 12.6% 3.2% 11.0% 8.5% 3.0% 3.0%50 Berlin 11.7% 10.6% 1.7% 8.7% 6.0% 1.5% 1.6%

scenarios Paris 4.1% 2.2% 0.2% 1.8% 1.1% -0.1% 0.2%Average 8.9% 7.4% 0.9% 6.1% 4.1% 0.7% 0.8%

Malaga 12.2% 11.3% 4.1% 10.2% 9.9% 5.7% 4.0%Best Stockholm 17.5% 16.1% 7.1% 16.1% 16.7% 10.2% 6.8%

improvement Berlin 13.9% 13.2% 4.8% 13.3% 14.5% 7.9% 4.6%achieved Paris 8.9% 11.6% 3.8% 10.4% 5.1% 3.9% 3.8%

Average 13.1% 13.0% 5.0% 12.5% 11.5% 6.9% 4.8%





PERCENTAGE OF SCENARIOS IMPROVED

We have improved more than 58% of 200 scenarios on average

Case Study T .Time CO CO2 HC PM NO FuelMalaga 90.0% 88.0% 24.0% 82.0% 58.0% 36.0% 22.0%

% Stockholm 100.0% 100.0% 92.0% 100.0% 98.0% 78.0% 92.0%scenarios Berlin 100.0% 100.0% 90.0% 100.0% 98.0% 74.0% 84.0%improved Paris 94.0% 74.0% 52.0% 74.0% 66.0% 46.0% 50.0%

Average 96.0% 90.5% 64.5% 89.0% 80.0% 58.5% 62.0%

Each scenario consists of different traffic distributions

We have worked with 50 different scenarios of each case study (200)





ACCUMULATED VALUES OF THE VEHICLES’ EMISSIONS

CO [g]

PM [g]

CO2 [Kg]

NO [g]

HC [g]

Fuel [l]




CONCLUSIONS AND FUTURE WORK

We have addressed the reduction of greenhouse gas emissions, traveltimes and fuel consumption in Malaga, Stockholm, Berlin, and Paris

We have designed an effective evolutionary algorithm to optimize thescenarios

Our proposal has achieved average reductions up to 13.0% in CO,12.5% in HC, 11.5% in PM, and above 5% in the rest of emissions andfuel consumption

Additionally, we have shortened travel times up to 13.1% on average

Results were influenced by the different characteristics of vehicles aswell as the distribution of the cities’ streets

As a matter for future work, we are testing different strategies to furtherimprove upon our results

We are also implementing the rerouting by city districts to be able toinstall Red Swarm throughout the entire city




http://neo.lcc.uma.eshttp://danielstolfi.com/redswarm/


http://neo.lcc.uma.es

http://danielstolfi.com/redswarm/



http://neo.lcc.uma.eshttp://danielstolfi.com/redswarm/

Questions?


http://neo.lcc.uma.es

http://danielstolfi.com/redswarm/

Presentations & Public Speaking

Eco-friendly Reduction of Travel Times in European Smart Cities (GECCO'14)