1. ECO-FRIENDLY REDUCTION OF TRAVEL TIMES
IN EUROPEAN SMART CITIES
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
July 2014
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2. Introduction
Proposal
Experiments
Conclusions
CONTENTS
1 INTRODUCTION
2 PROPOSAL
3 EXPERIMENTS
4 CONCLUSIONS
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3. Introduction
Proposal
Experiments
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
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4. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
RED SWARM
Our proposal, Red Swarm, consists of:
A few spots distributed throughout the city
I Installed at traffic lights
I Linked to vehicles by using Wi-Fi
Our Evolutionary Algorithm
Our Rerouting Algorithm
Several User Terminal Units
I They visualize the alternatives routes
suggested
I They could be smartphones, tablets, or
On Board Units
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5. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
RED SWARM
Red Swarm offers:
Personalized information for each vehicle (online, distributed)
Prevention of traffic jams
Reduction of greenhouse gas emissions
Sensing of the city’s state
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6. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
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)
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7. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
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 calculated
by our EA.
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8. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
REROUTING EXAMPLE
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9. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
SCENARIO BUILDING
We work with real maps imported from OpenStreetMap
We 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
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10. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
CASE STUDIES (I)
Malaga
I 2.5 Km2
I 262 traffic lights
I 10 Red Swarm spots
I 1200 vehicles
I 169 routes
Stockholm
I 2.9 Km2
I 498 traffic lights
I 12 Red Swarm spots
I 1400 vehicles
I 131 routes
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11. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
CASE STUDIES (II)
Berlin
I 7 Km2
I 770 traffic lights
I 10 Red Swarm spots
I 1300 vehicles
I 122 routes
Paris
I 5.6 Km2
I 575 traffic lights
I 10 Red Swarm spots
I 1200 vehicles
I 125 routes
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12. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
SYSTEM CONFIGURATION
If a vehicle which is driving to Destination 2 enters by Street 1
in the coverage area of a red swarm spot, a new route will be
suggested by the Rerouting Algorithm according to the
probability values stored in the system configuration.
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13. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
STATUS VECTOR
It represents the configuration of the N streets which are input
to a junction controlled by a red swarm spot. There are M
chunks of probabilities values in each street block in order to
hold different configurations depending on the vehicles’ final
destination.
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14. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
EVOLUTIONARY ALGORITHM
The result of the algorithm is the configuration for all
the spots
The configuration is calculated in the offline stage.
(10+2)-EA
Evaluates individuals by using the SUMO traffic
simulator
The rerouting made by the Rerouting Algorithm is
implemented in SUMO by TraCI.
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15. Introduction
Proposal
Experiments
Conclusions
Red Swarm
Architecture
Case Studies
Evolutionary Algorithm
FITNESS FUNCTION
F = 1( n) +
+ 2
1
n
Xn
i=1
COi + 3
1
n
Xn
i=1
CO2i + 4
1
n
Xn
i=1
HCi +
+ 5
1
n
Xn
i=1
PMi + 6
1
n
Xn
i=1
NOi + 7
1
n
Xn
i=1
Fueli (1)
: Total amount of vehicles
n: Vehicles that end their itinerary during the period analyzed
1 to 7: Normalize each variable
The lower, the better
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16. Introduction
Proposal
Experiments
Conclusions
Results
50 Scenarios
Graphs
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 Fuel
Malaga 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%
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17. Introduction
Proposal
Experiments
Conclusions
Results
50 Scenarios
Graphs
PERCENTAGE OF SCENARIOS IMPROVED
We have improved more than 58% of 200 scenarios on average
Case Study T:Time CO CO2 HC PM NO Fuel
Malaga 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)
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18. Introduction
Proposal
Experiments
Conclusions
Results
50 Scenarios
Graphs
ACCUMULATED VALUES OF THE VEHICLES’ EMISSIONS
CO [g]
PM [g]
CO2 [Kg]
NO [g]
HC [g]
Fuel [l]
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19. Introduction
Proposal
Experiments
Conclusions
CONCLUSIONS AND FUTURE WORK
We have addressed the reduction of greenhouse gas emissions, travel
times and fuel consumption in Malaga, Stockholm, Berlin, and Paris
We have designed an effective evolutionary algorithm to optimize the
scenarios
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 and
fuel consumption
Additionally, we have shortened travel times up to 13.1% on average
Results were influenced by the different characteristics of vehicles as
well as the distribution of the cities’ streets
As a matter for future work, we are testing different strategies to further
improve upon our results
We are also implementing the rerouting by city districts to be able to
install Red Swarm throughout the entire city
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20. Introduction
Proposal
Experiments
Conclusions
http://neo.lcc.uma.es
http://danielstolfi.com/redswarm/
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21. Introduction
Proposal
Experiments
Conclusions
http://neo.lcc.uma.es
http://danielstolfi.com/redswarm/
Questions?
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