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

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This article proposes an innovative solution for reducing polluting gas emissions from road traffic in modern cities. It is based on our new Red Swarm architecture which is composed of a series of intelligent spots with WiFi connections that can suggest a customized route to drivers. We have tested our proposal in four different case studies corresponding to actual European smart cities. To this end, we first import the city information from OpenStreetMap into the SUMO road traffic micro-simulator, propose a Red Swarm architecture based on intelligent spots located at traffic lights, and then optimize the resulting system in terms of travel times and gas emissions by using an evolutionary algorithm. Our results show that an important quantitative reduction in gas emissions as well as in travel times can be achieved when vehicles are rerouted according to our Red Swarm indications. This represents a promising result for the low cost implementation of an idea that could engage the interest of both citizens and municipal authorities.

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

  1. 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 Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 1 / 20
  2. 2. Introduction Proposal Experiments Conclusions CONTENTS 1 INTRODUCTION 2 PROPOSAL 3 EXPERIMENTS 4 CONCLUSIONS Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 2 / 20
  3. 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 Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 3 / 20
  4. 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 Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 4 / 20
  5. 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 Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 5 / 20
  6. 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) Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 6 / 20
  7. 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. Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 7 / 20
  8. 8. Introduction Proposal Experiments Conclusions Red Swarm Architecture Case Studies Evolutionary Algorithm REROUTING EXAMPLE Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 8 / 20
  9. 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 Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 9 / 20
  10. 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 Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 10 / 20
  11. 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 Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 11 / 20
  12. 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. Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 12 / 20
  13. 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. Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 13 / 20
  14. 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. Daniel H. Stolfi & Enrique Alba Eco-friendly Reduction of Travel Times. . . 14 / 20
  15. 15. Introduction Proposal Experiments Conclusions Red Swarm Architecture Case Studies Evolutionary Algorithm FITNESS FUNCTION F = 1(

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