Red Swarm: Smart Mobility in Cities with EAs (GECCO'13)

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This work presents an original approach to regulate traffic by using an on-line system controlled by an EA. Our proposal uses computational spots with WiFi connectivity located at traffic lights (the Red Swarm), which are used to suggest alternative individual routes to vehicles. An evolutionary algorithm is also proposed in order to find a configuration for the Red Swarm spots which reduces the travel time of the vehicles and also prevents traffic jams. We solve real scenarios in the city of Malaga (Spain), thus enriching the OpenStreetMap info by adding traffic lights, sensors, routes and vehicle flows. The result is then imported into the SUMO traffic simulator to be used as a method for calculating the fitness of solutions. Our results are competitive compared to the common solutions from experts in terms of travel and stop time, and also with respect to other similar proposals but with the added value of solving a real, big instance.

http://dx.doi.org/10.1145/2463372.2463540

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Red Swarm: Smart Mobility in Cities with EAs (GECCO'13)

  1. 1. Red Swarm: Smart Mobility in Cities with EAs Daniel H. Stolfi and Enrique Alba Amsterdam, The Netherlands July 06-10, 2013
  2. 2. RReedd Red SSwwaarrmm:: Swarm: SSmmaarrtt MMoobbiilliittyy iinn CCiittiieess wwiitthh EEAAss Daniel H. Stolfi dhstolfi@lcc.uma.es http://www.danielstolfi.com Enrique Alba eat@lcc.uma.es http://www.lcc.uma.es/~eat University of Malaga University of Malaga July 2013 July 2013 Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs
  3. 3. TTaabbllee ooff CCoonntteennttss Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 3 3 Introduction Proposal Experiments Conclusions 1 2 4
  4. 4. Introduction Proposal Experiments Conclusions Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 4 IInnttrroodduuccttiioonn People are living or thinking about moving to big cities A higher number of vehicles are moving by the streets The number of traffic Jams is rising Tons of greenhouse effect gases are emitted to the atmosphere The citizens' quality of life is decreasing The optimization of road traffic is necessary in modern big cities
  5. 5. Introduction Proposal Experiments Conclusions Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 5 RReedd SSwwaarrmm Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Our Red Swarm consists of: Several spots distributed throughout the city Installed at traffic lights Linked to vehicles by using Wi-Fi Our Optimization Algorithm Our Rerouting Algorithm On Board Units (OBU) Installed inside vehicles Smartphones or tablets could be used instead
  6. 6. produces CENTRALIZED OFFLINE ONLINE DISTRIBUTED Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 6 AArrcchhiitteeccttuurree Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions The Evolutionary Algorithm produces a configuration for the Red Swarm spots The configured Red Swarm spots are deployed in several junctions of the city
  7. 7. RReerroouuttiinngg ((tt == 1100 ss..)) Introduction Proposal Experiments Conclusions Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Experts' solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 7
  8. 8. RReerroouuttiinngg ((tt == 1111 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 8
  9. 9. RReerroouuttiinngg ((tt == 1122 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 9
  10. 10. RReerroouuttiinngg ((tt == 1133 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 10
  11. 11. RReerroouuttiinngg ((tt == 1144 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 11
  12. 12. RReerroouuttiinngg ((tt == 1155 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 12
  13. 13. RReerroouuttiinngg ((tt == 1166 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 13
  14. 14. RReerroouuttiinngg ((tt == 1177 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 14
  15. 15. RReerroouuttiinngg ((tt == 1188 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 15
  16. 16. RReerroouuttiinngg ((tt == 1199 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 16
  17. 17. RReerroouuttiinngg ((tt == 2200 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 17
  18. 18. RReerroouuttiinngg ((tt == 2211 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 18
  19. 19. RReerroouuttiinngg ((tt == 2222 ss..)) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Experts’ solution Red Swam Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 19
  20. 20. Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 20
  21. 21. Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Constructing tthhee SScceennaarriioo:: MMeetthhooddoollooggyy We have worked with real maps imported from OpenStreetMap We have added 10 Red Swarm spots in junctions controlled by a traffic light We have imported the map into SUMO We have defined traffic flows for vehicles (experts' solution) This process can be adapted to any modern city in the world NETCONVERT and DUAROUTER are part of the SUMO package Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 21
  22. 22. SScceennaarriioo:: MMaallaaggaa MALAGA (SPAIN) Real Scenario 261 traffic lights 10 Red Swarm spots 800 vehicles 4 vehicle types 3 different traffic patterns ( Scen1, Scen2 & Scen3 ) Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Sedan Van Wagon Transport Our goal is to reduce the travel time of the vehicles in high density conditions Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 22
  23. 23. Experiments Conclusions EEvvoolluuttiioonnaarryy AAllggoorriitthhmm Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal (10+2)-EA The fitness value is calculated by using the SUMO traffic simulator The rerouting produced by the Red Swarm spots is implemented by using TraCI and the Rerouting Algorithm which runs in each spot The result of the EA is a configuration for all the Red Swarm spots placed in the city Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 23
  24. 24. Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Evolutionary Algorithm: RReepprreesseennttaattiioonn ((SSeennssoorrss)) Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 24 SENSORS They represent the inputs to Red Swarm spots in the simulated scenario Vehicles trigger the rerouting algorithm when they are detected by a sensor In a real city they would be radio links
  25. 25. Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Evolutionary Algorithm: RReepprreesseennttaattiioonn ((EExxaammppllee)) For example, if a vehicle which is going to Destination 2 is detected by Sensor 1, one of the routes from R121 to R12K will be chosen by the Rerouting Algorithm, depending on the values of the probabilities P121 to P12K Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 25
  26. 26. Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Evolutionary Algorithm: RReepprreesseennttaattiioonn REPRESENTATION RSDK: Available routes from Sensors to Destinations and to other spots Each new route will be selected by the Rerouting Algorithm depending on the probabilities Conceptual representation Solution vector made up of 1119 floats (probabilities) Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 26
  27. 27. Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Evolutionary Algorithm: FFiittnneessss FFuunnccttiioonn FITNESS FUNCTION N: Total number of vehicles (800 in this work) ntrips: Number of vehicles which have ended their itineraries ttrip: Trip time of each vehicle tdelay: Waiting time of each vehicle before entering the analyzed zone The three terms are weighted by α1, α2, and α3, respectively We want to minimize the fitness value (the lower the better) Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 27
  28. 28. Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Evolutionary Algorithm: RReeccoommbbiinnaattiioonn OOppeerraattoorr RECOMBINATION OPERATOR We use the standard two-point recombination The offspring are obtained by exchanging a range of sensor configurations from the selected parents TPX Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 28
  29. 29. Red Swarm Architecture Rerouting Scenario Evolutionary Algorithm Introduction Proposal Experiments Conclusions Evolutionary Algorithm: MMuuttaattiioonn OOppeerraattoorrss MUTATION OPERATOR Changes the probabilities of the routes in the sensor configurations 1) All Destinations – One Sensor It changes all probabilities in a Sensor block (i.e. all the probabilities in Sensor 4) 2) One Destination – One Sensor It changes probabilities in a Destination block in a Sensor block (i.e. only the probabilities of Destination 8 in Sensor 4) Exploration Exploitation Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 29
  30. 30. Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 30 RReessuullttss Introduction Results Proposal Experiments Conclusions The 800 vehicles leave the city in a lower time when we use Red Swarm Results show a reduction of the average waiting time and the average travel time Vehicles have to travel a longer distance because they are using alternative routes
  31. 31. Introduction Results Proposal Experiments Conclusions Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 31
  32. 32. Introduction Results Proposal Experiments Conclusions Results: Simulation Time vvss.. NNuummbbeerr ooff VVeehhiicclleess Figures show that the higher the number of vehicles is, the more effective Red Swarm becomes Under the threshold, Red Swarm still being useful Advantages are evident when the traffic density raises Travel Time (Scen1) Travel Time (Scen2) Travel Time (Scen3) Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 32
  33. 33. Introduction Results Proposal Experiments Conclusions GGeenneerraalliizzaattiioonn ooff RReessuullttss This figure shows the fitness values of the execution of Red Swarm in 30 extra scenarios compared with the experts' solution Red Swarm has not only worked in all these scenarios but has also achieved lower travel times in 20 of them (66.7%) Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 33
  34. 34. Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 34 CCoonncclluussiioonnss Introduction Proposal Experiments Conclusions This is an innovative approach to the prevention of traffic jams The results confirm road traffic can be improved by using Red Swarm We have reduced the travel times and waiting times of the vehicles Currently, we are working on: The expansion of the analyzed region Collecting information from vehicles (on-line and historical data of the city) The reduction of greenhouse gas emissions
  35. 35. http://neo.lcc.uma.es http://en.danielstolfi.com/redswarm/ Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 35
  36. 36. http://neo.lcc.uma.es http://en.danielstolfi.com/redswarm/ dhstolfi@lcc.uma.es Questions? Daniel H. Stolfi and Enrique Alba Red Swarm: Smart Mobility in Cities with EAs 36

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