TU Dortmund Chair Computer Science 3. BeeJamA. Honey Bee Inspired Traffic Jam Avoidance

Transcription

1 TU Dortmud Chair Computer Sciece 3 Operatig Systems & System Architecture BeeJamA Hoey Bee pired Traffic Jam Avoidace Highly Dyamic ad Adaptive Traffic Cogestio Avoidace i Real-Time pired by Hoey Bee Behavior H. F. Wedde, S. Lehhoff, B. va Bo, Z. Bay, S. Becker, S. Böttcher, C. Bruer, A. Büscher, T. Fürst, A. M. Lazarescu, E. Rotaru, S. Sege, B. Steibach, F. Yilmaz, T. Zimmerma

2 TU Dortmud Chair Computer Sciece 3 Cotet Itroductio Distributed Traffic Model Quality Ratig Fuctio Distributed Traffic Jam Avoidace (The BeeJamA Algorithm) Simulatio Studies Experimetal Results Coclusio Future Work 2

3 TU Dortmud Chair Computer Sciece 3 Itroductio Traffic cogestio a highly dyamic distributed problem i metropolita areas world-wide. Serious ad complex problems regardig the timely arrival of goods or persos. Expertise i developig distributed routig algorithms from Nature (BeeHive, BeeAdHoc) Dyamic multipath routig algorithms High adaptability / flexibility High throughput High fault tolerace Distributed o-lie traffic cotrol model BeeJamA 3

4 TU Dortmud Chair Computer Sciece 3 Distributed Traffic Model Idividual hop-to-hop routig Probabilistic ext hop determiatio Regioally resposible avigators Maagemet of local routig tables Real-time respose Layered routig model Area-Layer: odes correspod to itersectios, edges correspod to roads Net-Layer: odes correspod to areas, edges represet roads coectig adjacet areas 4

5 TU Dortmud Chair Computer Sciece 3 Distributed Traffic Model (Partitioig) Net-Layer: partitioig ito Foragig Zoes ( IFZ Net -table) ad Foragig Regios ( IFR Net -table) FZ(N): all odes (areas) withi a limited hop rage of ode (area) N FR: fixed partitios with a sigle Represetative Node (area) Area-Layer: partitioig ito areas Size must allow for sufficiet routig alteratives Real-time costraits must be met FZ: each ode s FZ cosists of all odes withi its area ( IFZ Area -table) Border-odes coect adjacet areas 5

6 TU Dortmud Chair Computer Sciece 3 Quality Ratig Fuctio Routig tables reflect estimated travel times Extesive empirical studies i traffic cogestio developmet Fuctioal depedecy betwee vehicle speed ad desity o a road sectio α β v A B 6-Lae-Freeway 4-Lae-Freeway Highway Vehicle Desity ρ [Vehicles/km] 6

7 TU Dortmud Chair Computer Sciece 3 The BeeJamA Algorithm Lookup destiatio ode i FRM Area table dest. ode withi curret area? to dest. ode Move! At destiatio? dest. ode s area i curret area s FZ? area to border-ode to dest. area Lookup dest. regio s represetative ode i FRM Net regio s represetative ode Net-Layer Area-Layer 7

8 TU Dortmud Chair Computer Sciece 3 The BeeJamA Algorithm Lookup destiatio ode i FRM Area table dest. ode withi curret area? to dest. ode Move! At destiatio? dest. ode s area i curret area s FZ? area to border-ode to dest. area Lookup dest. regio s represetative ode i FRM Net regio s represetative ode Vehicle drivig from ode 4 (area A) to ode 14 (area B) Net-Layer Area-Layer 8

9 4 14 TU Dortmud Chair Computer Sciece 3 The BeeJamA Algorithm Lookup destiatio ode i FRM Area table dest. ode withi curret area? FRM Area A A B B E to dest. ode Move! At destiatio? dest. ode s area i curret area s FZ? area to border-ode to dest. area Lookup dest. regio s represetative ode i FRM Net regio s represetative ode Net-Layer Area-Layer 9

10 A B TU Dortmud Chair Computer Sciece 3 The BeeJamA Algorithm Lookup destiatio ode i FRM Area table dest. ode withi curret area? to dest. ode Move! At destiatio? dest. ode s area i curret area s FZ? area to border-ode to dest. area Lookup dest. regio s represetative ode i FRM Net regio s represetative ode Net-Layer Area-Layer 10

11 A B Lookup destiatio ode i FRM Area table TU Dortmud Chair Computer Sciece 3 The BeeJamA Algorithm dest. ode withi curret area? to dest. ode dest. ode s area i curret area s FZ? area to border-ode to dest. area B c BB c CB c EB c FB c HB c IB Move! At destiatio? IFZ Net (A) B C E F H I E c BE c CE c EE c FE c HE c IE c BB c BE Lookup dest. regio s represetative ode i FRM Net regio s represetative ode Net-Layer Area-Layer 11

12 4 B (via 6) TU Dortmud Chair Computer Sciece 3 The BeeJamA Algorithm Lookup destiatio ode i FRM Area table dest. ode withi curret area? to dest. ode IFZ Area (4) dest. ode s area i curret area s FZ? area to border-ode to dest. area Move! 1 c 11 c 21 c 31 c 41 c 51 c 61 c 71 3 c At 13 c 23 c 33 c 43 c 53 c 63 c 73 destiatio? 5 c 15 c 25 c 35 c 45 c 55 c 65 c 75 c 61 c 63 Lookup dest. regio s represetative ode i FRM Net c 65 regio s represetative ode Net-Layer Area-Layer 12

13 1 14 TU Dortmud Chair Computer Sciece 3 The BeeJamA Algorithm Lookup destiatio ode i FRM Area table dest. ode withi curret area? to dest. ode Move! At destiatio? dest. ode s area i curret area s FZ? area to border-ode to dest. area Lookup dest. regio s represetative ode i FRM Net regio s represetative ode Net-Layer Area-Layer 13

14 TU Dortmud Chair Computer Sciece 3 Simulatio Studies Traffic Simulator Realistic traffic model (Nagel/Schreckeberg) Cellular automato based Commercially available avigatioal data (AND) Sectio of the Germa Ruhr District Source Nodes Destiatio Node New Vehicles per Secod Simulatio Time Dijkstra Update Iterval Tempo Limits Max. Speed for Vehicles Vehicular Desity Limits Highways 4-Lae-Freeways 6-Lae-Freeways A, B, C, D E 4 (1 per Node) 3600 secods 600 secods 135 km/h (Freeways), 85 km/h (Highways) 135 km/h α=35, β=40 [vehicles/km], A=50, B=10 [km/h] α=40, β=55 [vehicles/km], A=70, B=30 [km/h] α=45, β=65 [vehicles/km], A=75, B=35 [km/h] Table 1: Experimetal Setup 14

15 TU Dortmud Chair Computer Sciece 3 Experimetal Results Traffic cogestio avoidace with respect to idividual travel times BeeJamA routig agaist Dijkstra-based fastest path routig (10mi update iterval) 15

16 TU Dortmud Chair Computer Sciece 3 Coclusio We developed our ow simulator, due to the lack of software for dyamic, distributed routig algorithms i vehicular traffic etworks. Distributed layered traffic model based o BeeHive/BeeAdHoc routig. Dyamic cost model: Miimal travel times to destiatios, cogestio avoidace as objectives. Although there is o global iformatio the avigators work very efficietly. Demostratio available after the sessio 16

17 TU Dortmud Chair Computer Sciece 3 Future Work Distributed o-lie simulator Realistic commuicatio modelig Dyamic rule sets OpeStreetMap itegratio Optimal off-lie clusterig of avigatio areas Hardware implemetatio ad evaluatio OpeMoko hadsets Deadlie graularity 1 sec. Olie versio of the simulator: 17

18 TU Dortmud Chair Computer Sciece 3. Thak you! 18