A Distributed and Self-Regulating Approach for Organizing a Large System of Mobile Objects S´ andor P. Fekete Stefan Fischer Bj¨ orn Hendriks Horst Hellbr¨ uck Sebastian Ebers Algorithms Group, Department of CS Institute of Telematics Universit¨ at zu L¨ ubeck October, 8th 2010
Review Hovering Data Clouds Improving Flow Urban traffic Outlook Goal: Improving traffic Cars as self-organizing entities Ad-hoc C2C communication Distributed algorithms Avoidance of centralized coordination and organization 2 11th Colloquium of SPP 1183
Review Hovering Data Clouds Improving Flow Urban traffic Outlook Rules, Data and Strategies Hovering Data Cloud ( HDC ): Mix of system of rules and data structure which is independent of physical carriers (e.g., vehicles) Organic Information Complex ( OIC ): Aggregation of information of several HDCs Advanced Distributed Strategy ( ADS ): Strategies pursued by equipped vehicles to deal with detected traffic phenomena 3 11th Colloquium of SPP 1183
Review Hovering Data Clouds Improving Flow Urban traffic Outlook AutoNomos architecture OIC Layer Aggregation into Detection of higher-level-data matching HDCs HDC Layer Local Sensor Data Adaptation Data Correlation Information e.g. of Driving mining Handling Extraction GPS, speed, Behavior Maintaining acceleration, HDC Repository ABS, ESP AutoCast Transport Layer Data Units Car-to-Car Communication 4 11th Colloquium of SPP 1183
Review Hovering Data Clouds Improving Flow Implementation of HDC and OIC layers Urban traffic Outlook Implementation of HDC and OIC layers OIC Layer Aggregation into Detection of higher-level-data matching HDCs HDC Layer Local Sensor Data Adaptation Data Correlation Information e.g. of Driving mining Handling Extraction GPS, speed, Behavior Maintaining acceleration, HDC Repository ABS, ESP AutoCast Transport Layer Data Units Car-to-Car Communication 5 11th Colloquium of SPP 1183
Review Hovering Data Clouds Improving Flow Implementation of HDC and OIC layers Urban traffic Outlook Class diagram HDCManager * 1 +correlate(ein data : HDCDataUnit) : HDC #integrate(ein data : HDCDataUnit, ein hdc : HDC) #aggregate(ein hdcs... : HDC) «interface» #informationExtraction(ein hdc : HDC) HDCUpdateListener +update(ein event : UpdateEvent) 1 * «interface» * * HDC +doUpdate(ein dataUnit : HDCDataUnit) +registerListener(ein listener : HDCUpdateListener) 1 1 HDCDataUnit -ttl : long «interface» «interface» -maxLifetime : long HDCDataUnitListener SensorUpdateListener -creationTime : long +add(ein dataUnit : HDCDataUnit) +update(ein event : UpdateEvent) -lastAccess : long -expansion : object -name : string * * -distributionArea : object 1 1 +hashValue() : long 1 * TransportLayer Sensor +registerListener(ein listener : HDCDataUnitListener) +getValue() : object +onReceive(ein dataUnit : HDCDataUnit) +registerListener(ein listener : SensorUpdateListener) +distribute(ein dataUnit : HDCDataUnit) 6 11th Colloquium of SPP 1183
Review Hovering Data Clouds Improving Flow Implementation of HDC and OIC layers Urban traffic Outlook Receiving a message TransportLayer HDCManager HDC onReceive(HDCDataUnit) add(HDCDataUnit) correlate(HDCDataUnit) integrate(HDCDataUnit, HDC) doUpdate(HDCDataUnit) update(UpdateEvent) informationExtraction(HDC) distribute(HDCDataUnit) HDCDataUnit 7 11th Colloquium of SPP 1183
Review Hovering Data Clouds Improving Flow Implementation of HDC and OIC layers Urban traffic Outlook Receiving a message << HDCDataUnitListener >> TransportLayer HDCManager HDC onReceive(HDCDataUnit) add(HDCDataUnit) correlate(HDCDataUnit) integrate(HDCDataUnit, HDC) doUpdate(HDCDataUnit) update(UpdateEvent) informationExtraction(HDC) distribute(HDCDataUnit) HDCDataUnit 7 11th Colloquium of SPP 1183
Review Hovering Data Clouds Improving Flow Implementation of HDC and OIC layers Urban traffic Outlook Receiving a message << HDCUpdateListener >> TransportLayer HDCManager HDC onReceive(HDCDataUnit) add(HDCDataUnit) correlate(HDCDataUnit) integrate(HDCDataUnit, HDC) doUpdate(HDCDataUnit) update(UpdateEvent) informationExtraction(HDC) distribute(HDCDataUnit) HDCDataUnit 7 11th Colloquium of SPP 1183
Review Hovering Data Clouds Avoiding traffic jam with Jam-ADS Improving Flow Improve flow through successive traffic lights Urban traffic Outlook Review of Jam-ADS Goal Increase traffic flow on a highway while decreasing the fuel consumption Idea Recommend a velocity which is a convex combination of desired velocity and average velocity of the cars ahead v recommend = λ v desired + (1 − λ ) v average 8 11th Colloquium of SPP 1183
Review Hovering Data Clouds Avoiding traffic jam with Jam-ADS Improving Flow Improve flow through successive traffic lights Urban traffic Outlook Patent granted Our patent application for the Jam-ADS procedure was successful. Granted on 2010-09-09 9 11th Colloquium of SPP 1183
Review Hovering Data Clouds Avoiding traffic jam with Jam-ADS Improving Flow Improve flow through successive traffic lights Urban traffic Outlook Scanning parameter space We still run many simulations with variations of independent parameters car density, number of lanes λ , distance to take average over, consider neighbor lanes penetration (fraction of equipped cars) 10 11th Colloquium of SPP 1183
Review Hovering Data Clouds Avoiding traffic jam with Jam-ADS Improving Flow Improve flow through successive traffic lights Urban traffic Outlook Scanning parameter space II Applying different simulators our own simulator SUMO together with the network simulator Shawn to find capabilities and limits of Jam-ADS. 11 11th Colloquium of SPP 1183
Review Hovering Data Clouds Avoiding traffic jam with Jam-ADS Improving Flow Improve flow through successive traffic lights Urban traffic Outlook Scanning parameter space III Average fuel consumptions 12 11th Colloquium of SPP 1183
Review Hovering Data Clouds Avoiding traffic jam with Jam-ADS Improving Flow Improve flow through successive traffic lights Urban traffic Outlook Improve flow through successive traffic lights Advanced Distributed Strategy to improve traffic flow over successive traffic lights 13 11th Colloquium of SPP 1183
Review Hovering Data Clouds Avoiding traffic jam with Jam-ADS Improving Flow Improve flow through successive traffic lights Urban traffic Outlook General idea Traffic Lights-Advanced Distributed Strategy Cars receive information from traffic lights: next cycles from other cars: positions and velocities Cars estimate arrival time at stop line considering other cars ⇒ Adapt velocity ⇒ Save time and fuel by crossing the stop line with maximal speed 14 11th Colloquium of SPP 1183
Review Hovering Data Clouds State of the art Improving Flow Rerouting flows in reply to a collapse Urban traffic Outlook Going beyond Up to now we have improved the flow within a road. Now we look at the road network as a whole. 15 11th Colloquium of SPP 1183
Review Hovering Data Clouds State of the art Improving Flow Rerouting flows in reply to a collapse Urban traffic Outlook Max-flow problem Offline optimization on a road network Given roads (edges) with known capacities u e Given a fixed flow demand from a source s to a sink t We know algorithms to optimally distribute flow, e.g., augmenting paths (1 , 2) v 1 v 2 (1 , 3) (2 , 1) (2 , 1) s t (1 , 3) (1 , 1) v 3 16 11th Colloquium of SPP 1183
Review Hovering Data Clouds State of the art Improving Flow Rerouting flows in reply to a collapse Urban traffic Outlook Dynamic flows Moreover we know algorithms for flows with limited transit times τ e on each edge (1 , 2) v 1 v 2 θ = 0 Demand 6 flow units x = 0 (1 , 3) (2 , 1) from s to t x = 0 (2 , 1) x = 0 x = 0 x = 0 x = 0 Goal minimize time s t (1 , 3) (1 , 1) Result need 5 time x = 0 x = 0 units v 3 Furthermore the transit times τ e could be load-dependent ⇒ NP-hardness 17 11th Colloquium of SPP 1183
Review Hovering Data Clouds State of the art Improving Flow Rerouting flows in reply to a collapse Urban traffic Outlook Dynamic flows Moreover we know algorithms for flows with limited transit times τ e on each edge (1 , 2) v 1 v 2 θ = 1 Demand 6 flow units x = 0 (1 , 3) (2 , 1) from s to t x = 0 (2 , 1) x = 0 x = 2 x = 0 x = 0 s Goal minimize time t (1 , 3) (1 , 1) Result need 5 time x = 0 x = 1 units v 3 Furthermore the transit times τ e could be load-dependent ⇒ NP-hardness 17 11th Colloquium of SPP 1183
Review Hovering Data Clouds State of the art Improving Flow Rerouting flows in reply to a collapse Urban traffic Outlook Dynamic flows Moreover we know algorithms for flows with limited transit times τ e on each edge (1 , 2) v 1 v 2 θ = 2 Demand 6 flow units x = 1 (1 , 3) (2 , 1) from s to t x = 0 (2 , 1) x = 1 x = 1 x = 0 x = 0 Goal minimize time s t (1 , 3) (1 , 1) Result need 5 time x = 1 x = 1 units v 3 Furthermore the transit times τ e could be load-dependent ⇒ NP-hardness 17 11th Colloquium of SPP 1183
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