Mediamatics / Knowledge based systems Dynamic vehicle routing using Ant Based Control Ronald Kroon Leon Rothkrantz Delft University of Technology October 2, 2002 Delft

Mediamatics / Knowledge based systems2 Contents Introduction Theory Ant Based Control Simulation environment and Routing system Experiment and results Conclusions and recommendations

Mediamatics / Knowledge based systems3 Introduction (1) Dynamic vehicle routing using Ant Based Control: Routing cars through a city Using dynamic data Using an Ant Based Control algorithm

Mediamatics / Knowledge based systems4 Introduction (2) Design and implement a prototype of dynamic Routing system using Ant Based Control Design and implement a simulation environment for traffic Test Routing system Goals:

Mediamatics / Knowledge based systems5 Introduction (3) Navigate a driver through a city Find the closest parking lot Divert from congestions Possible applications:

Mediamatics / Knowledge based systems6

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8 Schematic overview of the PITA components

Mediamatics / Knowledge based systems9 3D Model of dynamic traffic data

Mediamatics / Knowledge based systems10 Theory (1) Natural ants find the shortest route

Mediamatics / Knowledge based systems11 Theory (2) Choosing randomly

Mediamatics / Knowledge based systems12 Theory (3) Laying pheromone

Mediamatics / Knowledge based systems13 Theory (4) Biased choosing

Mediamatics / Knowledge based systems14 Theory (5) Earlier pheromone (trail completed earlier) More pheromone (higher ant density) Younger pheromone (less diffusion) 3 reasons for choosing the shortest path:

Mediamatics / Knowledge based systems15 Mobile agents Probability tables Different pheromone for every destination Ant Based Control (1) Application of ant behaviour in network management

Mediamatics / Knowledge based systems16 Ant Based Control (2) (Node 2)Next135 Destination ………… Probability table

Mediamatics / Knowledge based systems17 Generated regularly from every node with random destination Choose route according to a probability Probability represents strength of pheromone trail Collect travel times and delays Ant Based Control (3) Forward agents

Mediamatics / Knowledge based systems18 Move back from destination to source Use reverse path of forward agent Update the probabilities for going to this destination Ant Based Control (4) Backward agents

Mediamatics / Knowledge based systems19 Probability for choosing the node the forward agent chose is incremented Depends on: Sum of collected travel times Delay on this path Update formula: Δp = A / t + B Probabilities for choosing other nodes are slightly decremented Ant Based Control (5) Updating probabilities

Mediamatics / Knowledge based systems20 Simulation environment and Routing system (1) Architecture GPS-satellite Vehicle Routing system Simulation

Mediamatics / Knowledge based systems21 GPS-satellite Vehicle Routing system Position determination Routing Dynamic data Simulation environment and Routing system (2) Communication flow

Mediamatics / Knowledge based systems22 Routing system (1) Routing system Route finding system Memory Timetable updating system Dynamic data Routing

Mediamatics / Knowledge based systems … … … … … … …………… Routing system (2) Timetable

Mediamatics / Knowledge based systems24 Routing system (3) Update information t1t1 t2t2 20

Mediamatics / Knowledge based systems25

Mediamatics / Knowledge based systems26 Simulation environment (1) Map of Beverwijk

Mediamatics / Knowledge based systems27 Simulation environment (2) Map representation for simulation

Mediamatics / Knowledge based systems28 Simulation environment (3) Simulation with driving vehicles

Mediamatics / Knowledge based systems29 Traffic lights Roundabouts One-way traffic Number of lanes High / low priority roads Simulation environment (4) Features Precedence rules Speed variation per road Traffic distribution Road disabling

Mediamatics / Knowledge based systems30 Experiment

Mediamatics / Knowledge based systems31 Results 32 % profit for all vehicles, when some of them are guided by the Routing system 19 % extra profit for vehicles using the Routing system In this test case (no realistic environment):

Mediamatics / Knowledge based systems32 Conclusions Successful creation of Routing system and simulation environment Test results: – Routing system is effective: Smart vehicles take shorter routes Other vehicles also benefit from better distribution of traffic – Routing system adapts to new situations: 15 sec – 2 min

Mediamatics / Knowledge based systems33 Recommendations Let vehicle speed depend on saturation of the road Update probabilities using earlier found routes compared to new route Use the same pheromone for all parkings near a city center

Mediamatics / Knowledge based systems34 Start demo Demo