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Explore a system that routes cars through cities dynamically using an Ant-Based Control algorithm for efficient navigation and congestion management. Test results show improved traffic flow and shorter routes.
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Dynamic vehicle routingusing Ant Based Control Ronald Kroon Leon Rothkrantz Delft University of Technology October 2, 2002 Delft
Contents • Introduction • Theory • Ant Based Control • Simulation environment and Routing system • Experiment and results • Conclusions and recommendations
Introduction (1) Dynamic vehicle routing using Ant Based Control: • Routing cars through a city • Using dynamic data • Using an Ant Based Control algorithm
Introduction (2) Goals: • Design and implement a prototype of dynamic Routing system using Ant Based Control • Design and implement a simulation environment for traffic • Test Routing system
Introduction (3) • Navigate a driver through a city • Find the closest parking lot • Divert from congestions Possible applications:
Theory (1) Natural ants find the shortest route
Theory (2) Choosing randomly
Theory (3) Laying pheromone
Theory (4) Biased choosing
Theory (5) 3 reasons for choosing the shortest path: • Earlier pheromone (trail completed earlier) • More pheromone (higher ant density) • Younger pheromone (less diffusion)
Ant Based Control (1) Application of ant behaviourin network management • Mobile agents • Probability tables • Different pheromone for every destination
3 2 1 6 4 5 7 Ant Based Control (2) Probability table
Ant Based Control (3) Forward agents • 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 (4) Backward agents • Move back from destination to source • Use reverse path of forward agent • Update the probabilities for going to this destination
Ant Based Control (5) Updating probabilities • 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
Simulation GPS-satellite Vehicle Routing system Simulation environment and Routing system (1) Architecture
GPS-satellite • Position • determination Vehicle • Routing • Dynamic data Routing system Simulation environment and Routing system (2) Communication flow
Routing system Route finding system Timetable updating system Dynamic data Routing Memory Routing system (1)
3 2 1 6 4 5 7 Routing system (2) Timetable
t2 20 3 2 1 6 4 5 7 Routing system (3) Update information t1
Simulation environment (1) Map of Beverwijk
Simulation environment (2) Map representation for simulation
Simulation environment (3) Simulation with driving vehicles
Simulation environment (4) Features • Traffic lights • Roundabouts • One-way traffic • Number of lanes • High / low priority roads • Precedence rules • Speed variation per road • Traffic distribution • Road disabling
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):
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
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
Start demo Demo