Mediamatics / Knowledge based systems

Dynamic vehicle routingDynamic vehicle routingusing Ant Based Controlusing Ant Based Control

Ronald Kroon

Leon Rothkrantz

Delft University of Technology

October 2, 2002

Delft

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ContentsContents

IntroductionTheoryAnt Based ControlSimulation environment and Routing systemExperiment and resultsConclusions and recommendations

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Introduction (1)Introduction (1)

Dynamic vehicle routing

using Ant Based Control:

Routing cars through a city Using dynamic data Using an Ant Based Control algorithm

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Introduction (2)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:

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Introduction (3)Introduction (3)

Navigate a driver through a city Find the closest parking lot Divert from congestions

Possible applications:

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Schematic overview of the PITA Schematic overview of the PITA componentscomponents

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3D Model of dynamic traffic data3D Model of dynamic traffic data

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Theory (1)Theory (1)

Natural ants find the shortest route

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Theory (2)Theory (2)

Choosing randomly

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Theory (3)Theory (3)

Laying pheromone

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Theory (4)Theory (4)

Biased choosing

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Theory (5)Theory (5)

Earlier pheromone (trail completed earlier) More pheromone (higher ant density) Younger pheromone (less diffusion)

3 reasons for choosing the shortest path:

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Mobile agents Probability tables Different pheromone for every destination

Ant Based Control (1)Ant Based Control (1)

Application of ant behaviourin network management

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Ant Based Control (2)Ant Based Control (2)

(Node 2) Next 1 3 5

Destination

1 0.90 0.02 0.08

3 0.03 0.90 0.07

4 0.44 0.19 0.37

5 0.08 0.05 0.87

… … … …

Probability table

13

2

4 5

6

7

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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)Ant Based Control (3)

Forward agents

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Move back from destination to source Use reverse path of forward agent Update the probabilities for going to this

destination

Ant Based Control (4)Ant Based Control (4)

Backward agents

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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)Ant Based Control (5)

Updating probabilities

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Simulation environment and Simulation environment and Routing system (1)Routing system (1)

Architecture

GPS-satellite

Vehicle

Routing system

Simulation

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GPS-satellite

Vehicle

Routing system

• Position determination

• Routing

• Dynamic data

Simulation environment and Simulation environment and Routing system (2)Routing system (2)

Communication flow

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Routing system (1)Routing system (1)

Routing system

Route finding system

MemoryTimetable updating system

Dynamic data

Routing

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1 2 4 5 …

1 - 12 15 - …

2 11 - - 18 …

4 14 - - 13 …

5 - 18 14 - …

… … … … … …

13

2

4 5

6

7

Routing system (2)Routing system (2)Timetable

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Routing system (3)Routing system (3)

Update information

13

2

4 5

6

7

t1

t220

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The effect of new information on an entry in the timetable

02468

10121416182022

time

tim

etab

le v

alu

e

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Simulation environment (1)Simulation environment (1)

Map of Beverwijk

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Simulation environment (2)Simulation environment (2)

Map representation for simulation

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Simulation environment (3)Simulation environment (3)

Simulation

with driving

vehicles

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Traffic lights Roundabouts One-way traffic Number of lanes High / low priority roads

Simulation environment (4)Simulation environment (4)

Features

Precedence rules Speed variation per road Traffic distribution Road disabling

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ExperimentExperiment

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ResultsResults

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):

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ConclusionsConclusions

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

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RecommendationsRecommendations

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

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Start demoStart demo

DemoDemo