Transcript Dynamic vehicle routing using an Ant Based Control algorithm

Dynamic vehicle routing
using Ant Based Control
Ronald Kroon
Leon Rothkrantz
Delft University of Technology
October 2, 2002
Delft
Mediamatics / Knowledge based systems
Contents
Introduction
 Theory
 Ant Based Control
 Simulation environment and Routing system
 Experiment and results
 Conclusions and recommendations

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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)
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
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Introduction (3)
Possible applications:

Navigate a driver through a city
 Find the closest parking lot
 Divert from congestions
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Schematic overview of the
PITA components
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3D Model of dynamic traffic
data
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Theory (1)
Natural ants find the shortest route
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Theory (2)
Choosing randomly
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Theory (3)
Laying pheromone
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Theory (4)
Biased choosing
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Theory (5)
3 reasons for choosing the shortest path:

Earlier pheromone (trail completed earlier)
 More pheromone (higher ant density)
 Younger pheromone (less diffusion)
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Ant Based Control (1)
Application of ant behaviour
in network management

Mobile agents
 Probability tables
 Different pheromone for every destination
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Ant Based Control (2)
Probability table
1
2
3
(Node 2)
5
7
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1
3
5
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
…
…
…
…
Destination
6
4
Next
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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
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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
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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
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Simulation environment and
Routing system (1)
Architecture
Simulation
GPS-satellite
Vehicle
Routing
system
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Simulation environment and
Routing system (2)
Communication flow
GPS-satellite
• Position
determination
Vehicle
• Routing
• Dynamic data
Routing
system
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Routing system (1)
Routing system
Dynamic
data
Timetable
updating
system
Memory
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Route
finding
system
Routing
Routing system (2)
Timetable
1
2
3
1
2
4
5
…
6
4
5
7
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1
2
4
-
12 15
5 …
-
…
11
-
-
18 …
14
-
-
13 …
-
18 14
-
…
… … … … …
Routing system (3)
Update information
t1
1
3
2
t2
20
6
4
5
7
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timetable value
The effect of new information on an entry in
the timetable
22
20
18
16
14
12
10
8
6
4
2
0
tim e
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Simulation environment (1)
Map of
Beverwijk
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Simulation environment (2)
Map
representation
for simulation
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Simulation environment (3)
Simulation
with driving
vehicles
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Simulation environment (4)
Features





Traffic lights

Roundabouts

One-way traffic

Number of lanes

High / low priority roads
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Precedence rules
Speed variation per road
Traffic distribution
Road disabling
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Experiment
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Results
In this test case (no realistic environment):

32 % profit for all vehicles, when some of
them are guided by the Routing system
 19 % extra profit for vehicles using the
Routing system
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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
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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

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Start demo
Demo
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