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Dynamic routing versus static Dynamic routing versus static routingrouting
Prof. drs. Dr. Leon Rothkrantz
http://www.mmi.tudelft.nl
http://www.kbs.twi.tudelft.nl
Outline presentationOutline presentation
• Problem definition• Static routing Dijkstra shortest path algorithm• Dynamic traffic data (historical data, real time data)• Dynamic routing using 3D-Dijkstra algorithm• Travel speed prediction using ANN• Personal intelligent traveling assistant (PITA)• PITA in cars and in trains
IntroductionIntroduction
Problem definition• Find the shortest/fastest route from A to B
using dynamic route information.• Research if dynamic routing results in shorter
traveling time compared to shortest path• Is it possible to route a traveler on his route in
dynamically changing environments ?
(Non-) congested road(Non-) congested road
TrafficTraffic
Testbed: graph of highwaysTestbed: graph of highways
MONICA networkMONICA networkMany sensors/wires along the road to Many sensors/wires along the road to
measure the speed of the carsmeasure the speed of the cars
Smart RoadSmart Road
Many sensors (smart sensors) along a road Sensor devices set up a wireless ad-hoc network Sensor in the car is able to communicate with the road Congestion, icy roads can be detected by the sensors
and communicated along the network, to inform drivers remote in place and time
GPS, GSM can be included in the sensornetworks Wireless communication by wired
lamppost/streetlights
Real speed on a road segment Real speed on a road segment during peak hourduring peak hour
3 dimensional graph3 dimensional graphUse 3D DijkstraUse 3D Dijkstra
Why not search in this 3 dim. Why not search in this 3 dim. graph ?graph ?
This will become a giant graph:
- constructing such a 3 dimensional graph (estimating travel times) would take too
much time
- performance of shortest path algorithm for such a graph will be very poor
Shortest path via dynamic routingShortest path via dynamic routing
Expert systemExpert systemBased on knowledege/experience of daily cardriverBased on knowledege/experience of daily cardriver
(entrance kleinpolderplein ypenburg)
(route ypenburg prins_claus)(file prins_claus badhoevedorp)(route badhoevedorp nieuwe_meer)(exit nieuwe_meer coenplein)
Translate routes to trajectories between junctions and assign labels entrance, route, file and exit to each trajectory
Design (1)Design (1)
Schematic overview of a P+R route.
Design (2)Design (2)
Static car and public transport Static car and public transport routesroutes
Dynamic car routeDynamic car route
P+R routeP+R route
Expert systemExpert system
(entrance kleinpolderplein ypenburg)
(route ypenburg prins_claus)
(file prins_claus badhoevedorp)
(route badhoevedorp nieuwe_meer)
(exit nieuwe_meer coenplein)
Translate routes to trajectories between junctions and assign labels entrance, route, file and exit to each trajectory
Example alternative routesExample alternative routesusing expert knowledgeusing expert knowledge
Implementation in CLIPSImplementation in CLIPS
Results of dynamic routingResults of dynamic routing
Based on historical traffic speed data dynamic routing is able to save approximately 15% of travel time
During special incidents (accidents, road work,…) savings in travel time increases
During peak hours savings decreases
User preferencesUser preferences
Shortest travel timePreference routing via highways, secondary
roads minimizedPreferred routing (not) via toll routesFastest route or shortest routeRoute with minimal of traffic jams
TrafficTraffic
Current systems developed at TUDelft
• Prediction of travel time using ANN (trained on historical data)
• Model of speed as function of time average over road segments/trajectories
• Static routing using Dijkstra algorithm• Dynamic routing using 3D Dijkstra• Dynamic routing using Ant Based Control algorithm• Personal Traveling Assistant online end of 2008
NN ClassifiersNN Classifiers
Feed-Forward BP Network– single-frame input– two hidden layers– logistic output function in
hidden and output layers– full connections between layers– single output neuron
NN ClassifiersNN Classifiers
Time Delayed Neural Network– multiple frames input– coupled weights in first hidden layer for time-
dependency learning– logistic output
function in hidden and output layers
(continued)
NN ClassifiersNN Classifiers
Jordan RecursiveNeural Network– single frame input– one hidden layer– logistic output function
in hidden and output layer– context neuron for time-dependency learning
(continued)
Factors which have impact on the Factors which have impact on the speedspeed
Factors• Time• Day of the week• Month• Weather• Special events
Impact on speedImpact on speed
Time
Impact on speedImpact on speed
Day of the week
Impact on speedImpact on speed
Day of the week
Impact on speedImpact on speed
Month
Impact on speedImpact on speed
Month
Impact on speedImpact on speed
Weather
Impact on speedImpact on speed
Special events
Model 1Model 1
Is it possible to predict average speed on a special location and time?
Model 1Model 1
P r e d i c t o r
o x ( t )
t
d ( t )
d a ( t )
w x ( t )
p e ( t )
s e ( t )
e e ( t )
s i e ( t )
h ( t )
Model 2Model 2
Is it possible to predict average time 25 minutes ahead on a special location with an error of less then 10% ?
Model 2Model 2
P r e d i c t o r
t
d ( t )
d a ( t )
w x ( t )
p e ( t )
s e ( t )
e e ( t )
s i e ( t )
h ( t )
o x ( t - t ) … o x ( t - 2 t ) o x ( t - d t )
o x ( t ) … o x ( t + t ) o x ( t + k t )
Model 3Model 3
…
Predictor
t
d(t)
da(t)
wx(t)
pe(t)
se(t)
ee(t)
sie(t)
h(t)
ox (t) … ox (t + t) ox (t + kt)
ox-ix (t - t) … ox-ix (t - 2t) ox-ix (t - dt)
ox-x (t - t) … ox-x (t - 2t) ox-x (t - dt)
ox(t - t) … ox (t - 2t) ox (t - dt)
Test results Model 1Test results Model 1
• 6 networks tested• Tuesday• A12 in the direction of Gouda• Best results with 5 neurons in hidden layer
Test results Model 1Test results Model 1
Test results Model 2Test results Model 2
• 9 networks tested• Tuesday• A12 in the direction of Gouda• Best results with 9 neurons in the hidden layer
Test results Model 2Test results Model 2
Test resultsTest results
Test resultsTest results
Results of the best performing network:
• 76% of the values with difference of 10% or less
• Average error is more than 20%• Deleting outliers: average error less than 9%
ConclusionsConclusions
• Existing research• Formula of Fletcher and Goss• Impact• Results
Current systemCurrent system
• Model (based on historical data)• Accidents and work on the road• Travel time (based on Recurrent neural
networks)• Data collection (average speed per segment, per
road)
Ant Based Control Ant Based Control Algorithm (ABC)Algorithm (ABC)
Is inspired from the behavior of the real antsIs inspired from the behavior of the real ants
Was designed for routing the data in packet switch networksWas designed for routing the data in packet switch networks
Can be applied to any routing problem which assumes dynamic Can be applied to any routing problem which assumes dynamic data like:data like:
Routing in mobile Ad-Hoc networks Routing in mobile Ad-Hoc networks Dynamic routing of traffic in a cityDynamic routing of traffic in a city Evacuation from a dangerous area ( the routing is done to multiple Evacuation from a dangerous area ( the routing is done to multiple destinations )destinations )
Natural ants find the Natural ants find the shortest routeshortest route
Choosing randomlyChoosing randomly
Laying pheromoneLaying pheromone
Biased choosingBiased choosing
3 reasons for3 reasons for choosing the shortest choosing the shortest
pathpathEarlier pheromone (trail completed
earlier)More pheromone (higher ant density)Younger pheromone (less diffusion)
AApppplication of ant lication of ant behaviourbehaviour in network in network
managementmanagement
Mobile agentsProbability tablesDifferent pheromone for every destination
Traffic mTraffic mooddel el inin oneone node node
i j k
1 pi1 pj1 pk1
2 pi2 pj2 pk2
.. ..
N piN pjN PkN
Routing tableRouting table
Local TrafficLocal Traffic StatisticsStatistics
NetworkNetworknodenode
des
tin
atio
ns
des
tin
atio
ns
neighboursneighbours
1 2 .... N
μ1;σ1; W1 μ2; σ2; W2 … μN; σN; WN
Routing tableRouting table
To forward the packets, each node has a routing table
6 8 101 0.4 0.5 0.1
2 0.7 0.2 0.1
…11 0.4 0.1 0.5
All possible destinations
Neighbours
1
4
2
3
7
9
8
10
11
65
Generating virtual ants Generating virtual ants (agents)(agents)
1. ants are launched on regular intervals
- it goes from source to a randomly chosen destination
1
4
2
1 3
7
9
8
10
11
65
11
Chosing the next nodeChosing the next node
2
1 5
2. Ant chooses its next node according to a probabilistic rule:
-probabilities in routing table;
-traffic level in the node;
2 5
11 0.4 0.6
neighbours
destination
2
Sniffing the networkSniffing the networkAnt moves towards its destination
…and it memories its path
2
11 t5
10 t4
9 t3
3 t2
2 t1
1 t0
11
8
4
3
7
9
10
11
65
11
3 9
10
2
8
The backward antThe backward ant
Ant goes back using the same path
11 t5
10 t4
9 t3
3 t2
2 t1
1 t0
1
10
1
11
10
2
4
3
7
9
65
3 9
Updating the Updating the probability tablesprobability tables
On its way to the source, ant updates
routing tables in all nodestable in 1 before update
table after update 2 511 0.4 0.6
2 511 0.8 0.2
2
8
1
10
1
11
10
2
4
3
7
9
65
3 9
SimpleSimple formula formulaee
Calculate reinforcement:
Update probabilities:
Complex formulaeComplex formulae
P’jd=Pjd + r(1-Pjd)
P’nd=Pnd - rPnd , n<>j
Map representation for
simulation
Simulation Simulation environmentenvironment
ResultsResults
Number of timesteps8,0006,0004,0002,0000
Ave
rage
sm
art r
oute
tim
e
160
140
120
100
80
60
40
20
0
Number of timesteps8,0006,0004,0002,0000
Ave
rage
sta
ndar
d ro
ute
time
180
160
140
120
100
80
60
40
20
0
Average trip time for the cars using the routing system
Average trip time for the cars that not use the routing system
Simulation environmentSimulation environment
Architecture
GPS-satellite
Vehicle
Routing system
Simulation
GPS-satellite
Vehicle
Routing system
• Position determination
• Routing
• Dynamic data
Communication flowCommunication flow
Routing systemRouting system
Routing system
Route finding system
MemoryTimetable updating system
Dynamic data
Routing
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
ExperimentExperiment
Personal intelligent Personal intelligent travel assistanttravel assistant
PITA is multimodal, speech, touch, text, picture,GPS,GPRSPITA is able to find shortest route in time using dynamic traffic
dataPITA is able to launch robust agents finding information on
different sites (imitating HCI)PITA computes shortest route using AI techniques (expertsystems,
case based reasoning, ant based routing alg, adaptive Dijkstra alg.)
PDAPDA
Digital AssistantDigital Assistant
Digital assistant has characteristics of a human operatorAmbient IntelligentContext awarenessAdaptive to personal characteristicsIndependent, problem solverComputational, transparent solutionsMultimodal input/output
Schematic overview of Schematic overview of the PITA componentsthe PITA components
Overview of Overview of communicationcommunication
Wireless network Wireless network layers:layers:
human human communication communication layerlayer
virtual virtual communicationcommunication
virtual virtual coordinating agentcoordinating agent
Actors, Agents and Actors, Agents and ServicesServices
Layers of Layers of communication:communication:
overlapping overlapping clouds of actors clouds of actors ( human sensors, ( human sensors, perception perception devices)devices)
corresponding corresponding clouds of clouds of representative representative agentsagents
clouds of clouds of servicesservices
Mobile Ad-Hoc Mobile Ad-Hoc NetworkNetwork
PITA system in a trainPITA system in a train
Travelers in train have device able to set up a wireless network in the train or to communicate via e-mail, connected to GPS
Position of traveler corresponds to position of trains
(de-)Centralized systems knows the position of train at every time and is able to reroute and inform travelers in dynamically changing environments
A technical view of the PITA system
The personal agent
The handheld interface model
The handheld application model
A handheld can be connected to the rest of the system by only an ad-hoc wireless connection
Sequence diagram of the addition of a new delay
The distributed agent platform architecture
User profiles
THE MAPPING BETWEEN THE USER PROFILES AND THE SEARCHPARAMETERS
The route plan to Groningen Noord
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