Transcript Average speed prediction using Artificial Neural Networks

Dynamic routing versus static
routing
Prof. drs. Dr. Leon Rothkrantz
http://www.mmi.tudelft.nl
http://www.kbs.twi.tudelft.nl
Outline presentation
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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
Introduction
Problem definition
• Find the shortest/fastest route from A to B using
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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
Traffic
Testbed: graph of highways
MONICA network
Many sensors/wires along the road to
measure the speed of the cars
Smart Road
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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
during peak hour
3 dimensional graph
Use 3D Dijkstra
Why not search in this 3 dim.
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 routing
Expert system
Based on knowledege/experience of daily cardriver
 Translate routes to trajectories between
junctions and assign labels entrance, route, file
and exit to each trajectory
(entrance
kleinpolderplein
ypenburg)
(route ypenburg prins_claus)
(file prins_claus badhoevedorp)
(route badhoevedorp nieuwe_meer)
(exit nieuwe_meer coenplein)
Design (1)
Schematic overview of a P+R
route.
Design (2)
Static car and public transport
routes
Dynamic car route
P+R route
Expert system

Translate routes to trajectories between
junctions and assign labels entrance, route, file
and exit to each trajectory
(entrance
kleinpolderplein
ypenburg)
(route ypenburg prins_claus)
(file
prins_claus
badhoevedorp)
(route
badhoevedorp
nieuwe_meer)
(exit nieuwe_meer coenplein)
Example alternative routes
using expert knowledge
Implementation in CLIPS
Results 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 preferences

Shortest travel time
 Preference routing via highways, secondary
roads minimized
 Preferred routing (not) via toll routes
 Fastest route or shortest route
 Route with minimal of traffic jams
Traffic
Current systems developed at TUDelft
• Prediction of travel time using ANN (trained on
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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 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 Classifiers

(continued)
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
NN Classifiers

(continued)
Jordan Recursive
Neural Network
– single frame input
– one hidden layer
– logistic output function
in hidden and output layer
– context neuron for time-dependency learning
Factors which have impact on the
speed
Factors
• Time
• Day of the week
• Month
• Weather
• Special events
Impact on speed
Time
Impact on speed
Day of the week
Impact on speed
Day of the week
Impact on speed
Month
Impact on speed
Month
Impact on speed
Weather
Impact on speed
Special events
Model 1
Is it possible to predict average speed on a
special location and time?
Model 1
d(t)
t
wx(t)
da(t)
se(t)
pe(t)
Predictor
ox(t)
sie(t)
ee(t)
h(t)
Model 2
Is it possible to predict average time 25
minutes ahead on a special location with an
error of less then 10% ?
Model 2
d(t)
t
ox(t - t)
ox (t - 2t)
…
wx(t)
da(t)
se(t)
pe(t)
sie(t)
ee(t)
h(t)
ox (t - dt)
Predictor
ox (t)
ox (t + t)
…
ox (t + kt)
Model 3
d(t)
t
ox(t - t)
ox (t - 2t)
…
wx(t)
da(t)
se(t)
pe(t)
sie(t)
ee(t)
h(t)
ox (t - dt)
Predictor
ox-x (t - 2t)
…
ox-ix (t - t)
ox-ix (t - 2t)
…
ox-x (t - dt)
…
ox-x (t - t)
ox-ix (t - dt)
ox (t)
ox (t + t)
…
ox (t + kt)
Test results Model 1
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6 networks tested
Tuesday
A12 in the direction of Gouda
Best results with 5 neurons in hidden layer
Test results Model 1
Test results Model 2
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9 networks tested
Tuesday
A12 in the direction of Gouda
Best results with 9 neurons in the hidden layer
Test results Model 2
Test results
Test results
Results of the best performing network:
• 76% of the values with difference of 10% or
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less
Average error is more than 20%
Deleting outliers: average error less than 9%
Conclusions
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Existing research
Formula of Fletcher and Goss
Impact
Results
Current system
• Model (based on historical data)
• Accidents and work on the road
• Travel time (based on Recurrent neural
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networks)
Data collection (average speed per segment, per
road)
Ant Based Control
Algorithm (ABC)
 Is inspired from the behavior of the real ants
 Was designed for routing the data in packet switch networks
 Can be applied to any routing problem which assumes dynamic
data like:
Routing in mobile Ad-Hoc networks
 Dynamic routing of traffic in a city
 Evacuation from a dangerous area ( the routing is done to multiple
destinations )

Natural ants find the
shortest route
Choosing randomly
Laying pheromone
Biased choosing
3 reasons for
choosing the shortest
path
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Earlier pheromone (trail
completed earlier)
 More pheromone (higher ant
density)
 Younger pheromone (less
diffusion)
Application of ant
behaviour in network
management

Mobile agents
 Probability tables
 Different pheromone for every destination
Traffic model in one node
Routing table
Network
node
i
j
k
1
pi1
pj1
pk1
2
pi2
pj2
pk2
piN
pjN
PkN
.. ..
destinations
neighbours
N
Local Traffic
Statistics
1
2
....
N
μ1;σ1; W1
μ2; σ2; W2
…
μN; σN; WN
Routing table
To forward the packets, each node has a routing table
Neighbours
8
4
7
6
5
1
6
11
10
3
9
1
2
…
11
8
10
0.4 0.5
0.1
0.7 0.2
0.1
0.4 0.1
0.5
2
All possible
destinations
Generating virtual ants
(agents)
1.
8
4
11
11
7
6
5
1
10
3
2
ants are launched on
regular intervals
9
- it goes from source
to a randomly chosen
destination
Chosing the next node
2. Ant chooses its next node according to a
probabilistic rule:
-probabilities in routing table;
-traffic level in the node;
1
neighbours
2
5
destination 11 0.4 0.6
5
2
Sniffing the network
8
4
11
11
7
6
5
10
10
3
1
2
9
Ant moves towards its
destination
…and it memories its
path 11 t
5
10
9
3
2
1
t
t
t
t
t
4
3
2
1
0
The backward ant
8
4
11
7
6
5
10
10
3
1
2
9
Ant goes back using the
same path
11
10
9
3
2
1
t
t
t
t
t
t
5
4
3
2
1
0
Updating the
probability tables
8
4
11
7
6
5
10
10
3
1
2
On its way to the source,
ant updates
routing tables in all nodes
 table in 1 before update
11
9
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2
5
0.4 0.6
table after update
11
2
0.8
5
0.2
Simple formulae
Calculate reinforcement:
Update probabilities:
Complex formulae
P’jd=Pjd + r(1-Pjd)
P’nd=Pnd - rPnd , n<>j
Simulation
environment
Map
representation
for
simulation
Average smart route time
Results
Average trip time for the
cars using the routing
system
160
140
120
100
80
60
40
20
0
0
2,000
4,000
6,000
Number of timesteps
8,000
0
2,000
4,000
6,000
Number of timesteps
8,000
Average standard route time
180
Average trip time for the
cars that not use the routing
system
160
140
120
100
80
60
40
20
0
Simulation environment
Architecture
Simulation
GPS-satellite
Vehicle
Routing
system
Communication flow
GPS-satellite
Vehicle
Routing
system
• Position
determination
• Routing
• Dynamic data
Routing system
Routing system
Dynamic
data
Timetable
updating
system
Memory
Route
finding
system
Routing
Routing system (2)
Timetable
1
2
3
6
4
5
7
1
2
4
5
…
1
2
4
-
12 15
5 …
-
…
11
-
-
18 …
14
-
-
13 …
-
18 14
-
…
… … … … …
Experiment
Personal intelligent
travel assistant
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PITA is multimodal, speech, touch, text,
picture,GPS,GPRS
 PITA is able to find shortest route in time
using dynamic traffic data
 PITA 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.)
PDA
Digital Assistant
Digital assistant has characteristics of a human
operator
 Ambient Intelligent
 Context awareness
 Adaptive to personal characteristics
 Independent, problem solver
 Computational, transparent solutions
 Multimodal input/output
Schematic overview of
the PITA components
Overview of
communication
Wireless network
layers:
human
communication
layer

virtual
communication

virtual
coordinating agent

Actors, Agents and
Services
Layers of
communication:
overlapping
clouds of actors (
human sensors,
perception
devices)

corresponding
clouds of
representative
agents

clouds of
services

Mobile Ad-Hoc
Network
PITA system in a train
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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
THE MAPPING BETWEEN THE USER
PROFILES AND THE SEARCH
PARAMETERS
User profiles
Search times
The route plan to Groningen Noord