Using Inductive Loop Signature Re-Identification for Travel Time

Transcription

commeaT.E.C.
traffic engineering & consulting
Using Inductive Loop Signature Re-Identification
for Travel Time Measurement
– Use Case Bremerhaven
Jonas Lüßmann – TU München, Chair of Traffic Engineering and Control (TUM-vt)
Florian Schimandl – TUM-vt
Friedrich Maier – commeaT.E.C. – traffic engineering & consulting
Fritz Busch – TUM-vt
6th International Symposium “Networks for Mobility”
Stuttgart, September 27, 2012
0
Friedrich Maier: Using Inductive Loop Signature Re-identification for Travel Time Measurement
Networks for Mobility, September 27, 2012
Inductive loops, vehicle signatures
commeaT.E.C.
60
Detuning %o
Verstimmung in %o
50
50
40
30
20
10
0
49800
0
0
1
49850
2
49900
3
49950
4
50000
5
50050
6
50100
7
50150
8
50200
9
50250
Time [s]
Num m erierung der Stützstellen (unnorm iert)
ISAR: Inductive loops – Signature Analysis for vehicle Re-identifcation and
travel time measurement – project at TUM-vt 2004-2006
1
ISAR-Method – basics
commeaT.E.C.
Standardised signatures !!!
Relevant signature
features:
2
›
Signature
derivative
›
maximum
detuning
Filter criteria:
› Vehicle „class“
› Temporal
distance
› Small
similarity
ISAR-Method – basics
commeaT.E.C.
yA
yB
y‘A
y‘B
U… uniformness, similarity
cal… calibration factor
>
x
Matching equation:
U A,B
xEnd min( y , n; y ) 
 min(max y A  cal A ; max y B  cal B )  
1
A ,n
B, n








max(max
y

cal
;
max
y

cal
)
(
x
End

x
Start

1
)
max(
y
;
y
)
n xStart
A
A
B
B  
A ,n
B, n 



Matching of the max. detuning
(Signatures not standardised)
3
Matching of the signature derivatives‘ shapes
Field test Munich 2006
commeaT.E.C.
›
>2.000 vehicles
at each cross
section
›
~250 vehicle
crossing both
cross sections
›
38 correct reidentifications,
7 errors
Error
Examples
4
Project AMONES
commeaT.E.C.
Aim:
›
a
Evaluation of different methods of
network control by analysing simulation
results and collected data
Test site Bremerhaven (Northern Germany):
›
9 intersections controlled by traffic lights
›
Intersections equipped with inductive loops
›
Some ANPR-systems temporarilly installed
Side effects of collecting inductive signatures:
›
Evaluation of the ISAR-method using ANPRdata as reference
›
If the ISAR-method works fine:
additional data to evaluate the traffic light
control algorithms
Adapted from Openstreetmap
5
Test site installations
commeaT.E.C.
(1)
(2)
(3)
(4)
Picture: Example from Munich
(1) Control box with detectors and electric power supply
(2) Computer to collect the loop detuning (10 PCs in
Bremerhaven placed in the control boxes and
connected to 20 detectors, sampling rate 125 Hz)
(3) ANPR-Camera with
(4) Camera computer (in Bremerhaven integrated in the
camera body, supervised by the Institute of Road and
Transportation Science of the University of Stuttgart)
6
AMONES: data collection reality
commeaT.E.C.
› Signature analysis for driving direction south:
› 2 PCs at (1) – 2 approaches – and (5)
› 1 PC at (3), (8) and (9)
X
› 3 PCs at (4), 2 approaches
› ANPR-detection at (1), (5) and (9)
› wrong volumes and useless signatures at (8)
› High in- and outflow rate between (5) and (9) due
to large parking decks
› Bad prospects for vehicle re-identification
› ~ 25 re-identifications at (5)-(9) per day
›
Between 80 and 90 re-identifications on the other
sections
›
No improvements with the data from (4)
Let‘s see the results from (1)-(3) and (3)-(5)
X
X
X
Adapted from Openstreetmap
7
AMONES: Route (1,3)
commeaT.E.C.
Travel time [s]
Travel times at route R(1,3) on February 17th 2009
Time of day
8
›
No ANPR-Installation at (3)
›
Qualitatively similar results on route (3,5)
AMONES: Route (1,5)
commeaT.E.C.
Travel time [s]
Travel times at route R(1,5) on February 17th 2009
Time of day
›
9
ISAR travel times: addition of routes (1,3) and (3,5)
AMONES: Re-ident. frequency
commeaT.E.C.
10
Potential of travel times
commeaT.E.C.
v/v0 [-]
ANPR-travel time [min]
(route with many links)
Function to estimate link-related v/v0:
11
›
Using a segemented regression approach
›
Using ANPR travel times as input data
commeaT.E.C.
Temporal
offset
Temporal
offset
Functions to estimate
link-related v/v0 [-]:
Functions to estimate travel
times on a route [min]:
12
›
Using ANPR-travel times
[min] as input data
›
Using local occupancy
[%] as input data
›
With different
temporal offsets
›
With different
temporal offsets
commeaT.E.C.
›
Estimation of link-related v/v0 with ANPR-travel times as input data
Sun,24:00
v/v0
Mon, 0:00
A9
B13
13
A99
Last slide
commeaT.E.C.
›
There is more information in vehicle signatures than only classification:
they also allow the collection of travel time data to a certain extend
›
These travel times can enrich the data basis for the evaluation of traffic
management measures
›
But: the data collection with our algorithm and equipment is still
difficult
›
Travel times include more than only travel times between two cross
sections:
in combination with historic fleet data they also offer estimations of the
current spatial distribution of travel time loss on the detected route
There is still a lot of information covered in the data we already collect
– we just have to elaborate it!
14
The end.
commeaT.E.C.
Contact information:
15
›
jonas.luessmann@vt.bv.tum.de
›
florian.schimandls@vt.bv.tum.de
›
friedrich.maier@commea-tec.de
›
fritz.busch@vt.bv.tum.de

Using Inductive Loop Signature Re-Identification for Travel Time

Transcription

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