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ABSTRACT
Title: AIMSUN2 Simulation of a Congested Auckland Freeway
Author: John T Hughes, BE (Civil), MIPENZ Regional
Transportation Engineer Transit New Zealand, P. O. Box 1459
Auckland, New Zealand
Contact Details: Tel: 64-9-377 7092
Fax: 64-9-307 6843 Email: [email protected]
Introduction and Objectives The objective of this paper is to
present an outline of the model building process and selected
findings from a traffic simulation model of a congested 9.7 km
section of an urban freeway in Auckland, New Zealand. Field data
was collected to provide a comprehensive set of New Zealand traffic
characteristics for the freeway micro-simulation model. The data
set constitutes a detailed "snapshot" of traffic conditions over
one week (23 to 29 September 1997), with additional detail on
particular days. The data, which include traffic speeds, volumes,
headways, accelerations and lane change counts, are being used to
build an AIMSUN2 traffic simulation model. This is a new tool that
can provide input into transport investment decisions in
Auckland.
Simulation Model AIMSUN2 (Advanced Interactive Microscopic
Simulator for Urban and Non-Urban Network) is a microscopic,
stochastic model for simulating traffic on road networks. It is
part of the GETRAM (Generic Environment for Traffic Analysis and
Modelling) software suite developed at the Universitat Politcnica
de Catalunya in Barcelona, Spain GETRAM (Barcelona and Ferrer 1997,
Monteroet al 1998) consists of a user-friendly graphical interface,
a traffic network graphical editor (TEDI, Traffic Editor)
supporting any kind of road type or network geometry, a network
database and a module for storing and presenting results. It
includes an animated simulation display, which shows vehicles
moving through the network. The model can simulate a range of
traffic management features including incident detection and
surveillance systems, variable message signs and wide area traffic
control strategies. Simulating predictive control and guidance
strategies are also potentially feasible. AIMSUN2 has a wide
variety of possible applications in traffic management of Aucklands
congested arterial road network. In particular it may be an
important enabling technology to serve as a testbed for an Advanced
Traffic Management System (ATMS) being developed in Auckland.
Transit New Zealand (Transit), the national State Highway
authority, is implementing the ATMS, the first portions of which
are scheduled to be operational by mid 1999.
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The traffic data collected on Aucklands Southern Motorway are
being used to calibrate and validate the model to determine the
accuracy with which it can represent real traffic flows. If it is
shown that the model is accurate over a section of motorway by
comparison with extensive measured data then confidence can be had
in simulations of other motorway sections and features where less
comprehensive data is available. Study Area and Scope Auckland is
located towards the north of New Zealands North Island. With a
population of some 1.1 million people it is the countrys largest
urban centre and is growing at 2.5% per year. The study area
(Figure 1) is located south of the central business district (CBD)
and passes through the regions core industrial areas of Penrose and
Mt Wellington. The study section is a 9.7 km length of the Southern
Motorway extending from Panama Road (just south of Mt Wellington
Highway) in the south to the Khyber Pass in the north. The motorway
section crosse relatively flat terrain with an isolated maximum
grade of 4.0% and the balance at 3.0% or less. In 1997 it carried
bi-directional Average Annual Daily Traffic (ADDT) volumes ranging
from 109,000 vehicles per day (vpd).
Figure 1 Study Area
Spaghetti Junction (CBD)
Ellerslie
Gillies Avenue Market
Road Greenlane
Panama Road
Mt Wellington Highway
Tamaki River
Khyber Pass
Main Highway
Penrose Road
Southern Motorway (SH1)
Southern Motorway (SH1)
0 1 2 km
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To obtain information that was comprehensive, yet at a
sufficiently high level of detail, many different types of data
were collected simultaneously at varying intervals along the
motorway. Automatic count data was collected over a full 7-day
week. Resource-intensive data collection methods, including video
taping, aerial photography and laser-gun speed profiles, were
measured on a single day, mostly within that week. Most of the data
are being used to calibrate the AIMSUN2 model. Some data not used
in the calibration are being used as benchmarks to which simulation
model output is being compared in order to validate the model. The
work reported in this paper is a preliminary investigation into the
ability of the model to reproduce traffic flows in the northbound
direction of this motorway corridor. The general approach was to
define the motorway links, apply field-measured traffic flows at
the network boundaries and then calibrate the model by fine-tuning
sensitive parameters to seek agreement with measured data at
intermediate points within the corridor. These steps are repeated
to seek a good agreement with the field results. Model Requirements
In common with many simulation models (Wang and Cassidy 1995, Hua
Heng 1989, Quadstone 1996) AIMSUN2 requires input information
defining the road network geometry, traffic stream conditions and
driver and vehicle characteristics. Geometric Information For this
project the motorway geometric layout was obtained as a CAD (dxf)
file showing curb lines and edges of the road pavement. The map was
geometrically accurate, having been produced by aerial
photogrammetry. However it was not particularly detailed and
omitted key features, such as lane lines and other pavement
markings. Additional information on the widths and number of lanes
and the lane configuration at ramp locations was obtained from a
variety of sources. These included historic construction drawings
dating back to the 1960's, a series of 1:1,000 scale
ortho-corrected black and white photographic prints from 1994 and
uncorrected colour photography flown during the traffic surveys on
26 September 1997. The dxf file was imported into TEDI and the
roadway links created by mouse dragging and clicking the
appropriate section drawing tools over the map background. While
this is the usual method of creating a new AIMSUN2 model an
alternative is available. That is to import an EMME/2 network
model, with it's centriod connector structure, directly into GETRAM
using the optional module available for this purpose (Montero et al
1998). Table 1 shows the basic road section parameters adopted in
this model from field studies of the southern motorway (Hughes
1998).
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Traffic Flow Information Traffic flow information includes trip
demands across the network, fleet composition (cars, trucks, buses
etc) and traffic control mechanisms such as traffic signals and
intersection priority signage. As this model involves only the
motorway through lanes and ramps, and not the adjacent arterial
network, it has no at-grade intersections, priority signs or
signals. During one week in September 1997, additional traffic data
was obtained over and above that normally collected in Transits
ongoing traffic monitoring program. Permanent count stations with
inductive loop detectors are located on the motorway through lanes
at each end of, and at 2 locations within, the study area. These
record time-interval count and speed data plus vehicle headways and
length classifications. For this study data was obtained by using a
video classification system for 2 hours each, at four additional
locations in the morning and afternoon peaks on Friday 26
September. Unclassified vehicle counts were collected on all 17
ramps entering and leaving the motorway study section (Hughes
1998). In the past micro-simulation models have tended to require
defined traffic flows on each entry link to the network and
specified turning percentages (by vehicle type) at each
intersection or off-ramp. With this approach the vehicles entering
the network have no "knowledge" of their intended route or
destination. The more recent trend in microscopic simulation is for
traffic inputs to be defined as time-sliced origin-destination
matrices. This allows greater flexibility in modelling traffic
scenarios and problems involving route assignment can be
investigated at the microscopic level. AIMSUN2 allows the use of
both methods of traffic data input. Route choice is not an issue in
this Auckland Motorway model, and the generation of time-sliced
matrices can also be difficult and time consuming. However the
matrix method has been used in this study because the alternative,
requiring the calculation of turning percentages from count data by
vehicle type and time interval, would also not have been straight
forward. The latter method could give incorrect flows when
simulating transient phenomena such as lane blockages. Errors could
occur, for example, if the motorway through lanes were blocked up
stream of a bottle neck that is normally caused by a heavy on-ramp
merge. The through lane blockage would allow higher than normal
flows to enter the motorway at the ramp. Application of the normal
(un-blocked) percentage for vehicles leaving the motorway at the
next down stream off ramp would probably be erroneous if most of
the through traffic had actually been blocked from reaching that
point by an accident. Trip matrices were obtained from a 1992
origin-destination postcard survey for each of three different
vehicle types during the 7.00am too 9.00am morning commuter peak .
These were manually factored in a spreadsheet to approximately
match measured traffic flows entering and leaving the motorway
study section on 26 September 1997. The end result was a total of
48 matrices (3 vehicle types per quarter hour from 6am to 10am)
which were applied to the study network.
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Driver and Vehicle Information Vehicles and drivers have a range
of characteristics which effect the way they travel through a road
network. These include mechanical attributes of the vehicle (eg:
size, performance levels) and aspects of driver behaviour (eg:
desired speed, acceleration and gap preferences). In GETRAM this
type of information is input as parameters pertaining to vehicle
types, any number of which can be defined by the user. The data
which may be entered for each vehicle type are shown in Figure 2.
These include desired speed, acceleration, normal and emergency
deceleration, maximum yield time and minimum vehicle spacing when
stopped in a queue. The queuing up and queue leaving speeds control
whether or not a vehicle will enter an intersection that contains
vehicles which are "queued", as defined by these parameters. In a
traffic stream these data may vary stochastically between vehicles.
For each data item (desired speed, acceleration etc) the values
attributed to individual vehicles are considered normally
distributed and the user may define the distribution parameters
(mean, standard deviation, minimum and maximum values).The three
vehicle types currently being used for this study are shown in
Table 2. Table 2 Vehicle Type Classifications
Vehicle Type Classification Length Class (metres) Weight Class
(tonnes) Passenger Car (CAR) < 5.5m < 3.5t Light Commercial
(LCV) 5.5m to 11.0m < 3.5t Heavy Commercial (HCV) > 11.0 >
3.5t AIMSUN2 does not use vehicle weight as a model parameter.
However, pending more specific data, the weight classes have been
assumed to correspond to the length classes shown in the table.
This enabled trip matrices from an earlier postcard Origin
Destination survey to be used in the study. The following data was
obtained from individual vehicles recorded at three of the motorway
ATMS sites on 26 September 1997. Table 3. Vehicle Length (m)
Mean Min Max Std. Dev. Sample Size Cars 4.39 ? 5.5 0.43 82,392
LCVs 7.73 7.5 11.5 1.49 4,535 HCVs 16.64 11.5 ? 3.55 2,168 Total:
89,095
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Table 4. Vehicle Desired Speeds
Free Speed (km / hr) Mean Min Max Std. Dev. Sample Size
Cars 96 ? ? 15.3 3217 LCVs 88 ? ? 12.9 306 HCVs 90 ? ? 14.9 173
Total: 3,696 The free speed data were derived from a sample of
vehicles travelling at a time gap of 10 seconds or more behind the
vehicle ahead. A small number of data records were discarded as
outliers. These were erroneous length records (ie. < ?? or >
??m) and suspect free-speed records (ie. < ?? or > ?? km/h).
Maximum Vehicle Acceleration As each simulated vehicle enters the
modelled road network it is assigned three speed-change parameters.
These are its maximum acceleration rate and its normal and maximum
(or emergency braking) deceleration rates. Although it is difficult
to measure speed change parameters which are characteristic of
whole fleet and driver populations, some information was gleaned
from three local sources. These were a study of traffic
decelerating on a motorway off ramp (Bennett 1993), instrumented
vehicle trials for development of a fleet emissions control
strategy (Ministry of Transport 1997), and some laser gun speed
change measurements by the author. Bennett recorded average
deceleration rates from 2000 vehicles on Aucklands Grafton off ramp
of between from 0.46 m/s and 2.34 m/s, for various approach and
final speeds. He also cites a study on urban streets in Palmerston
North, New Zealand (ATS 1990) which reported the following maximum
rates for vehicles travelling at less than 70km/hr . Table 5. ATS
Study
Maximum Rate m/s Vehicle Acceleration Deceleration Passenger Car
1.08 -1.72 Heavy Trucks 0.40 -1.19 The New Zealand Ministry of
Transport (MOT) conducted an extensive series of trials using 23
instrumented vehicles following qualitatively defined drive cycles
on Auckland area roads (Ministry of Transport 1997). The vehicles
were all cars of various ages and conditions with engine capacities
ranging from 1.3 to 4.1 litres. The aggregated results included the
following:
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Table 6. NZ MOT Study
Road Type Traffic Type Average Speed (km/hr)
Max Speed (km/hr)
Max Acceleration
(m/s)
Acceleration Standard
Deviation (m/s) Suburban Interrupted
Aggressive 31.8 74.9 4.0 1.17
Suburban Interrupted 23.4 58.0 2.8 0.79 Urban Interrupted 15.4
54.9 3.2 0.88 Urban Congested 7.7 49.3 2.8 0.65 Motorway Congested
32.9 74.1 1.8 0.66 The author measured vehicle speeds, and hence
accelerations at one location on Aucklands Southern Motorway and at
signalised urban arterial intersection on Quay Street. Speed
changes were measured over successive pairs of observations of each
vehicle. Scatter plots of the resulting data are shown in Figure 2
and summarised in Table ? below. Figure 2 Laser-measured Speed
Changes. Each of the three sources of field data give an indication
of the range of speed-change values appropriate for the model.
However none of them closely corresponds to the desired statistics,
namely the probability distributions for the maximum accelerations
and decelerations experienced by the population of vehicles and
drivers. Figure 3 shows the vehicle parameters adopted from the
model runs reported in this paper.
HeavyCommercial Vehicles
-4
-3
-2
-1
0
1
2
3
4
0 50 100
Speed (km/hr)
LightCommercial
Vehicles
-4
-3
-2
-1
0
1
2
3
4
0 50 100
Speed (km/hr)
Cars
-4
-3
-2
-1
0
1
2
3
4
0 50 100
Speed (km/hr)
Acc
. (m
/s^2
)
So uth ern Mo to rw ay Qu ay S tre et
Acc
eler
atio
n (m
/s)
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Figure 3 Vehicle Parameters Used in this Model Motorway Model A
model has been constructed in GETRAM of the northbound lanes of the
study section of the Southern Motorway. It consists of the three
motorway through lanes and a short length of each interchange ramp.
Traffic has been applied to this network as three vehicle types
(cars, LCVs and HCVs). A trip matrix was produced for each vehicle
type during each 15 minute time period for an extended morning
commuter peak from 6.00am to 10.00am. The traffic flows applied to
the model are an approximation to the actual flows that existed on
the motorway on Friday 26 September 1997. The raw traffic data
measured in the field were manually adjusted to make up for several
deficiencies including the lack of length classification on the
ramps, under - counting due to equipment faults and the fact that
the video classifier sites operated only for the middle 2 hours
rather than the full 4 hour extended peak. These gaps in the field
data were filled by comparison with flow data from other days and
missing length classification percentages were assumed from the
1992 postcard survey.
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An accident just south of the study area blocked lane 1 for
about 5 minutes at 7.30am on the Friday morning. This resulted in a
reduction in flows entering the study area and a corresponding
increase in vehicle speeds. The effect was removed from the traffic
entering the model by averaging the flow rates before and after the
blockage period. After inputting the road sections and trip
matrices the model was run and some parameters adjusted by trial
and error to try to replicate traffic conditions observed in the
field. Model Outputs Figure 4 shows total vehicle flows by lane at
several points within the model. It can be seen during periods of
low flow ( the left most, outside lane ) and lane 3 is little
utilised. While this is normal drive behaviour the Figure 4 Model
Spreads and Flows (5 minute intervals)
Greenlane On-Ramp
0500
1,0001,5002,000
6:006:3
07:0
07:3
08:0
08:3
09:0
09:3
010
:00
0
50
100
Greenlane - Lane 1
0500
1,0001,5002,000
6:006:3
07:0
07:3
08:0
08:3
09:0
09:3
010
:000
50
100
Greenlane Off-Ramp
0500
1,0001,5002,000
6:006:3
07:0
07:3
08:0
08:3
09:0
09:3
010
:000
50
100
Ellerslie On-Ramp
0500
1,0001,5002,000
6:006:3
07:0
07:3
08:0
08:3
09:0
09:3
010
:000
50
100
Ellerslie - Lane 1
0500
1,0001,5002,000
6:006:3
07:0
07:3
08:0
08:3
09:0
09:3
010
:000
50
100
Penrose Road (3 Lanes)
02,0004,0006,000
6:006:3
07:0
07:3
08:0
08:3
09:0
09:3
010
:00
0
50
100
F low (veh /h r)S pe ed (k m/hr)
Legend
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Figure 5 Field Flow Rates (on 26 September 1998)
Figure 6 Speed-Flow Scatter Plots (5 minute intervals)
Ellerslie: M'way Through Lanes
0500
10001500200025003000
6:00
6:20
6:40
7:00
7:20
7:40
8:00
8:20
8:40
9:00
9:20
9:40
10:00
Lane 1 Lane 2 Lane 3
Ellerslie: On-Ramp
0200400600800
10001200140016001800
06:00
:00
06:20
:00
06:40
:00
07:00
:00
07:20
:00
07:40
:00
08:00
:00
08:20
:00
08:40
:00
09:00
:00
09:20
:00
09:40
:00
10:00
:00
Ellerslie On-Ramp
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Figure 7 Field and Modelled Speeds
Dilworth - Lane 1
0102030405060708090
0 500 1000 1500 2000 2500Flow (veh/hr)
Spee
d (k
m/h
r)
ModelField amField pm
Panama - Lane 1
0
20
40
60
80
100
120
0 500 1000 1500 2000 2500Flow (veh/hr)
Spee
d (k
m/h
r)
ModelField am
Dilworth - Lane 2
0
20
40
60
80
100
120
0 500 1000 1500 2000 2500Flow (veh/hr)
Spee
d (k
m/h
r)
ModelField amField pm
Panama - Lane 2
0
20
40
60
80
100
120
0 500 1000 1500 2000 2500Flow (veh/hr)
Spee
d (k
m/h
r)
ModelField am
Dilworth
0
50
100
6:00
6:30
7:00
7:30
8:00
8:30
9:00
9:30
10:00
Greenlane
10
60
110
6:00
6:30
7:00
7:30
8:00
8:30
9:00
9:3010
:00
Main Highway
0
50
100
6:00
6:30
7:00
7:30
8:00
8:30
9:00
9:3010
:00
Ellerslie
050
100150
6:00
6:30
7:00
7:30
8:00
8:30
9:00
9:3010
:00
Penrose Road
0
50
100
6:00
6:30
7:00
7:30
8:00
8:30
9:00
9:3010
:00
Mt Wellington
0
50
100
6:00
6:30
7:00
7:30
8:00
8:30
9:00
9:3010
:00
Legend 56:307:
007:
308:
008:
309:
009:3
010
:00M od elled Sp ee d (km /h r)
Field S pe ed (k m/hr)
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Figure 8 Figure 9 Aerial Photo and Model Outputs, north of
Greenlane Interchange
Vehicle T ypes
D e n s ityV e h ic le s /k m
050
100150
7:30
7:40
7:50
8:00
8:10
8:20
8:30
Cou
nt (5
min
)
050
100150
7:30
7:40
7:50
8:00
8:10
8:20
8:30
Cou
nt (5
min
)
050
100150
7:30
7:40
7:50
8:00
8:10
8:20
8:30
Cou
nt (5
min
)
020406080
7:30
7:40
7:50
8:00
8:10
8:20
8:30
Spee
d (k
m/h
r)
020406080
7:30
7:40
7:50
8:00
8:10
8:20
8:30
Spee
d (k
m/h
r)0
20406080
7:30
7:40
7:50
8:00
8:10
8:20
8:30
Spee
d (k
m/h
r)
020406080
7:30
7:40
7:50
8:00
8:10
8:20
8:30
Spee
d (k
m/h
r)
A1
23
3 2
1
AA
1
2
Figure 8: AIMSUN2 output showing five random seed replications
of a weaving area with the auxiliary lane blocked from 8:00 to
8:15.
A
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Run Times The 4 hour simulation of this 9 km section of
northbound traffic took 9 minutes and 6 seconds to run in batch
mode on a Pentium 166MMX personal computer.
Conclusion About The Author
The author leads Transit New Zealands Transportation Planning
Section in Auckland. In this role he manages the transportation
planning phases of new State highway and freeway projects in the
Auckland urban area. This motorway simulation-modelling project is
the subject of the authors Master of Engineering thesis at the
University of Auckland.
Disclaimer
The opinions expressed in this paper are those of the author and
do not necessarily represent those of Transit New Zealand.
Acknowledgements
Transit New Zealand funded the work described in this paper.
Transportation Simulation Systems, Barcelona supported GETRAM and
graduate student M. Kamruzzaman assisted collection and reduction
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