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Simulation of a Large Freeway/Arterial Network with CORSIM, INTEGRATION and WATSim
PANOS D. PREVEDOUROS, Ph.D. Associate Professor
and
YUHAO WANG
M.S.C.E.
Department of Civil Engineering University of Hawaii at Manoa
2540 Dole Street, 383 Honolulu, HI 96822 tel.: 808-956-9698 fax: 808-956-5014
E-mail: [email protected]
Final version for publication in the Transportation Research Record
April 19, 1999
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ABSTRACT
Simulation of a large integrated (street/freeway) network with three state-of-the art software is
presented. The 20 centerline km network includes three on-ramps, three off-ramps, an on/off-
ramp weaving section, and a high design arterial with eleven signalized intersections. All three
software were able to replicate field measured volumes well, after considerable modifications to
default settings. INTEGRATION required extensive modifications to approximate complex
signal timing plans and had problems with lane alignment on the wide arterial. CORSIM’s
FRESIM component had a problem with vehicles that miss their destination and required car-
following parameter settings corresponding to unusually high capacities to produce good results.
WATSim needed the fewest modifications and it was primarily sensitive to merging/acceleration
lengths. WATSim and CORSIM speeds were close to each other. INTEGRATION’s simplified
treatment of signalization produced higher street link speeds.
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INTRODUCTION
A traffic engineering literature search for comprehensive comparisons of traffic software
based on real-world applications will yield little, particularly with respect to newer software.
Overviews of software can be found through McTrans, PC-Trans, developers’ publications and a
long term study at Leeds University (1) which is the most up-to-date and comprehensive software
summary yet.
This paper presents simulation results from the application of three state-of-the-art
software, INTEGRATION (2), CORSIM (3), and WATSim (4), on a rather large (20 centerline
kilometers) freeway/arterial integrated network.
Smaller-scale applications which also include comparisons with field measured
characteristics (e.g., speed) were first investigated (5). INTEGRATION, TSIS/CORSIM, and
WATSim were applied to three heavily loaded traffic networks for which exact volumes and
speeds (on specific lanes and locations) were known. The models produced reasonable and
comparable simulated results on most of the tested network links. The experiments also revealed
that the main limitation of these models is the large number of parameters that need to be
modified in order to replicate the real traffic conditions. In no case did the default parameters
offer satisfactory results. Specific strengths and weaknesses of the three software examined in (5)
were as follows:
• CORSIM has the most realistic lane-changing maneuvers. Oddly, car-following parameter
settings corresponding to freeway capacities as high as 3,000 vphpl were used to duplicate the
real traffic conditions. (CORSIM does not utilize capacity; see discussion later.) As in
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NCHRP 385, we concluded that the percentile input for off-ramps is inconvenient and causes
difficulty in replicating field conditions.
• INTEGRATION is the only model which can simulate the U-turn movement among these
models, but it also has the most limited ability to simulate signalized intersections. The
optional lane-striping file enhances flexibility in simulating a variety of traffic operations, but
the lane-changing mechanism may not reflect Honolulu driver behavior (e.g., most weaving
maneuvers occur at the first 1/3 of a given weaving length) and is not user-adjustable.
• WATSim required the least modification to default parameters to achieve good results but its
animation is relatively inferior. The globally (both the surface street and freeway) applied
NETSIM car-following and lane-changing parameters are derived internally based on user-
input capacity; direct user-input would be more desirable.
Although in our earlier research we concluded that given some effort for parameter
calibration, the three models are capable of fitting detailed field data, it is uncertain whether this
capability is applicable to large integrated networks. Thus, the objective of the second part of the
research was to:
• simulate the existing traffic conditions of a large integrated network,
• assess the ability of each model to replicate existing traffic flows, and
• compare the relative magnitudes of the resultant measures of effectiveness (MOEs),
primarily speed.
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DATA DESCRIPTION
The network consists of the eastbound H-1 freeway, the one-way eastbound School St.,
east- and westbound Vineyard Blvd. and nine bi-directional north-south streets. Within this
network, the Liliha St. on-ramp merge and Pali Hwy. on-ramp/Kinau St. off-ramp weaving
sections are heavily congested during the morning peak period, as is the eastbound traffic on
Vineyard Blvd. This network is depicted in Figure 1 along with a large part of the data.
Additional specifications for the data are given below.
1. A 15-minute period (7:30-7:45 AM) was simulated after proper initialization. The
initialization was also 15 minutes long, so each run simulated one half hour. The
aforementioned period is the peak 15-minutes. The entire peak hour was not simulated due to
time limitations considering the size of the network, the processing speed (first generation 586
processor) and the large number of runs required for parameter adjustment with each
software.
2. Freeway volumes were counted from videotapes using AUTOSCOPE and by manually
checking selected ramps and mainline segments. All freeway data used in the analyses are
from simultaneous and contemporaneous tapes.
3. O-D data are needed to run INTEGRATION. There are 23 origin-destination nodes in the
network. The O-D matrix was derived on the basis of the observed link volumes, turning
movements, and on-ramp destination surveys. An iterative process assured that differences
between actual and estimated (O-D) volumes were within 1% (6).
4. Intersection volumes were obtained in the field. All counts were taken by a team of 10
people. At two critical locations (nodes 1 and 7 in Figure 1), intersection counts were done at
the same time with freeway counts. The counts in other locations were taken within two
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weeks from the date of freeway counts, excluding Mondays and Fridays. In general, the
intersection counts were stable and consistent.
5. Traffic signal data were collected simultaneously with volume data. All signals in this network
operate in actuated mode, but most are not coordinated. Actuated data were averaged and
the signalization was modeled as pretimed. This was necessary for two reasons: (i)
INTEGRATION can only model pretimed signal timings, and (ii) field measurements are not
sufficient for deriving all the parameters required for NETSIM (which is a part of CORSIM
and WATSim) simulation. The latter problem could not be overcome because the responsible
agency did not provide signal controller settings. Intersections 1 and 7 (Figure 1) operated on
a fixed 150 second cycle from 6:30 to 9:00 A.M. During the most part of the peak hour, the
rest of the signals were running in a pretimed mode (maxed out) since all critical approaches
were saturated. As a result, a de facto pretimed operation was in effect during the simulated
peak 15 minutes.
6. Since traffic flow changes with space, 150 m link increments were modeled along the freeway
mainline to compare the three models' results over space.
7. H-1 freeway has 3 lanes per direction throughout the simulated segment. Vineyard Blvd. is a
high-design arterial with 3 lanes per direction and left turn lanes at all intersections. Several
streets intersecting Vineyard Blvd. also have exclusive right and left turn lanes.
The base settings for essential network simulation parameters are shown below. Several
of these settings were subsequently modified to improve model fit.
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Vineyard Other Freeway Ramp Blvd. streets sat. flow rate (vphpl) 2300 2000 1900 1900
free-flow speed (km/h) 105 65 55 50
speed at capacity (km/h) 55 32 32 25
jam density (v/km/l) 150 150 150 150
TESTED SOFTWARE
TSIS/CORSIM and WATSim are similar in many respects, whereas INTEGRATION’s logic
is distinctly different. The essentials of the tested software are reviewed next.
CORSIM is virtually a combination of two microscopic models, NETSIM and FRESIM.
These two predecessor models are reviewed first. NETSIM (NETwork SIMulation) is the only
microscopic model available for urban street networks. NETSIM, formerly called UTCS-1, was
initially released in 1971 and integrated within the TRAF (an integrated traffic simulation system)
in the early 1980s. NETSIM can simulate most operational conditions experienced in an urban
street network environment. It provides a high level of detail and it may be the most widely-used
traffic simulation model (7). The TRAF-NETSIM model uses an interval-scanning simulation
approach to move vehicles each second according to car-following logic and in response to traffic
control and other conditions (8). Like most other stochastic models, TRAF-NETSIM uses Monte
Carlo simulation to represent real-world behavior. Therefore, the individual vehicle/driver
combinations, the vehicle turning movements on new links, and many other behavioral and
operational decisions are all represented as random processes. The recent version of TRAF-
NETSIM uses an identical seed number technique to represent identical traffic streams and reduce
output variability (9).
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INTRAS (INtegrated TRAffic Simulation) is a microscopic, stochastic simulation model,
developed by KLD Associates in the late 1970s and was enhanced continuously through the
1980s (10). It uses a vehicle-specific, time-stepping, highly detailed lane-changing and car-
following logic to realistically represent traffic flow and traffic control in a freeway corridor and
surrounding surface street environment. INTRAS requires fairly detailed geometric and traffic
information, including link length, lane numbers, location, free-flow speeds, vehicle composition,
traffic volumes, O-D data, etc. This model has been used to evaluate the freeway reconstruction
alternatives (11) and weaving area capacity analysis (12,13). These research results pointed out
that INTRAS was not yet fully operational, especially in the freeway weaving areas. JFT and
Associates reprogrammed INTRAS with support from the FHWA according to structure design
techniques and made more user-friendly. The revised model was called FRESIM and it also
became a part of the TRAF family (10,14). The FRESIM model can simulate complex freeway
geometrics, such as lane add/drop, inclusion of auxiliary lanes, and variation in slopes,
superelevation, and radius of curvature. The model can handle freeway operational features such
as lane-changing, on-ramp metering, and representation of a variety of traffic behaviors in freeway
facilities. FRESIM has become the most complete and updated microscopic freeway simulation
model.
CORSIM is capable of simultaneously simulating traffic operations on surface streets as
well as freeways in an integrated fashion. However, within the earlier integrated traffic simulation
system (TRAF), the total freeway/urban street systems simulated by the combination of NETSIM
and FRESIM could only be called "composite" networks rather than the fully "integrated"
networks, in terms of the TRAF system characteristics of distinct separation of the assignment
and simulation phases of the analysis, independent control strategies in each subnetwork, data
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transfers between models/modules, and the lack of rerouting capability (15). At the present, a
Windows version of TSIS (Traffic Software Integrated System) (3) is available to provide an
integrated, user-friendly, graphical user interface and environment for running CORSIM. A traffic
assignment module with two assignment options, system optimal or user equilibrium, is available
in NETSIM. It utilizes user input O-D trip information to generate turning fractions for
intersections. TSIS/CORSIM was released for public use late in spring 1996; version 4.2 became
available in spring 1998.
WATSim (Wide Area Traffic Simulation) is based entirely on NETSIM and was first
presented at the 1996 annual meeting of the Transportation Research Board. At the time of the
tests WATSim was not marketed as a stand-alone software but it is offered as part of a contract
with its developer, KLD Associates. However, the developer planned to offer WATSim as a
standalone software sometime in 1999. WATSim extends the functionality of TRAF-NETSIM to
incorporate both freeway and ramp operations simulation with surface street traffic simulation.
WATsim’s operational features include those in TRAF-NETSIM plus HOV configurations, light
rail vehicles, toll plazas, path tracing, ramp metering, and real time simulation and animation (4).
The WATSim simulation model also includes an interface with a traffic assignment model.
INTEGRATION was developed in the late 1980s (16). It is a mesoscopic routing-
oriented simulation model of integrated freeway and surface street networks. In the model,
individual vehicle movements through the network are traced to monitor and control the unique
behavior of vehicles that belong to a certain subpopulation. The model differs from most other
models in that only the aggregate speed-volume interactions of traffic and not the details of a
vehicle's lane-changing and car-following behavior are explicitly considered (17), thus, its
classification as mesoscopic. The model is routing- based in that only a vehicle's trip origin,
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destination, and departure times are specified external to it, leaving the actual trip path and the
arrival times at each link along the path to be derived within the simulation based on the modeled
interactions with any other vehicles. Another distinctive feature of the INTEGRATION is that it
may be the first simulation model which considers the ITS route guidance information in the
vehicle routing/rerouting mechanism (18,19). While INTEGRATION provides a graphical
capability to view vehicles as they move through the network, it provides no graphical user
interface (GUI) for viewing and editing network data. Some view this as a main drawback of
INTEGRATION because the ability to view/edit data is essential for model setup, calibration, and
scenario testing (20). A much more sophisticated version of INTEGRATION (v.20 able to run
under Windows) became available in late 1998 but was not used in our tests.
INTEGRATION Simulation
Two major problems were revealed during the simulations with INTEGRATION. The
first is the traffic congestion in the 4-lane Pali Hwy. on-ramp/Kinau St. off-ramp weaving section
(see Figure 1). This section consists of a 3-lane freeway mainline and an auxiliary lane which
begins at the Pali Hwy. on-ramp and ends at the Kinau St. off-ramp.
The animation showed that almost all weaving maneuvers were completed at the
divergence point of the weaving section. Many weaving vehicles from the upstream mainline to
the Kinau St. off-ramp remained on the two left lanes while they approached the divergence point.
Then, they stopped in search of gaps for lane change and exit through the off-ramp. As
simulation time elapsed, the queue extended backward both on the mainline and on the Pali Hwy.
on-ramp, a phenomenon that does not occur in reality.
10
This simulation problem did not improve by using a higher freeway capacity (2,500 vphpl).
Instead, a lane-stripping file was used to force the weaving vehicles from the upstream mainline to
the off-ramp to change lanes earlier. By using the striping file with a 2,300 vphpl capacity, the
weaving congestion (which was largely a software artifact) was greatly reduced.
A more extensive problem was the signal control at intersections. In INTEGRATION, any
link can be served by up to two signal phases. However, links with protected left turns often are
served in three phases (i.e., left only, left and through, through only). In small networks this
problem can be solved by using a separate link configuration for the left turn movement, as
explained later. However, link prohibition codes must be defined for each link, otherwise some
through (or left-turn) vehicles may get into and clog the left-turn (or through) link. To avoid this
occurrence, different vehicle types need to be specified for left-turn and through movement
vehicles in the INTEGRATION O-D file.
The problem with larger networks such as the one presented herein is that there are 9
intersections along Vineyard Blvd. and most have protected left turns, but there are only 5 vehicle
types which can be defined in the O-D file. As a result, up to 4 intersections per direction can be
modeled with left turn bays and exclusive left turn phasing, i.e., vehicle types 1, 2, 3 and 4 along
the north-bound direction are destined to the left turn bays at intersections 1, 2, 3 and 4,
respectively and vehicle type 5 are through vehicles; similarly for the east-, west-, and south-
bound approaches. Thus, a separate link configuration can be utilized to deal with the signal
problem for only a part of this large network.
For links on which a separate link configuration was not specified, the left-turn operation
is, by default, modeled as permitted (the impact of permitted left turns on the simulated speeds is
11
discussed later). An example of separate link configuration is depicted in Figure 2 and is
described below.
Three distinct issues were present at the Vineyard/Punchbowl intersection, node 1 in
Figure 1. First, phase 4 does not permit left turns, but INTEGRATION cannot prohibit left turns
as long as they are modeled as a part of the through link. This issue is present at all the
intersections along the Vineyard Blvd. arterial. Second, U-turns are permitted on (and are a
sizeable portion of) the west-bound left turn operation (twin-lane). This issue applies to a few
other intersections along this arterial. Third, INTEGRATION internally determines the proper
alignment between an upstream link and its downstream link. This is true for a 3-lane link which
is followed by a 3- or 2-lane downstream link. However, a lane alignment problem was found for
a 4-lane link (Link 13) or a 5-lane link (Link 11) which are followed by 3-lane downstream links.
INTEGRATION assigned the two through lanes to the middle lane of the downstream link. This
issue also applied to other intersections along Vineyard Blvd.
To solve these signalization and lane alignment problems, the geometry was modified to
include a separate link for each protected left turn movement. For example, Link 11.1 in Figure 2
is created for left-turn (and U-turn) movements only; through movement vehicles are prohibited
from using it. In INTEGRATION, no two links can have identical starting and ending nodes.
This is bypassed by introducing an extra node (i.e., Node k in Figure 2), which splits the through
movement into two sublinks (Link 11.2 and Link 11.3). In this way, the through and left turn
movements no longer have nodes i and j as common start and end. This simple node insertion
and link assignment resolved both the lane alignment and signal phasing issues. A sample
resolution is given next.
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Phases 2 and 3 were modeled for Link 11.1, and phases 3 and 4 were modeled for Link
11.3, exactly as in real conditions; the phasing diagram is shown below node 1 in Figure 1. The
three through movement lanes of Link 11.3 were all correctly aligned to the corresponding lanes
of the downstream link because there is no lane drop between Link 11.3 and Link 16. Destination
node m is all that was required for the U-turns on Link 11.1 to work.
The separate link configuration was modeled for the eastbound Vineyard Blvd. links to
Palama St., Liliha St. and Aala St. intersections and for westbound Vineyard Blvd. links to
Punchbowl St., Queen Emma St., Pali Hwy. and Nuuanu Ave. intersections. All these Vineyard
Blvd. links have three signal phases.
Table 1 lists the actual and simulated volumes on critical freeway links and street links.
The errors are very small on freeway links and within ±5% on most street links.
CORSIM Simulation
CORSIM’s NETSIM component had no problems with default model parameters for the
surface street subnetwork, but low freeway volumes and a large number of destination-reassigned
and missed vehicles were produced by FRESIM. Car-following and lane-changing parameters
have significant effects on FRESIM results. The FRESIM default car-following parameters
usually cannot produce good results (5).
For this network simulation, car-following parameters were decreased from the default
array [15,14,13,12,11,10,9,8,7,6] to [4,4,4,4,3,3,3,3,2,1] which, according to Payne (21),
increases the capacity from the default level of 2,350 vphpl to about 3,300 vphpl. The lane-
changing parameters were set to 2.5 seconds of lane-changing duration and 25% of cooperative
drivers; the defaults are 3.0 seconds and 20%, respectively. Lane change activating distances
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were modified from the default 2,500 ft. (750 m) to 4,500 ft. (1,350 m) for vehicles diverging to
the Pali Hwy. off-ramp and 3,000 ft. (900 m) to the Punchbowl St. off-ramp.
Table 2 lists the simulated volumes on critical links of the network. All volume errors are
small. Multiple replications gave surprisingly (given the size of the network) consistent results
with speeds on selected critical links varying by only 2 to 5 km/h.
WATSim Simulation
Freeway capacities can be defined explicitly in WATSim, which is a desirable feature.
Capacities of 2,300 vphpl and 2,000 vphpl were assigned to freeway mainline links and ramps,
respectively. Most lane-changing default parameters remained unchanged, except for the distance
for activating the mandatory lane-changing maneuvers. This distance parameter was modified
from the default 150 ft. (45 m) to 600 ft (180 m).
There were no simulation problems for the surface streets with the default model
parameters. However, a significant problem was present at the Liliha St. on-ramp merge
segment. The Liliha St. on-ramp vehicles merged into the mainline smoothly, but the upstream
mainline vehicles were excessively impeded. By changing the capacity of the merge link from
2,300 vph to 2,500 vph and increasing the link length from 215 ft. (65 m) to 400 ft. (120 m) --the
added length was subtracted from the immediate downstream mainline link-- both mainline and
on-ramp vehicles could merge into the downstream mainline segment.
The simulated volumes are listed in Table 3. Again, good proximity between actual and
simulated volumes was achieved.
COMPARISON OF MOEs
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The simulated travel speeds along the freeway are shown in Figure 3. The speed plots
from the three simulation models exhibit similar trends. There are three obvious bottlenecks for
eastbound vehicles: the Liliha St. on-ramp merge, the Pali/Kinau weaving section and the
Vineyard on-ramp merge. Traffic conditions at the Liliha St. on-ramp merge segment are the
worst: The simulated speeds are below 30 km/h. Notably, the CORSIM and WATSim speeds
began to decrease at least 450 to 600 m upstream of the Liliha St. on-ramp link whereas the
INTEGRATION speed decreased only about 150 m from the merge link. CORSIM and
WATSim better represent the actual merge condition seen through the surveillance camera.
Vehicle speeds gradually increase from the “valley” before the Liliha St. on-ramp merge
and reach a maximum on the link following the Punchbowl St. on-ramp merge. The CORSIM and
WATSim speed curves are close to each other. The INTEGRATION speed estimates were
always lower than the CORSIM and WATSim speeds on off-ramp divergence segments because,
as shown in the animation, some diverging vehicles on the left lanes did not change lanes early
enough. Subsequently, they stopped in search of a gap and blocked the vehicles behind them.
These results were produced using a lane capacity of 2,300 vph in both INTEGRATION and
WATSim. As mentioned earlier, the car-following parameters in CORSIM were set to a level
corresponding to a capacity of 3,300 vph (see Payne et al. (21)).
The 355 m length, four-lane Pali/Kinau weaving section was modeled as two equal-length
links. The minimum speeds of CORSIM (53.4 km/h) and WATSim (50.6 km/h) on the weaving
section were very close, but the former was reached on the second part of the weaving section
and the latter on the first part. vehicles reached minimum speed on the second part of the weaving
section as well; its output differs from CORSIM’s in that almost all weaving vehicles completed
lane-changing maneuvers at the off-ramp divergence point.
15
The Vineyard Blvd. on-ramp merge segment also is a major bottleneck along the
eastbound H-1 freeway. CORSIM and WATSim produced comparable speeds on this segment
whereas INTEGRATION’s speed was high. Simulated speeds on the link downstream of the
Vineyard Blvd. on-ramp merge increase rapidly within a short distance.
Actual speed profiles are available from HDOT instrumented vehicle surveys. Surveys
from four days in 1997 and 1998 (at 7:30 and 8:00 A.M.) were averaged to generate the speed
profile labeled “ACTUAL” in Figure 3. The plots of model speed output and actual speed
averages are similar. One exception is the Pali-on/Kinau-off section which all models tend to
over-represent as a bottleneck. Also, congestion caused by the Vineyard on-ramp bottleneck
propagates backward and the predicted speeds of about 80 km/h at the location labeled “upstream
of Vineyard merge” are not realized. This is a likely consequence of the short simulation period.
Figure 4 illustrates the simulated speeds along the eastbound Vineyard Blvd. Travel speed
on a street link is defined as the link length divided by the total time, including stop time, that all
vehicles experience on the link. The curves from all software are very close to each other, except
for INTEGRATION’s speed at the Nuuanu Ave. intersection. Since the separate link
configuration was not modeled for the left turn lane of this link, vehicles on left-turn pockets were
permitted to turn left during the through movement phase. Therefore, the simulated stop times
for left-turn vehicles were shorter than the actual times, resulting in higher speeds in comparison
with reality and with the protected left-turn movements simulated in CORSIM and WATSim. For
eastbound Vineyard Blvd. links to the Pali Hwy., Queen Emma St. and Punchbowl St.
intersection, the INTEGRATION speeds are close to the CORSIM and WATSim speeds
although the separate link configuration was not modeled. This is because both the through
movement phase length at these intersections is relatively shorter than at the Nuuanu Ave.
16
intersection and the opposing through movements are heavy, so few, if any, permitted left-turns
occurred. Actual travel speeds are not available for comparison.
The simulated speeds on westbound Vineyard Blvd. links are presented in Figure 5. The
link speeds from the three models are close to each other for 5 out of 9 intersections. The
separate link configuration was specified for these links in INTEGRATION. For westbound
Vineyard Blvd. links west (to the left in Figure 5) of Nuuanu Ave., the INTEGRATION speeds
are considerably different from the CORSIM and WATSim speeds. Permitted left-turn operations
adopted due to software limitations resulted in higher INTEGRATION speeds. Actual travel
speeds are not available for comparison.
Summary results from the 2,165 m freeway mainline segment between the Liliha St. on-
ramp and the Vineyard Blvd. on-ramp, and the 1,725 m segment of Vineyard Blvd. between
Palama St. and Punchbowl St. are shown below. All CORSIM and WATSim results are close.
The INTEGRATION results are somewhat different, especially on the freeway mainline and
westbound arterial. Using CORSIM as the base, WATSim estimates vary between -9% and 5%;
INTEGRATION estimates vary between -22% and 28%.
TRAVEL TIME (sec/veh)
FREEWAY EB Arterial WB Arterial
INTEGRATION 135 395 246
CORSIM 116 415 316
WATSim 117 431 331
17
AVERAGE TRAVEL SPEED (km/h)
FREEWAY EB Arterial WB Arterial
INTEGRATION 58.6 15.9 25.6
CORSIM 68.6 15.1 20.0
WATSim 67.6 14.7 19.0
CONCLUSIONS AND DISCUSSION
INTEGRATION’s two main problems are in modeling complex signalization and lane-
changing. INTEGRATION speeds are higher than the speeds obtained from the other two
models on links where protected left-turn operation cannot be modeled using the separate link
modification developed and presented herein. While in CORSIM and WATSim the distance for
activating mandatory lane-changing maneuvers can be adjusted so that vehicles may activate lane
changes earlier, INTEGRATION does not permit user input. Instead, the lane-changing
propensity gradually increases as a vehicle approaches a "hardwall" (2), at which point, a
mandatory lane change must be completed. As a result, vehicles may give up lane-changing
opportunities on upstream, less congested links and finally cannot find gaps to change lanes under
heavy traffic conditions near the "hardwall." This usually leads to some stopped vehicles on
freeway divergence segments and surface streets with heavy turn movements.
CORSIM and WATSim employ a similar logic in simulating surface street networks, and
both produced close results for the Vineyard Blvd. arterial and other street links. However, the
difference in freeway simulation is apparent. CORSIM evokes FRESIM which usually produced
lower simulated speeds on freeway on-ramp merge segments. The CORSIM parameters
(especially the car-following factors that affect capacity) needed radical modifications to duplicate
18
observed volumes, which is not a desired feature. Relatively speaking, WATSim can produce
reasonable results with fewer adjustments. Within the examined network, only the Liliha St. on-
ramp merge with its short acceleration length presented a difficulty for WATSim.
The latest version of TSIS/CORSIM (4.2) produced more missed and re-assigned
vehicles. It is hoped that answers to the questions such as: 1) what did the model do with these
vehicles? 2) why these vehicles did not reach their intended destination? and 3) what parameters
affect the growth and decay of missed/re-assigned vehicles? posed by Jacobson (22) are answered
soon.
Judging by the comparisons of actual versus simulated volumes for freeway and street
links, all three software examined produced acceptable results for the simulated network. These
software were able to handle the integrated freeway/arterial network and produced helpful
animation in addition to a multitude of numerical outputs. Best results were achieved with the
NETSIM-based models which could be improved further by encoding the actuated signal
operation. INTEGRATION execution is hampered by the tedious O-D table determination and
the modeling of complex signalization and lane-changing. However, after reliable O-D flows have
been estimated, then, INTEGRATION is more efficient and arguably more suitable for planning
analyses that involve significant vehicle routing changes (e.g., lane closures, directional changes,
turn prohibitions, ITS applications, etc.).
Acknowledgment
The authors wish to thank FHWA and McTrans-University of Florida for the supply of
and assistance with TSIS/CORSIM as well as Mr. R. Goldblatt of KLD Associates, Inc. for the
supply of and assistance with WATSim.
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23
10
16
86
28
3
129
13
13
2
30
20
13
52
11
15
84
30
15
115
15
20
4
70
46
19
0
41
14
15
41
40
20
2
78
50
20
16
50
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5 55
5
5
5
5
5
5
5
5
5
5
5
5
1
2
3
45
23 22
6 7
21
8
20
9
10
111213141516171819
Pal
ama
St.
Pu
a S
t.
Lili
ha
St.
Aal
a S
t.
Mau
nak
ea S
t.
Nu
uan
u A
ve.
Pal
i Hw
y.
Q. E
mm
a S
t.
Pu
nch
bo
wl S
t.
11
9 8 7 6 5 4 3 2 1
10Liliha
On-ramp
H-1 F reeway
Vineyard Blvd.
School St.
To Lusitana St.
Kinau
Off-ram
p
Pali Hwy.
On-ramp
Kinau Weaving
Section
Pali Hwy. Off-rampPunchbowl
Off-ramp
2064 1804 1895 1638 1433
671 6325746051051
1405 1439 1350 1625 933
17511106976822638
8454933
1260 940 5471550
1675
5267256
2167
1177
655
116449
2
642
402
1703
585
744
720
379
834
539
1968 39
3
867
868
2370
526
332
176
36 158
918
593
280
283
241
262
283
594
797
829
818
25
125
5
5
15
55
45
5
5
5
677
Origin Node
Destination Node
Origin/Destination Node
5 Signal
1751 Actual Volume (vph)
9
1
8
N
Figure 1. The simulated Vineyard Boulevard and east-bound H-1 Freeway network.
Node iNode j Node k
Link 11.1
Link 11.3 Link 11.2
Link
14
Link
12
Node j Node m
Link 15
Link 16
Link 13
Figure 2. The separate link modification for the Vineyard Blvd.
intersection with Punchbowl St.
24
Figure 3: Simulated Travel Speeds Along Eastbound H-1 Freeway
PunchbowlOff-rampDivergePali Hwy.
Off-rampDiverge
Upstream ofPali On-ramp
Merge
Kinau Off-rampDiverge
Upstream ofVineyardOn-rampMerge
Liliha On-rampMerge
VineyardOn-rampMerge
0
10
20
30
40
50
60
70
80
90
100
0 500 1000 1500 2000 2500 3000
H-1 Freeway Distance (m)
Sp
eed
(km
/h)
INTEGRATIONCORSIMWATSimACTUAL
Direction of traffic
25
Figure 4. Simulated Travel Speeds Along Eastbound Vineyard Blvd.
Lusitana
Punchbowl
Palama
To H-1
Nuuanu
Aala
Pua
Liliha
Maunakea
Pali Queen Emma
0
10
20
30
40
50
60
0 500 1000 1500 2000 2500 3000
Vineyard Blvd. Distance (m)
Sp
eed
(km
/h)
INTEGRATION
CORSIM
WATSim
Direction of traffic
26
Figure 5. Simulated Travel Speeds Along Westbound Vineyard Blvd.
Punchbowl
Palama
Pali
Nuuanu
Aala
Pua
Liliha
Maunakea
Queen Emma
0
10
20
30
40
50
60
0 500 1000 1500 2000 2500 3000
Vineyard Blvd. Distance (m)
Sp
eed
(km
/h)
INTEGRATION
CORSIM
WATSim
Direction of traffic
27
H-1 Freeway
Volume (vph)
H-1 Entry Link
Liliha On-ramp
Pali Off-ramp
Punchbowl Off-ramp
Punchbowl On-ramp
Kinau Off-ramp
Vineyard On-ramp
H-1 Exit Link
Actual 4933 1260 940 547 1550 1675 677 5267Simulated 4928 1242 936 556 1568 1648 662 5294% error -0.1 -1.4 -0.4 1.6 1.2 -1.6 -2.2 0.5
Vineyard Blvd. Link (eastbound, approaching the listed intersection)
Volume (vph)
Palama Pua Liliha Aala Maunakea Nuuanu Pali Queen Emma Punchbowl
Actual 2064 1804 1895 1638 1433 1405 1439 1350 1625Simulated 2036 1896 2002 1608 1406 1390 1418 1374 1640% error -1.4 5.1 5.6 -1.8 -1.9 -1.1 -1.5 1.8 0.9
Vineyard Blvd. Link (westbound, approaching the listed intersection)
Volume (vph)
Palama Pua Liliha Aala Maunakea Nuuanu Pali Queen Emma Punchbowl
Actual 605 574 632 671 638 822 976 1106 1751Simulated 616 602 614 662 634 754 1000 1136 1774% error 1.8 4.9 -2.8 -1.3 -0.6 -8.3 2.5 2.7 1.3
Other Critical Street Link
Volume (vph)
SB Palama NB Palama SB Liliha NB Liliha SB Pali NB PaliSB
PunchbowlNB
Punchbowl
Actual 720 379 829 818 1703 402 1164 655Simulated 768 376 794 790 1746 422 1154 664% error 6.7 -0.8 -4.2 -3.4 2.5 5.0 -0.9 1.4
Table 1. INTEGRATION Volume Comparison
28
H-1 Freeway
Volume (vph)
H-1 Entry Link
Liliha On-ramp
Pali Off-ramp
Punchbowl Off-ramp
Punchbowl On-ramp
Kinau Off-ramp
Vineyard On-ramp
H-1 Exit Link
Actual 4933 1260 940 547 1550 1675 677 5267Simulated 4912 1274 919 514 1550 1692 674 5269% error -0.4 1.1 -2.2 -6.0 0.0 1.0 -0.4 0.0
Vineyard Blvd. Link (eastbound, approaching the listed intersection)
Volume (vph)
Palama Pua Liliha Aala Maunakea Nuuanu Pali Queen Emma Punchbowl
Actual 2064 1804 1895 1638 1433 1405 1439 1350 1625Simulated 2060 1799 1937 1720 1458 1405 1420 1301 1604% error -0.2 -0.3 2.2 5.0 1.7 0.0 -1.3 -3.6 -1.3
Vineyard Blvd. Link (westbound, approaching the listed intersection)
Volume (vph)
Palama Pua Liliha Aala Maunakea Nuuanu Pali Queen Emma Punchbowl
Actual 605 574 632 671 638 822 976 1106 1751Simulated 578 565 626 680 629 778 952 1110 1748% error -4.5 -1.6 -0.9 1.3 -1.4 -5.4 -2.5 0.4 -0.2
Other Critical Street Link
Volume (vph)
SB Palama NB Palama SB Liliha NB Liliha SB Pali NB PaliSB
PunchbowlNB
Punchbowl
Actual 720 379 829 818 1703 402 1164 655Simulated 722 381 821 818 1674 397 1161 642% error 0.3 0.5 -1.0 0.0 -1.7 -1.2 -0.3 -2.0
Table 2. CORSIM Volume Comparison
29
H-1 Freeway
Volume (vph)
H-1 Entry Link
Liliha On-ramp
Pali Off-ramp
Punchbowl Off-ramp
Punchbowl On-ramp
Kinau Off-ramp
Vineyard On-ramp
H-1 Exit Link
Actual 4933 1260 940 547 1550 1675 677 5267Simulated 4859 1262 951 549 1531 1634 692 5142% error -1.5 0.2 1.2 0.4 -1.2 -2.4 2.2 -2.4
Vineyard Blvd. Link (eastbound, approaching the listed intersection)
Volume (vph)
Palama Pua Liliha Aala Maunakea Nuuanu Pali Queen Emma Punchbowl
Actual 2064 1804 1895 1638 1433 1405 1439 1350 1625Simulated 2068 1828 1924 1696 1427 1376 1382 1312 1620% error 0.2 1.3 1.5 3.5 -0.4 -2.1 -4.0 -2.8 -0.3
Vineyard Blvd. Link (westbound, approaching the listed intersection)
Volume (vph)
Palama Pua Liliha Aala Maunakea Nuuanu Pali Queen Emma Punchbowl
Actual 605 574 632 671 638 822 976 1106 1751Simulated 593 565 625 677 642 784 972 1113 1750% error -2.0 -1.6 -1.1 0.9 0.6 -4.6 -0.4 0.6 -0.1
Other Critical Street Link
Volume (vph)
SB Palama NB Palama SB Liliha NB Liliha SB Pali NB PaliSB
PunchbowlNB
Punchbowl
Actual 720 379 829 818 1703 402 1164 655Simulated 719 369 840 823 1729 403 1163 652% error -0.1 -2.6 1.3 0.6 1.5 0.2 -0.1 -0.5
Table 3. WATSIM Volume Comparison
