This document analyzes traffic flow on a 6.5 mile stretch of I-4 in downtown Orlando using CORSIM simulation software. It simulates existing conditions and proposes alternatives for medium and high density traffic. For the medium density scenario, trucks are restricted to the two right lanes, but this makes little difference compared to existing conditions. For high density, alternatives adding auxiliary lanes, ramp metering, and HOV lanes are analyzed. Ramp metering combined with HOV lanes shows the best improvement in reducing vehicle hours and increasing speeds compared to the base high density scenario.

1. The Need for Speed: Modeling I-4 in Downtown Orlando to Improve
Traffic Flow
Prepared for: Dr. Scott Washburn, Professor
CGN 6905
Prepared by: Johns
John-Mark Palacios
13 December 2006
University of Florida

2. Johns/Palacios i
Table of Contents
List of Tables............................................................................................................................... ii
List of Figures .............................................................................................................................iii
Introduction .................................................................................................................................1
Simulation Analysis .....................................................................................................................2
Existing Conditions ..................................................................................................................2
Freeway Configuration .........................................................................................................2
Network Coding ...................................................................................................................4
Calibration............................................................................................................................4
Performance Measures ........................................................................................................6
Deficiency Contributors ........................................................................................................7
Alternatives Analysis................................................................................................................8
Medium Density Improvement ..............................................................................................8
Truck Lane Restrictions ....................................................................................................8
Comparison with Base......................................................................................................8
High Density Improvement Scenarios.................................................................................10
Auxiliary Lanes ...............................................................................................................10
HOV Lanes.....................................................................................................................11
Ramp Metering...............................................................................................................11
HOV and Ramp Metering................................................................................................14
Comparisons with Base ..................................................................................................14
HCM Freeway Facilities Analysis...............................................................................................18
Methodology..........................................................................................................................18
Performance Measures..........................................................................................................18
Comparison with CORSIM .....................................................................................................19
References................................................................................................................................23
Appendix and Tables.................................................................................................................24

3. Johns/Palacios ii
List of Tables
Table 1. Entry Link Volumes (vehicles per hour). .........................................................................3
Table 2. Exit volume percentages................................................................................................3
Table 3. Detector Station Network Placement..............................................................................3
Table 4. Reference Volumes (vehicles per hour)..........................................................................4
Table 5. Factors Adjusted for Calibration .....................................................................................5
Table 6. Simulation Volumes .......................................................................................................5
Table 7. Percent Error .................................................................................................................6
Table 8: Ramp Metering Algorithms...........................................................................................11
Table 9. Ramp Metering Algorithm Network Statistics ................................................................12
Table 10. Low Density Network-wide Statistics Comparison.......................................................19
Table 11. Medium Density Network-wide Statistics Comparison.................................................19

4. Johns/Palacios iii
List of Figures
Figure 1. Satellite Photo/Map of I-4 from Church St. to Maitland Blvd...........................................1
Figure 2. TRAFVU screenshot taken at the entry. ........................................................................7
Figure 3. TRAFVU screenshot taken at Colonial on-ramp. ...........................................................7
Figure 4. Volume Comparison along the Length of the Facility. ....................................................9
Figure 5. Density Comparison along the Length of the Facility. ....................................................9
Figure 6. Speed Comparison along the Length of the Facility.....................................................10
Figure 7. TRAFVU screenshot before and after an auxiliary lane is added. ................................11
Figure 8. TRAFVU screenshot of a ramp meter..........................................................................13
Figure 9. TRAFVU screenshot of HOV bypass lanes on entry ramp. ..........................................14
Figure 10. High Density Condition Traffic Volume Comparison...................................................15
Figure 11. High Density Condition Average Vehicle Speed Comparison.....................................15
Figure 12. High Density Condition Average Density Comparison ...............................................16
Figure 13. High Density Alternative Vehicle Hours Comparison..................................................17
Figure 14. Low-Density Speed Comparison. ..............................................................................20
Figure 15. Low-Density Density Comparison..............................................................................21
Figure 16. Medium-Density Speed Comparison. ........................................................................21
Figure 17. Medium-Density Density Comparison........................................................................22

5. Johns/Palacios 1
Introduction
I-4 serves as Orlando’s primary facility route. Large
volumes of vehicles use I-4 to commute between work
and home everyday. Orlando also has a large tourist
population, so out-of-towners also use the facility to
travel around the city. This use of I-4 has led to
congestion, especially near downtown, and efforts to
correct these issues have been relatively unsuccessful in
the past.
Using CORSIM simulation software, we modeled a 6.5
mile stretch of east-bound I-4 from Church Street to
Maitland Boulevard, through downtown Orlando, just
north of the East-West expressway. There are 8 off-
ramps and 5 on-ramps on this stretch. A satellite photo
of the section is shown in Figure 1. This simulation will
have two parts to it: the first part is to simulate base-case
scenarios and calibrate them with empirical data. The
second part offers alternatives to the cases for
comparison in effectiveness.
Three scenarios based on current roadway configuration
will be simulated for the following conditions: low density,
medium density and high density. Since low density
conditions have few problems, the low density scenarios
will only be used for calibration purposes. Alternative
scenarios will be used for the medium and high density
conditions only. For the medium density condition, the
proposed alternative is that trucks are restricted to the
two outside (two right-most) lanes. The high density
condition has the following proposed conditions:
• Addition of auxiliary lanes throughout the stretch
Source: Google Maps. 24 October
2006. <http://maps.google.com>.
Figure 1. Satellite Photo/Map of I-4
from Church St. to Maitland Blvd.

6. Johns/Palacios 2
between successive on- and off-ramps.
• Implementation of ramp metering at on-ramps to reduce the volume of vehicles entering
the freeway.
• Addition of high occupancy vehicle (HOV) lanes
• Implementation of ramp metering and HOV lanes
All simulation scenarios, both the base-case and alternatives, will use CORSIM for simulation.
CORSIM is a microscopic simulator that considers variables such as lane changing and car-
following sensitivity. Each vehicle’s movement and speed information is recorded. From each
individual vehicle, CORSIM can output aggregate network statistics such as average speed and
vehicle-miles. A possible downside to CORSIM is that vehicle actions may not necessarily
reflect a realistic driver decision. For example, in some runs of the simulation, heavy vehicles
and trucks stop at the end of the acceleration lane and wait to merge in, affecting average
speeds for the network.
In addition to simulating these alternative scenarios with CORSIM, a freeway facilities analysis
will be conducted using HCS+ for the low and medium densities. HCS+ takes advantage of the
methods outlined in the Freeway Facilities chapter in the Highway Capacity Manual (HCM,
Chapter 22). The outputs of HCS+ will provide an additional check to verify CORSIM outputs for
the existing configuration. HCS+ does not have the capacity to deal with lane changing events
or driver-specific parameters. It provides macroscopic analysis and outputs data such as
average speed and volume on a given segment.
Simulation Analysis
Existing Conditions
Freeway Configuration
The freeway is composed of nodes and links, each having its own properties such as number of
lanes and length for links and entrance and exit volumes and metering schemes for nodes.
The on-ramp entrance volumes for each base case are shown in Table 1. The off-ramp exit
volume percentages for each base case are shown in Table 2.

7. Johns/Palacios 3
Table 1. Entry Link Volumes (vehicles per hour).
Entry # Low Medium High
Main Entry 5307 5000 9999
41 850 800 1000
42 630 800 1000
43 60 100 400
45 50 100 250
46 95 100 300
Table 2. Exit volume percentages
Exit # Low Medium High
41 10 6 5
42 4 4 2
43 1 1 1
44 1 1 1
45 1 1 1
46 2 1 1
47 (A) 1 1 1
47 (B) 1 1 1
In addition to configuring entry and exit ramps volumes and percentages, detector stations were
also set up to record traffic data such as speeds and traffic volumes per lane. There were 11
stations throughout the study region. Detector station placement is listed in Table 3.
Table 3. Detector Station Network Placement
Station ID Network Placement
40 After Colonial Dr. exit (#41)
41 At the end of the acceleration lane of Colonial Dr.
42 Between Ivanhoe Blvd. off and on-ramps
43 Before Princeton St. exit (#43)
44 After Princeton St. on-ramp
45 After Par St. exit (#44)
46 Before Fairbanks Ave. exit (#45)

8. Johns/Palacios 4
47 Between Fairbanks Ave. off and on-ramps
48 After Fairbanks Ave. on-ramp
49 After Lee Rd. exit (#46)
50 After Lee Rd. on-ramp
51 Before Maitland Blvd. exit ramps (#47A,B)
Network Coding
The network was coded into CORSIM using a combination of .trf text files and .tno Network
Editor files. TSIS, the shell program for CORSIM, provided the capability to translate between
the two formats as necessary. For some steps it was quicker to input the record type data by
typing directly into a .trf file, while for other steps it was simpler to use the Network Editor’s
visual interface to edit the links and nodes.
CORSIM offers some flexibility for coding different networks into the system, but it does have a
few limitations. For instance, acceleration lanes or deceleration lanes that span more than one
link cannot simply be coded as an acceleration lane or a deceleration lane. They have to be
coded as an auxiliary lane first, then as an additional regular lane, then as a lane add or drop
wherever they begin or end. Drawbacks to this workaround include the possibility of vehicles
trying to use the longer acceleration/deceleration lanes as travel lanes, but this seemed to be
minimal.
Calibration
The three base case models were calibrated in order to match as closely as possible the
volumes observed in the field. These observed volumes are shown in Table 4.
Table 4. Reference Volumes (vehicles per hour)
Density Level
Nearest street Station ID Low Medium High
Church None (40) 5300 5000 3700
Ivanhoe 42 5400 5250 4400
Par 45 5900 5950 5500
Kennedy 51 5950 6100 5700

9. Johns/Palacios 5
The model was calibrated by adjusting the on-ramp volumes and the off-ramp percentages and
by adjusting various factors in CORSIM. These factors are shown in Table 5 with their CORSIM
default values and our adjusted values. In addition, the car following sensitivity was adjusted to
140% of its default value for the high density condition in the first section of the facility where the
bottleneck occurs. This was done in order to account for drivers’ tendency to follow more closely
in queuing conditions.
Table 5. Factors Adjusted for Calibration
Factors used values
Default
values
Car following sensitivity (in increments of 0.1) 1.45-0.55 1.25-0.35
% drivers yielding to merging vehicles 8% 20%
Discretionary lane change multiplier 0.4 0.5
Discretionary lane change threshold 0.5 0.4
Max Decel. (non-Emergency) for Vehicle Types 1,2,8,9 10 8
The base-case scenarios did not all match up perfectly with the observed volumes in Table 4,
but the model was calibrated using the same factors for low, medium, and high volumes so that
they all came reasonably close to the observed. The results obtained for volumes are noted in
Table 6. The percent error for each detector station and scenario is calculated in Table 7. The
error was lowest overall for the medium density scenario, but all the percent errors for the
streets corresponding to loop detectors were under 7% for all scenarios. The percent error was
higher for Church St.; but this could partly be due to the measurement being taken at detector
station number 40, after the first off-ramp. The observed volumes were measured at node 100,
the beginning of the facility.
Table 6. Simulation Volumes
Density Level
Nearest street Station ID Low Medium High
Church 40 4762 4680 4160
Ivanhoe 42 5215 5250 4395
Par 45 5637 5800 5854

10. Johns/Palacios 6
Density Level
Nearest street Station ID Low Medium High
Kennedy 51 5596 5817 6034
Table 7. Percent Error
Density Level
Nearest street Station ID Low Medium High
Church 40 10.2% 6.40% 12.4%
Ivanhoe 42 3.43% 0% 0.1%
Par 45 4.46% 2.52% 6.44%
Kennedy 51 5.95% 4.64% 5.86%
Additional calibration was needed for high density conditions. In order to simulate the
congestion at the beginning of the freeway, an additional “pseudo” on-ramp was added. This
addition allowed the simulation to begin under congested conditions. A flow of 1500 vehicles per
hour was used as the volume of cars for that ramp.
Performance Measures
CORSIM outputs several useful performance measures. The measures we considered
important included network-wide vehicle-miles, vehicle-hours (of move time, delay time, and
total time), average speed, volumes, and the move time/total time ratio. A few common
performance measures, such as Density and Level of Service, are not directly output by
CORSIM. We calculated the density from other variables in the loop detector data generated in
CORSIM. CORSIM gave us a volume in vehicles per hour for each lane and an average speed
in miles per hour for each lane. We calculated the average speed for each station, weighting the
speed for each lane according to the volume in that lane. Then we calculated the total volume
by summing the volumes for all the lanes. We calculated the average density by dividing this
volume by the weighted average speed. Knowing the density, we could go on to calculate the
Level of Service, but the letter grade LOS will not be as useful as the numerical density in
comparing the base scenarios versus the alternative scenarios. A single letter does not
distinguish whether the Level of Service is high on the density range for that letter, low on the
range, or somewhere in the middle. If the alternative scenarios only change the density within

11. Johns/Palacios 7
the same letter LOS, the change would go unnoticed if comparing letters. We will, therefore, use
numerical density as our LOS performance measure.
The results from the base case scenarios will be thoroughly presented in a later section in order
to more easily compare them with the alternatives and with the HCS+ results.
Deficiency Contributors
During the high density base-case condition, there were several spots along the freeway that
experienced congestion. The first area of concern is located at the entry of this section. Vehicles
from the East-West expressway merge onto I-4 and create a queue. Shortly after Church Street,
however, the queue begins to discharge. Figure 2 below is a screenshot from TRAFVU showing
the congestion at the entry.
Figure 2. TRAFVU screenshot taken at the entry.
The second area of concern lies shortly after the Colonial exit-ramp. Vehicles merging onto the
interstate create congestion for the vehicles already on the mainline. These high levels of
congestion extend until the Ivanhoe exit. The remainder of the segment operates with relatively
small amounts of congestion. A possible explanation for this problematic area is the high
volume of vehicles entering I-4 from Colonial. These vehicles are forced to merge into the I-4
mainline which becomes congested easily. Figure 3 below demonstrates the congestion level as
simulated by CORSIM and displayed by TRAFVU at the Colonial entry ramp.
Figure 3. TRAFVU screenshot taken at Colonial on-ramp.

12. Johns/Palacios 8
Alternatives Analysis
Medium Density Improvement
Truck Lane Restrictions
In this scenario, trucks were restricted to the two outside lanes of the freeway for the medium
density condition. While this seems pretty straightforward to implement, CORSIM has some
issues with restricting the proper lanes when it has a lane add or a lane drop. The coding of
acceleration and deceleration ramps longer than one link required us to specify an additional
lane of width for the excess, which either got added for an off-ramp or dropped for an on-ramp.
CORSIM specified truck restrictions as trucks being allowed to use only the two rightmost lanes.
Since the stretches of the freeway with long acceleration lanes were coded as four lanes
instead of three, the truck restriction coding allowed trucks to use the three rightmost lanes.
Where the lane was dropped, CORSIM still held to the three rightmost lane rule. Technically,
then, trucks were allowed to use the far left lane in some places for stretches of a few hundred
feet. In the TRAFVU simulation viewer, we did not see any trucks taking advantage of those few
feet of liberty, so the coding is sufficient to accurately simulate trucks restricted to the two right
lanes.
Comparison with Base
Adding truck restrictions did little to improve capacity or level of service over the existing
medium-density conditions. As shown in Figure 4, the volume was virtually the same over the
entire freeway facility. In some places the existing condition had the higher volume, while in
other places the truck restriction condition had the higher volume. Figure 5 compares the
densities over the length of the facility. While for the most part the two are pretty close, it is
worth noting that the truck restriction alternative has a higher maximum density at the peak at
detector 43. Figure 6 correlates with the density, as the truck restriction scenario gives a
significant drop in speed at detector station 43. It is quite possible that the trucks are hindered
from moving to the left to avoid merging traffic at on-ramps, thus they have to slow down to the
speed of the incoming traffic. If the trucks slow down, vehicles behind them slow down, and so
on.

13. Johns/Palacios 9
Figure 4. Volume Comparison along the Length of the Facility.
Figure 5. Density Comparison along the Length of the Facility.

14. Johns/Palacios 10
Figure 6. Speed Comparison along the Length of the Facility.
Since the percent of trucks in the medium density condition is only 4% at the entrance to the
facility, it is not too surprising that restricting trucks to the two rightmost lanes makes little
difference. In fact, the truck restriction alternative is either the same as the original or worse.
The safest solution, then, is not to implement any truck restrictions for this facility.
High Density Improvement Scenarios
Auxiliary Lanes
The first high density alternative scenario we will test will be to connect all on-ramps to an
adjacent upstream off-ramp with auxiliary lanes. As noted above under Deficiency Contributors,
forcing vehicles to merge onto the mainline may cause the problematic areas from Colonial to
Ivanhoe. By adding these auxiliary lanes, we hope that the traffic entering and exiting the
highway will have more time to merge into traffic. This should decrease the on-ramp delays by
providing an exclusive lane for entering and exiting vehicles.
The following auxiliary lanes were coded to the network:
• Colonial Drive on-ramp to Ivanhoe Blvd. exit
• Ivanhoe Blvd on-ramp to Princeton Street exit
• Princeton Street on-ramp to Par Street exit

15. Johns/Palacios 11
• Fairbanks Avenue on-ramp to Lee Road exit
• Lee Road on-ramp to the Maitland Blvd. exits
The CORSIM network representation of an auxiliary lane is shown below in Figure 7.
Figure 7. TRAFVU screenshot before and after an auxiliary lane is added.
The simulation results are displayed and explained in the Comparisons with Base section.
HOV Lanes
This alternative will add an extra outside lane to the entire stretch of the freeway for HOV
purposes only, increasing the number of lanes from 3 to 4. The lane will allow vehicles with 2 or
more people or buses to travel in it. It is expected that both the volume of the freeway and the
average vehicle speeds will increase as a result of the HOV lane addition. The simulation
results are displayed and explained in the Comparisons with Base section.
Ramp Metering
The installation of ramp meters is also being considered. The ramp meters will control on-ramp
access to the freeway based on current freeway conditions. Meters reduce demand and break
up groups of cars. The reduction in demand may come from vehicle diversion—vehicles
bypassing the freeway to avoid ramp queues—and from the limited access rate. There are
various metering algorithms to use, each with their own benefits. Table 8 shows some common
metering algorithms along with pros and cons for each:
Table 8: Ramp Metering Algorithms
Metering Algorithm Pros Cons
Clock-time Control Easy to implement
No additional equipment other
than the signaling device and
controller is needed
Does not respond to current
traffic conditions

16. Johns/Palacios 12
controller is needed
Demand/Capacity Can be based on flow or
occupancy
Responsive to current traffic
conditions
Freeway flow measurement
not enough to determine
congestion state
Speed Control Meters vehicles based on
immediate speed measurements
to avoid speed drops
Low speeds do not
necessarily suggest
congestion and high speeds
may not suggest free-flow
state. Accuracy of speed
measurements is vital
Occupancy Control Attempts to meter vehicles based
on occupancy. Occupancy is
directly related to congestion
Reliance on a lookup table to
determine the rates.
ALINEA Allows a minimum rate to be set None
We tested each algorithm with default input parameters and simulated them with CORSIM.
Table 9, below, shows network data that was used to determine the appropriate algorithm to
use for metering.
Table 9. Ramp Metering Algorithm Network Statistics
Network Statistic Base Clock Speed Demand ALINEA Occupancy
Total Vehicle Miles 9692.108 9539.85 9700.6 9694.28 9333.55 9051.37
Vehicle Hours of
Move Time
152.112 148.71 152.29 151.41 145.41 141.14
Vehicle Hours of
Delay Time
126.925 94.83 136.27 132.09 63.87 85.82
Vehicle Hours of
Total Time
279.037 243.54 288.56 283.51 209.28 226.97
Average Speed 34.742 39.17 33.62 34.19 44.6 39.88

17. Johns/Palacios 13
Move/Total 0.545 0.61 0.53 0.53 0.69 0.62
Minutes/Miles of
Delay Time
0.786 0.6 0.84 0.82 0.41 0.57
Minutes/Miles of
Total Time
1.727 1.53 1.78 1.75 1.35 1.5
We chose to use the ALINEA algorithm due to the increase in average speed and the large
reduction in vehicle hours of delay time.
The ALINEA ramp metering algorithm was developed by Papageorgiou and Smaragdis. It uses
the following formula to calculate the metering rate:
[ ])t(K)1t(R)t(R outCR ο−ο+−=
Where:
)t(R is the meter rate at time interval t
RK is a constant regulator parameter
Cο is the occupancy at capacity
)t(outο is the current occupancy measurement
The occupancy at capacity is predefined. The difference between it and the measure occupancy
is what controls the metering rates.
Figure 8, below, shows how TRAFVU displays meters at ramps.
Figure 8. TRAFVU screenshot of a ramp meter.
Simulation results are displayed and explained in the Comparisons with Base section.

18. Johns/Palacios 14
HOV and Ramp Metering
This scenario will simulate the network with both HOV lanes added and ramp metering
implemented. Each on-ramp will now have two lanes. The left lane will be used as a HOV
bypass lane while the right will be metered using the same ALINEA metering algorithm as in the
previous metering alternative. All other network configuration will be the same as the HOV
alternative mentioned above. Figure 9, below, shows the geometry of the new on-ramps with
HOV bypass lanes added. Simulation results are displayed and explained in the Comparisons
with Base section.
Figure 9. TRAFVU screenshot of HOV bypass lanes on entry ramp.
Comparisons with Base
Figure 10 shows the volume of vehicles passing through each station. It is evident that the
combination of HOV and metering alternative provides the greatest likelihood of increasing
traffic volume.

19. Johns/Palacios 15
Figure 10. High Density Condition Traffic Volume Comparison.
Figure 11. High Density Condition Average Vehicle Speed Comparison.
Figure 11 shows the average speed of vehicles traveling through the detector stations. As you
can see from the figure above, each alternative scenario provides drivers with the opportunity of
traveling at higher speeds than the base-case. The alternatives which provided the steadiest
speed throughout the freeway are the metering and the combination metering and HOV.

20. Johns/Palacios 16
Figure 12. High Density Condition Average Density Comparison
Figure 12 above displays station densities from the alternatives. Each alternative succeeded in
lowering the density throughout the network. Towards the end of the freeway segment, near
Maitland, the auxiliary alternative lowers the density the most, but it also has the highest density
of any alternative at station 42.
Figure 13 is a comparison of each alternative scenario’s vehicle hours of move and delay time.
The HOV alternatives had the highest move vehicle hours while the metering alternative
provided the lowest delay time. This is true because metering the ramps allows for a decrease
in congestion on the mainline. The HOV alternatives provided the highest number of move
vehicle hours due to an extra lane being added to allow for a greater volume of vehicles to be
on the freeway. Figure 10 demonstrated that the combination of HOV and metering allowed for
the greatest volume of cars, so this makes sense.
For a more detailed display of simulation results, see the first table in the Appendix. That table
shows a direct comparison between each alternative scenario and the base-case with regards
to overall network statistics and station data.

21. Johns/Palacios 17
Figure 13. High Density Alternative Vehicle Hours Comparison.
The chart above shows that the scenarios involving HOV had the highest total vehicle hours,
possibly due to a larger volume of vehicles loaded onto the network. The scenario with the least
amount of vehicle hour delay time was the scenario with only ramp metering. Also, the
difference in delay time from combination HOV and ramp metering and only HOV suggests that
ramp metering minimizes delay time that mainline drivers experience.
Based on the simulated data, we recommend a ramp metering system be adopted throughout
this stretch of I-4. The two scenarios that seem to offer the best improvements are ramp
metering and ramp metering with HOV lanes. While the ramp metering combined with HOV
lanes more effectively reduces the congestion seen in high density conditions, adding HOV
lanes is much more expensive than simply implementing ramp metering. Hardware such as the
control system and light signals need to be installed, but preexisting detectors can be used for
each meter. Without doing any kind of cost comparison, it seems like ramp metering offers the
most “bang for the buck.”

22. Johns/Palacios 18
HCM Freeway Facilities Analysis
Methodology
We used HCS+ to analyze the same freeway facility according to the methods in the Highway
Capacity Manual chapter 22. This involved dividing the freeway into segments that changed
wherever the number of lanes changed for acceleration lanes of on-ramps and deceleration
lanes of off-ramps. The entire facility was divided into 26 segments, but only the first 25
segments could be input into HCS+ due to a bug in the software. Only the low and medium
density base-case scenarios were input into HCS+. The high density scenario had congestion at
the first segment of the facility, making it impossible to perform a complete analysis by the HCM
methods (HCM 22–1).
The data required for HCS+ was taken from the data input into CORSIM. On-ramp volumes
were taken directly as input in the .trf file, while off-ramp volumes were calculated using the
percent from the .trf file as the percent of the calculated demand in HCS+. Truck percentages
were assumed to be constant throughout the facility and equal to the entry node truck
percentages from CORSIM. This percentage was 6% for low density and 4% for medium
density. While percentages of trucks varied from on-ramp to on-ramp in the CORSIM file, these
on-ramp percentages were assumed to be negligible in the overall percentage of trucks on the
main freeway segments.
For segments spanning only one grade, the same grade was used that was input into CORSIM,
as shown in the Network Diagram (see Appendix). The composite grade method was used to
calculate the grades for the longer segments that spanned varying grades on the freeway. The
desired free flow speed CORSIM was input into HCS+ as the Base Free Flow Speed.
Performance Measures
The performance measures output by CORSIM were already discussed in the Existing
Conditions section. These measures were still useful when comparing results with HCS+, but
the chief limitations lie in whether HCS+ output the same measures. HCS+ output some of the
same network-wide statistics, including vehicle-miles of travel, vehicle-hours of travel, vehicle-

23. Johns/Palacios 19
hours of delay, average speed, and average density. The move time/total time ratio was
calculated using the vehicle-hours of travel (move time) and the vehicle-hours of delay. The
average density was not output in CORSIM, but it was calculated by averaging the densities
found at each loop detector. HCS+ output densities and capacities for each analysis segment,
so these values were also considered useful to provide a more detailed comparison throughout
the length of the facility.
Comparison with CORSIM
The network-wide performance measures were compared easily with CORSIM’s data. The data
and the percent differences are presented in Table 10 for the low density scenario and in Table
11 for the medium density scenario. The largest difference occurred in the vehicle-hours of
delay. This does not seem too surprising given that HCS+ does not perform a simulation of
actual traffic conditions, and thus cannot account for delay caused by queuing when congestion
occurs due to randomized traffic conditions.
Table 10. Low Density Network-wide Statistics Comparison
Network-wide statistics
HCS+
CORSIM % Diff.
Veh-Mi of Travel 8992 9497 5.46%
% Difference of total
time
Veh-Hr of Travel 153.4 147 4.16% 3.74%
Veh-Hr of Delay 8.4 25.6 101% 10.3%
Average Speed 58.6 55.0 6.38%
Average Density 30.5 32.4 6.06%
Move/Total 0.948 0.852 10.7%
Table 11. Medium Density Network-wide Statistics Comparison
Network-wide statistics HCS+ CORSIM % Diff.
Veh-Mi of Travel 9476 9674 2.08%
% Difference of total
time
Veh-Hr of Travel 163.4 150 8.54% 7.50%
Veh-Hr of Delay 10.6 32.8 102% 12.4%
Average Speed 58 53.0 9.02%
Average Density 32.3 33.5 3.45%
Move/Total 0.939 0.821 13.4%

24. Johns/Palacios 20
HCS+ was also compared directly to CORSIM on individual segments. Since HCS+ measured
data over the length of the segment, and CORSIM measured data at certain points (the loop
detectors), errors are quite likely. We compared only those HCS+ segments that contained a
loop detector in CORSIM. Where there were two detectors in one HCS+ segment, the average
values of the two were taken. Figure 14 and Figure 15 show graphs of speed and density
respectively for the low-density scenario over the length of the facility. Figure 16 and Figure 17
show the same graphs for the medium-density scenario. The most glaring difference for both
speed and density seems to be between detector station 43 and the corresponding segment.
Close inspection of the Network Diagram (see Appendix) shows that detector 43 is merely a
couple hundred feet from off-ramp 6, so it is nearly at the end of the HCS+ segment. It is
possible that traffic is backing up just before the off-ramp in the CORSIM simulation, while the
HCS+ analysis does not show any congestion.
Figure 14. Low-Density Speed Comparison.

25. Johns/Palacios 21
Figure 15. Low-Density Density Comparison.
Figure 16. Medium-Density Speed Comparison.

26. Johns/Palacios 22
Figure 17. Medium-Density Density Comparison.
Another large difference is at detector station 41, where the density values for both the medium
and the low scenarios jump up for CORSIM, while remaining low for HCS. Detector 41 only
consists of loops in the three thru lanes, while the on-ramp acceleration lane has no detector.
Since it is near the end of a long acceleration lane, there are probably few vehicles in the
acceleration lane and CORSIM’s data is probably accurate. HCS+, however, takes into account
the whole segment of the freeway with the acceleration lane, essentially a 4 lane freeway. This
is the most likely explanation for this large difference in density.
The larger differences aside, HCS+ still provides different results from CORSIM. These
differences no doubt stem from the inherent differences between HCS+ and CORSIM. As
mentioned earlier, HCM is a deterministic analysis, while CORSIM is a stochastic simulation.
CORSIM is a microscopic simulator, and in general it looks at more detail than HCS+. CORSIM
considers the horizontal alignment, more frequent grade changes (since the links are shorter
than the HCM segments), car-following behavior, lane-changing, and heavy vehicles. The HCM
method considers heavy-vehicles, but only using a passenger car equivalency factor. The HCM
methods offer a few advantages, such as the ability to consider the effects of lane width and
shoulder width; however, this facility has standard lane and shoulder widths that do not affect
the flow of traffic.

27. Johns/Palacios 23
References
Google Maps. 24 October 2006. <http://maps.google.com>.
Transportation Research Board. Highway Capacity Manual. National Academy of Sciences.
Washington, D.C.: 2000

28. Johns/Palacios 24
Appendix and Tables
Base Auxiliary HOV Meter HOV & Meter
Measure
Obs. Obs. Diff
%
Change
Obs. Diff
%
Change
Obs. Diff
%
Change
Obs. Diff
%
Change
Total Vehicle Miles 9692 9819 127 1.31% 12083 2391 24.7% 9353 -339 -3.50% 12401 2709 28.0%
Vehicle Hours of
Move Time 152 155 3.22 2.12% 187 35.7 23.5% 146 -6.45 -4.24% 193 40.4 26.6%
Vehicle Hours of
Delay Time 126.9 98.6 -28.3 -22.3% 101.4 -25.5 -20.1% 65.1 -61.8 -48.7% 88.6 -38.3 -30.2%
Vehicle Hours of
Total Time 279 254 -25.1 -9.00% 289 10.2 3.67% 211 -68.3 -24.5% 281 2.13 0.76%
Average Speed 34.7 38.7 3.94 11.4% 41.8 7.05 20.3% 44.4 9.64 27.8% 44.1 9.37 27.0%
Move/Total 0.545 0.612 0.067 12.3% 0.65 0.105 19.3% 0.691 0.146 26.8% 0.684 0.139 25.5%
Minutes/Miles of
Delay Time 0.786 0.602 -0.184 -23.4% 0.502 -0.284 -36.1% 0.417 -0.369 -47.0% 0.429 -0.357 -45.4%
Minutes/Miles of
Total Time 1.727 1.553 -0.174 -10.1% 1.436 -0.291 -16.9% 1.354 -0.373 -21.6% 1.36 -0.367 -21.3%

29. Johns/Palacios 25
Network Diagram:
I-4 Diagram.vsd
CORSIM project file:
Freeway Operations and Simulation project.tcf
CORSIM .trf files:
project_low.trf
project_med.trf
project_high.trf
project_med_truck_restrictions.trf
project_high_aux.trf
project_high_hov.trf
project_high_meter_alinea.trf
project_high_hov_meter.trf
HCS+ files:
final_proj_low_facilities.hcx
final_proj_low_facilities.txt
final_proj_med_facilities.hcx
final_proj_med_facilities.txt
Excel calculations file:
final_corsim_summary.xls