The document discusses improvements to the modeling of right turns on red (RTOR) in the Highway Capacity Manual 2010 (HCM 2010). It summarizes the existing HCM 2010 procedures that do not adequately account for RTOR flows. A new proposed methodology is described that estimates maximum RTOR flow rates and incorporates them into flow profiles to generate more accurate performance measures. Experimental results demonstrate that the proposed logic improves the modeling accuracy of flow profiles, consistency with microscopic simulations, and control delay estimates compared to not accounting for RTOR.
Application of a Markov chain traffic model to the Greater Philadelphia RegionJoseph Reiter
A macroscopic traffic model based on the Markov chain process is developed for urban traffic networks. The method utilizes existing census data rather than measurements of traffic to create parameters for the model. Four versions of the model are applied to the Philadelphia regional highway network and evaluated based on their ability to predict segments of highway that possess heavy traffic.
A macroscopic traffic model based on the Markov chain process is developed for urban traffic networks. The method utilizes existing census data rather than measurements of traffic to create parameters for the model. Four versions of the model are applied to the Philadelphia regional highway network and evaluated based on their ability to predict segments of highway that possess heavy traffic.
A New Paradigm in User Equilibrium-Application in Managed Lane PricingCSCJournals
Ineffective use of the High-Occupancy-Vehicle (HOV) lanes has the potential to decrease the overall roadway throughput during peak periods. Excess capacity in HOV lanes during peak periods can be made available to other types of vehicles, including single occupancy vehicles (SOV) for a price (toll). Such dual use lanes are known as “Managed Lanes.” The main purpose of this research is to propose a new paradigm in user equilibrium to predict the travel demand for determining the optimal fare policy for managed lane facilities. Depending on their value of time, motorists may choose to travel on Managed Lanes (ML) or General Purpose Lanes (GPL). In this study, the features in the software called Toll Pricing Modeler version 4.3 (TPM-4.3) are described. TPM-4.3 is developed based on this new user equilibrium concept and utilizes it to examine various operating scenarios. The software has two built-in operating objective options: 1) what would the ML operating speed be for a specified SOV toll, or 2) what should the SOV toll be for a desired minimum ML operating speed. A number of pricing policy scenarios are developed and examined on the proposed managed lane segment on Interstate 30 (I-30) in Grand Prairie, Texas. The software provides quantitative estimates of various factors including toll revenue, emissions and system performance such as person movement and traffic speed on managed and general purpose lanes. Overall, among the scenarios examined, higher toll rates tend to generate higher toll revenues, reduce overall CO and NOx emissions, and shift demand to general purpose lanes. On the other hand, HOV preferential treatments at any given toll level tend to reduce toll revenue, have no impact on or reduce system performance on managed lanes, and increase CO and NOx emissions.
Application of a Markov chain traffic model to the Greater Philadelphia RegionJoseph Reiter
A macroscopic traffic model based on the Markov chain process is developed for urban traffic networks. The method utilizes existing census data rather than measurements of traffic to create parameters for the model. Four versions of the model are applied to the Philadelphia regional highway network and evaluated based on their ability to predict segments of highway that possess heavy traffic.
A macroscopic traffic model based on the Markov chain process is developed for urban traffic networks. The method utilizes existing census data rather than measurements of traffic to create parameters for the model. Four versions of the model are applied to the Philadelphia regional highway network and evaluated based on their ability to predict segments of highway that possess heavy traffic.
A New Paradigm in User Equilibrium-Application in Managed Lane PricingCSCJournals
Ineffective use of the High-Occupancy-Vehicle (HOV) lanes has the potential to decrease the overall roadway throughput during peak periods. Excess capacity in HOV lanes during peak periods can be made available to other types of vehicles, including single occupancy vehicles (SOV) for a price (toll). Such dual use lanes are known as “Managed Lanes.” The main purpose of this research is to propose a new paradigm in user equilibrium to predict the travel demand for determining the optimal fare policy for managed lane facilities. Depending on their value of time, motorists may choose to travel on Managed Lanes (ML) or General Purpose Lanes (GPL). In this study, the features in the software called Toll Pricing Modeler version 4.3 (TPM-4.3) are described. TPM-4.3 is developed based on this new user equilibrium concept and utilizes it to examine various operating scenarios. The software has two built-in operating objective options: 1) what would the ML operating speed be for a specified SOV toll, or 2) what should the SOV toll be for a desired minimum ML operating speed. A number of pricing policy scenarios are developed and examined on the proposed managed lane segment on Interstate 30 (I-30) in Grand Prairie, Texas. The software provides quantitative estimates of various factors including toll revenue, emissions and system performance such as person movement and traffic speed on managed and general purpose lanes. Overall, among the scenarios examined, higher toll rates tend to generate higher toll revenues, reduce overall CO and NOx emissions, and shift demand to general purpose lanes. On the other hand, HOV preferential treatments at any given toll level tend to reduce toll revenue, have no impact on or reduce system performance on managed lanes, and increase CO and NOx emissions.
Detailed description of Capacity and Level of service of Multi lane highways based on Highway Capacity Manual (HCM2010) along with one example for finding LOS of a highway
Modeling Truck Movements: A Comparison between the Quick Response Freight Man...inventionjournals
In recent years, with a growing realization of the important impacts of truck traffic on the economy as well as urban congestion and pollution levels, there is a keen interest in modeling truck movements with greater accuracy, robustness, and detail. This paper examines two different approaches for explicitly including truck trips into travel demand forecasting models. The approaches considered are (a) the truck modeling methodology published in the Quick Response Freight Manual (QRFM) and (b) an emerging truck tour-based approach. In this paper, the two approaches are demonstrated and compared using the Birmingham, AL region as a case study and statistical analyses are conducted to evaluate the level of accuracy of both approaches. The results demonstrate that the model using tour-based approach performs better than the one based on the QRFM approach with respect to model accuracy, when compared to field data from the study area. However, the tourbased approach requires a comprehensive data collection and processing effort, whereas the QRFM approach uses the publicly available data such as household and employment data. The decision on the best approach for adoption should be made on a case-by-case basis after considering the tradeoffs between accuracy and data availability and processing requirements. Overall, the findings from this study can be used to support the development of efficient freight truck modeling applications for the Birmingham region. Moreover, lessons learned from the Birmingham case study provide valuable insights that can guide freight modeling efforts of planning agencies in other medium sized communities in the future
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Application of Cumulative Axle Model To Impute Missing Traffic Data in Defect...IJERDJOURNAL
Abstract: An automatic vehicle classification (AVC) station is typically composed of three sensors per lane. Instances of data missing from the traffic datasets collected at such stations can occur as a result of issues such as one of the sensors malfunctioning. Although various data imputation methods, such as autoregressive integrated moving average (ARIMA), exponential smoothing, and interpolation, have been proposed to deal with this problem, they are either too complicated or have significant errors. This paper proposes a model, called the “cumulative axle model,” that minimizes such errors in traffic volume data resulting from a malfunctioning sensor at AVC stations. Evaluations conducted in which missing traffic volume data imputation was simulated using the proposed cumulative axle model indicate that our method has a mean absolute percentage error (MAPE) of 2.92%. This is significantly more accurate than that of conventional imputation methods, which achieve a MAPE of only 10% on average.
Accessibility Analysis and Modeling in Public Transport Networks - A Raster b...Beniamino Murgante
Accessibility Analysis and Modeling in Public Transport Networks - A
Raster based Approach
Morten Fuglsang, - National Environmental Research Institute, Aarhus
University and Aalborg University Copenhagen
Henning Sten Hansen - Aalborg University Copenhagen
Bernd Münier - National Environmental Research Institute, Aarhus University
A Framework for Traffic Planning and Forecasting using Micro-Simulation Calib...ITIIIndustries
This paper presents the application of microsimulation for traffic planning and forecasting, and proposes a new framework to model complex traffic conditions by calibrating and adjusting traffic parameters of a microsimulation model. By using an open source micro-simulator package, TRANSIMS, in this study, animated and numerical results were produced and analysed. The framework of traffic model calibration was evaluated for its usefulness and practicality. Finally, we discuss future applications such as providing end users with real time traffic information through Intelligent Transport System (ITS) integration.
Detailed description of Capacity and Level of service of Multi lane highways based on Highway Capacity Manual (HCM2010) along with one example for finding LOS of a highway
Modeling Truck Movements: A Comparison between the Quick Response Freight Man...inventionjournals
In recent years, with a growing realization of the important impacts of truck traffic on the economy as well as urban congestion and pollution levels, there is a keen interest in modeling truck movements with greater accuracy, robustness, and detail. This paper examines two different approaches for explicitly including truck trips into travel demand forecasting models. The approaches considered are (a) the truck modeling methodology published in the Quick Response Freight Manual (QRFM) and (b) an emerging truck tour-based approach. In this paper, the two approaches are demonstrated and compared using the Birmingham, AL region as a case study and statistical analyses are conducted to evaluate the level of accuracy of both approaches. The results demonstrate that the model using tour-based approach performs better than the one based on the QRFM approach with respect to model accuracy, when compared to field data from the study area. However, the tourbased approach requires a comprehensive data collection and processing effort, whereas the QRFM approach uses the publicly available data such as household and employment data. The decision on the best approach for adoption should be made on a case-by-case basis after considering the tradeoffs between accuracy and data availability and processing requirements. Overall, the findings from this study can be used to support the development of efficient freight truck modeling applications for the Birmingham region. Moreover, lessons learned from the Birmingham case study provide valuable insights that can guide freight modeling efforts of planning agencies in other medium sized communities in the future
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Application of Cumulative Axle Model To Impute Missing Traffic Data in Defect...IJERDJOURNAL
Abstract: An automatic vehicle classification (AVC) station is typically composed of three sensors per lane. Instances of data missing from the traffic datasets collected at such stations can occur as a result of issues such as one of the sensors malfunctioning. Although various data imputation methods, such as autoregressive integrated moving average (ARIMA), exponential smoothing, and interpolation, have been proposed to deal with this problem, they are either too complicated or have significant errors. This paper proposes a model, called the “cumulative axle model,” that minimizes such errors in traffic volume data resulting from a malfunctioning sensor at AVC stations. Evaluations conducted in which missing traffic volume data imputation was simulated using the proposed cumulative axle model indicate that our method has a mean absolute percentage error (MAPE) of 2.92%. This is significantly more accurate than that of conventional imputation methods, which achieve a MAPE of only 10% on average.
Accessibility Analysis and Modeling in Public Transport Networks - A Raster b...Beniamino Murgante
Accessibility Analysis and Modeling in Public Transport Networks - A
Raster based Approach
Morten Fuglsang, - National Environmental Research Institute, Aarhus
University and Aalborg University Copenhagen
Henning Sten Hansen - Aalborg University Copenhagen
Bernd Münier - National Environmental Research Institute, Aarhus University
A Framework for Traffic Planning and Forecasting using Micro-Simulation Calib...ITIIIndustries
This paper presents the application of microsimulation for traffic planning and forecasting, and proposes a new framework to model complex traffic conditions by calibrating and adjusting traffic parameters of a microsimulation model. By using an open source micro-simulator package, TRANSIMS, in this study, animated and numerical results were produced and analysed. The framework of traffic model calibration was evaluated for its usefulness and practicality. Finally, we discuss future applications such as providing end users with real time traffic information through Intelligent Transport System (ITS) integration.
Focused on the lane occupancy phenomenon, this paper analyzes the roads during two different accidents to the evacuation period. Firstly, according to the statistical data, this paper calculated the correction coefficients under the road traffic condition, and then obtained the actual traffic capacity result at each moment of the road when combining the function model of the actual traffic capacity corrected by the running speed and the road traffic condition. Next the actual traffic capacity results are fitted to the Smooth spline interpolation, and then the actual traffic capacity is further verified by the traffic congestion situation. The actual traffic capacity of the road during the accident to evacuation is summarized as follows: the actual traffic capacity shows a nonlinear trend, that is, ascending-attenuating-recovering and gradually stabilizing. Finally, using Mann-Whitney U test to carry out the difference test on the actual traffic capacity, it is found that there is significant difference between the two groups of data, and the actual traffic capacity of the second case is stronger than that of the first one, and the reasons for the difference are analyzed as follows: the ratio of the steering traffic volume at the downstream intersection is different; this road section includes the community intersection and there are vehicles entering and leaving; meanwhile the speed of each lane is different and there are buildings near the lane. The above conclusions will provide theoretical basis for the traffic management department to correctly guide the vehicle driving, approve the road construction, design the road channelization plan, set the roadside parking space and the non-port-type bus stations.
Evaluation of level of service at chatikara, MathuraSHASHANK KAMAL
Evaluation of level of service. Level of service (LOS) is a qualitative measure used to relate the quality of traffic service. LOS is used to analyze highways by categorizing traffic flow and assigning quality levels of traffic based on performance measure like speed, density,etc.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Macroscopic Traffic Flow model for nepalese roadsHemant Tiwari
This research deals with the calibration of various conventional macroscopic traffic flow models of Nepalese Roads and recommend the best suitable model after undergoing calibration and validation process.
Operatioal analysis of any road is necessary for its design,planning and implementation procedure.This article mostly deals with preliminary proposal of two lane road of eastern region of Nepal.due to increased traffic condition servicabilty and level of service of koshi higway is found to be very poor hence Dharan submetropolitan city's part is analysed.
1. 29
Transportation Research Record: Journal of the Transportation Research Board,
No. 2553, Transportation Research Board, Washington, D.C., 2016, pp. 29–40.
DOI: 10.3141/2553-04
The Highway Capacity Manual 2010 (HCM 2010) contains computational
procedures for evaluating traffic operational efficiency of urban street seg-
ments. These procedures have been implemented within several commer-
cial software packages and are likely used by thousands of engineers and
planners across the United States.The procedures for urban street capacity
analysis contain no logic for handling right turns on red (RTORs) or for
handling special cases of RTORs such as shielded and free right turns. A
new proposed RTOR modeling framework is described for urban streets
in the HCM 2010. When significant upstream RTOR flows exist, the pro-
posed logic is designed to generate more realistic flow profiles. Three types
of experimental results are presented: they demonstrate the improved
modelingaccuracyoftheproposedlogic.First,itisshownthatmacroscopic
flow profile shapes are now more visually sensible because they now illus-
trate RTOR flows moving at the appropriate times. Second, macroscopic
flow profile shapes are now more consistent with microscopic vehicle tra-
jectories. Third, a statistical analysis shows that when the proposed logic is
used, HCM 2010 performance measures become more consistent with
the performance measures generated by microsimulation. Finally, case
study results show that when the proposed RTOR logic is not used, control
delays are sometimes be inaccurate by more than 30%. Given the experi-
mental evidence presented, it is urgent that the proposed improvements
be adopted and implemented so that RTOR corridors can be accurately
analyzed by the HCM 2010 procedures.
The Highway Capacity Manual 2010 (HCM 2010) contains compu-
tational procedures designed to evaluate traffic operational efficiency
of urban street segments, signalized intersections, and interchange
ramp terminals (1). The HCM 2010 is one of the world’s most impor-
tant transportation documents. These procedures are implemented by
commercial software packages including the Highway Capacity Soft-
ware (HCS) 2010, Synchro,Vistro, andTransmodeler.There has been
a natural interest in integrating these interrupted-flow procedures
because they all require detailed analysis of signalized intersection
operation. The TRB Standing Committee on Highway Capacity and
Quality of Service, which maintains computational engines to test and
demonstrate their procedures, has worked on integrating their engines
related to urban streets and signalized intersections. In recent years
the McTrans Center, at the University of Florida, integrated three pro-
cedures (i.e., urban streets, signalized intersections, and interchange
ramp terminals) into a single software module within HCS.
This integration presented various challenges, including the treat-
ment of right turn on red (RTOR). For many years, HCM 2010 proce-
dures for signalized intersections have required the analyst to specify
an expected number of RTOR vehicles per time period. Subsequently,
RTOR volumes would be subtracted from right-turn volumes before
the full procedure was executed. Although the signalized intersection
procedures continue to support this treatment of RTOR, computational
engines for urban streets and ramp terminals do not support RTOR.
Some calculations related to flow profiles within the urban street and
ramp terminal engines become even less accurate when the signalized
intersection treatment occurs. Ideally, the HCM 2010 signalized pro-
cedures would be expanded to include methods for explicit analysis
of permissive right turns. However, this expansion would not resolve
problems with the flow profile model, which currently has no logic for
addressing RTOR.
Flow profiles should properly address RTOR to allow accurate
computation of the percentage of downstream vehicles arriving on
green (PVG).When the discharge flow rate of RTOR vehicles is deter-
mined, the procedures should account for conflicting flow rates. PVG
values significantly affect performance measures including control
delay and level of service (LOS). It is also desirable that the signalized
procedures reflect operation of free right turns, in which right-turning
vehicles move freely throughout the cycle. The operation of shielded
right turns, in which exclusive left-turn phases allow right turns on
the conflicting approach to move without yielding, should also be
explicitly recognized.
The objective of this research was to develop improvements to the
RTOR treatment for the HCM 2010 and to test their accuracy. The
next section summarizes existing HCM 2010 treatment of RTOR,
including free and shielded right turns. Next, shortcomings of the
existing treatment are demonstrated, and the following section pro-
poses corresponding improvements. The subsequent section provides
a statistical comparison of existing and proposed logic in terms of
consistency with microsimulation. The final section provides a case
study to demonstrate the impacts of the proposed logic.
Background
HCM 2010 Computational Engines
Certain chapters of the HCM 2010 refer to computational engines
that implement the HCM 2010 methodologies (1). Although these
methodologies have improved since the publication of HCM 2000,
Right-Turn-on-Red Flow Profile Impacts
on Urban Street Capacity Analysis
David K. Hale, Jiaqi Ma, Alexandra Kondyli, Jia Hu,
Zhitong Huang, and Peng Su
D. K. Hale, J. Ma, and P. Su, Leidos, Inc., 11251 Roger Bacon Drive, Reston,
VA 20190. A. Kondyli, Department of Civil, Environmental, and Architectural
Engineering, School of Engineering, University of Kansas, 2159A Learned Hall,
Lawrence, KS 66045. J. Hu, Federal Highway Administration, 6300 Georgetown
Pike, McLean, VA 22101. Z. Huang, Department of Civil and Environmental
Engineering, James Worth Bagley College of Engineering, Mississippi State
University, Mississippi State, MS 39762. Corresponding author: D. K. Hale,
david.k.hale@leidos.com.
2. 30 Transportation Research Record 2553
some issues have not been addressed. One example is that RTOR is
fully supported in the engine for signalized intersections but not in
the one for urban streets. A unified engine should accurately model
RTOR movements on all major- and minor-street approaches. Until
then, users of unified engines will face compromised results when
RTOR conditions exist. The ramp terminal procedures also require
modeling of PVG at downstream intersections. So although there is
nothing erroneous within the chapter on ramp terminals regarding
RTOR, urban street RTOR deficiencies compromise the accuracy of
ramp terminal analysis in a unified engine.
From 2011 to 2014, the McTrans Center received problem reports
from users of the HCS software (2), which implements the HCM
2010 procedures. Heavy RTOR input volumes caused analysis vol-
umes to be incorrect by 10% to 50%. In 2014, the TRB Standing
Committee on Highway Capacity and Quality of Service eliminated
RTOR-related flow balancing problems in the computational engine
by making appropriate changes to the programming logic. Flow
profile and PVG errors caused by RTOR, free rights, and shielded
rights were not reported by HCS users during the period from 2011 to
2014. However, the complexity of PVG calculations made it difficult
to detect problems. Thus, users were likely accepting performance
measures (and LOS) compromised by PVG errors.
Permissive Movement Models
The HCM 2010 chapter on two-way stop control provides a rea-
sonable model for permissive right turns from the minor street, a
prerequisite to modeling upstream intersection RTOR effects on
downstream intersection flow profiles. Upstream intersection flow
profiles also affect sign-controlled movement capacity, but such
analysis is outside the scope of this paper. HCM 2010 equations
19-4 through 19-9 determine conflicting flows as a function of
conflicting lanes. Exhibits 19-10 and 19-11 suggest critical head-
way and follow-up values, respectively, for right turns made from
a minor street. Equation 19-32 (shown here as Equation 1) then
uses conflicting flow rate, critical headway, and follow-up values
to determine permissive right-turn maximum flow rate (SRT). For
permissive maximum flow rates, HCM 2010 computational engines
use the SLT or SRT terminology, whereas the HCM 2010 chapters use
the cp,x terminology. The signalized intersection procedure already
prescribes Equation 19-32 (Equation 1 here) to estimate permissive
left-turn maximum flow rates (SLT). Extending this logic to SRT is
thus easily accomplished by applying right-turn values of tc and tf;
and by obtaining vc from the 90-degree conflicting approach, instead
of the 180-degree opposing approach, as follows:
1
(1), ,
3,600
3,600
, ,
, ,
=
−
−
−c v
e
e
p x c x
v t
v t
c x c x
c x f x
where
cp,x = potential capacity of movement x [vehicles per hour (vph)];
vc,x = conflicting flow rate for movement x (vph);
tc,x = critical headway for minor movement x (s), typically 4.5 s
for left turns and 6.9 s for right turns; and
tf,x = follow-up headway for minor movement x (s), typically
2.5 s for left turns and 3.3 s for right turns.
When RTOR maximum flow from shared lanes is estimated, the
HCM 2010 prescribes an extra adjustment. Creasey (3) and Creasey
et al. (4) noted that RTOR research was lacking in shared lanes,
and they developed a shared-lane probability model. Creasey
et al. also summarized methods to estimate RTOR maximum flow
(RTORCap) from exclusive lanes (4), such as Equation 2 from work
by Abu-Lebdeh et al. (5):
a
g
C
s Vc{ }( )= − −
RTOR max 1 , 0 (2)Cap
where
α = ratio of saturation headway of intersecting through traffic
to that of RTOR traffic; values of α range from 0.73 (cor-
responding to a right-turn saturation headway of 2.6 s per
vehicle) to 0.85 (corresponding to 2.2 s per vehicle);
g/C = ratio of green to cycle length;
s = saturation flow rate on green (vph); and
Vc = total conflicting volume (vph).
Portions of the HCM 2010 platoon dispersion logic are based on
Robertson’s original algorithms (6–8) within the TRANSYT-7F arte-
rial analysis software (9). TRANSYT-7F estimates RTOR maximum
flow rate by applying the FHWA-TRC (10) regression model shown
in Equation 3, with special coefficients developed for minor-street
right turns:
i iMFR exp (3)( )( ) ( )= −t A B v ti i i o
Ci
where
MFR(t)i = maximum flow rate for permitted traffic at time t for
minor-street rights;
Ai = statistically derived intercept, MFR at zero opposing
flow, for minor-street rights;
Bi, Ci = statistically derived model parameters; and
vo(t) = opposing flow rate at time t.
Problem Scenario: Upstream Right Turns Made
from Minor Street
Flow profiles from the urban street computational engine are not
yet designed to reflect incoming flows from special-case right turns
(RTORs, shielded, free). When special-case right turns exist, PVG
becomes less accurate at the downstream intersection. Accuracy of
the downstream PVG is inversely proportional to the volume of
minor-street vehicles serving as special-case right turns. Moreover,
PVG is known to have a significant effect on delay and LOS in
many situations.
Summary of Background Information
In summary, the existing HCM 2010 computational engines for urban
streets and ramp terminals have significant shortcomings related to
RTOR, some of which have been reported by HCS users. Several
modeling methods for permissive right turns exist in the literature,
and three of them were summarized in this section. Permissive right-
turn models are a prerequisite in generating flow profiles that account
for RTOR vehicles.Anew flow profile methodology is needed so that
all RTOR corridors can be accurately analyzed by the HCM 2010
procedures.
3. Hale, Ma, Kondyli, Hu, Huang, and Su 31
Proposed Methodology
Maximum Flow Rates
The proposed methodology requires exactly the same input data as
the current HCM methodology. For free right turns and shielded
right turns, saturation flow rates are provided by the analyst. Units of
measurement are typically vehicles per lane per hour of green. How-
ever for RTOR, permissive-period maximum flow rates are needed.
“Unadjusted” maximum flow rate estimates can be obtained from
models available in the literature, as described earlier in the section on
permissive movement models. Subsequently, HCM 2010 procedures
and computational engines contain logic for making final adjust-
ments. These final adjustments for pedestrian, bicycle, lane utiliza-
tion, and other effects, which were previously implemented for the
permissive left-turn maximum flow rate, should now be applicable to
the maximum flow rate for RTOR.
The computational engines classify 14 cases of combined lane
geometry and signal phasing. Cases 1 through 4 denote protected
left-turn cases under various geometries. Cases 5 through 8 cover
permissive left turns, whereas Cases 9 through 12 involve split phas-
ing. Cases 13 and 14 cover protected–permitted left turns made from
a shared lane. The RTOR adjustments should be applied to Case 3
(exclusive right turns moving while left turns are not allowed to
move), Case 5 (exclusive right turns moving during a permissive
left-turn phase), and Case 9 (exclusive right turns moving during an
unopposed split phase). When an RTOR made from a shared lane is
modeled, the adjustment for lane utilization would be omitted.
Discharge Flow Rates
The urban street computational engine does not yet recognize the effect
of special-case right turns (RTOR, shielded, free) on flow profiles, and
thechapteronurbanstreetsdoesnotmentiontheirinfluence.Toaddress
this deficiency, adjusted maximum flow rate from the previous section
can now be used to synthesize more accurate flow profiles. To accom-
plish this, new logic is needed to transform upstream maximum flow
rates into an improved upstream discharge profile. The computational
engine currently contains logic for four types of discharge profiles, but
a new category, for special-case upstream right turns, is now suggested.
The new category is suggested as Option 3, because these options are
typically checked in a first-to-last sequence. Options at the top of the
list capture the most specific and complex cases. Options toward
the bottom of the list contain simpler and more generalized logic; they
catch all scenarios not already addressed by the earlier options:
1. Upstream left turns made from an approach with protected–
permitted left-turn phasing;
2. Upstream left turns made from an approach with permitted-
only left-turn phasing;
3. Upstream right turns made from an approach with RTOR, free
right turns, and shielded right turns (new);
4. Upstream through movements and right turns made from an
approach with permitted-only left-turn phasing and no exclusive
left-turn lanes; and
5. All other upstream phase and movement combinations.
Figure 1 illustrates discharge flow rates for up to eight periods within
the cycle in the most complex case of protected–permissive right turns.
Durations of these eight periods are determined by the HCM 2010
signalized intersection procedure, which is essentially a subset of the
urban street procedure. It is fairly simple to determine discharge pro-
files for upstream free right turns made from the minor street. Their
discharge rate remains unchanged throughout the cycle, and they do
not yield to major-street vehicles. The discharge rate should reflect
their saturation flow rate (S) or demand volume (QG), whichever is
lower. For shielded right turns, queue service times are needed to syn-
thesize more accurate flow profiles. The computational engine com-
putes shielded right-turn queue service time (T_Que_1), but this value
should now be used to determine discharge flow rates. Additional
logic is needed to identify green windows in which shielded right
turns can move. The green window should account for T_Que_1 as
Free Right Turn
S or QG, whichever is smaller
QG
Cycle Length
Protected Red RedShielded RTOR
S QG
QR
SRT
S(zero) (zero) (zero) or
Right-turn
queue
service
Right-turn
queue
service queue
service
Conflicting
through
FIGURE 1 Right-turn discharge flow rates for up to eight periods within cycle [S 5 protected
right-turn saturation rate (vehicles per second; vps); SRT 5 permissive right-turn saturation
flow rate (vps; from section on background); QG 5 protected right-turn arrival flow rate during
green (vps); QR 5 protected right-turn arrival flow during red (vps)].
4. 32 Transportation Research Record 2553
well as phase start and end times for the complementary left turn.
Finally, it is necessary to define discharge profiles in a similar manner
to that for protected–permitted left turns, because shielded right turns
also have multiple green phases. Discharge profile values during the
shielded phase should reflect saturation flow (S) while the queue is
being served and arrival rate on red (QR) after the queue is served.
For upstream RTORs made from the minor street, it is first nec-
essary to identify the green window in which RTORs are allowed to
move.The green window is affected by queue service times and phase
start and end times for conflicting movements (this research focused
on conflicting through movements instead of opposing left-turn move-
ments). Then it is necessary to convert adjusted RTOR maximum flow
rates into discharge profile flow rates. Finally, it is necessary to define
discharge profiles in a similar manner to that for protected–permitted
left turns, because RTOR movements also have protected and permis-
sivephases.ThedischargerateduringtheRTORperiodshould be equal
to the arrival rate on red (QR) or the adjusted RTOR maximum flow
rate (SRT), whichever is lower. The most complex case involves both
RTOR and shielded right turns, causing up to three green windows
(protected, shielded, RTOR) within the cycle. Figures 2 through 4
Cycle Length Loop (L = active time step)
Conventional RTORs with no shielded right turns, and no free right turns
The following flowchart logic is applied when “m = upRT”
(i.e., when one computes discharge profiles for upstream right-turn movements)
Identify TimeToStartOfPermPhase, t_psT, and t_peT
TimeToStartOfPermPhase = time until beginning of permissive right-turn phase (new)
t_psT = start of queue service during permissive phase (new)
t_sT = start of queue service during protected phase
t_peT = start of permissive phase effective red time (new)
t_rT = end of queue service during protected phase
t_eT = start of red time after protected phase
s_T = saturation flow rate of protected phase
s_pT = maximum flow rate of permissive phase (new)
Q_gT = flow rate during protected phase
Q_rT = flow rate arriving on red
t_psT t_peT?
t_sT ≤ L t_rT?
s_pT Q_rT?
t_rT ≤ L t_eT?
t_psT ≤ L t_peT?
t_psT = t_peT
Yes
Discharge flow = s_T
Discharge flow = Q_gT
Discharge flow = s_pTDischarge flow = Q_rTDischarge flow = 0
Yes
Yes
Yes
Yes
FIGURE 2 RTOR-only discharge flow rate methodology.
5. Hale, Ma, Kondyli, Hu, Huang, and Su 33
FIGURE 3 Shielded-only discharge flow rate methodology.
Cycle Length Loop (L = active time step)
Shielded right turns with no conventional RTORs and no free right turns
The following flowchart logic is applied when “m = upRT”
(i.e., when computing discharge profiles for upstream right-turn movements)
Identify TimeToStartOfProRTPhase, t_xsT, t_xrT, and t_xeT
TimeToStartOfProtPhase = time until beginning of complementary left-turn phase (new)
t_xsT = start of queue service during permissive phase (new)
t_xeT = start of permissive phase effective red time (new)
t_xrT = end of queue service during permissive phase (new)
t_xsT t_xeT?
t_sT ≤ L t_rT?
t_rT ≤ L t_eT?
t_xsT ≤ L t_xrT?
t_xrT ≤ L t_xeT?
t_xsT = t_xeT
Yes
t_xrT t_xeT? t_xrT = t_xeT
Yes
Discharge flow = s_T
Discharge flow = Q_gT
Discharge flow = s_T
Discharge flow = Q_rT
Discharge flow = 0
Yes
Yes
Yes
Yes
6. 34 Transportation Research Record 2553
Cycle Length Loop (L = active time step)
Conventional RTORs AND shielded right turns but no free right turns
The following flowchart logic is applied when “m = upRT”
(i.e., when computing discharge profiles for upstream right-turn movements)
Identify TimeToStartOfPermPhase, t_psT, and t_peT
Identify TimeToStartOfProtRTPhase, t_xsT, t_xrT, and t_xeT
t_xsT t_xeT?
t_sT ≤ L t_rT?
t_rT ≤ L t_eT?
t_psT ≤ L t_peT?
t_xsT ≤ L t_xrT?
t_xrT ≤ L t_xeT?
t_xsT = t_xeT
Yes
t_xrT t_xeT? t_xrT = t_xeT
Yes
Discharge flow = s_T
Discharge flow = Q_gT
s_pT Q_rT?
Discharge flow = s_T
Discharge flow = s_pTDischarge flow = Q_rT
Discharge flow = Q_rT
Discharge flow = 0
Yes
Yes
Yes
Yes
Yes
Yes
FIGURE 4 RTOR-plus-shielded discharge flow rate methodology.
7. Hale, Ma, Kondyli, Hu, Huang, and Su 35
illustrate the RTOR-only methodology, shielded methodology, and
RTOR-plus-shielded methodology, respectively.
Summary of Proposed Improvements
The proposed improvements are summarized next. In the event that
the TRB Standing Committee on Highway Capacity and Quality of
Service develops an explicit RTOR permissive movement model,
Items 1 and 2 would become unnecessary but proposed Items 3
through 5 would still be needed:
1. Base maximum flow calculation for upstream RTOR from the
minor street (section on permissive movement models) (provides
necessary maximum flow values for RTOR discharge flow profile
calculations),
2. Maximum flow rate adjustment for upstream RTOR from the
minor street (section on maximum flow rates) (unadjusted maximum
flow rates should be adjusted by typical HCM 2010 methods),
3. Discharge flow logic for upstream free right turns from the
minor street (section on discharge flow rates) (generates free right
arrival profile and prevents flow profiles from ignoring free rights),
4. Discharge flow logic for upstream shielded right turns from
the minor street (section on discharge flow rates) (generates shielded
arrival profile and prevents flow profiles from ignoring shielded
rights), and
5. Discharge flow logic for upstream RTOR from the minor street
(section on discharge flow rates) (generates RTOR arrival profile and
prevents flow profiles from ignoring RTOR).
Comparison of Original
and Proposed Logic
This section provides three categories of evidence to show that the
proposed logic improves modeling accuracy. First, flow profiles are
visibly shown to reflect upstream RTOR more realistically. Second,
a statistical analysis of performance measures shows significantly
reduced error between the HCM 2010 and microsimulation when the
proposed logic is in effect. Third, vehicle trajectories from micro-
simulation exhibit improved pattern matching when compared with
flow profiles under the proposed logic. Finally, case study results
demonstrate the potential impact of these changes.
To test the proposed logic, a synthetic traffic network was cre-
ated in HCS and exported to CORSIM (11). Figure 5 illustrates the
synthetic traffic network and upstream signal phasing. Comparisons
were then made between a new HCS (containing the proposed cor-
rections), the old HCS, and CORSIM Version 6.3. The synthetic
network was designed so upstream RTORs from an exclusive lane
made from the northbound minor street onto the eastbound major
street would significantly affect flow profiles at the downstream sig-
nal with a 152-m distance between signals. Signal spacing exceeding
300 m would smooth out flow profiles and reduce their impact. Signal
spacing of less than 100 m would cause downstream flow profiles to
match upstream saturation flows; this outcome would make it difficult
to assess the proposed logic. Similarly, an RTOR flow rate of 500 vph
was chosen for the experiments to illustrate that the proposed logic
has significant impacts under typical conditions. RTOR flow rates of
less than 500 vph would produce less impressive impacts, and more
than 500 vph is considered uncommon.
The synthetic network scenarios are as follows:
SN1. No RTOR allowed, no shielded right-turn phase exists;
SN2. RTOR allowed, no shielded right-turn phase exists;
SN3. No RTOR allowed, shielded right-turn phase does exist; and
SN4. RTOR allowed, shielded right-turn phase exists.
Visual Assessment of Flow Profiles
Figures 6 through 8 illustrate arrival profiles for the downstream
through movement. PVG values generated by these profiles sig-
nificantly affect control delay and other performance measures. In the
complex case (SN4) of both RTORs and shielded right turns, the first
platoon spike (far left) shown in Figure 8a is caused by vehicles
from the protected right-turn phase. The second spike is caused by
vehicles from the shielded phase, but it dissipates after the right-turn
queue is served. The third spike is caused by major-street through
phase vehicles but dissipates into combined through plus right-turn
arrivals after the through queue is served. In contrast, flow profiles
from the original logic ignore RTOR and shielded right-turn vehicle
movements.
Proposed Improvement 1 (out of 5) involves base maximum flow
calculation for upstream RTOR from the minor street. Under the traf-
fic conditions that generated Figures 6 through 8, performance mea-
sures are identical when either the HCM 2010 two-way stop control
FIGURE 5 Testing conditions for RTOR and shielded right turns as shown in CORSIM.
8. 36 Transportation Research Record 2553
(a)
FlowRate(vph)
Cycle Time (s)
(b)
SegmentLength(ft)
Cycle Time (s)
FIGURE 6 Case SN2 (RTOR only): (a) macroscopic flow profiles and (b) microscopic vehicle trajectories.
(a)
FlowRate(vph)
Cycle Time (s)
FIGURE 7 Case SN3 (shielded only): (a) macroscopic flow profiles.
(continued)
9. Hale, Ma, Kondyli, Hu, Huang, and Su 37
(b)
SegmentLength(ft)
Cycle Time (s)
FIGURE 7 (continued) Case SN3 (shielded only): (b) microscopic vehicle trajectories
(a)
FlowRate(vph)
Cycle Time (s)
(b)
SegmentLength(ft)
Cycle Time (s)
FIGURE 8 Case SN4 (both RTOR and shielded): (a) macroscopic flow profiles and (b) microscopic
vehicle trajectories.
10. 38 Transportation Research Record 2553
calculation or the calculation by Abu-Lebdeh et al. (5) shown in
Equation 2 is used. This result is because base maximum flow rates
predicted by the HCM 2010 (943 vph) and by Abu-Lebdeh et al.
(1,234 vph) both exceed the arrival rate on red (500 vph):
HCM 2010 (1):
1
100
1
943 vphRT ,
3,600
3,600
100 6.9
3,600
100 3.3
3,600
, ,
, ,
p
p
p=
−
=
−
=
−
−
−
−S v
e
e
e
e
c x
v t
v t
c x c x
c x f x
Abu-Lebdeh et al. (5):
S a
g
C
s Vcmax 1 , 0
1,525
1,714
1 0.025 1,525 100 1,234 vph
RT
p p
{ }( )
[ ]( )( )
= − −
=
− − =
Thus the discharge flow rate is 500 vph during the RTOR phase,
regardless of which model is in effect. Although this study did
not investigate the ideal model for Proposed Improvement 1, results
shown in Figures 6 through 8 imply that flow profiles under the pro-
posed logic are more consistent with commonsense RTOR operation.
Moreover, the new flow profiles are more consistent with vehicle
trajectories from microscopic simulation. Vehicle trajectories from
Figures 6 through 8 sometimes cross because there were two through
lanes, and some vehicles passed others by selecting the lane with the
shortest queue.
In Case SN2 (Figure 6), RTOR movements occur throughout the
signal cycle except during the protected green phase, when right-
turners briefly move at a higher flow rate. Thus under the proposed
logic, the SN2 flow profile remains flat throughout the cycle, at a
flow rate reflecting the combined upstream through and right-turning
vehicles. Microscopic vehicle trajectories also reflect steady vehicle
arrivals throughout the cycle. In Case SN3 (Figure 7), only the pro-
posed logic correctly reflects right-turning vehicles flowing heavily
between Time Steps 25 and 75. Most microscopic vehicle trajectories
also reflect vehicle arrivals during the first half of the cycle. Finally, in
Case SN4, right-turning vehicles flow steadily throughout the cycle
under the proposed logic. However, the flow rate during the shielded
phase (Steps 25 through 75) is significantly lower in SN4 than in
SN3 because so much demand volume has already been served as
RTOR during other parts of the cycle. Microscopic vehicle trajec-
tories, which show steady vehicle arrivals throughout the cycle, are
again more consistent with the proposed logic.
Statistical Analysis of Performance Measures
Visual analysis demonstrates that the proposed logic better reflects
real-world traffic flow. However, statistical analysis can demon-
strate that under the proposed logic, performance measures are more
consistent with microsimulation. In addition, microsimulations
provide a much larger sample size of results than field data; this
size produces more confidence in the findings. In this experiment,
the four basic scenarios (SN1 through SN4) were examined at
20 evenly spaced offsets (10-s intervals) throughout the 200-s
cycle. Evenly distributed offsets throughout the cycle make it
possible to analyze the full shape of the flow profile. For each of
80 total scenarios, an HCM 2010 analysis was performed with
and without the proposed logic. In addition, 10 CORSIM simu-
lations were performed with 10 sets of random seed numbers to
generate a reliable average result for each scenario. Thus a total
of 160 HCM 2010 and 800 CORSIM runs was conducted for the
statistical analysis experiment.
A paired t-test compares two population means in which observa-
tions in one sample are paired with observations in the other sample
(12). In this experiment, two samples of observations were derived
from the same scenarios and should thus be paired for compari-
son. The primary performance measure was the percentage of differ-
ence between HCM 2010 and CORSIM results. For the combined set
of four basic scenarios (SN1 through SN4) and for each individual
scenario, the data in Table 1 imply that the proposed logic produces
statistically better results when CORSIM results are considered as
the ground truth. On average, differences between the HCM 2010
and CORSIM were approximately 5.2% lower under the proposed
logic for both control delays (5.25%) and travel times (5.22%).
TABLE 1 Statistical Analysis Results for Control Delay and Travel Time
Degrees of
Freedom
Difference Between Old and
New Methods (95% confidence interval)
Scenario t-Statistic P-Value Mean Lower Bound Upper Bound
Percentage of Difference in Control Delay
Overall 2.0172 79 .0471 0.0525 0.0007 0.1043
SN1 na 19 na 0 0 0
SN2 2.7804 19 .0119 0.0360 0.0089 0.0631
SN3 1.8474 19 .0803 0.1757 −0.0234 0.3747
SN4 −0.0526 19 .9586 −0.0016 −0.0661 0.0629
Percentage of Difference in Travel Time
Overall 3.0893 79 .0027 0.0522 0.0185 0.0857
SN1 na 19 na 0 0 0
SN2 1.3981 19 .1782 0.0155 −0.0077 0.0387
SN3 2.6922 19 .0144 0.1569 0.0349 0.2789
SN4 1.7741 19 .0920 0.0362 −0.0065 0.0789
Note: na = not applicable.
11. Hale, Ma, Kondyli, Hu, Huang, and Su 39
Overall P-values less than .05 imply a 95% confidence level for both
of these improvements (control delay and travel time).
In the data analysis, control delay and travel time improved the
most under Scenario SN3 (shielded right turns only), by 17.6%
and 15.7%, respectively. However control delays improved by
3.6% for Scenario SN2 (RTOR only), and travel times improved
by 3.6% under Scenario SN4 (both RTOR and shielded right
turns). This finding appears consistent with flow profiles observed
in Figures 6 through 8 because the shielded right-turn scenario
experiences the biggest shape change, whereas the RTOR-plus-
shielded scenario exhibits the biggest cycle-wide magnitude change.
According to the HCM 2010 urban street procedure, flow pro-
file shapes and magnitudes are converted into PVG and segment
flow rates, respectively. Subsequently, the HCM 2010 signalized
intersection procedure uses PVG to determine control delay, and
the urban street procedure uses segment flow rates to compute
travel time.
According to various microsimulation guidelines, a statistical
check can determine the appropriate sample size (13, 14). If the for-
mula from the Virginia Department of Transportation (13) is applied
to the data in Table 1, the appropriate sample size for control delay
would be 5.1 simulations, and for travel time it would be 3.6 simu-
lations. Thus, it can be concluded that 10 simulations per scenario
produced reliable results.
Impact of Flow Profiles
on Performance Measures
Although the statistical analysis illustrated flow profile impacts
on performance measures, a case study of the same RTOR flow rate
(500 vph) at only one offset point can illustrate these impacts. In the
abstract, it is stated that omission of RTOR in the flow profiles can
cause vehicle delays to be inaccurate by more than 30%.According to
Table 2, vehicle delays were more than 30% different (67 versus 96 s
per vehicle) under the original procedure and the proposed logic in
Case SN3. Because HCM 2010 control delay is considered an impor-
tant performance measure by decision makers and is used to determine
LOS, the significant change implies an incentive to incorporate the
proposed logic sooner rather than later.
Two observed values of PVG results under two random seed num-
bers were obtained by time-consuming inspection of CORSIM anima-
tion, because PVG statistics were not provided by the software. Each
observed PVG result was the average of 18 PVG results observed
during a 1-h simulation. Under the proposed logic, special-case right
turns (RTORs, shielded, free) changed the PVG result and travel time
(TT) by less than 10% and 6 s, respectively. These changes are more
consistent with those in CORSIM, in which special-case right turns
change PVG and TT by less than 6% and 6 s, respectively. Although
absoluteTTcorrelates better under the old logic, the impact of special-
case right turns on PVG and TT results correlates better under the new
logic. Eliminating absolute differences between the HCM 2010 and
microsimulation is beyond the scope of this paper, but special-case
right-turn effects were more consistent with microsimulation under
the new logic.
Conclusions
There has been natural interest in integrating HCM 2010 procedures
for signalized intersections, urban street segments, and interchange
ramp terminals because they all require detailed signal analysis.
This integration has presented various challenges, including the
treatment of RTORs. The treatment of RTORs by the signalized
intersection procedure causes urban street and ramp terminal pro-
cedures to lose accuracy. Rather than remove RTOR support from
the unified computational process, it would be preferable to improve
RTOR support. Five specific modeling enhancements are proposed;
they allow the urban street procedure and computational engine to
model RTORs, free right turns, and shielded right turns more accu-
rately in a way that does not change preexisting support for RTORs
in the signalized intersection procedure. In a unified engine, the
five enhancements will also improve the accuracy of ramp terminal
analysis.
These computational software improvements could facilitate accep-
tance of the HCM 2010. They have been shown to affect delays and
LOS greatly, by making the status quo less acceptable and change
more desirable.Acase study is used to demonstrate how significant the
impacts can be when a reasonable RTOR methodology is implemented
instead of a nonexistent RTOR methodology.
As for possible future enhancements, the interrupted-flow proce-
dures and engines could be expanded for explicit analysis of “chan-
nelized” right turns, which yield to conflicting through vehicles.
Unlike RTORs, the channelized rights would not have a protected
green phase. The new logic could therefore be a hybrid of RTOR
and free right-turn logic, with new input parameters to indicate the
existence of channelized right turns. Interrupted-flow procedures
could also be expanded to model RTOR explicitly as a permis-
sive movement and make left-turn and right-turn treatments more
consistent. Engineers would no longer need to estimate the number
of RTOR vph. Finally, it would help to incorporate adjustments
recommended by Creasey et al. (4) and Chen et al. (15), such as
RTORs made from a shared lane or a dual right-turn lane or yielding
to opposing left-turners.
TABLE 2 Downstream Performance Under Original and Proposed Logic
Control Delay
(s/vehicle) Arrivals on Green (%) Travel Time (s)
HCS HCS CORSIM HCS CORSIM
Scenario Old New Old New Run No. 1 Run No. 2 Old New Run No. 1 Run No. 2
None (SN1) 92 92 10 10 13 18 105 105 91 88
RTOR (SN2) 69 88 38 19 11 18 82 101 95 85
Shielded (SN3) 67 96 40 8 18 17 77 110 88 90
Both (SN4) 69 89 39 18 12 15 82 102 88 90
12. 40 Transportation Research Record 2553
References
1. Highway Capacity Manual 2010. Transportation Research Board of the
National Academies, Washington, D.C., 2010.
2. HCS 2010 Urban Streets Users Guide. McTrans Center, University of
Florida, Gainesville, 2012.
3. Creasey, F. T. Right-Turn-on-Red Volume Estimation and Incremental
Capacity Models for Shared Lanes at Signalized Intersections. PhD
dissertation. University of Kentucky, Lexington, 2010.
4. Creasey, F. T., N. Stamatiadis, and K. Viele. Right-Turn-on-Red Volume
Estimation and Incremental Capacity Models for Shared Lanes at Sig-
nalized Intersections. In Transportation Research Record: Journal of
the Transportation Research Board, No. 2257, Transportation Research
Board of the National Academies, Washington, D.C., 2011, pp. 31–39.
5. Abu-Lebdeh, G., R. F. Benekohal, and B. Al-Omari. Models for Right-
Turn-on-Red and Their Effects on Intersection Delay. In Transportation
Research Record 1572, TRB, National Research Council, Washington,
D.C., 1997, pp. 131–139.
6. Robertson, D. I. TRANSYT: Traffic Network Study Tool. In Proceedings
of the Fourth International Symposium on the Theory of Traffic Flow,
University of Karlsruhe, Germany, 1968, pp. 98–104.
7. Robertson, D. I. TRANSYT: A Traffic Network Study Tool. Report LR 253.
UK Transport and Road Research Laboratory, Crowthorne, Berkshire,
England, 1969.
8. Robertson, D. I., and P. Gower. User Guide to TRANSYT Version 6. Sup-
plementary Report 255. UK Transport and Road Research Laboratory,
Crowthorne, Berkshire, England, 1977.
9. Hale, D. K. TRANSYT-7F Users Guide. McTrans Center, University of
Florida, Gainesville, 2009.
10. Wallace, C. E., and F. White. Development of Algorithms for Permit-
ted Traffic Movements in TRANSYT-7F. FHWA, U.S. Department of
Transportation, 1986.
11. CORSIM 6.3 Users Guide. McTrans Center, University of Florida,
Gainesville, 2013.
12. Shier, R. Statistics: 1.1 Paired t-tests. Mathematics Support Centre,
Loughborough University, United Kingdom, n.d. www.statstutor.ac.uk
/resources/uploaded/paired-t-test.pdf. Accessed May 17, 2015.
13. Traffic Operations Analysis Tool Guidebook,Version 1.1. Traffic Engineer-
ing Division, Virginia Department of Transportation, Richmond, 2013.
http://www.virginiadot.org/business/resources/traffic_engineering
/VDOT_Traffic_Operations_Analysis_Tool_GuidebookV1
.1-August2013.pdf. Accessed Dec. 15, 2013.
14. Hale, D. K. How Many NETSIM RunsAre Enough? McTrans Newsletter,
1997, pp. 4–5.
15. Chen, X., Y. Qi, and D. Li. Estimating Right-Turn-on-Red Capacity
for Dual Right-Turn Lanes at Signalized Intersections. In Transporta-
tion Research Record: Journal of the Transportation Research Board,
No. 2286, Transportation Research Board of the National Academies,
Washington, D.C., 2012, pp. 29–38.
The Standing Committee on Highway Capacity and Quality of Service peer-reviewed
this paper.