University of Florida

Capacity and Level of Service at Two-

way, Two-lane Highways

Traffic Flow Theory Course Project

Kiarash Fariborzi

December 2015

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Abstract

This study concentrates on the performance measures of two-way two-lane highways. First, the

measures and methodologies used by HCM over time are discussed, followed by the shortcomings

of these methodologies addressed in the literature, which justifies the need for more research to

develop alternative performance measures. Second, a discussion on the alternative performance

measures proposed on various studies are provided, so are the empirical models relating those

measures to platooning factors such as flow, percentage of heavy vehicles, etc. In addition, a

framework is presented that can be used to compare a set of performance measures based on their

correlation with platooning factors. Finally, recommendations for future research in this area are

provided so as to overcome the drawbacks found in the reviewed studies.

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1. Introduction

Two-Lane Two-Way Highways (TLTWHW) are undivided roads with two lanes where the

direction of traffic in one is the opposite of that in the other. Two-lane highways comprise the

majority of the road system in the US. Measuring capacity and quality of service of TWTLHW’s

are crucial for planning purposes as well as decision to upgrading them to a multilane highways.

Ever since its first edition, the HCM has offered models estimating TWTLHW capacity. The HCM

method is to come up with a capacity corresponding to the base conditions, and then to account

for the prevailing conditions, adjustment factors are applied. For example, in the HCM 1965 the

capacity of a two-lane highway is estimated 2000 pcph to which two factors are applied capturing

the effects of truck and lane width. However, there are many other contributing factors that are

overlooked such as directional distribution, road environment, presence of no-passing zones,

access points etc. Aside from the analytical approach adapted by the HCM, several studies have

developed simulation models to estimate TWTLHW capacity which attempt to explicitly deal with

the factors that are ignored or implicitly considered in the HCM analyses. Also, since there are a

few TWTLHW’s operating near their capacity, estimation of capacity based on field observation

is not always feasible, hence the need for simulation.

In terms of TWTLHW quality of service, various performance measures are proposed in literature.

Earlier versions of the HCM would use “percent time delay” meaning the percentage of

accumulative travel time of a vehicle spent in queue which is a space-averaged value. The

surrogate measure of percent time delay which could be measured in the field was percentage of

vehicles traveling at headways less than or equal to 5 seconds, recorded at certain points along the

road. More recent versions use “percent time spent following” and the average travel speed. These

performance measures are also reported as the outputs of simulation tools which are capable of

modeling TWTLHW. Several studies have discussed these measures and the HCM procedures.

The intent of this paper is to provide an extensive review of the studies dealing with capacity and

level of service of TWTLHW. It provides a discussion of relevant factors found in literature which

are affecting the TWTLHW capacity estimations and the way they are incorporated in the models.

Furthermore, the various performance measures used in literature as representatives of the level of

service will be compared and contrasted.

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2. Literature Review

Section 2.1 presents a review of the performance measures, analytical models and procedures of

HCM used to determine the level of service in two-lane highways. Section 2.2 provides the

advantages and shortcomings of the HCM methods addressed in the literature. Section 2.3 goes

over other performance measures relevant to the LOS of two-lane, two-way highways that are

discussed in the literature and can potentially be a substitute for current methods of HCM. Section

2.4 reviews the empirical studies that have compared a few of these performance measures and

their findings regarding the “best” indicator of quality of service.

2.1. HCM Approach

The 1965 HCM presents the capacity and a service measure procedure for two-lane highways.

Accordingly, the quality of service is attributed to a combination of volume-to-capacity ratio and

the operating speed of traffic with six levels (A through F) being defined. Certain conditions such

as passing sight distance and average highway speed are also taken into account in the 1965 HCM

procedure. Furthermore, the capacity of two-lane highways is estimated 2000 passenger car per

hour for both directions combined, regardless of directional split.

The LOS metrics proposed by the 1965 HCM remained unchanged until the subsequent edition of

the HCM in 1985. Meanwhile, Morall and Werner (1982) questioned the criteria of the 1965 HCM

through a study conducted on two-lane rural highways in western Canada. They found that using

HCM, high level of service were obtained in the rural two-lane highways due to relatively low

volumes. But nevertheless, drivers perceived a low level of service. They attributed their finding

to the fact that although vehicles drive with fairly high speed, they are stock in platoons for a long

time with limited opportunity to overtake which causes frustration for road users, hence adopting

a negative perception. Therefore, this study suggests that aside from operating speed and flow, the

time vehicles spend in platoons as well as overtaking opportunities should be reflected in the level

of service of two-lane highways. This notion is adapted in the later edition of the HCM (i.e. 1985

HCM).

In the 1985 HCM two performance measures are used to determine the LOS for two-lane

highways; average upgrade travel speed for specific upgrades and percent time delay (PTD) for

general terrain segment. PTD is defined as the average percent of time that the vehicles are delayed

due to their inability to overtake a slow-moving leading vehicle, which is approximated by a

surrogate measure, that is, the percentage of vehicles driving at headways less than or equal to 5

seconds at certain points along the road segment. Capacity under ideal conditions is estimated

2,800 pcph for both directions. For particular conditions, capacity (i.e. flow rate corresponding to

LOS E) is adjusted for directional split, percent of heavy vehicle, percent of passing zone, lane and

shoulder width, terrain type (in general terrain procedure) and percent grade (in specific grade

procedure).

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Harwood et al. (1999) did a research to identify the drawbacks of the 1985 HCM through surveying

the HCM users. Their conclusions were threefold: 1) the term “percent time delay” caused

confusion for users, and its surrogate measure based on 5-second headway should be modified, 2)

There should be only one LOS applicable to the sections of the same highway, 3) Design speed as

well as the effect of passing and climbing lanes should be reflected on LOS procedure. To address

these issues thus providing modifications to the next edition of the HCM (i.e. HCM 2000), they

collected data and used TWOPAS models to modify the procedures. As the result, PTD was

renamed as percent time spent following (PTSF), which is a more intuitive term. It is an indicator

of freedom of maneuver and comfort of travel. As for the surrogate measure of PTSF practicable

for field measurements, the simulation study indicated that it can be defined as the percent of

vehicles driving with a headway of 3 seconds or less at a particular point down the roadway.

In the 2000 HCM, the LOS measures are PTSF for class II highways (roads where drivers do not

expect traveling with high speed), and PTSF and average travel speed (ATS) combined for class I

highways (where high travel speeds are expected), thereby making LOS sensitive to design speed,

and in turn, allowing for using the same procedure for both general grade and specific grade

conditions. In this version, “directional segment” procedure is provided for specific up- and down-

grade analysis. Unlike the previous version, the estimation of capacity is irrespective of

distributional split, with 1,700 pcph for each direction and 3,200 pcph for both directions.

2.2. Shortcomings of the HCM methodologies

Various studies are conducted to evaluate the validity of the HCM 2000 methods for two-lane

highways. Lutinnen (2001) applied the 2000 HCM method to the traffic data obtained in Finish

roadways. He found that the HCM overestimate the PTSF, and that the impact of directional split

on PTSF model is higher than HCM estimation whereas the impact of no-passing zone is lower.

He, therefore, recommended that HCM procedure be adjusted for Finish two-lane highways

environment. In addition, it was suggested that an alternative measure based on space headway

should be replaced for oversaturated conditions in that PTSF is based on time headway which

cannot be representative of those conditions. Nevertheless, it is pointed out frequently in the

literature that two-lane highways rarely operate under such conditions.

Romana and Perez (2006) and Lutinnen (2001) pointed out that the HCM 2000 has not proposed

guidelines regarding the location(s) in which headway data collection should be conducted.

Romana and Perez (2006) that sometimes it is not intuitive that in which type (I or II according to

HCM classification of two-lane roads) a road should be categorized, and they question the use of

the same set of thresholds by HCM in determining LOS for all types of two-lane roads. Lutinnen

(2001) mentions that since PTSF is an exclusive measure to two-lane highways, it cannot be useful

for planning purposes as it is not comparable with service measures of multi-lane facilities. For

this reason, it is suggested that the HCM methodology for freeways be extended to multi-lane

highways.

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Al-Kaisy and Durbin (2008) discussed the inconsistencies of modeling assumptions and the HCM

surrogate measure with the definition of PTSF. In fact, the time a vehicle spends following a lead

vehicle even with short headways should not be considered as PTSF unless the follower has a

higher desired speed than the speed of the lead vehicle. This fact is not taken into account in the

HCM 2000 methodologies, neither in the analytical models nor in the field measurement method.

2.3. Alternative Service Measures

This section provides a review of different approaches presented in the literature in regard to the

LOS of two-lane, two-way roads. Restricting assumptions as well as merits of each method are

also discussed.

Al-Kaisy and Durbin (2008) suggest “percent following” (PF) (or “percent impeded” (PI), so

called by Al-Kaisy and Freedman (2010)) as an alternative to the HCM surrogate measure (i.e.

PTSF). They also come up with two methods estimating PF using field data such as headway and

speed distributions. The first method is called probabilistic method which says that the PF is the

product of two probabilities:

𝑃𝐹 = 𝑃𝑝𝑃𝑖 (1)

Where 𝑃𝑝 is the probability that a vehicle is traveling in a platoon—platoons are differentiated

based on headway data, and 𝑃𝑖 is the portion of vehicles whose desired speeds are greater than the

speed of the platoon. The former is calculated based on headway distribution. That is, the portion

of vehicles traveling with headways less than 6 seconds (headway data is collected at particular

section(s)). The latter is computed based on the desired speed distribution obtained using headway

data. In other words it is the portion of vehicles that would drive faster than the average speed of

slow-moving vehicles if not impeded (the lead vehicles in platoons). This method is based on the

assumption that the distribution of desired speed of the vehicles inside platoons is identical to that

for vehicles out of platoon as well as platoon leaders (Al-Kaisy and Freedman (2010))

The other method used by Al-Kaisy and Durbin (2008) to estimate PF is called “weighted- average

method”. In this method vehicles are divided to heavy vehicles and passenger vehicle. This method

is based on the assumption that the average speed is weighted average of the speed of passenger

and heavy vehicles, where the weights are the percentage of these two categories. In other words:

𝑆𝑎𝑡𝑜𝑡 = 𝑆𝑎𝑝𝑣𝑃𝑝𝑣 + 𝑆𝑎ℎ𝑣𝑃ℎ𝑣 (2)

Where 𝑆𝑎𝑝𝑣 and 𝑃𝑝𝑣 are the speed and proportion of passenger vehicles, and 𝑆𝑎ℎ𝑣 and 𝑃ℎ𝑣 are those

of heavy vehicles. Dividing the passenger cars into impeded and not impeded (whose speed is

denoted by 𝑆𝑖𝑝𝑣 and 𝑆𝑑𝑝𝑣, respectively). Therefore, we have:

𝑆𝑎𝑡𝑜𝑡 = 𝑆𝑑𝑝𝑣𝑃𝑝𝑣1 + 𝑆𝑖𝑝𝑣𝑃𝑝𝑣2 + 𝑆𝑎ℎ𝑣𝑃ℎ𝑣 (3)

𝑃𝑝𝑣 = 𝑃𝑝𝑣1 + 𝑃𝑝𝑣2 (4)

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In which 𝑃𝑝𝑣1 is the percentage of vehicles driving at their desired speed whereas 𝑃𝑝𝑣2 is the

proportion of passenger vehicles impeded by slow-moving vehicles. The equations above can be

solved for 𝑃𝑝𝑣2 (which is equal to 𝑃𝑝 in the previous method). Assuming that 𝑆𝑖𝑝𝑣 is approximately

equal to 𝑆𝑎ℎ𝑣, 𝑃𝑝𝑣2 will be calculated easily from the available field data. This method is based on

the assumption that heavy vehicles cannot be impeded by passenger vehicle which needs further

investigations to be validated.

The two methods explained above together with the HCM field estimation method were applied

to field data gathered from three sites. They found that all the three approaches generally result in

comparable PF values and all of them are significantly less than the PTSF estimated using the

HCM analytical models. It was concluded that none of the proposed approaches are superior to the

HCM method.

Romana and Perez (2006) presents an LOS estimation approach based on ATS and PTSF (the

same MOE’s used the HCM methodology). The motivation behind this method was to eliminate

the need for facility classification, and to provide different thresholds considering different road

environments. This method determines a threshold speed as the minimum speed that road users

deem acceptable under heavy traffic conditions, which can be set by polling the users. If the ATS

is more than the specified threshold, PTSF will be used as the measure distinguishing the LOS’s

ranging from A through D; otherwise LOS is determined based on ATS. Figure 1 shows the LOS

ranges based on the threshold speed of 80 km/h. In this method it is not made clear how the ranges

of LOS should be determined, and how the survey for estimating the threshold speed should be

conducted. The authors, however, believe that this method better reflects road users’ perception of

LOS.

Fig. 1. LOS thresholds suggested by Romana and Perez (2006)

Source: Romana, M., & Pérez, I. (2006). Measures of effectiveness for level-of-service assessment of two-lane

roads: An alternative proposal using a threshold speed. Transportation Research Record: Journal of the

Transportation Research Board, (1988), 56-62

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Cohen and Polus (2011) suggest a theoretical model based on queuing theory for calculating PTSF.

This study assumes that a platoon is a one-server queuing system. Given that, “service time” is the

time interval that a fast vehicle in the second position in platoon drives right behind the first vehicle

in platoon. “Customers” are the vehicles joining the queue. This model uses only headway data as

its input. It is considered an advantage that unlike the HCM model this model is not based on

simulation since the author believes that assumptions regarding driver behavior in passing

maneuvers are not realistic. The model is as follow:

𝑃𝑇𝑆𝐹 =

{

100.

�̅�

�̅� + �̅� 𝑖𝑓 𝜋 = 0

100.�̅� − 1

�̅� + �̅� − 1 𝑖𝑓 𝜋 = 1 − 𝜌

100.

𝑄2̅̅̅̅

�̅�− 1

�̅� +𝑄2̅̅̅̅

�̅�− 1

𝑖𝑓 𝜋 = 𝑃𝑟𝑜𝑏(𝑄 = 2)

(5)

Where the parameters of the model �̅� and �̅� that are average number of headways inside a platoon

and average number of headways between two platoons, respectively. 𝜋 is the probability that a

free-moving vehicle will create a platoon. This parameter is unobserved from field data.

The assumptions in this method are as follow:

1. Service time should not exceed the inter-arrival time.

2. An M/M/1 queuing model is assumed to apply to this queuing system, implying that the

headway distribution must be exponential and the flow should be relatively low.

3. Platoons are distinguished based on headways greater than or equal to 3 seconds.

4. Number of headways in queue (Q) (i.e. queue length) follows a geometric distribution. This

should be verified using chi-square test.

𝑃𝑟𝑜𝑏 (𝑄 = 𝑖) = (1 − 𝜌). 𝜌𝑖−1 𝑖 = 1,2,3, … (6)

𝜌 = 1 −1

�̅�

Where 𝜌 is traffic intensity, and �̅� is the average number of headways in platoon.

5. Drivers are assumed to overtake as soon as a suitable gap is available, so presence of no-

passing zones are ignored in the model.

This model was applied to the field data and the estimated PTSF was plotted against flow as

shown in diagram below. Each data point corresponds to a 1-hour headway data. It is seen that

the estimated values are significantly less than the estimation of the analytical model of HCM

2000. This finding is consistent with other studies comparing HCM analytical models for PTSF

estimation with the PTSF estimated from field data.

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Fig. 2. Empirical model relating PTSF to flow developed by Cohen and Polus (2011)

Source: Cohen, M., & Polus, A. (2011). Estimating percent-time-spent-following on two-lane rural

highways. Transportation research part C: emerging technologies, 19(6), 1319-1325.

2.4. Comparison of Performance Measures

Regarding the comparison of performance measures, a few methodologies have been proposed in

literature. One of these methodologies (Al-Kaisy and Karjala (2008), and Al-Kaisy and Freedman

(2010)) is based on exploring a relationship between these measures and such platooning factors

as traffic flow in the direction of travel, opposing traffic flow, percent heavy vehicle, standard

deviation of free-flow speed, presence of no-passing zone. To figure out this relationship, this

method uses correlation analysis and/or regression analysis where the measures are dependent

variables and the platooning factors are explanatory variables. Subsequently, an F-test will be

carried out to compare the regression model with the bench mark model (i.e. constant-only model).

Based on the r-squared value of the regression model, the suitability of performance measures are

evaluated, and the t-value of the platooning factors (i.e. the independent variables of the model)

indicates the contribution of that factor to platooning. This methodology assumes that platooning

is the major indicator of operational performance in two-way two-lane highways.

Al-Kaisy and Karjala (2008) explored the correlation of platooning factors with six indictors of

quality of service, namely average travel speed, average travel speed of passenger cars, average

travel speed as a percentage of free flow speed, average travel speed as a percentage of free flow

speed for passenger cars, percent follower and follower density. Percent follower was calculated

using 3-second cut-off point as the criterion to distinguish between platoons. Follower density by

definition is the “number of followers in a directional traffic stream over a unit length”. The

surrogate measure of that for field measurement could be determined based on occupancy

percentage of traffic detectors, or based on measuring flow and speed and usage of fundamental

formula. The regression analyses are conducted both within each of the sites and across the sites.

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Regarding across-site examination, the F-test showed that except for the regression models

corresponding to percent follower and follower density, the other four regression models were not

better than the base model according to the F-test results. The regression model corresponding to

follower density had the highest r-squared value. Accordingly, It is concluded that among the

measures studied, follower density is best correlated with platooning factors followed by percent

follower (currently used by HCM), and that traffic flow in direction of travel is the best indicator

of platooning with having the only statistically significant t-value in the models. Also, it is pointed

out that follower density is superior to percent follower in that it is reflective of traffic level

whereas percent follower is not. That is, a traffic flow with low AADT could have the same percent

follower value as a high-AADT traffic flow if say the variability in latter was higher than that in

former. In this situation one would expect that the low-AADT flow should have a lower LOS than

the high-AADT one, but percent follower alone cannot capture that. Therefore, follower density

represents the quality of flow better than percent follower does, and in turn, this study suggest the

use of follower density as the performance measure of two-way two-lane highways. This measure

is currently used as the LOS measure in South Africa (Van As (2003)). A shortcoming of the study

is that the transformed version of dependent variable were not experimented in analysis, which

implies that a curvilinear relationship between platooning factors and performance measures is not

investigated.

In a similar study conducted by Al-Kaisy and Freedman (2010), beside the performance measures

experimented by Al-kaisy and Karjala (2008), another measure called percent impeded (PI) (PI is

discussed in the preceding section) is tested. Unlike Al-Kaisy and Karjala (2008), Al-Kaisy and

Freedman (2010) found that the corresponding regression model to PI has by far the highest r-

squared value followed by percent follower, follower density and ATS/FFS. The flow of the same

and opposing direction of travel turned out to be significant in the regression model of PI and

follower density whereas it is insignificant in the other two models. This is consistent with the

notion that percent follower and ATS/FFS are not necessarily dependent on traffic flow level (Al-

Kaisy and Karjala (2008)).

Polus and Cohen (2009) proposed and evaluated five parameters that could be indicators of the

quality of flow by developing theoretical models based on the queuing theory. The model

parameters include number of headways inside and outside platoon, which are easy to observe in

the field. In-platoon headways based on the HCM suggestion are assumed 3 seconds although

other studies (e.g. Alkaisy and Durbin (2008)) used headway of 6 sec to distinguish platoons. PTSF

was one of the parameters studied. In comparison to the HCM model, this model predicts lower

PTSF values, which is consistent with the premise mentioned frequently in the literature (e.g. Al-

Kaisy and Durbin (2008)) that HCM overestimates PTSF. Furthermore, the issue of defining a

surrogate measure for PTSF for field observation does not exist in this model in that it relates PTSF

to headways observed in the field. Furthermore, this study maintains that “freedom of flow” is the

best indicator of level of service. Freedom of flow by definition is the ratio between the average

travel time between platoons and the expected time that the first vehicle behind a slow-moving

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vehicle in the platoon spends before finding a suitable gap to overtake, so the higher the freedom

of flow, the higher the LOS.

3. Conclusions

LOS concept was first introduced in the 1965 HCM suggesting that quality of flow for two-lane

two-way highways should be measured based on the operating speed and volume-to-capacity ratio

taking into account the impact of passing sight distance and the average speed. In this version of

HCM, capacity was estimated 2,000 pcph for both directions irrespective of directional

distribution. Travelling in platoons for a long time without having enough passing opportunity

causes frustration for drivers which should be reflected in LOS (Morall and Werner (1982)).

However the LOS measures used by the 1965 HCM cannot take that into account.

In the 1985 HCM, two different set of models for specific grade and general terrain were proposed

which caused confusion for HCM users arguing that one procedure should apply to all the sections

of the same roadway, and that two methods would result in different LOS for the same section

(Harwood et al. (1999)). Two service measures (i.e. percent time delay (PTD) and average travel

speed) were used by the 1985 HCM admitting that measurement of PTD in the field is not feasible,

hence suggesting a surrogate measure which would differentiate between platoons using headways

of 5 seconds or more. The 1985 HCM estimates 2,800 pcph as the maximum flow rate of a two-

lane highway corresponding to a 50/50 split.

The 2000 HCM proposes LOS models for two classes of highways, and for two-way and

directional segments. Percent time spent following (PTSF) and average travel speed (ATS) are

used as performance measures for type I highways whereas PTSF alone is the determinant of LOS

for type II highways. The surrogate measure of PTSF is based on 3- second headways. The 2000

HCM estimate the capacity as 1,700 for each direction and 3,200 for both directions combined

regardless of directional split.

An issue associated with the surrogate measure of PTSF is that it suggests that all the vehicles in

platoon desire to overtake if a suitable gap is found. But in fact, even in presence of a long gap,

they overtake as long as the speed of the platoon is less than their desired speed. This results in

overestimation of PTSF. This notion has also been confirmed by the empirical studies (Lutinnen

(2001), and Al-Kaisy and Durbin (2008)) saying that the PTSF estimated based on the surrogate

measure is less than the HCM analytical model estimation.

Given the shortcomings of the measures suggested by HCM, the following studies have

recommended alternative measures which partly deal with these shortcomings:

1- Given the headway and speed data and desired speed distribution, Al-Kaisy and Durbin

(2008) come up a method to calculate a measure called “percent impeded” as the surrogate

measure for PTSF that deals with the issue stated above.

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2- To eliminate the need for classifying roads and account for unique road environments,

Romanna and Perez (2006) offers a methodology for evaluating LOS in Spanish roads

using the same performance measures as HCM but a different way of determining the LOS

boundaries.

3- An alternative approach to estimate PTSF using field data is proposed by Cohen and Polus

(2011) based on queuing theory which has quite a few simplifying assumptions.

Nevertheless, the PTSF estimation of this method using headway data collected in Israel is

less than the PTSF estimated by HCM, so it could solve the issue of HCM PTSF

overestimation.

4- To deal with overestimated values of PTSF by the HCM models, Polus and Cohen (2009)

developed a PTSF model whose parameters were number of headways inside and out of

platoon, which are easy to observe in the field as well.

5- A model based on “Freedom of flow” as a measure explaining quality of flow was

established and evaluated by Polus and Cohen 2009.

Al-Kaisy and Karjala (2008) provided a framework for comparison of quality of service measures

based on regression and/or correlation analysis where the independent variables are the measures

to be tested, and the dependent variables are platooning factors such as traffic flow, percent heavy

vehicle, presence of no-passing zone, etc. Based on the r-squared value of the regression model,

the most suitable performance measure will be selected. This framework was applied to field data

by Al-Kaisy and Karjala (2008) and Al-Kaisy and Freedman (2010) who found follower density

and percent impeded as the best indicator of quality of flow, respectively.

4. Recommendations

Almost all the studies comparing the PTSF estimated by HCM analytical models and the surrogate

measure of PTSF for field calculations mentioned that there is inconsistencies between them, and

that the analytical models overestimate PTSF. This should be considered in later versions of HCM.

There is inconsistencies between the minimum time headways used in different studies to

differentiate between platoons. For example, Cohen and Polus (2011) went with 3-second merely

because it is suggested by HCM 2000 whereas Al-Kaisy and Durbin (2008) used 6 seconds since

they believed that the interaction between the lead and the following vehicle vanishes when their

headway exceeds 6 seconds. Also it should be noted that HCM 1985 uses 6-second headway as

the cut-off point of platoons whereas in HCM 2000 this value has decreased to 3 seconds.

Therefore, as for future research, it is recommended that a systematic study be conducted to

provide a more objective guideline for headway cut-off point.

Different countries suggest different methodologies and/or performance measures for LOS

evaluation. For instance, in South Africa follower density is used as the performance measure of

two-way, two-lane highways (Van As (2003)), or In Germany average travel speed is used as they

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care more about the functionality of their facilities than comfort of the road users (Brillon and

Weiser (2006)). Therefore, different countries can independently do studies on two-way, two-lane

highway performance considering their own concerns rather than blindly following the HCM

propositions.

5. References

1. Manual, H. C. (1965). Highway capacity manual. Washington, DC.

2. Manual, H. C. (1985). Highway capacity manual. Washington, DC.

3. Manual, H. C. (2000). Highway capacity manual. Washington, DC.

4. Morrall, J. F., & Werner, A. (1982). Measurement of level of service for two-lane rural

highways. Canadian Journal of Civil Engineering, 9(3), 385-398.

5. Brilon, W., & Weiser, F. (2006). Two-lane rural highways: the German

experience. Transportation Research Record: Journal of the Transportation Research

Board, (1988), 38-47.

6. Luttinen, R. (2001). Percent time-spent-following as performance measure for two-lane

highways. Transportation Research Record: Journal of the Transportation Research

Board, (1776), 52-59.

7. Al-Kaisy, A., & Durbin, C. (2008). Evaluating new methodologies for estimating

performance on two-lane highways. Canadian Journal of Civil Engineering,35(8), 777-

785.

8. Van As, C. (2003). The development of an analysis method for the determination of level

of service of two-lane undivided highways in South Africa. Project Summary, South

African National Roads Agency, Limited, Pretoria.

9. Polus, A., & Cohen, M. (2009). Theoretical and empirical relationships for the quality of

flow and for a new level of service on two-lane highways. Journal of Transportation

Engineering, 135(6), 380-385.

10. Cohen, M., & Polus, A. (2011). Estimating percent-time-spent-following on two-lane

rural highways. Transportation research part C: emerging technologies,19(6), 1319-

1325.

11. Romana, M., & Pérez, I. (2006). Measures of effectiveness for level-of-service assessment

of two-lane roads: An alternative proposal using a threshold speed. Transportation

Research Record: Journal of the Transportation Research Board, (1988), 56-62.

12. May, A. D., Anderson, I. B., Leiman, L., & Archilla, A. R. (1999). Capacity and quality

of service of two-lane highways. Kansas City, MO: Midwest Research Institute.

13. Al-Kaisy, A., & Karjala, S. (2008). Indicators of Performance on Two-Lane Rural

Highways: Empirical Investigation. Transportation Research Record: Journal of the

Transportation Research Board, (2071), 87-97.

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