oldfgfddf

PLEASE SCROLL DOWN FOR ARTICLE

This article was downloaded by: [2009 Universiti Putra Malaysia]On: 19 April 2011Access details: Access Details: [subscription number 781191584]Publisher RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Transportation Planning and TechnologyPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t713653693

Establishing speed-flow-density relationships for exclusive motorcyclelanesH. Hussaina; R.S. Radin Umarb; M.S. Ahmad Farhanc

a Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang,Selangor, Malaysia b Department of Higher Education, Ministry of Higher Education, Putrajaya,Malaysia c Malaysian Institute of Road Safety Research, Kajang, Selangor, Malaysia

Online publication date: 14 April 2011

To cite this Article Hussain, H. , Radin Umar, R.S. and Ahmad Farhan, M.S.(2011) 'Establishing speed-flow-densityrelationships for exclusive motorcycle lanes', Transportation Planning and Technology, 34: 3, 245 — 257To link to this Article: DOI: 10.1080/03081060.2011.565175URL: http://dx.doi.org/10.1080/03081060.2011.565175

Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf

This article may be used for research, teaching and private study purposes. Any substantial orsystematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply ordistribution in any form to anyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss,actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directlyor indirectly in connection with or arising out of the use of this material.

http://www.informaworld.com/smpp/title~content=t713653693

http://dx.doi.org/10.1080/03081060.2011.565175

http://www.informaworld.com/terms-and-conditions-of-access.pdf

Establishing speed�flow�density relationships for exclusive motorcyclelanes

H. Hussaina*, R.S. Radin Umarb and M.S. Ahmad Farhanc

aDepartment of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400,Serdang, Selangor, Malaysia; bDepartment of Higher Education, Ministry of Higher Education,Level 3, Block E9, Parcel E, 62505, Putrajaya, Malaysia; cMalaysian Institute of Road SafetyResearch, Lot 125-135, Jalan TKS 1, Taman Kajang Sentral, 43000, Kajang, Selangor, Malaysia

(Received 4 November 2009; final version received 1 February 2011)

The motorcycle is a popular mode of transport in Malaysia and developing Asiancountries, but its significant representation in the traffic mix results in high ratesof motorcycle accidents. As a result, the Malaysian Government decided tosegregate motorcycle traffic along its new federal roads as an engineeringapproach to reduce accidents. However, traffic engineers needed to know themaximum traffic a motorcycle lane could accommodate. Despite substantialliterature related to speed�flow�density relationships and capacities of varioustransport facilities, there is a knowledge gap regarding motorcycle lanes. Thispaper establishes motorcycle speed�flow�density relationships and capacities ofexclusive motorcycle lanes in Malaysia. Observations of motorcycle flows andspeeds were conducted along existing and experimental motorcycle lanes.Motorcycle speed�density data were aggregated and plotted for two types ofobservable motorcycle riding behaviour patterns that were influenced by thewidths of a motorcycle lane: the headway pattern (lane width 5 1.7 m) and thespace pattern (lane width � 1.7 m). For both riding patterns, regression analysisof motorcycle speed�density data best fits the logarithmic model and conse-quently the motorcycle flow�density and speed�flow models are derived.Motorcycle lane capacities for headway and space riding patterns are estimatedas 3300 mc/hr/lane and 2200 mc/hr/m, respectively.

Keywords: motorcycle; accidents; motorcycle lane facility; motorcycle speed�flow�density relationships; motorcycle lane capacity

1. Introduction

As a result of the low socioeconomic status of its people and relatively poor public

transport services, especially in busy urban areas, motorcycle riding is an alternative

and cheaper mode of transport which provides the freedom of door-to to door travel

in countries such as Malaysia and other developing Asian countries. In addition, it is

part of the travelling culture in these hot climate countries. The motorcycle

population in Malaysia represents 47% of the total vehicle mix (Malaysian Road

Transport Department 2007), and motorcycle riders and pillion passengers account

*Corresponding author. Email: [email protected]

Transportation Planning and Technology

Vol. 34, No. 3, April 2011, 245�257

ISSN 0308-1060 print/ISSN 1029-0354 online

# 2011 Taylor & Francis

DOI: 10.1080/03081060.2011.565175

http://www.informaworld.com

Downloaded By: [2009 Universiti Putra Malaysia] At: 02:51 19 April 2011

http://www.informaworld.com

for 57% of road accident fatalities (Royal Malaysian Police 2007). It clearly indicates

that motorcycle accidents are a key accident problem in the country and that

something must be done to tackle the road safety problem of this major and

vulnerable road user.

The idea of segregating motorcycles from the traffic mix in Malaysia was initiated

during the early 1970s. The first exclusive motorcycle lane was constructed along

Federal Highway Route 2 (F02) in the state of Selangor. But as there were no proper

guidelines available at that time, the geometric design of this facility was based on a

‘rule-of-thumb’ approach. Studies later showed that the introduction of this 30-km-

long (per direction) exclusive motorcycle lane led to a significant reduction in

motorcycle accidents � by 39% on that stretch (Radin Umar et al. 1995, 2000). This

supported the notion that segregation is the best engineering approach to avoid

conflicts between motorcyclists and other vehicles in the traffic mix, thus reducing

serious injuries and fatalities.

Recently, the Malaysian Government decided to adopt a policy to provide

exclusive motorcycle lanes along its new federal roads as part of its engineering

approach to reduce motorcycle accidents (The Star Newspaper 2007). This policy

involves a large-scale project with usually limited financial resources. Therefore, it is

essential for traffic engineers to have some idea regarding the maximum number of

motorcycles that can be accommodated in a motorcycle lane (i.e. its capacity) in their

attempt to minimise the costs of construction and to maximise the operating

performance of the motorcycle lane.

Despite the widely available literature related to the fundamental speed�flow�density relationships, capacity and level of service for roadways, pedestrians and

bicycle lane facilities (HCM 2000), there has been a knowledge gap for motorcycle

lanes. This paper is seen as an initial effort towards a better understanding

of motorcycle traffic science, operations and facility design. It attempts to

develop motorcycle speed�flow�density relationships along an uninterrupted

exclusive motorcycle lane from which the values of critical density, critical speed

and capacity may be determined.

2. Related motorcycle traffic studies

The most relevant available literature to motorcycle traffic science and facilities is the

guidelines for the design of a cycle track published by the Malaysian Ministry of

Works (Public Works Department 1986). Although the design elements of this cycle

track seem to be a cross between a highway and a bicycle track, there is no mention

of the capacity of this facility and it is not known if the lane width recommendations

(minimum 2.0 m and maximum 3.5 m) were actually derived from any scientific

studies.

In a recent study that relates to motorcycle/rider characteristics, it was reported

that small- and medium-sized motorcycles (less than 150 c.c. engine) represented

99% of all motorcycles in Malaysia (Hussain et al. 2005). The static handlebar width

of a motorcycle/rider unit is 0.8 m, while the operating width ranged from 0.9 to

1.7 m (a mean width of 1.3 m). There are two observable behaviour patterns with

regard to motorcyclists’ riding manner, which are influenced by the width of the

246 H. Hussain et al.


motorcycle lane. The first pattern is for situations when the motorcycle lane width is

1.7 m or less, thus constraining the available riding space of the motorcyclists and as

a consequence forcing them to ride in a single file regardless of low- or high-flow

traffic conditions. This riding behaviour is known as headway (or platoon) behaviour

and the motorcycle flow is measured in mc/hr/lane. For the second riding pattern,

motorcyclists are able to pass other motorcyclists as the width is more than 1.7 m.

The formation of two lines during low- or high-flow traffic conditions is observable

and this pattern is referred to as the space pattern and the motorcycle flow is

expressed in mc/hr/m width.

2.1. Speed�density models from past studies

There have been various established forms for the shape of the speed�density

relationships covering the linear as well as various logarithmic and exponential

curves. Greenshields (1934) first proposed the linear speed�density model, which is

simple and straightforward. Both free-flow speed and jam density are easily

determined, and the model is easily manipulated to find flow�speed and flow�density relationships. Most recent studies, however, have indicated that speed�density data are not perfectly linear.

Greenberg (1959) hypothesised a logarithmic shape for the speed�density

relationship, but the major flaw in this model is that it collapses at low densities.

For this reason, a maximum free-flow speed must be independently assumed or

observed and superimposed on this model. Underwood (1961) proposed an

exponential model of speed�density. This model is reasonable at low densities, but

is rather unreliable at high densities because speed asymptotically approaches zero

without ever reaching it.

The fitting of data to these simple models is accomplished using multiple linear re-

gression analysis. The speed�density equations developed by Greenshields, Underwood

and Greenberg in relation to their hypothesised basic shapes are summarised in Table 1.

Given a speed�density model, corresponding parabolic equations for flow�speed

and flow�density may be defined algebraically from the general relationship: Volume

� Speed � Density. Once speed�flow�density relationships are established,

capacity can therefore be defined either graphically or mathematically from the

peak of the flow�density curve.

Table 1. Summary of basic speed�density models.

Model Speed�density equation Simple linear equation

Linear (Greenshields) S ¼ Sf �Sf

DfD S ¼ Sf �

Sf

DfD

Exponential (Underwood) S ¼ Sf e�DDc

� �ln Sð Þ ¼ ln Sfð Þ � 1

DcD

Logarithmic (Greenberg) S ¼ Sc lnDj

D

� �S ¼ Sc ln Dj

� �� Sc ln Dð Þ

Note:S�speed; Sf�free-flow speed; Sc�critical speed; D�density; Dc�critical density; Dj�jam density.

Transportation Planning and Technology 247


3. Methodology

3.1. Field parameters

As in the general vehicular traffic stream, the understanding of the macroscopic

parameters of a motorcycle traffic stream, such as motorcycle speed, motorcycle flow

and motorcycle density, is essential. Parameters recorded in the field were individual

motorcycle spot speeds, motorcycle volumes and total paved width of the motorcycle

lanes. Time-mean speeds were converted to space-mean speeds according to

established traffic flow theories (Wardrop 1952). Data collection was carried out to

cover the two observable riding behaviour patterns, i.e. the headway pattern (where the

motorcycle lane is 1.7 m wide or less) and also the space pattern (where the motorcycle

lane is more than 1.7 m wide). Motorcycle volumes collected in 1-minute intervals

were converted to an equivalent rate of flow in motorcycles per hour. The motorcycle

flow rate for the headway riding pattern was computed as motorcycles per hour per

lane. For the space riding pattern, motorcycles per hour per unit width was obtained

by dividing the rate of flow by the total width of the motorcycle lane.

3.2. Field and experimental study sites

In an attempt to collect data ranging from stable-flow to unstable-flow conditions,

the study was conducted in three stages. In the initial stage, data were collected at

three sites of the motorcycle lane along Federal Highway Route 2. This is the stretch

of exclusive motorcycle lane noted earlier and shown in Figure 1. The availability of

elevated positions or concealed locations such as pedestrian overhead bridges

ensured that the observed motorcyclists were not inhibited by any external factors

such as the presence of study observers at the vicinity of the study site.

All sites were level and straight basic segments with total widths of 2.4, 3.0 and

3.3 m, respectively, representing the minimum to maximum available lane width.

Figure 1. Motorcyclists riding along exclusive motorcycle lane, Federal Highway Route 2,

Selangor, Malaysia.



Observations from these three sites exhibited only the initial portion of a stable flow

condition (i.e. low density, high speed) even under peak-hour conditions and

minimum width of 2.4 m. It indicated that the lane width provided using the rule-of-

thumb approach is overdesigned in comparison to the volume of motorcycles ridingalong this stretch. As such, the chances of recording motorcycle flow at capacity and

near-jam density conditions along this exclusive motorcycle lane are rather slim.

Observations of motorcycle flow at capacity could be made possible if the widths

were narrower than 2.4 m and coupled with considerably higher motorcycle flows.

The second stage involved three experimental studies representing three different

widths of less than 2.4 m conducted on the Universiti Putra Malaysia campus. These

studies involved 100 motorcyclists who volunteered to ride along level and straight

basic segments that were narrowed on one side by safety cones. The experimentaltotal lane widths were 1.5, 1.7 and 1.9 m, respectively.

The success of these experimental studies in obtaining data covering capacity and

near-jam density conditions led to similar experimental studies which were

conducted along the motorcycle lane on Federal Highway Route 2. In this third

stage, the experimental studies were conducted during the morning peak hour of

motorcycle traffic riding along the level and straight basic segments of narrowed

experimental total widths 1.4, 1.6 and 2.0 m, respectively.

4. Regression models

A total of 193 data points measured at 1-minute intervals were aggregated from the

study sites, covering total widths ranging from 1.4 to 3.3 m. Adopting earlier findings

pertaining to headway and space riding patterns (Hussain et al. 2005), the model for the

headway pattern covered data points belonging to observations for total widths of 1.4-

1.7 m. For comparison purposes, the space pattern model encompassed all 193 data

points (i.e. total widths from 1.4 to 3.3 m). Scatter plot diagrams for three relationships

� flow (F)-speed (S), flow (F)-density (D) and speed (S)� density (D) � were obtained.The initial calibrations focused on the S�D relationships because S�D curves are

monotonically decreasing and involve simpler mathematical forms than the other

two curves. Linear regression analysis at a 95% confidence interval was employed for

the model fitting and model validation process using the Statistical Package for

Social Sciences (SPSS) software. The three underlying assumptions � that random

errors are independent of one another (Durbin�Watson, du), have constant variance

(scatter diagram) and are distributed normally (P�P plot) � were tested.

The outcome of the model-building process for the S�D relationship suggestedthat the data are best described by Greenberg’s logarithmic model (Greenberg 1959).

Since F � S � D, the calibration of the S�D relationship leads to the derivation of

the F�D and S�D relationships.

4.1. Motorcycle headway riding pattern (total lane width 5 1.7 m)

From the linear regression analysis, the motorcycle speed versus motorcycle density

relationship may be described in a linear form as

S ¼ 84� 13 ln Dð Þ (1)



where S is the motorcycle speed and D is the motorcycle density.

Using Greenberg’s logarithmic model which is expressed in linear form as

S ¼ Sc ln Dj

� �� Sc ln Dð Þ (2)

the following results were obtained:

Jam motorcycle density; Dj ¼ 640 mc=km=lane

Critical motorcycle speed; Sc ¼ 13 km=hr

Critical motorcycle density; Dc ¼ 235 mc=km=lane

Maximum motorcycle flow; Fmax ¼ 3060 mc=hr=lane

Hence, the motorcycle speed�flow�density relationships may be expressed as

follows:

(a) Motorcycle speed versus motorcycle density (Figure 2):

S ¼ 13 ln 640=Dð Þ (3)

(b) Motorcycle flow versus motorcycle density (Figure 3):

F ¼ 13D ln 640=Dð Þ (4)

Figure 2. Relationship between motorcycle speed and motorcycle density (headway riding

pattern).



Figure 3. Relationship between motorcycle flow and motorcycle density (headway riding

pattern).

Figure 4. Relationship between motorcycle speed and motorcycle flow (headway riding

pattern).



(c) Motorcycle speed versus motorcycle flow (Figure 4):

F ¼ 640S e�S=13 (5)

(Note that D ] 11 mc/km/lane.)

4.2. Motorcycle space riding pattern (total lane width � 1.7 m)

Similarly, the linear regression analysis of the motorcycle speed (S) versus motorcycle

density (D) relationship may be described in the following linear form:

S ¼ �10; 759� 13; 330 ln Dð Þ (6)

From Greenberg’s model, the following results were computed:

Jam motorcycle density; Dj ¼ 0:45 mc=m2 ðor space; M j ¼ 1=Dj ¼ 2:2 m2=mcÞCritical motorcycle speed; Sc ¼ 13; 330 m=hr ð13 km=hrÞCritical motorcycle density; Dc ¼ 0:166 mc=m2 ðor space; Mc ¼ 1=Dc ¼ 6:0 m2=mcÞMaximum motorcycle flow; F max ¼ 2207 mc=hr=m

The motorcycle speed�flow�density relationships are described as follows:

(a) Motorcycle speed versus motorcycle density (Figure 5):

S ¼ 13; 330 ln 0:45=Dð Þ (7)

(b) Motorcycle flow versus motorcycle density (Figure 6):

F ¼ 13; 330D ln 0:45=Dð Þ (8)

Figure 5. Relationship between motorcycle speed and motorcycle density (space riding

pattern).



(c) Motorcycle speed versus motorcycle flow (Figure 7):

F ¼ 0:45Se�S=13;330 (9)

(Note that D ] 0.003 mc/m2.)

Figure 6. Relationship between motorcycle flow and motorcycle density (space riding

pattern).

Figure 7. Relationship between motorcycle speed and motorcycle flow (space riding pattern).



5. Discussion

In this study, 99% of motorcycles observed along the uninterrupted exclusive

motorcycle lane (one-way) comprised small- and medium-sized motorcycles with

engine capacities of 150 c.c. and below. It therefore described an almost

homogeneous motorcycle traffic stream under ideal conditions. It may also be

hypothesised that under non-ideal conditions where there exists a reasonable mixture

of small- and large-sized motorcycles, together with effects of side frictions and

psychological barriers, riding manners would be rather similar.

5.1. Motorcycle speed�density relationship (headway and space patterns)

The logarithmic function is the best model that describes the data of the motorcycle

speed�density relationships for both headway pattern (Figure 2) and space pattern

(Figure 5). The establishment of the motorcycle speed�density models led to the

derivation of the motorcycle flow�density and motorcycle speed�flow relationships

for the headway pattern (Figures 3 and 4) and the space pattern (Figures 6 and 7).

The three fundamental motorcycle speed�flow�density relationships developed in

this study exhibited trends that are similar to the ones established for pedestrians,

bicycles and automobiles. In fact, all of them obeyed the theory of flow that was

originally established by Greenshields (1934). A noticeable difference between

motorcyclists and other transport modes is that motorcyclist riding behaviour

utilised both headway and space patterns depending on the availability of safe space

within the motorcycle lane. In comparison, cars followed a headway pattern while

bicycles and pedestrians followed a space pattern.

The motorcycle speed�density models for both the headway and space patterns

(Figures 3 and 5) indicated that as the motorcycle density increases, motorcycle

speed would continue to drop. This is because as more motorcycles are present along

the motorcycle lane, motorcyclists have less freedom to select their own desired

speed. The fastest motorcyclists are slowed down first, but eventually even the slow

motorcyclists are affected. As the motorcycle speed drops to about 5 km/hr, the

motorcyclists will have problems to maintain the stability of their vehicles and will be

forced to put their feet on the ground. Eventually, all motorcyclists will practically

come to a stop as the traffic stream approaches the jam density, which occurred at

640 mc/km/lane (in the headway pattern) and 0.46 mc/m2 (in the space pattern) � as

shown in Figures 2 and 5, respectively.

In the headway pattern, the motorcycle speed�density relationship (Figure 2) along

the uninterrupted exclusive motorcycle lanes shared similar trends to that for the speed�density of cars. The car following car movements along a lane is based on headway

(HCM 2000). But unlike motorcycles, cars can travel at speeds lower than 5 km/hr.

As for the space pattern, the motorcycle speed�density relationship (Figure 5)

along the uninterrupted exclusive motorcycle lanes is comparable to cyclists riding

along exclusive bicycle lanes. But due to their low operating speeds (15�25 km/hr),

the bicycle speeds remain relatively insensitive over a wide range of flows (HCM

2000). It is reasonable to expect that the riding manner of both motorcyclists and

cyclists to be equivalent within the riding speed of 25 km/hr and below.



5.2. Motorcycle flow�density relationship (headway and space riding patterns)

With respect to motorcycle flow�density relationships, both Figure 3 (headway

pattern) and Figure 6 (space pattern) showed that motorcycle flow is zero when

motorcycle density is zero. Theoretically, it is zero when there is no motorcyclist

present in the exclusive motorcycle lane. As motorcyclists begin to join the motorcycle

lane, the flow increases with further increments in density. As density reaches a critical

value of 235 mc/km/lane (headway pattern), the motorcycle flow reaches a maximum

point of the parabolic curve that corresponds to the maximum motorcycle flow of 3060

mc/hr/lane and critical speed of 13 km/hr. This maximum point is the capacity of the

motorcycle lane facility. Similar trends were exhibited for the space pattern. At a

critical density of 0.166 mc/m2 (or critical space of 6 m2/mc), capacity is reached at a

maximum motorcycle flow of 2207 mc/hr/m, corresponding to 13 km/hr.

Note that at capacity (headway and space patterns), motorcyclists are riding very

close to each other but within a stable motorcycle traffic flow. Even though the

maximum motorcycle flows for the headway and space patterns are different in their

units of measurement, the capacities are reached at the same critical speeds of

13 km/hr. The similarity in critical speeds for both the headway and space patterns

may be attributed to the earlier assumptions in the data analysis with regard to the

scatter plots for the space pattern. That is, in order to obtain the range of stable flow

through unstable flow conditions for the space pattern, the data points from

motorcycle lanes of 1.7 m or less (which fall under the headway pattern) were used

with the assumption that the motorcyclists were actually being constrained by the

narrower lane widths. This limitation of space within the motorcycle lane width

forced them to ride in a single file.

On the other hand, it also means that motorcyclists adjust their riding speeds in

reaction to the available space and proximity to other motorcyclists riding along the

exclusive motorcycle lanes. Thus, the same critical speeds of 13 km/hr indicated that

the motorcyclist perception of density or proximity with other motorcyclists under

the headway pattern and the space concept are similar. As the available space

approaches a critical condition, the riding speeds continue to drop but still within the

stable flow conditions. The motorcyclists were still able to maintain the stability of

their motorcycles.

After this point of maximum motorcycle flow, a further increase in the number of

motorcyclists caused the flow to become unstable. Within this region, motorcycle

speeds drop slowly from 13 to 5 km/hr over a wide range of motorcycle densities

because motorcyclists tend to utilise the lesser space more efficiently � by

occasionally putting their feet on the ground in their effort to move forward and

at the same time maintaining the stability of their motorcycles. However, when

motorcycle speeds drop to 5 km/hr due to the large number of motorcyclists along

the motorcycle lane, they are forced to put their feet down � which brings the

motorcycle flow to a halt.

5.3. Motorcycle speed�flow relationship (headway and space patterns)

The motorcycle speed�flow relationships (Figures 4 and 7) exhibited that after the

free-flow speed region, the motorcycle speed gradually dropped with further

increases in the motorcycle flow. This is because the faster motorcyclists had to



reduce their speeds due to the reduction in available space along the uninterrupted

exclusive motorcycle lanes. As more motorcyclists are present, this trend continues

until the maximum motorcycle flow condition where the motorcyclists are riding at a

minimum speed of 13 km/hr while maintaining a stable flow. Further increases in

motorcycles caused an unstable flow condition. The motorcycle speed continues to

drop as the motorcycle flow gets slower until it comes to a complete stop at 5 km/hr

and below, when motorcyclists were unable to stabilise their vehicles and forced to

put their feet on the ground.

Conclusions

This paper has established that the shape of the motorcycle speed�density relation-

ships for an uninterrupted exclusive motorcycle lane for both types of observable

motorcycle riding behavior (i.e. the headway or platoon pattern and space pattern)

takes the form of a logarithmic curve. Further, based on the fundamental theory that

flow is a product of speed and density, the motorcycle flow�density and motorcycle

speed�flow relationships were derived mathematically. The three fundamental

motorcycle speed�flow�density relationships exhibited trends similar to the ones

established for pedestrians, cyclists and automobiles, which followed the theory of

flow.

From the motorcycle speed�flow�density curves, it was determined that under

the headway riding pattern (lane width of 1.4�1.7 m) capacity is reached at a

maximum motorcycle flow of 3306 mc/hr/lane corresponding to a critical speed of

13 km/hr and critical density of 235 mc/km/lane. While under the space riding

pattern (lane width � 1.7 m), capacity occurs at a maximum motorcycle flow of

2207 mc/hr/m, which corresponds to a critical motorcycle speed of 13 km/hr and

critical motorcycle density of 0.166 mc/m2 (or space of 6.0 m2/mc). Practically, a 1.4�1.7-m-wide motorcycle lane is capable of carrying a maximum motorcycle flow of

3306 mc/hr. By doubling the lane widths would mean that a 2.8�3.4-m-wide

motorcycle lane is capable of carrying 6612 mc/hr as if the motorcyclists are riding in

two single files across the width of a motorcycle lane. In the case of the space riding

pattern, which has a capacity of 2207 mc/hr/m width, a motorcycle lane of 2.0 m

wide is, for instance, capable of carrying 4414 mc/hr.

Overall, this paper is seen as an initiative to fill the knowledge gap pertaining to

motorcycle speed�flow�density relationships and the capacity of exclusive motor-

cycle lanes under ideal conditions as well as contributing new knowledge to the field

of motorcycle traffic engineering. Results should provide useful guidance for traffic

engineers to decide on the appropriate width of motorcycle lanes in relation to the

expected motorcycle demand flows along particular segments so as to minimise

construction costs and maximise the performance of the facility.

Acknowledgements

Data, grants and support for field data collections were provided by the National ScienceCouncil IRPA Malaysia, the Public Works Department, Ministry of Works Malaysia andRoadcare (Malaysia) Sdn. Bhd.



References

Greenberg, H., 1959. An analysis of traffic flows. Operations Research, 7, 79�85.Greenshields, B., 1934. A study of traffic capacity, In Proceedings of the highway research

Board, Vol. 14. Washington, DC: Transportation Research Board, National ResearchCouncil, 448�477

HCM, 2000. Highway capacity manual, 4th ed. Washington, DC: Transportation ResearchBoard, National Research Council.

Hussain, H., et al., 2005. Key components of motorcycle-traffic system: a study along themotorcycle path in Malaysia. Journal of International Association of Traffic and SafetySciences, 29 (1), 50�56.

Malaysian Road Transport Department, 2007. Registered vehicle statistics. Kuala Lumpur:Ministry of Transport Malaysia.

Public Works Department, 1986. A guide to the design of cycle track. Public WorksDepartment 10/86. Kuala Lumpur: Ministry of Works Malaysia.

Radin Umar, R.S., Mackay, M.G., and Hills, B.L., 1995. Preliminary analysis of exclusivemotorcycle lane along the Federal Highway F02, Shah Alam, Malaysia. Journal ofInternational Association of Traffic and Safety Sciences, 19 (2), 93�98.

Radin Umar, R.S., Mackay, M.G., and Hills, B.L., 2000. Multivariate analysis of motorcycleaccidents and the effects of exclusive motorcycle lane in Malaysia. Journal of CrashPrevention & Injury Control, 2 (1), 11�17.

Royal Malaysian Police, 2007. Statistical report road accident. Bukit Aman, Kuala Lumpur:Traffic Branch, Police Headquarters.

The Star Newspaper, 2007 M-cycle lane for new roads [online]. Available from: http://www.kkr.gov.my/en/node/3110 [Accessed 20 January 2011].

Underwood, R., 1961. Speed, volume, and density relationships: quality and theory of trafficflow. New Haven, CT: Yale Bureau of Highway Traffic, 141�188.

Wardrop, J.G., 1952. Some theoretical aspects of road traffic research. In: Proceedings of theinstitution of civil engineers. Road Paper No. 2 (Part 1). London: Institution of CivilEngineers, 326�331.



http://www.kkr.gov.my/en/node/3110

http://www.kkr.gov.my/en/node/3110

oldfgfddf

Documents