30120130405028

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –

6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME

244

ANALYSIS OF KEY FACTORS THAT AFFECT BICYCLE LEVEL OF

SERVICE

Mr.Sc. Mevlan Bixhaku1 and Prof. Dr. Marija Malenkovska

2

1University of Pristina, Faculty of Mechanical Engineering, Kosovo

2University St. KlimentOhridski, Bitola, Macedonia

ABSTARCT

In this paper are analyzed the factors which influence the bicycle level of service in urban

streets andintended to give an overview of the factors most likely to affect bicycle Level of Service.

The most important variables were found to be analyzed: number of right hand side driveways,

segment length, parking and shoulder width, pavement condition, and parking occupancy. Auto

speed and all the variables that affect it can also influence the bike score with faster speeds making

the LOS score worse. The shoulder parking, lane width and pavemement condition will be analyzed

as main factors which affecting bicycle LOS.In this paper will be used Bicycle Level of Service

Model (Version 2.0) which is the most accurate model of evaluating the bicycling conditions of

shared roadway environments.

Keywords: level of service, traffic flow, pavement condition, parking occupancy, segment length,

shoulder width.

1. INTRODUCTION

Road suitability measures have been developed in recent years that helpplanners, engineers

and citizens to understand how well their roads serve bicyclists.

This study was done for the first time in Prishtina and focused to arterial, fast local roads and

local roads, because they have different geometric characteristics and different volumes of motor

vehicle traffic at the highest speeds and often provide the worst service bicyclists who choose to use

them (Fig.1).

In this paper are used data from Municipality of Prishtina (Department of Transportation) for

the arterial and collector road network. Road suitability measures use informations about a road, such

as traffic volumes and speeds, lane widths and sidewalk dimensions, to rate the bicycle friendliness

of the road.

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ISSN 0976 – 6340 (Print)

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Volume 4, Issue 5, September - October (2013), pp. 244-249

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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976

6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, Septembe

It should also be noted that the Microsoft Excel is used to calculate the geometric and other

input data, while LOS grade in the charts are

Figure 1. Arterial, fast local and local road in Prishtina

2. BICYCLE LEVEL OF SERVICE MODEL

Bicycle Level of Service (BLOS)

model by transportation researchers. The BLOS regression equation (version 2.0) provides a

discomfort and inconvenience score for bicycle travel by taking into account four prevailing roadway

and traffic conditions:

1. Peak traffic flow in the outsid

2. Speed of traffic and percent of heavy traffic

3. Pavement surface condition

4. Pavement width available for bicycling

The first three variables are impact scores and reflect perceived chal

fourth variable is a benefit score and reflects perceived opportunities to bicycling.

developed the Bicycle Level of Service (1997) using a different technique. Theresearch involved

riders on actual field courses, instead of cyclist reaction to filmed conditions.

BLOS is similar to BCI in its sensitivity to curb lane width. I

logarithmic, increasing the impact of changes at low and me

shoulder or bike lane width affect the BLOS score some

development type, parking, and right

vehicular traffic have a major impact. Further work is pla

business district roads with high parking

correlationcoefficient (R2 = 0.77) of any form of the


6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME

245

that the Microsoft Excel is used to calculate the geometric and other

in the charts are obtain with the help of Transcad and Mathcad software.

Arterial, fast local and local road in Prishtina

LEVEL OF SERVICE MODEL

Bicycle Level of Service (BLOS) model (Version 2.0) was developed as a linear regres



1. Peak traffic flow in the outside lane

2. Speed of traffic and percent of heavy traffic

3. Pavement surface condition

4. Pavement width available for bicycling

variables are impact scores and reflect perceived challenges to bicycling. The

score and reflects perceived opportunities to bicycling.


riders on actual field courses, instead of cyclist reaction to filmed conditions.

BCI in its sensitivity to curb lane width. Its traffic volume dependence is

logarithmic, increasing the impact of changes at low and medium traffic levels. Additional

shoulder or bike lane width affect the BLOS score somewhat more than the BCI. Ignored are

development type, parking, and right-turning traffic, but bad pavement surfaces and higher

vehicular traffic have a major impact. Further work is planned for rural highways and for

business district roads with high parking turnover. As a result, Version 2.0

correlationcoefficient (R2 = 0.77) of any form of the Bicycle LOS Model.


October (2013) © IAEME

that the Microsoft Excel is used to calculate the geometric and other

Mathcad software.

was developed as a linear regression



lenges to bicycling. The

score and reflects perceived opportunities to bicycling.Landis et al.


ts traffic volume dependence is

dium traffic levels. Additional paved

e BCI. Ignored are

ad pavement surfaces and higher heavy

nned for rural highways and for central

Version 2.0 has the highest



246

�� 0.507 ln � �� 0.199 ��1 � 10.38 �� ! � 7.066 � #

$%��! & 0.005 �'( ! � 0,760 (1)

Where:

�*+#, �Volume of directional traffic in 15 minutes � �-./ · . · 12 /�4 · ��. , -./ � Average Daily Traffic on the segment,

D = Directional Factor,

12 �Peak to Daily Factor,

��5 �Peak Hour Factor,

�� Number of directional through lanes,

�� Effective speed limit� 1.1199 · ln��$ & 20 � 0.8103, where ��$ is the posted speed limit,

�� Percentage of heavy vehicles (as defined in the 1994 Highway Capacity Manual)

�75 � 5�'-`9 5 &point pavement surface condition rating (5=best)

'( �Average effective width of outside through lane:

'( � ': & �10` · ��- ;<=>= '+ � 0, '( � ': & '+ �1 & 2 · ��- ;<=>= '+ ? 0&'AB � 0,

'( � ': � '+ & 2�10` · ��- ;<=>= '+ ? 0,'AB ? 0, CDE FGH= +CD= =IG9J9, '� �Total width of outside lane (and shoulder) pavement

��- �Fraction of segment with occupied on-street parking

'+ �Width of paving between outside lane stripe and edge of pavement

'AB �Width of pavement striped for on-street parking

': �Effective width as a function of traffic volume

': � '� GK -./ ? 4000 L=<ECM,

': � '� N2 & �OPQRSSS�T GK -./ U 4000 and road is undivided and unstriped.

Bicycle Level of Service ranges associated with level of service (LOS) designations:

BLOS ≤ 1.50 1.51-2.50 2.51-3.50 3.51-4.50 4.51-5.50 >5.50

LOS Level A B C D E F

3. APPLICATION OF BLOS MODEL - APPLICATION EXAMPLES

Based on the BLOS model, the factors that most significantly affect the bicycle level of

service on shared roadways in Pristina are the lane widths, the motor vehicle traffic volume and the

pavement surface condition. The presence of a bicycle lane is a major factor in the BLOS model,

indicating that bicyclists who participated in the BLOS study generally felt more comfortable on

roadways with space designated for their use.

Figure2, 3 and 4, provides several examples of the BLOS methodology applied to arterial,

fast local road and local road, with different geometric characteristics and different volumes of motor

vehicle.



Figure 2.BLOS Application Examples for Arterial road

Figure3.BLOS Application Examples for Fast Local Road

Figure 4.BLOS



247

BLOS Application Examples for Arterial road

BLOS Application Examples for Fast Local Road

BLOS Application Examples for Local Road





Previous research found that a large number of variables had an effect on bicycle LOS.

the most important variables were found to be analysed in this research are

and shoulder width, pavement condition, and parking occupancy. Graphical representations of these

factors’ affect on bicycle LOS are shown in

Figure.4.Effect of ADT on Bicycle LOS

Figure.6.Effect of Pavement Conditions on Bicycle LOS

As shown in Fig. 4, effect of increasing traffic volume

very high traffic and BLOS show relatively little change until higher traffic levels

The final two main factors affecting bicycle LOS are pavement condition and parking occupancy

(Fig.5 and Fig.6).

Pavement condition is rated on a 1

(FHWA) with 1 being the worst and 5 being the best. The bicycle LOS methodology developed by

NCHRP 3-70 has little sensitivity after pavement condition

Parked cars tend to force bicyclists closer to moving traffic which will reduce theiroverall

experience of the study corridor. With no parked cars on the street, the parking spacesessentially act

as a large shoulder for bikes to use. This improves the LOS score by about 10% over a street where

50% of the length is occupied by parked vehicles.



248

found that a large number of variables had an effect on bicycle LOS.

the most important variables were found to be analysed in this research are: traffic volume


factors’ affect on bicycle LOS are shown in Figure 4, 5 and 6.

Effect of ADT on Bicycle LOS Figure.5.Effect of Parking Occupancy on

Bicycle LOS

Effect of Pavement Conditions on Bicycle LOS

effect of increasing traffic volume worsens steadily from very low to

very high traffic and BLOS show relatively little change until higher traffic levels.

he final two main factors affecting bicycle LOS are pavement condition and parking occupancy

Pavement condition is rated on a 1-5 scale developed by the FederalHighway Administration


70 has little sensitivity after pavement condition 3 but is highly sensitive to bad pavement.



der for bikes to use. This improves the LOS score by about 10% over a street where

50% of the length is occupied by parked vehicles.



found that a large number of variables had an effect on bicycle LOS. But,

: traffic volume, parking


Effect of Parking Occupancy on

worsens steadily from very low to

he final two main factors affecting bicycle LOS are pavement condition and parking occupancy

5 scale developed by the FederalHighway Administration


3 but is highly sensitive to bad pavement.



der for bikes to use. This improves the LOS score by about 10% over a street where



249

4. CONCLUSIONS

Although bicycle LOS was found to be sensitive to a large number of factors, LOS score

could not improve dramatically unless the pavement condition was poor. Poor pavement condition

can result deterioration in bicycle LOS much more thanparking occupancy and traffic volume.The

study results underscore the influence that on-street parking has on bicycle route choice and the

space occupied by parked vehicles has a negative impact on biking. Finally, the analysis clearly

emphasizes, effect of increasing traffic volume worsens steadily from very low to very high traffic

and BLOS show relatively little change until higher traffic levels. Although these issues cannot be

resolved without previous research, this study offers important insights into the importance of key

factors which affect bicycle level of service for arterials.

REFERENCES

1. Highway Capacity Manual: Special Report 209. Third Edition, Updated 1994.Transportation

Research Board, Washington, DC, 1994.

2. Sorton, Alex. Measuring the Bicyclist Stress Level of Streets. In Transportation

Congress:Civil Engineers--Key to the World Infrastructure. Proceedings of the 1995

Conference,American Society of Civil Engineers, San Diego, CA, 1995, pp. 1077-1088.

3. Turner A. Howard, Suitability of Louisville Metro Roads For Bicycling and Walking, 2004.

4. NCHRP REPORT 616 Multimodal Level of Service Analysis for Urban Streets

Transportation Research Board, 2008.

5. Bicycle Level Of Service-Applied Model, Sprinkle Consulting Inc.April 2007

6. Bicycle Suitability Evaluation of Roadways, SCI-Sprinkle Consulting Inc.

7. Hamid Ahmed Awad, “Improvement the Level of Service for Signalized Arterial”,

International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 4, 2013,

pp. 84 - 97, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.

8. Kasula Nagaraju, Dr. Shivarudraiah and Dr. Chandrasekhar.B, “A New Approach to

Optimize Traffic Flow using Maximum Entropy Modeling”, International Journal of

Mechanical Engineering & Technology (IJMET), Volume 1, Issue 1, 2010, pp. 134 - 149,

ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

30120130405028

Technology

speed of traffic

traffic conditions

traffic volumes

volume of directional

bicycle los model

traffic volume dependence

peak traffic flow

bicycle travel