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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.
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4, Issue 5, September - October (2013), pp. 244-249
© IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com
IJMET
© I A E M E
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
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976
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
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
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.
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.
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.
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
October (2013) © IAEME
that the Microsoft Excel is used to calculate the geometric and other
Mathcad software.
was developed as a linear regression
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
lenges to bicycling. The
score and reflects perceived opportunities to bicycling.Landis et al.
developed the Bicycle Level of Service (1997) using a different technique. Theresearch involved
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
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME
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.
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, Septembe
Figure 2.BLOS Application Examples for Arterial road
Figure3.BLOS Application Examples for Fast Local Road
Figure 4.BLOS
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976
6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME
247
BLOS Application Examples for Arterial road
BLOS Application Examples for Fast Local Road
BLOS Application Examples for Local Road
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
October (2013) © IAEME
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, Septembe
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.
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976
6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME
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
and shoulder width, pavement condition, and parking occupancy. Graphical representations of these
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
(FHWA) with 1 being the worst and 5 being the best. The bicycle LOS methodology developed by
70 has little sensitivity after pavement condition 3 but is highly sensitive to bad pavement.
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
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.
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
October (2013) © IAEME
found that a large number of variables had an effect on bicycle LOS. But,
: traffic volume, parking
and shoulder width, pavement condition, and parking occupancy. Graphical representations of these
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
(FHWA) with 1 being the worst and 5 being the best. The bicycle LOS methodology developed by
3 but is highly sensitive to bad pavement.
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
der for bikes to use. This improves the LOS score by about 10% over a street where
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME
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.
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