2020-03-11
BAHIR DAR UNIVERSITY
SCHOOL OF RESEARCH AND GRADUATE STUDIES
FACULTY OF CIVIL AND WATER RESOURCES ENGINEERING
IDENTIFICATION OF BARRIERS TO BICYCLING AND
EVALUATION OF SUITABILITY OF SELECTED MAJOR
CORRIDORS FOR BICYCLING IN BAHIR DAR CITY, ETHIOPIA
By: Abebe Getamesay Mulu
SUITABILITY OF SELECTED MAJOR CORRIDORS FOR BICYCLING IN BAHIR
DAR CITY, ETHIOPIA
By: Abebe Getamesay
A thesis submitted to the school of Research and Graduate Studies of Bahir Dar
Institute of Technology, BDU in partial fulfillment of the requirements for the degree of
Master of Science in the Road and Transport Engineering in the School of Civil and Water
Resource Engineering.
Co-Advisor Name:
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ACKNOWLEDGMENT
First of all, I would like to thank God for supporting and being with me in all activities of
my life.
I would like to express my sincere gratitude to my advisor Dr. Bikila Teklu (Ph.D) in Addis
Ababa Institute of Technology for the continuous support of my Masters of Science Degree
study, for his patience, motivation, and immense knowledge. His guidance helped me in all
the time of the research and writing of this thesis.
Nobody has been more important to me next to God in the pursuit of this research than the
member of my family. I would like to thank my parents, whose love and guidance are with
me in whatever I pursue. Most importantly, I would like to give thank for my loved wife,
Betelhem Wondosen, and my wonderful parents, Debritu Eshetie and Getamesay Mulu.
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ABSTRACT
Despite bicycling is fun, healthy, economical, efficient, and considered as one of a
sustainable mode of transport, in Bahir Dar, the level of use of bicycle decreases time to
time due to the existence of different barriers to bicycling. The objectives of the research
were to identify the barriers and motivations to bicycling; to differenciate the degree of
influence of barriers as it differes on socio-demographic characterstics of residents of Bahir
Dar City; and to evaluate the suitability of selected road segments for riding a bicycle.
To accomplish the research objectives, questionnaires were developed after reviewing
different researches to identify barriers and motivations; to examine the interaction between
vehicle drivers and bicyclists; and to identify which road segments are relatively suitable to
ride a bicycle. Factor analysis and Mean score analysis by IBM SPSS, BLOS analysis by
HCM 2010 were done to identify the top barriers; motivations to bicycling; the interaction
between drivers and cyclists; and the suitability of selected road segments for riding a
bicycle. About 308 respondents including currently-cyclists and cyclists in the past were
participated in the rating of variables.
The result revealed that Infrastructure and road traffic; safety and security; and on-street
activities related barriers were identified as the top three barriers to bicycling. The
interaction between cyclists and vehicle drivers especially Bajaj drivers were also identified
as the major barriers. It was identified that Provision and improvements of cycling
infrastructure facilities such as segregated bicycle lane and bicycle parking can be the top
motivation to cyclists. Cyclists have been using bicycle as transport mode due to advanteges
cycling such as environment friendliness of bicycle; and healthiness of bicycle. Most of the
road links on selected road segments have a Bicycle Level of Service (BLOS) of “D” and
“C” and rated as unsuitable which implies that bicyclists feel unsafe and uncomfortable to
ride a bicycle. It is recommended that the City Municipality and Road and Transport Sectors
develop policies and implement soft and hard measures for the encouragements of
bicyclists. Treatments such as providing speed limits of 30km/hr for vehicle; removing the
on-street parking; and provision of bicycle lane identified as the top powerfull measures to
improve significantly the Bicycle Level of Services.
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1 INTRODUCTION ...................................................................................................... 1
1.1 Background .............................................................................................................. 1
1.1.1 History and existing condition of transport systems in Bahir Dar ................... 1
1.2 Advantages and influencing factors to the use of a bicycle ..................................... 4
1.3 Safety of cyclists ...................................................................................................... 4
1.4 Problem Statement ................................................................................................... 5
2 LITERATURE REVIEW .......................................................................................... 7
2.2 Barriers and motivations to bicycling ...................................................................... 9
2.3 Factor analysis ........................................................................................................ 11
2.4 Models for evaluation of suitability of road for bicycling ..................................... 13
2.5 Best practices of bicycle-friendly cities ................................................................. 22
2.5.1 The effects of policies and strategies on cycling level of countries ............... 22
2.5.2 Overall cycling level among best-practiced countries in the world ............... 24
2.5.3 How to make cycling safer and convenient.................................................... 25
2.6 Safety of cyclists .................................................................................................... 30
3 METHODOLOGY ................................................................................................... 32
3.3.1 Questionnaire.................................................................................................. 36
3.5 Materials Used ........................................................................................................ 43
3.6 Analysis Methods ................................................................................................... 43
3.6.2 Bicycle Level of Service (BLOS) analysis ..................................................... 45
3.6.2.1 Analysis period ....................................................................................... 46
3.6.3 SWOT analysis ............................................................................................... 48
4.1 Introduction ............................................................................................................ 49
4.2.1 Difference between factors on the basis of varies demographic variables..... 59
4.2.2 Interaction between cyclists and vehicle drivers ............................................ 64
4.3 Motivations to bicycling ......................................................................................... 65
4.4 Evaluation of the suitability of road corridors and links for riding a bike ............. 66
4.4.1 Analysis result of the questionnaire ............................................................... 66
4.4.2 Analysis result Bicycle level of service ......................................................... 78
4.5 SWOT analysis to encourage bicycling in Bahir Dar City .................................... 88
5 CONCLUSIONS AND RECOMMENDATIONS ................................................. 90
5.1 Conclusions ............................................................................................................ 90
5.2 Recommendations .................................................................................................. 91
BCI Bicycle Compatibility Index
BISI Bicyclists Intersection Safety Indexes
BLOS Bicycle Level of Service
ERA Ethiopian Road Authority
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LIST OF FIGURE
Figure 1 Bicycling level of Bahir Dar City by 2003 ............................................................ 3
Figure 2 Figurative presentation for link, segment, and facilities ...................................... 17
Figure 3 Types of bicycle facilities .................................................................................... 30
Figure 4 Bahir Dar City Kifle-Ketema as a study area ...................................................... 32
Figure 5 Major corridors .................................................................................................... 34
Figure 6 Road Links on segments ...................................................................................... 35
Figure 7 Locations where video and geometric variables recorded ................................... 38
Figure 8 Scree plot ............................................................................................................. 52
Figure 9 Barriers to bicycling ............................................................................................. 55
Figure 10 Comfort and convenience related barriers between categories of respondents 61
Figure 11 Attitude related barriers between categories of respondents ............................. 61
Figure 12 Interaction between vehicle drivers and cyclists ............................................... 64
Figure 13 Mean score of motivation to bicycling .............................................................. 65
Figure 14 Suitability of Routes for riding Bicycle ............................................................. 67
Figure 15 Mean score of suitability of road corridors for cyclists ..................................... 68
Figure 16 Deposits of construction materials on walkway and carriageway ..................... 69
Figure 17 Traffic volume of selected road segments counted in 12hr. .............................. 70
Figure 18 Percentages of Bajaj and Heavy vehicles on selected road segments ............... 71
Figure 19 Level of stress of the Gambi Roundabout - Azwa Hotel ................................... 72
Figure 20 Illegal on street venders on segment Gambi Roundabout - Azwa Hotel ........... 73
Figure 21 Illegal on-street venders on Gambi roundabout to St. Georg roundabout ......... 75
Figure 22 On-street parking on Gambi Roundabout - St. Georg Roundabout ................... 75
Figure 23 On-street parking on Gambi Roundabout to Wisdom Roundabout .................. 77
Figure 24 BLOS of segments ............................................................................................. 82
Figure 25 Existing Crossections of segment Gambi Roundabout-St.George Roundabout 85
Figure 26 Provision of bicycle lane and buffer to the existing road geometry .................. 86
Figure 27 Existing geometrical crossection of segment,Gambi Roundabout-Azwa Hotel.87
Figure 28 provision of Bike lane by reducing existing width of vehicular and on-street
parking lane ........................................................................................................................ 88
Table 1 Percent share of all-purpose trips ............................................................................ 2
Table 2 Annual growth of modes in Bahir Dar during 1990-2000 ...................................... 2
Table 3 Modal share by number in the year 1990-2000....................................................... 3
Table 4 Level of Traffic Stress Criteria .............................................................................. 15
Table 5 Description of variables for HCM BLOS model for intersection ......................... 19
Table 6 Acceptable road gradient for bicyclists ................................................................. 22
Table 7 Accident severity caused by cyclists 2008-2017 ................................................... 31
Table 8 Road links on segments ......................................................................................... 33
Table 9 Distribution of total sample size over demography of respondents ...................... 41
Table 10 Standard Deviations of Spot Speeds for Sample Size Determination ................. 42
Table 11 PCU values for different vehicle types ............................................................... 48
Table 12 Mean score and standard deviation for barriers .................................................. 50
Table 13 Kaiser-Meyer-Olkin measure of sampling adequacy .......................................... 50
Table 14 communality of variables .................................................................................... 51
Table 15 Total variance explained ..................................................................................... 52
Table 16 Rotated component matrix .................................................................................. 53
Table 17 Extracted Factors as barriers ............................................................................... 54
Table 18 Mean score result for factors ............................................................................... 55
Table 19 Comparison of factors between categories of respondents ................................. 60
Table 20 Comparison of factors between genders ............................................................. 62
Table 21Comparison of factors between the ages of respondents ..................................... 63
Table 22 Suitability mean score of road corridors ............................................................. 66
Table 23 Peak hour volume of vehicles (passenger cars) for links on the segment ........... 78
Table 24 Parking occupancy of links on segments ............................................................ 79
Table 25 BLOS for the existing condition of the road segment ......................................... 81
Table 26 BLOS if the existing condition of the road is treated .......................................... 84
Table 27 SWOT analysis on the effort to encourage bicycling in Bahir Dar .................... 89
1 INTRODUCTION
1.1 Background
1.1.1 History and existing condition of transport systems in Bahir Dar
In Bahir Dar, Bajaj, motor bicycle, walking, automobile, and taxi are dominant modes of
transport for the people of the city. Currently, Public buses and bicycles cover very small
percentages (Bahir Dar University, 2018). It is important to determine what type of
transport system the city wants to build that should be closely correlated with the current
global trends in urban transportation and the needs of the community reflecting the mode
distribution (Bahir Dar University, 2018).
Bajaj mode transportation was introduced around 2007 and now it is apparent that the Bajaj
and motorcycle duo comprises 57% of the total trip made on Bahir Dar’s main road sections.
Their number increased by 79% just in three years and the total routes served by Bajaj is
more than all the other public three public transport modes added together. These numbers
are marginally high by traffic analysis standards and appropriate traffic management
schemes need to be developed to efficiently and safely move the traffic. The distribution of
modes of transport is not well distributed. Currently, non-motorized bicycle mode takes
only 5% of the total trips within the city which is much less than 30.3% by 2000. For a city
that aspired to promote bicycle travel in its BDIDP, this value is far below the acceptable
quota (Bahir Dar University, 2018). Bicycle modal share by trip purpose was reported as
14.6%, 24.2%, and 30.3% in 1990, 1995, and 2000 correspondingly as shown in table 1.
The modal share of bicycle was very high compared to modal share of motorized vehicles.
It was 2.28, 2.32, and 1.78 times that of motorized vehicles in the year 1990, 1995, and 2000
respectively. Currently, the modal share of motorized vehicle is much higher than
modalshare of bicycle.
Modes
Other 1.3 2 0.7
Total 100 100 100
Source: (Bureau of Transport and Communication and Bahir Dar Special Zone,2000/2001,
as cited in (Yayeh, 2003))
Bahir Dar was known by non-motorized transport especially bicycling. The number of
bicycles was 3778 and 9383 in the year 1990 and 2000 respectively with annual growth of
15% shown in table 2.
Table 2 Annual growth of modes in Bahir Dar during 1990-2000
Mode type
Motorized wheeled vehicles
Public bus 0 1 -
Truck 8 27 7%
By the year 2003, there were about 12000 bicycles, one for every 13 inhabitants or 2 for
each household. In the share of trip per modes of transport, the role of the bicycle had been
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also significantly increased and public bus transport had played minor role in Bahir Dar
(Yayeh, 2003).
Figure 1 Bicycling level of Bahir Dar City by 2003
Source: (Yayeh, 2003)
Table 3 Modal share by number in the year 1990-2000
Mode type
1990 2000
Truck 0.2 0.3
Source: (Bureau of Transport, 1998, 2001, Bureau of Transport and Communication, 1998,
2000, as cited in (Yayeh, 2003)).
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Even though Bahir Dar City have suitable topography and weather condition for bicycling
and having a high number of large population and tourists who will be one of the potential
users of bicycling, it has been seen that bicycling habits of the society are decreasing time
to time. Most of elder residents of the city say Bahir Dar was known with high bicycling
habits of its residents before Bajaj has dominated the roads of the city with high volumes
and creating unsuitable environments to the bike users. This shows active transportation
(walking and cycling) in Bahir Dar city has been given low consideration by concerned
bodies of the city like Road and Transport office and Road Authority.
1.2 Advantages and influencing factors to the use of a bicycle
Improved walking and cycling environment help to reduce automobile dependence,
increase physical activity levels, improve public health, decreasing congestion and parking
shortages, reduce infrastructure demands, and create more livable and vibrant communities
((BBC Research and Consulting, 2014); (URBAN systems, 2012)). Although active
transport has many advantages, the selection of this mode over other modes of transport
(motor vehicle) is limited when land use being categorized as low density, important
destinations are far away, existence of a number of high volume of arterials that carry a high
proportion of large trucks and accessing of the destination is not direct (URBAN systems,
2012). Safety concerns, financial costs, lack of infrastructure, physically unable, bad
weather condition, lack of facilities at the destination and crime by a thief are also the
limiting factor to select bicycling (BBC Research and Consulting, 2014).
1.3 Safety of cyclists
Cycling is an extremely efficient means of transportation even if it is one of the
transportation modes with associated traffic risks. Cyclist, like a pedestrian, are considered
vulnerable road users since they are at greater risk of injury in collisions with motor vehicles
(Ministry of Transportation of Ontario (MTO) Traffic Office, 2013). Road traffic accident
on non-motorized road users in Bahir Dar city varies with the vehicle mixes on the road.
The mixed traffic use on the road leads to high risks on those road users. The existing
accident data on bicyclists reported in the city is criticized due to underreporting. It was
reported that Bicyclists caused 18 accidents in 1995-2001 (Yayeh, 2003).
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The international practices show that active transport modes (cycling and walking) and
public buss are considered as a sustainable type of transport and countries are now working
for the enhancement of these transport modes.
Even if Bahir Dar City have suitable topography and weather condition for bicycling and
known by strong bicycling habits of the city residents in the past, currently, it has been seen
that the bicycling level use of the residents is decreasing time to time.
As evidence, currently, most of the city elder residents said that the number of bicycle users
for commuting is decreasing time to time after Bajaj has dominated the road by covering
57% of trips of the total trips. non-motorized bicycle mode takes only 5% of the total trips
within the city (Bahir Dar University, 2018). This figure shows how much the use of a
bicycle is decreasing time to time as compared with the past numerical figures. During the
year 1990-2000, modal share by trips and by number increased from 14.6 to 30.3% and 84.7
to 87.1% respectively. By 2003, the ratio of the number of bicycle to the number of
inhabitants was 1:13 (Yayeh, 2003). As per Yayeh, there were an increase in the use of
bicycle up to the year 2003 and after that the level of use of bicycle not known when it
decreases. So, the year that the level of use of bicycle has begin to deacrease was not known
that is why this research compares the numerical figures in 2003 and 2017. It is assumed
that the year where the level of use of bicycle begins to decrease was around 2007 that Bajaj
was introduced to Bahir Dar City.
1.5 Objective of the study
General objective: To identify barriers and motivations to bicycling, assessing the
suitability of the City roads to bike, and what will be the possible treatments to improve the
Level of service of road for cyclists.
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Specific objectives:
1. To identify the contributing factors to the declining use of bicycle and motivations to
bicycling as a transport mode in the city.
2. To determine the level of influence of the identified factors on the respondents and to
check whether varies factors differ on the basis of varies demographic variables.
3. To evaluate the suitability of selected major corridors of Bahir Dar city for riding a
bicycle.
4. To propose possible treatments to inhance level of service of roads for cyclists.
1.6 Scope of the study
The scope of the study was limited to identify barriers and motivations through
questionnaires by rating predetermined lists of barriers and motivations, and to evaluate the
suitability of major road corridors (links on segments of four routes) of the city for bicycling
using HCM, 2010 BLOS criteria. The evaluation is only limited to selected links on
segments and not including roundabouts and intersections due to the necessity of very
intensive data.
1.7 Significance of the study
The research clearly identified the real factors that make the biking habit of the city residents
to decrease time to time and prioritize these factors to take immediate actions. When barriers
to bicycling are identified, it is easy to propose countermeasures to those problems. In
addition to identifying the barriers, the study also identified what motivations will motivate
the people of the city to begin and bike more. The possible measures to be taken to increase
the use of bicycling have been identified by using best practice countries experience.
Generally, the study will play important role in supporting the policy makers and planners
to develop rules and regulations, developing encouraging policies, and planning in order to
minimize barriers, creating motivations to bicycling and as result to promote sustainable
mobility to the city.
2.1 Benefits of bicycling
Walking and bicycling are known to be active transportation which has much importance
such as decreasing congestion, positive impacts on health and the environment. Walking
and bicycling as an attractive and convenient transportation choice which can help as to
reduce automobile dependability, increase physical activity levels, improve public health,
reduce infrastructure demands, create more livable and vibrant communities and they are
cost-effective and efficient transportation systems (URBAN systems, 2012).
Physical activities like walking and bicycling help to minimize risks of a number of costly
medical conditions especially strokes and heart diseases (BBC Research and Consulting,
2014). Cycling for young boys and girls have a greater positive influence on the
improvement of health; cardiorespiratory fitness and disease risk factor; risk minimization
for all caused and cancer mortality; and obesity morbidity in middle age and elderly men
and women (Sylvia Titze, 2011).
In addition to reducing congestion, bicycling also reduce parking shortages (BBC Research
and Consulting, 2014). As London school economy, active bicyclists in the workplace
misses one less day of work per year than that of non-active bicycles and this one day absent
referred as absenteeism results in loss of $ 341 productivity averagely (BBC Research and
Consulting, 2014).
However sports other than walking and bicycling have importance in the reduction of risk
to disease, walking and bicycling can also be used as a way of commuting to work and saves
time (Gotschi, 2011). Several studies have been conducted to show impacts of bicycling on
health and economy by considering different parameters which can be grouped under either
cost or benefits.
Thomas Gotschi (Gotschi, 2011), In his studies he calculated the cost-benefit ratio of
bicycling investments in Portland by considering investment costs of infrastructure for a
bicycle including user ride costs for bicycle purchase and maintenance grouped under costs.
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To calculate the benefits of bicycling, first, he estimates the level of bicycling by assessing
total mile traveled by bicycles and then total mile traveled is converted to minutes by
assuming the speeds of the bicycle as 10 mph averagely. These total number of minutes is
divided by recommended 30 minutes every day moderate physical exercise for disease
prevention and control. Therefore health benefits can be estimated by considering inactive
person expenditure for health based on 30-minute exercise per day. In addition to this, the
benefits of prevention of all caused mortality by bicycling are considered. The other
important benefit of bicycling considered in his calculation is saving of fuel which can be
calculated by using the price of fuel. Even though other benefits like reduction of Co2
emission is not considered, the cost-benefit ratio calculation was resulted in between 3.8
and 1.5 to 1.
Lynn Weigand (Weigand, 2008), reviewed studies in the US about the economic impact of
bicycling on manufacturing/industrial/retail/ and bicycle-related tourism and his review
result is discussed as follow. The primary finding was bicycle and related activities ales and
service activities have the power of increasing revenue and job creations. All papers he
reviewed agreed that combinations of manufacturing, sales, services, and tourism-related
activities produce jobs at a different level of work and income types at different locations.
The review summarized revenue or output at different location are $447,996,836,
$1,000,000,000 +, $36,300,000 and $63,000,000 in Wisconsin (measured by total output),
Colorado (measured by total revenue), Maine (measured by total spending by tourists) and
city of Portland (measured by total revenue) respectively (Weigand, 2008).
The effects of tourism on the economy is by making the tourists to be attracted and to be
last in the area for those on visiting for a long time and this is by having attractive bicycle
facilities. The economic impact of bicycle-related tourism is usually expressed by counting
the number of visitors and local residents who are participating in self-guided and organized
tours, rides and events. Average amounts these tourists spend on food, lodging, and other
services while visiting are considered for estimation and estimated result are $141,000,000-
193,000,000, $36,000,000 and $7,169,630 in Colorado (total revenue by cycling tourists at
CO resorts), Maine (total tourist spending) and city of Portland (income from tours, races,
and rides) respectively (Weigand, 2008).
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The economic impact of bicycling not only by manufacturing/industrial/retail/ and bicycle-
related tourism but also by bicycle facilities. The impact of bicycling facilities on the
economy, usually trail increases the number of visitors that spends money. The economic
impact of trails has found that they generate revenue from those who use them mostly
through the purchase made by trail users for food, lodging, and incidents. The other
economic impact of bicycling facilities is in the form of higher property values and
increased tax revenues. The concept of people that are willing to pay more for the home
located close to amenities such as trail facilities, parks, and open spaces is known as
“proximate principle” (Weigand, 2008).
2.2 Barriers and motivations to bicycling
Barriers and motivations to bicycling may differ from country to country and city to city
because the conditions vary such as presence of infrastructures, attitudinal change of the
peoples, the environment, the car ownership, etc.. In the following paragraphs, studies done
to identify barriers to bicycling or factors influencing the use of bicycles are discussed
briefly. General bicycle friendlessness of local area, comfortability of the route, the sufficiency of
road infrastructure, safety to ride on the street, protectiveness of regulations, level of care of
car driver for cyclists, parking and workplace facilities are identified as major issues for
bicyclists. In addition to these factors, cycling hazards experienced by cyclists due to the
driver such as overtaking without safe distance, speeding, honking on cyclists, forcing of
the right of way, blocking bicycle paths, lack of awareness while turning, and dangerous
maneuver (El_zbieta Biernat, 2018; Meghan Winters, 2010). Car availability, travel
distance and times were also identified as barriers to use to commute by bicycle in Budapest
(Ana Barberana, 2017).
Although there are barriers for cycling, there are also motivations that can motivate cyclists
to use cycle more than they can do currently and non-cyclists to start cycling. These
motivators can be seen in two groups: cycling advantages over other transport modes; and
infrastructure improvement motivators. Under cycling advantage over another transport
mode: improving health and fitness, pleasure and pro-environment, no time for another
physical exercise, no alternative for cycling, saving money, avoiding congestion in public
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transport, and reducing the time distances were identified as motivations in Poland
(El_zbieta Biernat, 2018). Under infrastructure motivators, in Metro Vancouver, bicycle
route away from traffic noise and pollution; bicycle routes with the beautiful appearance of
the place; and path separated from traffic were the top motivators (Meghan Winters, 2010).
The researcher tries to identify the influencing factors to bicycling by developing models
that affect the choice of a bicycle to commute and others by simply selecting the top barriers
or motivations that are frequently selected (mean) by questionnaire for specified barriers
and motivations. In Beijing, Pengjun Zhao, using a multinomial logit model, he has tried to
show how the infrastructure environment affects the choice of bicycling for commuting to
works. Based on the analysis result, destination accessibility has significant effects on
commuting by bicycle. The less accessibility to the destination leads to longer commuting
time, consequently related to less likelihood of cycling. Commuting distance shows, 0.919
correlation coefficient in the multinomial logit model. The other very important
infrastructure effects on bicycling are the exposure conditions of residents to accidents when
they are going to work, the existence of exclusive bike lane, and closer proximity of public
transport. Workers live in community with longer of main road crossing has very less
likelihood that she or he will choose bicycling. This is because in this area there will be a
higher risk of traffic accidents. He also found that the existence of exclusive bicycle lane
significantly increase the use of bicycling and the closer proximity of public transport tend
to a decreasing of use of the bicycle(Zhao, 2014).
The younger people who study and work at the same level have a higher likelihood of using
a bicycle in the daily commuters(El_zbieta Biernat, 2018). Car availability reduces the
probability of using a bicycle to commute(Ana Barberana, 2017). In supporting this, the
prevalence of cycling for commuting is inversely correlated with a car or public transport
use(El_zbieta Biernat, 2018).
In Poland, it was found that the prevalence of cycling for commuting is negatively correlated
with car or public transport use. Moreover, the higher the income, the lower bicycle
ownership. Only the largest cities (and only in recent years), have bucked this negative
correlation, most likely due to EU-funded large investments in bicycle paths. It was also
found that cyclists and non-cyclists have differing views on the perceived barriers and their
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relative importance even the weather. Specifically, bicycle user having a tendency to look
at infrastructure barriers as more important. Both cyclists and non-cyclists treat safety
concerns as important and safer environments of the road can motivate cyclists. Therefore
bicycle infrastructure and road safety are the most important issue to be given
priority(El_zbieta Biernat, 2018). Considering attitudes, cycling use likelihood seems to be
related to behaving that cycling to be efficient, pleasant and suited to your lifestyle, and not
uncomfortable nor unsafe(Ana Barberana, 2017). Attitudes can influence society in the use
of cyclists. In Netherland, the following variables: using bikes provide status, environmental
benefits, mentally relaxing, physically relaxing, comfortable, time-saving, flexible cheap,
pleasant, offers privacy, healthy benefits, traffic safety, socially safe, and suits life style as
social attitudes were investigated to know how much the influence cycling.
The influence of safety may be higher in countries where cycling is less common. This may
be due to a lacking bicycle infrastructure or a low cycling motivating attitude of the car.
Similarly, other influencing factors may be different in different countries. However, in the
Netherlands ‘direct benefit’ attitude influence the commute mode choice the most, but
awareness of long-term effects and safety also affect bicycle commuting and this may also
be true for other countries where cycling is common such as Denmark, and also elsewhere
where cycle is perceived as a mode of transport rather than a form of recreation (Eva Heinen
2011).
Study in Zagreb, Croatia, on safety aspects of the bicycle traffic and the needs of cyclists in
the city and its surrounding, using regression analysis, results in people who inclined to
participate in the city traffic by bike, they believe that the city should highly improve the
condition for cycling. More frequent user of bike per week significantly influenced by lack
of road cycling conditions. Cyclists who often ride a bike for different purposes, more
sloped to safe driving (Sindik, 2015).
2.3 Factor analysis
Factor analysis is a technique that is used to reduce a large number of variables into a fewer
number of factors in questionnaire analysis. The technique extracts maximum common
variance or similar patterns from all variables and put them into a common score. The goal
of factor analysis is to reduce the number of variables to explain and interpret results, and
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to prepare for further analysis. This analysis can be accomplished into two steps; factor
extraction (making a choice about the type of model as well the number of items to extract)
and factor rotation (achieving simple structure in order to improve interpretability) (Kline,
1994).
In the factor extraction step, principal components analysis can be used and the goal is to
replicate the correlation matrix using a set of components that are fewer in number and
linear combinations the original sets of items. It is uncorrelated (orthogonal) linear
combinations of actual scores and it is not complex. The extraction method will be good
enough if it is based on Eigenvalue greater than 1. Eigenvalues represent the total amount
of variance that can be explained by a given principal component. Eigenvalue greater than
zero, then it is a good sign. Eigenvalues are also the sum of squared component loadings
across all items for each component. Eigenvector represents a weight for each Eigenvalue.
The Eigenvectors times the square root of the Eigenvalue gives the component loadings
which can be interpreted as the correlation of each item with the principal component. The
output of the component matrix can be interpreted as the correlation of each item with the
components. The square of each loading in the component matrix represents the proportion
or percent of variance explained by a particular component. If we keep summing up the
squared of the loadings across components cumulatively, we will find that it sums to 1 or
100%. This is known as communality, and in principal component analysis, the
communality for each item is equal to the total variance. Communality explains how much
the variance of the item is explained by the new factors. It is better to consider communality
greater than 0.3. If we sum up squared loading down the items, gives you the Eigenvalues
(Kline, 1994).
In factor rotation step, if there is assumption of less correlation between components, then
orthogonal rotation especially varimax rotation is the most efficient and recommended to
be used (Kaiser, 1958 as cited in (Kline, 1994)).
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2.4 Models for evaluation of suitability of road for bicycling
Some researchers use the following terms interchangeably and others use
differently(Lowry, 2012).
Bicycle Suitability: “an assessment of the perceived comfort and safety of a linear section
of bikeway (the term bikeway includes shared used paths and any roadway where bicycle
travel is permitted)”.
Bikeabilty: “an assessment of the entire bikeway –network in terms of ability, comfort, and
convenience to access important destinations”. Can be also explained by understanding of
the entire bikeway condition to determine the bike riding decistion making.
Complete conection with no break
Safe intersection
Exclusive bike lane.
Bicycle Friendliness: “an assessment of a community for varies aspects of bicycle travel,
including Bikeabilty, laws, and politics to promote safety, education effect to encourage
bicycling, and the general acceptance of bicycling throughout the community”.
There are many methods and indices developed to assess the suitability of urban streets for
bicycling. The following indices are developed in different countries at different times based
on the survey of traffic characteristics, roadway characteristics, and environmental data
specific to that location. In the following section of the research, different methods will be
discussed to identify which parameters are important in affecting the convenience of cyclists
on the road and to use these parameters to evaluate Bahir Dar City major roads for bicycling.
In addition, it is important to choose the best method.
1) Bicycle Compatibility Indices (BCI)
This method was developed for FHWA which is used to rate cyclists comfort level from 1
to 6 for the urban street mid-block segment. The lower numbers indicating a higher level of
comfort. It is based on a survey of cyclists’ perceptions of roadway and traffic
characteristics. The main aim of Harkey’s study was to develop a new model to be used by
planners and decision makers to accommodate the streets to serve non-motorized users. Lab
14
videos were used to study road characteristics and list the factors affecting their
compatibility. Using the contribution of 200 volunteers in 3 different study areas, the model
was stated as shown below in the equation (1) (Ian Hallett, 2006). The BCI method has
been developed to allow practitioners to evaluate existing facilities to determine what
improvements may be required, as well as determine the geometric and operational
requirements for new bicycle facilities.
= 3.67 − 0.966 − 0.410 − 0.498 + 0.002 + 0.0004 + 0.022 + 0.506 − 0.264 + (1)
Where,
BL=Presence of a bicycle lane or paved shoulder >=0.9 m (no = 0, yes=1),
BLW=bicycle lane (or paved shoulder) width, m (to the nearest tenth),
CLW=curb lane width, m (to the nearest tenth),
CLV=curb lane volume, vph in one direction,
OLV=other lane(s) volume, same direction, vph,
SPD=85th percentile speed of traffic, km/h,
PKG=presence if parking lane with more than 30 percent occupancy (no=0, yes=1),
AREA=type of road side development (residential=1, other type=0),
AF= adjustment factors.
Even if this method has its own strengths, it has also some limitations. some of the
limitations of BCI method described by Gizaw Eshetu (Eshetu, 2015) are; during the
development of comfort level, the developer uses recorded videos of bicyclists perception
to the traffic and road characteristics by considering them as if they were cyclists. The other
important limitations are the model does not include the effect of grade; it does not work
for intersection; the model was developed before a long time so that there will be significant
changes in the parameters included.
2) Level of Traffic Stress (LTS) methodology
Low traffic stress is a fundamental attribute of a street network that attracts more people
who are “interested but concerned” about riding a bike for daily trips. The Federal Highway
Administration (FHWA) Separated Bike Lane Planning and Design Guide provides an
15
overview and discussion of the principles of low-stress networks and its application in the
process of planning and evaluating a bikeway network. Level of Traffic Stress (LTS)
methodology, developed for the proposed project based on Mineta Transportation Institute
Low-Stress Bicycling and Network Connectivity (2012) is used to evaluate bicycle and
pedestrian safety impacts of the proposed project in the study area (Andrew Martin, 2016).
Bicycle safety condition is described using a “level of traffic stress” (LTS) methodology,
which uses a numbered system from 1 to 4 to characterize the actual and perceived safety
of streets for people on bikes using the following criteria: speed limit, number of vehicle
lanes and presence or absence of a median, grade or slope, and average daily traffic (ADT)
volumes. LTS 1 represents the safest streets on which people are the most comfortable
riding a bike, while LTS 4 represents the least safe streets on which people are the least
comfortable riding a bike (Andrew Martin, 2016).
Table 4 Level of Traffic Stress Criteria
Criteria LTS≥1 LTS≥2 LTS≥3 LTS≥4
speed limit
vehicular through
with separating
with separating
median
grade 5% or less 5%-10% 10%-15% 15% or more
average daily trip 5000 or less 5000-10,000 10,000-15,000 15,000 or more
Source: Andrew Martin, Senior Regional Planner(Andrew Martin, 2016).
Limitations of this method are; the model is limited only for the normal section and the
model do not involve major variables in which bicyclists’ comfort or safety is highly
affected. Some of the parameters which are not included are parking condition, traffic
mixes, and presence of bicycle lane. The other important short come is that to determine the
LTS of road four parameters should fall at one specified criteria which will be always not
true.
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3) Bicycle Level of Service (BLOS) by HCM 2010
To calculate BLOS is to assign a grade, A through F, to a portion of roadway. This grade is
meant to correspond to the perceived level of service that roadway provides to the bicyclist.
BLOS comprise a portion of the HCM’s Multimodal Level of Service methodology
(MMLOS) (Liggett, 2014). This model was developed using 150 persons in Florida;
however, the model has been calibrated and extensively tested in numerous other locations.
To evaluate bicycle LOS, a statistically calibrated mathematical equation is used to estimate
bicycling conditions in a shared roadway environment. It uses the same measurable traffic
and roadway factors that transportation planners and engineers use for other travel modes.
This modeling procedure clearly reflects the effect on bicycling suitability or compatibility
because of factors such as roadway width, bike lane width, striping combinations, traffic
volume, pavement surface condition, motor vehicle speed and type, and on-street parking
(Federal Highway Administration, 2006).
Urban streets serve for the mobility of travel modes like an automobile, pedestrian, bicycle,
and transit modes. Each travel modes perceive service level in different ways. Operational
decision made to improve a particular travel mode will have an impact on the service
permitted by other modes.
For the purpose of analysis, urban streets are separated into individual elements operating
as a single unit and being physically adjacent. These components are presented in figure 2;
A point represents a boundary between links. Intersection and ramp terminal are
point example.
A Facility represents multiple continuous segments.
As presented in figure 2:
Point is the space of the intersection.
Link is the space between the stop line located at the downstream of the first
intersection to the stop line located at the upstream of the other intersection.
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Segment is the space between the stop line located at the downstream of the first
intersection to the stop line located at the downstream of the other intersection. It
means that it includes the downstream intersection.
Source: HCM 2010 Volume 3 (Transportation Research Board, 2010) .
The selection of the level of analysis depends on the analyst based on the capacity to have
extensive data. To analyze at segment or facility level, very extensive data is needed.
The methodology is by the assumption that traffic condition is steady during the analysis
period 0.25 to 1hr. if the evaluation of multiple analysis period is determined to be important
then, the performance estimate should be reported for each analysis period instead of
averaging.
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There are 3 approaches to be used for selection of analysis period. These are;
Approach A: study period is 1 hr and the analysis period is 15 min (pick hour).
Approach B: both study period and the analysis period are 1hr.
Approach C: the study period is 1hr and 4 analysis period is considered and the
result is reported for each analysis period.
For example for approach A, the analysis period is 15 min peak. The equivalent hourly flow
rate (veh/hr.) used for the analysis is based on 15-min traffic multiplied by 4 or 1h demand
volume divided by the peak hourly factor.
Limitations corresponding to each street components;
Point: the methodology works only for a signalized intersection.
Segment: since segment, is the combination of link and point, it does not work for
all condition. And also it does not work for a grade in excess of 2%.
Facility: this holds the limitation of all segments.
The variables and analysis are only specific to the subject direction of travel including the
links and intersections.
Signalized intersection
= 0.0066 Vlt + Vth + Vrt
4 (3)
Where, Ib,int = bicycle LOS score for signalized intersection
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Table 5 Description of variables for HCM BLOS model for intersection
Symbol Description of the symbols and specification Unit
Wcd curb-to-curb width of the cross street Ft
Wt total width of the outside through lane, bicycle lane, and paved shoulder Ft
Vlt left-turn demand flow rate veh/h
Vth through demand flow rate veh/h
Vrt right-turn demand flow rate veh/h
Nth number of through lanes (shared or exclusive) Ln
Wol width of the outside through lane Ft
Wbl width of the bicycle lane = 0.0 if bicycle lane not provided Ft
Ipk indicator variable for on-street parking occupancy = 0
if ppk > 0.0,1otherwise
(0 or 1)
Ppk proportion of on-street parking occupied on the approach or exit in the
subject of bicycle movement
Wos width of paved outside shoulder (on-street parking lane) Ft
Wos* adjusted width of paved outside shoulder; if curb is present Wos* = Wos-
1.5>0.0,otherwise Wos*=Wos
2) For urban street links
In this research, it is specified to use link-based analysis instead of full-scale analysis that
are segment and facility level because it offers the advantage of being less data-intensive
than the full scale.
= −0.0052 (6)
= 0.507(
20
Fw = cross-section adjustment factor,
Fp = pavement condition adjustment factor,
ln(x) = natural log of x,
We = effective width of outside through lane (ft),
Vma = adjusted mid segment demand flow rate (veh/h),
Nth = number of through lanes on the segment in the subject direction of travel (In),
SRa = adjusted motorized vehicle running speed (mi/h),
PHVa = adjusted percent heavy vehicles in mid segment demand flow rate (%), and
Pc = pavement condition rating as presented in appendix 5.
The brief description of the variable in the HCM 2010 BLOS model for a link is presented
in appendix 4. BLOS expressed by the letter score “A” to “F” as shown in appendix 6.
Even though BLOS is the latest and includes many variables that influence the convenience
of bicyclists, it has also many limitations (Liggett, 2014). Some of the limitations are;
Intersection
and markings through intersections.
Does not contain a measure of bicyclist delay, so not sensitive to improvements in
signal timing or detection that would reduce delay.
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Link
Not sensitive to colored paint in the bicycle lane, striped buffers, or cycle tracks.
Does not contain a measure of bicyclist crowding, so not sensitive to the improvements
in the capacity that would reduce crowding.
3) Bicycle Intersection Safety Indices (BISI)
The Federal Highway Administration (FHWA) developed Bike ISI on the basis of two
measures—safety ratings (expert opinion of the safety of sites) and observed behaviors
(observed interactions between bicyclists and motorists). These different measures provided
a multifaceted approach to determining the relative safety of a bicycle approach leg. Bicycle
Intersection Safety Indices (BISI) are a set of models that enables the user to identify
intersection approach legs that need priority for safety improvements rather than the
intersection as a whole as shown in appendix 7. The indices proactively prioritize bicyclists’
approaches with respect to safety. The higher score indicates that the intersection should
be assessed in depth for safety assessment. The intersection with score 1 is considered as
safest and with score 6 considered as least safe. The existence of high bicycle BISI does not
necessarily mean that the intersection is “hazardous” because there are many characteristics
and behaviors at an intersection that will result in a bike crash, and no method can include
all of these factors (Daniel L. Carter, 2007).
BISI was developed at urban and suburban intersections with the following characteristics
of intersection and used only for intersection which will meet three legs and four-leg
intersections; Signalized, two-way stop, and four-way stop; traffic volumes from 600 to
50,000 vehicles per day; One-way and two-way roads; One to four through lanes; Speed
limits from 24.1 to 72.4 kilometers per hour (Daniel L. Carter, 2007).
Limitations of this method are: It is developed for prioritizing intersection approaches legs
to be assessed more for safety improvements implies that it give the safety condition in
reference to other intersection legs. It does not directly tell suitability of intersection for
bicycling. The other limitation is that it is only used for intersection, not for normal sections.
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4) Suitability Assessment by Road Gradient
As discussed in the above methods, all methods do not include the effects of road gradient
except Level of Traffic Stress method. The acceptable road gradient specified by AASHTO,
Guide for developments of bicycle facilities, American States of Highway and
Transportation Officials 1999 is shown in the following table.
Table 6 Acceptable road gradient for bicyclists
Grade % Grade length in m
5-6 For up to 240
7 For up to 120
8 For up to 90
9 For up to 60
10 For up to 30
11 and above For up to 15
Source: Guide for developments of bicycle facilities, American States of Highway and
Transportation Officials 1999
2.5 Best practices of bicycle-friendly cities
Putting efforts in changing the behavior of the people towards cycling will not be effective
only by providing cycling infrastructure, but also changing the mobility behavior of the
society. Therefore, it is important to investigate the attitudes of specific groups to identify
homogeneous attitude groups defined by homogeneous information needs and to have
effective policies and strategies for corresponding behavior and attitudes classes. There are
four basic class of population segmentation in identifications of effective encouraging
policies. This segmentation is based on travel behavior, geographical features, socio-
demographic variables and attitudes(Nadine Haufe 2016).
2.5.1 The effects of policies and strategies on cycling level of countries
John Pucher & Ralph Buehler reviews the experience of cycling between the three best
bicycle friendly countries German, Denmark and Netherland as one group and USA and
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UK as another group. The researchers had tried to show how the difference in policies and
strategies affect the development of bicycling as a sustainable mode. Bicycling in the
industrialized world is a marginalized mode which is used for recreational rather than a way
to workplaces because as industrialization increases, the environment will become highly
unsuitable for biking (Buehler, 2008).
Beside industrialization, there are countries like German, Denmark, and Netherlands that
are technically advanced and has efficient management to use a bicycle as a sustainable
mode to travel both to workplaces and recreation. While cultures, history, topography, and
climate are important, UK and USA had not given emphasis as Netherland, Denmark, and
German. The cycling habit of German, Denmark, and Netherlands is about ten times that of
UK and USA and women bike at the same level of men but slightly different in age group
(Buehler, 2008).
Moreover, Dutch, Danish and German cyclist uses simple, inexpensive bikes, a small
number of cyclists wear special cycling outfits and helpmates. In spite of lacking self-
confidence, safety-conscious and risk-averse, individual can be seen cycling. This shows
that how much these countries are close to cycling and being convertible to bike (Buehler,
2008).
Government policies are very important in utilizing the opportunities of the countries like
cultures, history, topography and climate to the efficient use of cycling as one of the
traveling modes. Transport policies, land-use policies, urban development policies, housing
policies, environmental policies, taxation policies, and parking policies have a great
influence on cycling. As an illustration, to show how these policies are, UK and USA have
given a green light to private car irrespective to social, economic, and environmental costs.
Subsequently in UK and USA cycling practice is much lower than that of German,
Netherland, and Denmark. In Netherland, Denmark, and German, private car have been
given red light which resulted in prospered cycling over the past years. Instead of providing
more vehicles, roads, and parking facilities, these countries have focused on people-friendly
cities, not car friendly cities and as result, they built more livable and more sustainable cities
than American and UK cities. By making policies in favor of giving more land use to
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cycling, it is possible to improve cycling dramatically and make it excellent alternatives to
the private car (Buehler, 2008).
2.5.2 Overall cycling level among best-practiced countries in the world
As reviewed by John Pucher & Ralph Buehler, cycling level in the world varies between
the least 1% of the trip by bicycle in Australia, USA, and UK and 11%, 18%, 27% of the
trip by bicycle in Finland, Denmark and Netherland respectively. Northern Europeans were
far more likely than Americans to cycle for practice, utilitarian purposes. Travel to work or
schools accounts only 11% all bikes trips in USA, compared to 30% in the UK, 28% in
German, 32% in Netherland and 35% in Denmark (Buehler, 2008).
The Netherlands, Denmark, and German have been among the most successful countries in
promoting cycling for daily trips even if these countries are leaders in car ownership. These
are not because these countries can’t afford expensive transport modes. Although car
ownership in German is much higher than UK, the bike share of trips by bike is much higher
than UK. This shows that a high level of car ownership does not preclude cycling(Buehler,
2008).
Some readers might think that bicycling experience in Europa has been constantly grown,
in reality, cycling fell sharply during the 1950s and 160s, where car ownership rises and
cities started spreading out. In the mid-1970s, land use policies in German, Denmark, and
Netherland shifted in favor of cycling, walking and public transport. The policy reform was
mainly to limit the increasingly harmful environment, energy and safety impacts of rising
car use. In most cities, cycling use increased by constructions of cycling facilities in
addition to making an imposing restriction on car use and making it expensive (Buehler,
2008).
In Netherland, German, and Denmark, not only do growing of level of cycling but also
covers both sexes. Women have been cycling as men, making 45% of all trips in Denmark,
49% in German and 55% in Netherland. In another dimension of these countries, cycling
has been practiced by all age groups and cycling level is the same between different income
groups (Buehler, 2008).
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Another possibility to show the level of cycling in different countries would be the safety
level of cycle users. The level of cycling is mainly influenced by the safety level that the
counties provided for their peoples. As an example, the most important reasons for a higher
level of cycling in German, Denmark, and Netherland than USA and UK among all ages,
sexes and income level is that cycling is much safer than in these countries. There is a reason
to believe that more cycling facilities to have safer cycling. Fatality rates and injuries per
km are much lower for countries with a higher share of bike transport to the total travel
(Buehler, 2008).
2.5.3 How to make cycling safer and convenient
Different Countries in the world follows two scenarios to increase the level of cycling and
make it safe and convenient. In the following paragraphs, these two scenarios are discussed
briefly.
Scenario-1: Develop bicycling encouragement policies
The first aspect is encouragement policies to promote bicycling and walking as affordable,
safe, and convenient mode of transport. The League of American Bicyclists’ bicycling
friendly State Program, launched a Bicycle-Friendly State program to work with states in
collaboration to improve their bicycling Environment. Through legislation, policies, and
programs, states can promote bicycling as healthy and affordable for transportation and
recreation purposes. A bicycling friendly state may wish to include “Five Es” to encourage
better bicycling (Teigen, 2008; URBAN systems, 2012). These are described as follow;
Engineering addresses the design, implementation, and maintenance of bikeway
facilities and how bikeway facilities fit into the broader transportation system.
Education includes teaching or training programs for cyclists and motorists, such as
cycling skills courses or bicycle maintenance courses, which are often targeted to key
populations such as children or new commuters.
Encouragement is the promotion of cycling through participatory events, such as
Bike to Work Week, Bike Month, community bike rides, commuter incentive
programs, or Safe Routes to School programs.
26
Enforcement refers to laws in regards to bicycle use and ensuring that bicyclists and
motorists know the rules of the road and share the road safely.
Evaluation is used to confirm that policies, programs, and facilities are meeting their
intended outcomes.
The other consideration to meet cycling targets and achieve the importance of bicycling,
strong cycling policies and must be adopted and implemented at both local and federal levels
of government. The following are recommendations based on policies adopted by cities and
countries that have achieved a high and sustained level of cycling based on high shift cycling
scenarios(Institute for Transport and Development Policies, 2015).
Rapidly constructing cycling and E-bike infrastructure in large scale;
Implementing bike-share programs in large- and medium-size cities, prioritizing
connections to transit;
Revise laws and enforcement policies to protect cycling and walking;
Coordinate metropolitan transport and land use plans, so that all new investments
result in more cycling, walking, and public transport trips.
Repeal policies that subsidize additional motor vehicle use, such as minimum
parking requirements, free on-street parking, and fuel subsidies;
Encourage cycling and active transport via pricing policies and information
campaigns;
Dedicate fuel taxes, driving fees, and other transport-system revenues toward
investment in sustainable transport.
In German, Netherlands, and Denmark, a very important practice in developing cycling is
that the local government plan, design and fund bicycling infrastructures. Municipalities
have a specific plan for a particular condition and context to promote cycling. Cycling
training, safety, and promotional programs are carried out at the local level even if they are
funded by a higher level. The construction of separate bicycle facilities is cornerstones of
Dutch, Danish and German policies to make cycling convenient and safe (Buehler, 2008).
27
The following key policies and innovative measures have been used in Dutch, Danish, and
German cities to promote safe and convenient cycling (Buehler, 2008).
Extensive systems of separate cycling facilities
Intersection modification and priority traffic signals
Traffic calming
Bike parking
Traffic calming: In Denmark, Netherland, and German, they have reduced residential street
speeds to 30km/hr and prohibiting through vehicles. These countries calmed most streets in
the residential neither hoods. The most advanced form of traffic calming was imposing of
limits to car speeds.
The other traffic calming were car-free zone in the center of cities and Bicycle Street which
has been strongly adopted in Dutch, and German cities. The available empirical evidence
shows that traffic calming enhance overall safety. The importance is more for pedestrian,
but serious cyclists injuries sharply decreased.
Intersection modification: “An intersection facilitates is the interchange between
bicyclists, motorists, pedestrians, and other competing modes in order to advance traffic
flow in a safe and efficient manner”. Designs for intersections with bicycle facilities should
enable to minimize conflict between cyclists and vehicles by enhancing the visibility,
marking a clear right-of-way, and helping to have continued eye contact and awareness with
competing modes. Intersection treatments used to resolve the queuing and merging
maneuvers and often assisted by timed and specialized signals (National Association of City
Transportation Officials, 2011).
While bike lane helps to protect cyclists between intersections and pose safety at the
intersection. Therefore it is important to care about the safety of cyclist at intersections.
28
Some of the modifications for cyclists that were practiced in best-practiced counties such as
German, Denmark, and Netherland are:
Special bike lane leading up to intersections with advance stop lines;
While cycle turning is allowed, restrict vehicle turning;
Advance green traffic signal for cyclists and extra green signal phases for cyclists;
Highly visible and distinctively colored cycling crossings at intersections;
Special cyclists traffic light;
Insertion of a traffic island.
Bike parking: Extensive bike parking is available in Dutch, Denmark and German cities.
Local governments and public transport sectors themselves provide vast parking and
building owners and private developers are required by local law to have minimum parking
space for cyclists.
Integration with public transport: The northern Europeans countries planner and public
service provider and distributor distinguish the role of bicycling as feeder and distributer to
public and transport. Bike parking at dominant stations of transit and other origin and
destination of tips of public transport.
Training and education: Dutch, Danish and German children learn training of safe and
effective cycling techniques as part of their regular school curriculum. The course is
extended up to and completed at fourth grade which includes instructions at the classroom
and on-road lessons. Real police officers test children, who have received official
certificates. Another crucial training is given to drivers to be aware of cyclists.
Traffic laws: Traffic laws in Netherland, German and Denmark are highly protective for
vulnerable groups such as cyclists of elders and children. Even crash occurs at the right of
way of motorists on the road, motorists will not be excused until it is identified that the crash
is due to cyclists.
Promotional events: In Dutch, Danish, and German, cities have extensive programs to
simulates interests and feeling of excitement for cycling by all age groups, sex and income
groups.
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Scenario-2: Construction of bicycle facilities
Bicycle facilities can be grouped and applied in various contexts and they have a different
level of comfort and safety. Based on the review of the best international bicycle facilities
and planning guidelines, six types of on-street and off-street bicycle facilities can be
considered throughout the city (URBAN systems, 2012).
Off-Street Pathways: are physically separated from motor vehicles and provide
sufficient width and supporting facilities to be used by cyclists, pedestrians, and other
non-motorized users.
Cycle Tracks: are physically separated from motor vehicle travel lanes but are located
within the road right-of-way. Cycle tracks are a hybrid type bicycle facility combining
the experience of an off-street path with the on-street infrastructure of a conventional
bicycle lane. In many cases cycle tracks are separated by landscaping or curbs from
the sidewalk, facilitating separation between cyclists and pedestrians as well.
“Compared with bicycling on a reference street…these cycle tracks had a 28% lower
injury rate” (Lusk et al.,2010 as cited in (National Association of City Transportation
Officials, 2011)).
Local Street Bikeways: are routes on streets with low vehicle speeds and volumes,
which include a range of treatments ranging from relatively basic facilities consisting
of signage and pavement markings to bikeways with varying degrees of traffic
calming implemented to improve safety for cyclists and other road users.
Bicycle Lanes: are separate lanes that are designated exclusively for bicycle travel
and also include pavement markings.
Shared Use Lanes: provide direct routes for experienced cyclists along the outer lane
of a roadway.
Shoulder Bikeways: are typically found on streets without curb and gutter with
shoulders wide enough for bicycle travel. Shoulder bikeways often, but not always,
include signage alerting motorists to expect bicycle travel along the roadway.
30
Source: Peel Pedestrian and Bicycle Facility Design Guidance, 2012.
2.6 Safety of cyclists
Based on the police accident records in Bahir Dar between 1995/6-2001/2, out of 47 people
were killed in road accidents, 26 (55 percent) were cycle related accidents, and out of 199
people injured, 101(slightly more than 50 percent) were cyclists. If one looks back over the
last seven years we find that cyclists accounted for about 18 percent of the total accidents
(Yayeh, 2003). As reported by Bahir Dar Police Memria office, the accidents caused by
cyclists in the year between 2008 and 2017 are presented in table 7.
31
Accident severity
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Property damage 4 4 2 6 1 2 4 4 2 4
Slight injury 12 2 8 4 - 3 1 2 1
Sever injury 1 2 1 - 2 - - - 4 -
Death - 1 2 - - 1 1 - - -
Total 17 9 13 10 3 6 6 6 6 5
Source: Bahir Dar Police Memria office, 2008-2017
Speeding has been one of the major causes of accident in Bahir Dar city, about 33% of total
accidents 1995-2001, was caused by speeding and the effect is more severe on a vulnerable
group of road users (Yayeh, 2003).
.
3.1 Study area
The study has been conducted in Bahir Dar city, a capital city of Amhara National Regional
State and located at the north-west part of Ethiopia. Depending upon the measure took place
somewhere around the center of the city, its absolute geographic location is 11037’north
latitude and 37025’ east longitude. Demographically, the population has more than doubled
which grew from 160,048 in 2007 to 362,297 in 2017 (Bahir Dar University, 2018).
The study area covers:
All Kifle-Ketemas to sample questionnaire respondents as presented in figure 4.
Figure 4 Bahir Dar City Kifle-Ketema as a study area
33
Selected major corridors and links on road segments
The selection of corridors and links on road segments were to consider the road approach
to downstream of the city characterized by a high volume of traffic, speed, on-street parking
and other conditions.
Selected major corridors to evaluate their suitability for riding a bike by questionnaire were
as shown in figure 5. The selected road corridors includes Gambi Roundabout -Wisdom
Roundabout, Wisdom Roundabout –Poly, Wisdom Roundabout to Peda Campus, Gambi
Roundabout - St. George Roundabout, St. George Roundabout - Abay Bridge, Abay Bridge
- Abay Mado End, St. George Roundabout - Geon traffic light, Geon traffic light - Geter
Menged, Gambi Roundabout - Azwa Hotel, and Azwa Hotel - China Camp.
Selected Links on segments to determine their Bicycle Level of Services were as shown in
table 8 and figure 6.
Table 8 Road links on segments
Code Direction of flow
2A Gion Traffic light to Dipo Intersection
2B Dipo Intersection to Gion Traffic light
3A Abay Bridge to Abay Mado Market
3B Abay Mado Market to Abay Bridge
4A St,George Roundabout to ANRS Roundabout
4B ANRS Roundabout to St.George Roundabout
5A Gambi Roundabout to Wisdom Roundabout
5B Wisdom Roundabout to Gambi Roundabout
6A Gambi Roundabout to Azwa Hotel
6B Azwa Hotel to Gambi Roundabout
7A St.George Roundabout to Gambi Roundabout
7B Gambi Roundabout to St.George Roundabout
8A St.George Roundabout to Geon Traffic light
8B Geon Traffic light to St. George Roundabout
34
36
3.2 Research approach
To answer the research questions and accomplish the objectives, both qualitative and
quantitative research approaches were used. Qualitatively, by using questionnaires, barriers
and motivations to bicycling were identified; and suitability of selected road segments of
the city was evaluated. The questionnaires were developed by including both stated and
revealed preference survey as presented in appendix 1.
Quantitatively, the suitability of the selected links on road segments of the city for riding
bicycle was evaluated using HCM 2010 BLOS model.
3.3 Data description and collection
3.3.1 Questionnaire
Questionnaires were developed after reviewing researches done on the issue of identifying
barriers and motivations to bicycling in different countries. Questionnaires were developed
by contextualizing to Bahir Dar conditions. The questionnaire was adopted with some
modification from researches done by (Ana Barberana, 2017; El_zbieta Biernat, 2018;
Meghan Winters, 2010; Sindik, 2015).
The survey questionnaire includes different components as cited in appendix 1. The first
component was to ask the respondent’s category which is multiple question with currently
cyclists and cyclists in the past. The second component was demographic and socio-
economic information including age, gender, level of education, place of residence
employment, and monthly expenditure. The third component was to ask interest to use a
bicycle in the future if safe and convenient bicycle facility is constructed – using five Likert
scale rating 1- strongly disagree through 5- strongly agree. The fourth component was to
rate barriers to bicycling not to use a bicycle or not to bike more than currently do by using
five Likert scale rating 1- strongly disagree through 5- strongly agree. The fifth component
was to rate motivations to bicycling that will motivate respondents using five Likert scale
rating 1- strongly disagree through 5- strongly agree.
In addition, currently cyclists were involved in rating statements describing the interaction
between cyclists and drivers characteristics (preventing right of way of cyclists; cut off
37
cyclists’ path; ruthlessness of driver during maneuvering; and illegal honking on cyclists)
using five frequency adverbs (never, rarely, sometimes, usually, and always). The other
question was to ask the suitability of existing conditions of selected road corridors and links
on road segments for riding a bicycle using five scale 1-extremely suitable through 5-
extremely unsuitable as shown in appendix 1.
The questionnaire was filled by respondents with the participation of data collector to clarify
questions which were unclear for respondents and not to have missing.
3.3.2 Field data
In addition to the use of a questionnaire to identify the suitability of segments for riding a
bicycle, field data were collected to be used in HCM BLOS analysis as discussed below.
Motorized vehicle running speed on mid segments: “Speed is an important measure for
traffic operations because highway users relate speed to economics, safety, time, comfort,
and convenience”(Anurag Pande and Brian Wolshon, 2016). The spot speed of vehicles was
measured on segments with a measured section between two points. Video recorders were
stood perpendicular to the measured distances between checkpoints to record the flow
characteristics in both directions. The position of the video recorder was at points suitable
to record both directions as seen in figure 7.
First, a video was recorded and then from the video, time taken by the vehicles to traverse
from the first point to the other were recorded. Figure7 shows the locations of the segment
of the road where the videos and geometric variables were recorded. The tail of the arrows
represents the positions of a video recorder for both directions and the head represents the
directions in which video was recorded.
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39
Percent of heavy vehicles in the mid-segment: Percentages of a heavy vehicle for
each segment for both direction was determined from peak hour volume counts.
Heavy vehicles include medium to large buses, medium to large tracks, recreational
vehicle, and other large vehicles according to (Transportation Research Board,
2010).
Mid-segment demand flow rate: is the sum of flow of vehicles in the analysis
direction converted to equivalent passenger cars (Transportation Research Board,
2010). Traffic volume was counted from video recorded on the field.
Division of the street: Division of the street is to identify whether the two directions
are separated with median or other barriers.
Presence of curb: Presence of curb is to differentiate whether the road has a curb at
the outside lane edge.
Proportion of on-street parking occupied: Proportion of on-street parking
occupied is the percentage of the space occupied by parked vehicles from the total
capacity of the parking lane(Transportation Research Board, 2010).
The other variables were;
Width of the bicycle lane: the width of bicycle lane is the distance between the
inner line separating the vehicle lane and the outer line of bicyle lane.
Width of the on-street parking lane: the space between the line separating the
outside through lane and the outside curb.
Width of outside through lane: is the distance between the line separating the inner
lane and the outside through lane and outside line of outside through lane.
Number of through lanes on the segment in the subject direction of travel: is
number of through vehicular lane excluding on-street parking lane and bicycle lane.
Pavement condition: this is the rating of the road surface quality as shown in
appendix 5.
There are different sample size determination methods for questionnaire respondents as
recommended by many researchers. The sample size for conducting the factor analysis, a
minimum of subject to variables ratio, 2:1 should be considered to rate statements by Likert
scales. Satisfying the ratio of subject to variables 10:1 is best to have good factor analysis
results(Kline, 1994).
The main consideration of this thesis was to focus on currently cyclists and cyclists in the
past by satisfying a sample size of 10:1 (subject to variable ratio). About 25 statements as
barriers were rated by a total of 308 respondents (197- currently cyclists, 111-cyclists in the
past). In this case, the ratio of the subject to variable ratio was 12:1 which is above the
satisfying ratio.
In addition to barriers, about 16 motivation statements were rated by 308 respondents
including currently cyclists and cyclists in the past with subject to the variable ratio of 19:1
and about 4 statements were rated to identify the major influencing interactions between
drivers and bicyclists by 197 currently cyclists only. The distributions of the respondent
demography were as shown in table 9.
The total respondents were 308 but the sum of the numbers presented in table 9 maynot
give 308. This was dueto the respondents were unwilling to fill all the demographic
informations.
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Table 9 Distribution of total sample size over demography of respondents
Demography of respondents
3. Age of respondents
Frequency 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69
52 63 62 45 20 26 18 13 4 2 1
4. Residence of the respondents
Frequency Shimbt Tana Fasilo
5. Education level of respondent
Frequency
First
level(1-8)
Second
level(9-10)
Preparatory(11-
6. Employment type
10000 >10000
101 38 20 19 30 16 13 9 2 7 12
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3.4.2 Sample size for field data
Motorized vehicle running speed on mid-segment: The sample size was determined by
using the following formula(Anurag Pande and Brian Wolshon, 2016);
= ( ∗
)2 (10)
S = estimated sample standard deviation, mph referred from table 10.
K = constant from the standard normal distribution corresponding to a certain confidence
Level
E = permitted error or tolerance in the average speed estimate, mph
Table 10 Standard Deviations of Spot Speeds for Sample Size Determination
Traffic area Highway type Average standard devaition
mph km/hr
Rounded volume 5 8
Source: Traffic Engineering Hand Book (Anurag Pande and Brian Wolshon, 2016)
From the formula taking S as 4.8 mph for two-lane urban roads, K as 1.96 for 95%
confidence level, and E as 1.5mph which can range from ±1 to ±5mph. The formula gives
a minimum sample size of;
= ( 4.8 ∗ 1.96
1.5 )2 = 40
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For all links on road segments, more than 100 vehicles including Baja were taken randomly.
Percent of heavy vehicles in the mid-segment: Percentage of the heavy vehicle for each
link on road segment for both direction were determined from peak hour volume counts
(one hour of the day) (Transportation Research Board, 2010).
Mid-segment demand flow rate: 1 hour traffic count volume were used because the
analysis period was 1hr according to HCM 2010 (Transportation Research Board, 2010).
Proportion of on-street parking occupied: The on-street parking was taken as the average
of the parking occupancy of two counts in the analysis period (1hr) (Transportation
Research Board, 2010).
3.5 Materials Used
Smartphones to record video of road segment traffic
Measuring tape to measure geometric characteristics of the road
Data collection formats for recording road traffic and geometric measurements
Stopwatch to record the time taken by vehicles to traverse the segments between
checkpoints in running speed measurement.
3.6 Analysis Methods
3.6.1 Analysis method for questionnaire
In this research both qualitative and quantitative analysis using SPSS software were used
for the analysis including:
Factor analysis was used to reduce and group barriers into a smaller number of
factors.
Mean score of likert scale of questionnaire rating was used to determine the level of
influence of the identified factors and the level of comfort of the selected road
segments for bicycling.
Non-parametric tests were used to check the identified factors to bicycling (as differ
on the basis of varies demographic variables).
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Factor analysis: was used to reduce the number of variables to explain and interpret results,
and to prepare for further analysis. This analysis was accomplished into two steps; factor
extraction (making a choice about the type of model as well the number of items to extract)
and factor rotation (achieving a simple structure in order to improve interpretability)(Kline,
1994).
In the factor extraction step, principal components analysis was used and the goal was to
replicate the correlation matrix using a set of components that are fewer in number and
linear combinations the original sets of items. It is uncorrelated (orthogonal) linear
combinations of actual scores and it is not complex. The extraction method will be good
enough if it is based on Eigenvalue greater than 1. In this thesis Eigenvalue greater than 1
was used. Eigenvalues represent the total amount of variance that can be explained by a
given principal component. Eigenvalue greater than zero, then it is a good sign. Eigenvalues
are also the sum of squared component loadings across all items for each component.
Eigenvector represents a weight for each Eigenvalue. The Eigenvectors times the square
root of the Eigenvalue gives the component loadings which can be interpreted as the
correlation of each item with the principal component. The output of the component matrix
can be interpreted as the correlation of each item with the components. The square of each
loading in the component matrix represents the proportion or percent of variance explained
by a particular component. If we keep summing up the squared of the loadings across
components cumulatively, we will find that it sums to 1 or 100%. This is known as
communality, and in principal component analysis, the communality for each item is equal
to the total variance. Communality explains how much the variance of the item is explained
by the new factors. It is better to consider communality greater than 0.3 and in this thesis
communality greater than 0.3 was useed. If we sum up squared loading down the items,
gives you the Eigenvalues (Kline, 1994).
In factor rotation step, if there is assumption of less correlation between components, then
orthogonal rotation especially varimax rotation is the most efficient and recommended to
be used (Kaiser, 1958 as cited in (Kline, 1994)). In this thesis varimax rotation were used
in the rotation step. The adequacy of the sample size is based on the ratio of 10 respondents
to 1 statement to be rated. Bartletts’ test of sphericity was used to check the correlation
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between items and the result shows the presence of at least one significant correlation
between items. After factors were extracted, the name of the extracted factor was given
according to the meaning of variables or statements grouped in each factor.
Mean score analysis: was done by averaging the rating of statements of:
likert scale (5-strongly agree through 1-strongly disagree) in the identifications of
barriers and motivations.
likert scale (5-extremly through 1-extremly unsuitable) in the evalautions of
suitability of selected roads for