Scholarly article on topic 'Towards an index of city readiness for cycling'

Towards an index of city readiness for cycling Academic research paper on "Social and economic geography"

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{"Non-motorized transport" / Cycling / "Bicycle-friendly city" / "Urban attributes" / "Cycle routes"}

Abstract of research paper on Social and economic geography, author of scientific article — Mohamed Anwer Zayed

Abstract After decades of overdependence on motorized means of transport, and in acknowledgement of major environmental challenges, most of the world’s cities are recognizing the importance of adopting non-motorized means of transport to enhance their quality of life. Local authorities are interested in providing facilities that enable city residents to efficiently traverse the urban fabric. Cycling, as an important non-motorized mode of transport, is considered to be an efficient alternative to motor vehicles. Establishing safe and convenient cycle routes has become one of the main development tasks in contemporary cities. Indeed, converting today’s traditional cities into bicycle-friendly ones is considered to be one of the principal goals of development plans. This paper addresses the urban readiness of cities to be bicycle-friendly. It focuses on the socioeconomic and urban characteristics that have to be present in a city for cycling to be adopted as a primary mode of transport. Through statistical analysis of 20 bicycle-friendly cities, the paper identifies the main requirements for urban cycling. Finally, it evaluates the readiness of Egyptian cities to become bicycle-friendly.

Academic research paper on topic "Towards an index of city readiness for cycling"

 International Journal of Transportation Science and Technology xxx (2017) xxx-xxx

© Contents lists available at ScienceDirect International Journal of Transportation Science and Technology journal homepage: Transportation Science &. Technology

Towards an index of city readiness for cycling

Mohamed Anwer Zayed

Department of Architecture Engineering, Cairo University, Cairo, Egypt


After decades of overdependence on motorized means of transport, and in acknowledgement of major environmental challenges, most of the world's cities are recognizing the importance of adopting non-motorized means of transport to enhance their quality of life. Local authorities are interested in providing facilities that enable city residents to efficiently traverse the urban fabric. Cycling, as an important non-motorized mode of transport, is considered to be an efficient alternative to motor vehicles. Establishing safe and convenient cycle routes has become one of the main development tasks in contemporary cities. Indeed, converting today's traditional cities into bicycle-friendly ones is considered to be one of the principal goals of development plans. This paper addresses the urban readiness of cities to be bicycle-friendly. It focuses on the socioeconomic and urban characteristics that have to be present in a city for cycling to be adopted as a primary mode of transport. Through statistical analysis of 20 bicycle-friendly cities, the paper identifies the main requirements for urban cycling. Finally, it evaluates the readiness of Egyptian cities to become bicycle-friendly.

© 2017 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (


Article history:

Received 7 October 2016

Received in revised form 12 January 2017

Accepted 15 January 2017

Available online xxxx


Non-motorized transport Cycling

Bicycle-friendly city Urban attributes Cycle routes


After decades of motorized mobility dominance in urban areas, the world is witnessing a revival of the notion of using bicycles as the main transport mode in cities. In fact, non-motorized transport as a whole is starting to receive special attention. This is due to the great environmental, health and social challenges that are threatening the liveability of contemporary cities. Developing cities to be more suitable for bicycle use and more encouraging of cycling is considered to be one of the most important urban trends, not only at the level of academic research but also in professional practice. The notion of the bicycle-friendly city (BFC) is emerging internationally as one of the main targets of urban development plans in the twenty-first century, especially in European countries. This is mainly as a result of recognizing the benefits of this efficient means of transport. The development of contemporary cities into BFCs firstly requires identification of the basic urban attributes that generally facilitate cycling. This will help to determine the priorities of development. The cities with cycling physical potentials will be nominated to be developed before those without. After determining these cities, the second step will start. This step will focus on the socio-economic and cultural characteristics of local communities in the chosen cities in order to orient the developing to BFC to fit it.

The current research had two main goals. The first was to identify the principal urban attributes of the city that affect utilitarian cycling. The second goal was to evaluate the readiness of Egyptian cities to be developed into BFCs so that cycling

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will be one of the main modes of transport. To achieve these two goals, a quantitative deduction methodology was adopted. A statistical tool, principal component analysis, was used to quantitatively investigate the attributes of the built environment of pioneer BFCs. It was then used again to evaluate the readiness of Egyptian cities to be developed into BFCs and rank them according to the urban attributes identified for utilitarian cycling. The research consists of three main parts. A literature review forms the first part, where the background to bicycle use as a principal mode of transport in cities is introduced. In this part, a cycling overview, the benefits of cycling, the characteristics of bicycle-friendly environments and the pioneer BFCs all over the world are considered. Then, in part two, a selected set of urban, environmental and social attributes in these pioneer BFCs are statistically investigated to identify urban attributes for utilitarian cycling, from which an index of city readiness for cycling is proposed. Based on these results, the research then highlights the status of cities in Egypt in terms of their readiness to adopt cycling. The availability of the required urban attributes is assessed for a selected set of Egyptian cities.

Literature review

Since the second half of the twentieth century, urban mobility has witnessed increasing motorization rates, which have resulted in negative impacts (Castillo-Manzano et al., 2015). Environmental pollution, increased fuel consumption, continuous traffic congestion and health deterioration are some of the main consequences that are threatening human life in cities (Stanley et al., 2011; Rybarczyk and Wu, 2010). In such circumstances, the world pursues sustainable transport as a principal goal of urban development plans (Blickstein, 2010; Kassens, 2009). Sustainable transport is defined as 'satisfying current transport and mobility needs without compromising the ability of future generations to meet these needs' (Biack, 1996). It offers to the local community the opportunity to feel free to reach a destination with minimum environmental, social and health impacts (Passafaro et al., 2014). It encompasses restructuring of current transport systems that are mainly dependent on private motorized transport means (Gossling, 2013). One of the main elements of sustainable transport systems is the facilitation of non-motorized modes such as walking and cycling (Rietveld and Daniel, 2004; European Commission, 2011). In particular, there is an international recommendation to adopt bicycles as a primary means of urban transport (European Commission, 2012; Eryigit and Ter, 2014; Lanzendorf and Busch-Geertsema, 2014; Heinen et al., 2011). This is due to the exceptional potential of cycling (Blair, 2002). Compared to private cars, the bicycle is an inexpensive door-to-door means of transport. Compared to walking, cycling is faster and requires less energy to transport someone the same distance. In fact, urban cycling is 3.6 times faster than walking (David and Sullivan, 2005; Bernardi and Rupi, 2015). For the same distance, cycling requires only 35% of the calories that are required for walking (Suzuki and Hanington, 2012; Lowe, 1989). Furthermore, there are various types of bicycles (Roman, 2014) to accommodate different characteristics of users (Stallard, 2004) (male vs. female and children vs. adults) and also different activities. Tandem, pedi-cab, cargo-bike and police-bike are all well-known types of utilitarian cycling means in many cities across the world. Fig. 1 presents some of the best-known types of bicycles.

In the twentieth century, many theories of urban planning and development addressed the importance of restructuring the urban fabric of cities to support better non-motorized urban mobility, especially cycling (Bickis, 2003). Ebenezer Howard in the theory of garden cities, Jane Jacobs in The Death and Life of Great American Cities, Christopher Alexander et al. in A Pattern Language: Towns, Buildings, Construction and Kevin Lynch in Good City Form were all directly or indirectly addressing how to make cities suitable for cycling. Today, many cities, especially in Europe, America and Australia, are adopting urban development policies to revive cycling as a primary transport mode (Buehler and Pucher, 2012). Bicycle master plans are an important element in urban restructuring strategies (Lowry et al., 2016). These plans are mainly aimed at creating a bike-friendly environment that will facilitate cycling in the city (Joo and Oh, 2013). Two main approaches are adopted to this end (Rietveld and Daniel, 2004). The first is to enhance the attractiveness of cycling as a mode of sustainable transport, to be achieved by both infrastructural development and strengthening of cycle culture (Lanzendorf and Busch-Geertsema, 2014). The second approach is to deter the competing modes (such as private car) through either financial (Sterner, 2007) or engineering constraints.

Research highlights the benefits of adopting bicycles as a primary transport means in urban areas. Utilitarian cycling is considered to be a sustainable and efficient mode of transport (Marshall and Garrick, 2011), especially for short-distance trips (Drumheller et al., 2001). The benefits of utilitarian cycling are clear and wide-ranging (Bauman et al., 2008). Generally, urban mobility, environmental, health and social benefits are realised as a result of replacing motor vehicles with bicycles (Rissel et al., 2013; Lindsay et al., 2011). These benefits not only directly affect users who are cycling to their destinations, but also indirectly affect the whole community (Krizec, 2007). For cyclists, health improvements, better mobility opportunities and economic savings are some of the main benefits. Utilitarian cycling offers a real opportunity for a routine physical activity that results in many potential health benefits for both adults and children (Oja et al., 2011). In particular, it helps the healthy growth of children's and adolescents' bodies and prevents many diseases, especially those affecting heart and blood vessels in adults (Bauman and Rissel, 2009). In addition, weight control and the minimization of obesity are positively associated with cycling (Bassett et al., 2008; Brown et al., 2008). In terms of the conditions for mobility, bicycles require a relatively small space to move in and park in Buehler (2012), as each lane on a typical road accommodates 14,000 bicycles per hour compared to only 2000 cars per hour (Proulx and Baron, 2015). Thus, cyclists are freed from traffic congestion and trip duration is relatively short. Furthermore, cycling offers an efficient transport mode at a relatively low monetary and time

Fig. 1. (Up) Different types of bicycles to suit various types of users (male, female, elderly, children, single user and group), (Down) different types of bicycles to suit various types of activities (taxi, cargo, vendor, commuter and police).

cost. A bicycle is considered to be more affordable than a car because it does not require fossil fuel to move, so cyclists can save the cost of fuel (Tiwari et al., 2015) in addition to maintenance and parking costs. As a result of adopting utilitarian cycling as a primary mode of transport, the whole community will also experience many benefits (Pedestrian and Bicycle Information Center, 2010). One of the most important of these is the improvement in quality of environment (Ma and Dill, 2015). Cycling helps in reducing both air pollution (Barwaldt et al., 2014) and traffic noise caused by motor vehicles (Cui et al., 2014; Reynolds et al., 2009). Bicycle-based urban development has positive ecological impacts by reducing the need for additional land development and lowering urbanization rates, preserving biodiversity and habitat (Meng et al., 2014). The routine physical activity of cyclists has a positive net effect on community public health (Rastogi, 2011; Pucher and Dijkstra, 2003) and generally alleviates chronic diseases (Taddei et al., 2015). Urban mobility is considered to gain a lot of benefits as a result of mass cycling (Dondi et al., 2011). There is clear evidence that community use of bicycles instead of automobiles helps to relieve traffic congestion (Stewart and Moudon, 2014; Alliance for Biking & Walking, 2014), saving time and cost in trips. In addition, road safety is enhanced in cities with higher cycling rates (Marshall and Garrick, 2011). The increase of cycling is correlated to a decrease in both automobile use and traffic speed as motorists change their behaviours, leading to reductions in road fatalities (Schepers and Heinen, 2013). Other researches highlights another urban benefit of cycling, as it supports other modes of urban transport through efficient integration between bicycle and public transit means in the same trip (Cui et al., 2014; Flamm et al., 2014). In addition, some social benefits can be recognized, such as liveability enhancement (Krizec, 2007), improved sense of community (Rissel et al., 2013) and promotion of healthier lifestyles (Owen et al., 2010). The economic advantage of all these community-level benefits is notable. In 2010, the gross economic benefit of cycling in the European Union achieved around US$1.80 per kilometre of cycled distance (Kuster and Blondel, 2013). This includes the economic impacts of health, environmental and urban benefits. Other research identifies the net profit for every kilometre cycled as 23 US cents, compared to a net loss of 16 cents for every car-driven kilometre (Moonen and Clark, 2013). It should be acknowledged that there are some risks associated with cycling in cities. Despite the fact that injuries and air pollution inversely correlate with cycling rates, it still presents a risk to the individual, especially

in cities with low cycling rates (Pucher et al., 1999). Overall, however, the benefits outweigh these risks (Lindsay et al., 2011; Winters et al., 2007).

Bicycle-friendly cities

In recognition of the importance of cycling as a sustainable transport mode, the term 'bicycle-friendly city' has started to emerge across the world. Developing the city to a bicycle-friendly one is considered to be one of the most important goals of urban development plans in many regions of the world. The definition and the characteristics of BFCs are considered here, followed by an introduction to the measurement initiatives that determine the cities that best exemplify BFCs across the world.


The term bicycle-friendly city, or bike-friendly city as used in North America, refers to a city that has efficient infrastructure, transportation policies and societal consensus to make cycling a main transport mode. It is where local authorities are focused on making the city people-friendly and environment-friendly rather than car-friendly (Kristinsdottir, 2012). This type of city shows a dedication to creating more suitable spaces for cycling (Williams, 2015). As a result, the bicycle is a realistic means of transport for the city's residents, especially for short-distance trips (Joo et al., 2015).


A BFC is mainly characterized by high-density urban development, diversified land-use planning and a safe and comfortable transport network (Stewart and Moudon, 2014; Alliance for Biking & Walking, 2014; Schepers and Heinen, 2013; Flamm et al., 2014; Owen et al., 2010; Küster and Blondel, 2013; Moonen and Clark, 2013; Pucher et al., 1999; Winters et al., 2007; Kristinsdottir, 2012; Williams, 2015; Joo et al., 2015; Majumdara and Mitra, 2013). Both high-density development and diversified land use give rise to nearby destinations. Thus, many residents' destinations will be located within small catchment areas; which means short trip distances from home to each destination. This encourages people to use non-motorized transport means, especially bicycles. A safe and comfortable transport network incorporates many other qualities. Secured paths especially with physical separation from the motorized network (Li et al., 2012), efficient facilities (Schoner et al., 2015; Basu and Vasudevan, 2013) and gently graded topography (Majumdara and Mitra, 2013) are the main qualities of such a network. Other geo-environmental characteristics such as temperature, precipitation and wind are of significance (Spencer et al., 2013). Warmer and drier climates are more suitable for cycling (Winters et al., 2007). The application of transport policies and strategies that prioritize non-motorized transport also characterizes BFCs (Sayers et al., 2012). Such policies are mainly focused on restricting motorized transport, promoting efficient cycling education, developing traffic-calmed neighbourhoods and considering people's perceptions and psychologies (Pucher and Buehler, 2006; Habib et al., 2014).

It is important to note that the previously mentioned characteristics of BFCs seem to be at various levels. It includes macro scale characteristics such as land use spatial distribution in city and on the other hand micro scale ones such as bicycle facilities. So, these characteristics could be classified in two main groups. The first group could be described as the passive characteristics. This group includes the macro scale characteristics that mostly are general urban and environmental attributes of city. It indicates to how much the physical realm of city is ready to adopt cycling. It indicates the readiness of a city for cycling. On the other hand, the second group represents the active characteristics. It includes some micro scale characteristics of urban, landscape, transportation and economic aspects in city that are essential to satisfy some of essential requirements of safe, comfortable and efficient utilitarian cycling. Secured bicycle parking racks is one of these characteristics. These micro-scale characteristics are depending on the socio-economic and cultural aspects of the local community. The passive characteristics determine whether the city has the potentials to adopt utilitarian cycling. They will be used to preliminary select the cities. Then, the active characteristics will be created by intended development programs or projects.

The most bicycle-friendly cities

Despite much research and professional practice having been devoted to the evaluation of bicycle environments (Joo et al., 2015) and their relationship to bicycle use rates (Schoner et al., 2015), only a limited number of them have focused on the city scale. Most existing indices are concerned with bicycle routes and infrastructure. Three city-scale measures have been identified. Two of these are limited to North American cities: the Bike Score index and the Bicycle-Friendly Community award. Only one index evaluates the bicycle-friendliness level of world cities: the Copenhagenize Index of bicycle-friendly cities (Vanoutrive, 2014). In 2011, the Copenhagenize Design Company, together with a technology consultant, developed the index and now run it every two years (Moonen and Clark, 2013). The index is basically calculated according to 13 parameters. These parameters are advocacy, bicycle culture, bicycle facilities, bicycle infrastructure, bike-sharing programmes, gender split, modal share for bicycles, modal share increase since 2006, perception of safety, politics, social acceptance, urban planning and traffic calming (Copenhagenize Design Company, 2016). To calculate the score for a city, a value between zero and four is awarded for each parameter, after which up to 12 bonus points can be awarded. Thus each city will be

assigned a score to a maximum of 64 points. Table 1 presents the 20 highest-scoring BFCs across the world according to this index.

Identifying the urban attributes of BFCs

Here the research focuses on the main urban attributes of BFCs. It examines the key requirements for selecting cities to be developed into bicycle-friendly ones. Such attributes can help decision-makers to identify the existence of genuine cycling potential in their cities. As a result, the development of policies and strategies can be determined according to the status of these attributes.

Analysis methodology

Statistical analysis was used to identify the urban attributes required of a BFC. Through the application of principal component analysis, a proposed set of urban attributes was examined for the selected case studies. The analysis extracted those variables that showed closest correlation with cycling.

Case studies

The top 20 BFCs around the world according to the 2015 Copenhagenize Index list were used as case studies. These cities have a worldwide reputation for adopting bicycles as one of their main means of transport.


A group of 12 variables has been selected. The variables are related to one or more of the passive group of characteristics of BFCs previously introduced. A brief description of each of these variables follows:

1. City Area. The gross area of the city as declared by the local authorities. The area is measured in square kilometres. Area is considered to be a clear indicator of average trip length. The smaller the city area, the shorter the length of trips.

2. City Population. The number of people who are living in the city according to the national census. It is measured in thousands of inhabitants and indicates the daily traffic volume in the city.

3. City Population Density. This is calculated by dividing city population by city gross area. It is measured in persons per square kilometre and indicates the crowdedness of city dwellers in the urban fabric.

4. City Form. This is a variable that indicates the compactness of a city's urban form. Compact urban form results in relatively shorter trip lengths. It is a ratio that is calculated by dividing the perimeter of a city by its gross area. The higher the ratio, the less compacted the city is and the longer the trip length. Fig. 2 illustrates different geometric forms with different perimeters for the same area.

Table 1

Top 20 BFCs according to Copenhagenize Index.

Rank 2011 2013 2015

1 Amsterdam* Amsterdam Copenhagen

2 Copenhagen Copenhagen Amsterdam

3 Barcelona Utrecht Utrecht

4 Tokyo Seville Strasbourg

5 Berlin Bordeaux Eindhoven

6 Munich Nantes Malmö

7 Paris Antwerp Nantes

8 Montréal Eindhoven Bordeaux

9 Dublin Malmö Antwerp

10 Budapest Berlin Seville

11 Hamburg Dublin Barcelona

12 Guadalajara Tokyo Berlin

13 Portland Munich Ljubljana

14 Stockholm Montréal Buenos Aires

15 Helsinki Nagoya Dublin

16 London Rio De Janeiro Vienna

17 San Francisco Barcelona Paris

18 Rio De Janeiro Budapest Minneapolis

19 Vienna Paris Hamburg

20 New York Hamburg Montréal

* Cities in bold have appeared in list on three successive occasions.

Fig. 2. Sketches of different city form ratios for the same area.

5. City Sectors. This variable indicates the existence of natural separators of the urban form such as rivers and water canals. These natural elements divide the city's urban fabric into smaller regions. As a result, accessibility within the city is affected and so too, indirectly, is trip length. Measured by a simple count, this variable specifies the number of divisions in the city. Fig. 3 presents an example of the analysis of natural dividers in one BFC.

6. Land Use Geography. This variable addresses the distribution of land uses in the city fabric. It indicates to what extent a mixture of land uses exists within the city. By using the land-use maps of the case study cities, this variable examines the existence of mixed land uses (mainly residential and commercial) within a catchment area of five kilometres' radius. This distance is considered as the average suited to cycling in urban areas (Blair, 2002; Lindsay et al., 2011). Fig. 4 presents an example of the analysis of land use geography for one BFC.

7. Road Network Length. This is the total length of the network of main roads in the city. It is measured in kilometres from maps of the case study cities. This variable indicates the potential infrastructure that may accommodate any future cycling transport.

8. Motorized Transport Modal Split. This is a ratio between the public and private modes of transport in the city. It provides an indication of the urban mobility culture of a city's residents. The higher the ratio, the more dependence on public transport exists.

9. Motorization Rate. This is the number of cars per 1,000 inhabitants. It indicates the level of car ownership in the city and, as a result, the crowdedness of city roads by private motor vehicles for urban mobility.

10. Terrain Slope. This is the average slope of the city terrain. It is calculated in percentage terms through measurement of elevation profiles in Google Earth software (Pro version). It generally indicates the slope of potential and existing cycle routes in the city.

Fig. 3. An analysis of natural dividers in the city of Nantes, France.

Fig. 4. An analysis of the geography of land use in the city of Malmo, Sweden.

11. Annual Temperature. This is the average annual temperature of a city. It is calculated in degrees Celsius from the maximum and minimum annual temperature records of each city.

12. Yearly Precipitation. This is the average precipitation level across the year for a city and is measured in millimetres.

Table 2 presents the list of the coded variables and associated BFC characteristics, and Table 3 presents the descriptive statistics of the variables.


The factor analysis was applied on the 12 selected variables for the 20 case study cities. By using principal component analysis as an extraction method, it took only one round to derive the final set of effective variables. In this round, the

Table 2

List of variables and related characteristics.

Code Variable

High-density urban development

Diversified land-use planning

Safe & comfortable transport Geo-environmental network characteristics

V01 City Area

V02 City Population

V03 City Population Density

V04 City Form

V05 City Sectors

V06 Land Use Geography

V07 Road Network Length

V08 Motorized Transport

Modal Split

V09 Motorization Rate

V10 Terrain Slope

V11 Annual Temperature

V12 Yearly Precipitation

Table 3

Descriptive statistics of variables.

Var. Measurement unit Min. Max. Mean Std. deviation Rel. std. deviation

V01 km2 49.4 891.6 218.6 227.8 104.2

V02 Inhabitants (000s) 223.2 3292.4 1051.6 969.9 92.2

V03 Person/km2 1702.0 21153.0 5854.7 5277.8 90.1

V04 Ratio 0.2 0.8 0.5 0.2 36.9

V05 Count 1.0 65.0 9.2 13.6 148.9

V06 % 75.0 100.0 93.8 7.7 8.2

V07 km 165.0 717.0 325.3 192.3 59.1

V08 Ratio 0.1 1.9 0.5 0.4 82.0

V09 Cars/1000 inhabitants 226.0 719.0 399.7 114.4 28.6

V10 % 0.1 4.2 0.7 1.0 142.5

V11 °C 7.0 18.5 11.2 3.1 27.9

V12 mm 523.0 1368.0 780.5 212.4 27.2

analysis extracted only four components with initial eigenvalues greater than 1.0. The percentages of explained variance for these components were 31%, 21%, 14% and 11%, respectively. The first component's power is greatest and the difference between it and the second one is also the largest between the four components. Thus, component one was used to filter the variables according to their loadings. The selected cut-off threshold was 0.5, which satisfies most of the cut-off criteria (Matsunaga, 2010; Yong and Pearce, 2013). As a result, five variables were extracted, and Table 4 presents the classification of the variables after the first round of analysis.

For verification, a second run was applied after eliminating variables with weak loadings. This time, only one component with initial eigenvalue greater than 1.0 was extracted. It is a very strong component as it alone accounts for 66% of the variance. In addition, there were no further weak variables to be eliminated. The results of both tests of the analysis sample adequacy - which were the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's Test of Sphericity (Field, 2005) - produced better scores in comparison to the initial round. Table 5 presents the evolution of the main results across the two rounds of factor analysis.

It should be noted that the extracted values of communalities for the five variables are high and this indicates that they are well positioned in the common factor space.


According to the results of the factor analysis, only five variables are strongly correlated with cycling rates in cities. Together, these variables account for 66% of the readiness of cities for cycling as a primary mode of transport. The resultant component structure could be used as an index of cycling readiness. Table 6 presents these five variables sorted according to their loadings and communalities.

A brief discussion of each variable follows:

• City Population was the most influential variable that was positively correlated. It achieved the highest score in the component loadings. It could be used to indicate the volume of passengers in the city. In the original sample, city population values ranged between 0.2 and 3.2 million inhabitants. Fig. 5 presents the scatter diagram for the City Population variable with the factor score of readiness index. The R2 value, which was 0.87, scored the highest among all extracted variables.

• Road Network Length was the second influential variable that was also positively correlated. It expresses the total length of the main roads in the city, which indicates the potential space to establish cycle routes as well as the level of accessibility in the city. Thus, the longer the main roads are, the better the opportunities to setup cycle routes. Fig. 5 presents the scatter diagram for the Road Network Length variable with the factor score for readiness index. The R2 value of 0.8 was the second highest among all of the extracted variables.

Table 4

Classification of variables after the first round of factor analysis.

Excluded variables

Included variables

V01 V02 V04 V07 V08

City Area City Population City Form

Road Network Length Motorized Transport Modal Split

V03 V05 V06 V09 V10 V11 V12

City Population Density City Sectors Land Use Geography Motorization Rate Terrain Slope Annual Temperature Yearly Precipitation

Table 5

The evolution of factor analysis results across the two rounds.

Item Run 1 Run 2

No. of variables excluded 7 0

% of variance explained by first two components 31 66

KMO measure of sampling adequacy 0.5 0.7

Bartlett's Test of Sphericity 131.2 54.4

Table 6

Variables of city readiness for cycling.

Code Name Component 1 loadings Communalities

V02 City Population 0.926 0.858

V07 Road Network Length 0.894 0.799

V04 City Form -0.819 0.671

V01 City Area 0.799 0.639

V08 Motorized Transport Modal Split 0.586 0.343

• City Form was an important variable that had a high loading in the principal extracted component. The City Form ratio indicates the compactness of the city form. As compacted urban forms encompass shorter trip lengths than other urban forms, the ratio value decrease for a compacted urban form confers a negative correlation on the loading for this variable. Fig. 5 presents the scatter diagram for the City Form variable with the factor score of readiness index. The R2 value, which was 0.78, scored the third highest among all extracted variables.

• City Area was also an important variable. It ranked fourth in correlation strength. It indicates the average length of trips in the city, which is one of the main factors in cycling in cities. In the original sample, city area values ranged between 50 and 890 square kilometres approximately. Fig. 5 presents the scatter diagram for the City Area variable with the factor score of readiness index. The R2 value of 0.71 was the fourth highest among all extracted variables.

• Motorized Transport Modal Split was the last correlated variable. There was a relatively wide gap between its loading and that of its predecessor. This variable indicates the level of public transport use compared to private. It is suggested that cities with high public transport modal splits have better opportunities to adopt cycling as a principal mode of transport. Fig. 5 presents the scatter diagram for the Motorized Transport Modal Split variable with the factor score of readiness index. The R2 value, which was 0.36, was the lowest among the extracted variables.

This suggests that city population is the most important indicator of a city's readiness for cycling, followed by the length of the main road network. Urban form and city area come next followed, lastly, by the motorized transport modal split.

It should be noted that other important variables were excluded by factor analysis, such as Land Use Geography (V06), Yearly Precipitation (V12) and Annual Temperature (V11). The first of these is an indication of the diversity of land use around users, and the others are indicative of climate conditions. In reviewing the descriptive statistics table (Table 3), these variables appear to exhibit the lowest values of relative standard deviation. Thus the variance within these variables is limited and cannot be extracted in the first component. This limitation in variance suggests that the top 20 BFCs have similar conditions in relation to these characteristics. As a result, these three attributes almost certainly have a role to play in evaluating the readiness of cities for cycling, despite their exclusion through the statistical analysis.

Evaluating the readiness of Egyptian cities for cycling

This element of the research analyzed the readiness of Egyptian cities for utilitarian cycling. Using the statistical analysis of the attributes of the top 20 BFCs, the research investigated the potential of cycling in Egyptian cities according to the status of urban attributes derived from the previous analysis.


At the outset, the analysis was intended to be based on five variables - City Population, City Area, City Form, Road Network Length and Motorized Transport Modal Split - because these were the best-correlated with the adoption of cycling in cities. However, because of a lack of requisite data, the analysis was applied on just four variables: there are no data in Egypt about transport modal split other than for the Greater Cairo region and this is no more recent than the year 2000. In addition, three other variables were considered in order to provide extra analysis of the case studies. These variables were Terrain Slope, Annual Temperature and Yearly Precipitation. Despite these three variables having been excluded in the analysis of the top 20 BFCs, they do have a direct effect on bicycle use.

Fig. 5. Scatter diagrams of the five excluded variables with factor score of readiness index.

Case studies

A set of 42 cities was selected. The selection criterion was based on city population (a lower threshold of 100,000 inhabitants). These cities are located in 24 of Egypt's 27 administrative governorates.

Analysis methodology

The statistical analysis was applied at two levels. Firstly, a descriptive analysis was applied to the three extra variables in relation to the cities, and then compared with the results of the analysis of the world's top 20 BFCs. Table 7 presents the results of this comparison.

According to the comparison, both the average terrain slope and the yearly precipitation in the selected Egyptian cities are below that of most of the world's top 20 BFCs. However, the average annual temperature of the selected cities in Egypt is higher than that of the 20 BFCs although still below the maximum optimal temperature for cycling of 28 °C (Ahmed et al., 2010). Thus these three natural characteristics of Egyptian cities are suited to cycling.

For the second level, factor analysis was used again, but this time to rank the Egyptian cities according to their readiness for cycling. This was done by calculating a numerical readiness index for each city according to the proposed index structure of the four remaining principal variables.


The results of the first run of factor analysis demonstrated the high level of adequacy of the sample. A KMO measure and Bartlett's Test produced scores of 0.77 and 150.66 respectively. Only one component with initial eigenvalue greater than 1.0 was extracted. This component was a substantial one as it alone accounted for 77.6% of the variance in the selected set. According to this component, the four variables were strongly correlated and there were no excluded variables. A second run of the analysis was then applied in order to calculate the factor score of each case study city according to the variables structure of the extracted component. Table 8 presents the ranking of the selected Egyptian cities according to their readiness to adopt cycling as a primary mode of transport.


According to the factor scores of city readiness to adopt cycling, the Egyptian cities vary widely in their readiness. Scores ranged between 436 (the maximum) and -99 (the minimum). Three distinct sets of cities could be identified. Set 1 incorporated those 11 cities that achieved positive scores. Set 2 covers the two cities that scored zero, which could therefore be considered as neutral for cycling adoption. One of them, Mansoura, is located in the Nile Delta, and the other one, Asyut, is located in Upper Egypt. By contrast, Set 3 incorporates the 29 cities that achieved negative scores. The 11 cities of Set 1, which have higher levels of cycling readiness, are Cairo, Alexandria, 6th October, New Cairo, 10th of Ramadan, Giza, Suez, Shubra el-Kheima, Ismailia, Port Said and Hurghada. Fig. 6 illustrates the geographical distribution of these cities.

Nine of these 11 cities are located in a triangle-shaped area that lies between the Suez Canal and the Greater Cairo region. The other two cities are located on the coast. With the exceptions of Giza and Shubra el-Kheima, all of the cities are located in urban governorates. It is notable that the readiness scores of these 11 cities vary widely, with the top-ranked city (Cairo) achieving a score of 436 and the eleventh-ranked city (Hurghada) achieving a score of just 3. Fig. 7 presents scatter diagrams illustrating the relationships between the readiness index and the four key variables. On the basis of these diagrams, the 11 cities could be classified into three sub-groups: Group 1 includes Cairo alone; Group 2 includes Alexandria, 6th October, New Cairo, 10th of Ramadan and Giza; Group 3 includes Suez, Shubra el-Kheima, Ismailia, Port Said and Hurghada. It seems likely that converting these cities to BFCs may require different approaches, frameworks and plans to adapt to the circumstances of each city group or individual city.

On the other hand, the 29 cities that have lower levels of cycling readiness are mainly scattered across the Delta and Upper Egypt governorates. All of these cities are located in rural governorates. In contrast to the first set, these cities achieved relatively convergent scores. Many cities had very close and sometimes identical factor scores, such as Benha, Girga, Luxor, Shibin el-Kom and Minya, which achieved scores of -53, -54, -55, -56 and -56, respectively. As a result, it is hard to classify them further into sub-categories in the manner of the first set. Fig. 8 presents scatter diagrams for the relationships between the readiness index and the variables.

Table 7

Comparison of natural characteristics between Egyptian cities and the top 20 BFCs.

Natural characteristic Terrain slope Annual temperature Yearly precipitation

Min. Max. Mean Min. Max. Mean Min. Max. Mean

Egyptian cities 0.1 2.7 0.8 20.0 27.0 21.3 0.0 183.0 42.8

World's top 20 BFCs 0.1 4.2 0.7 7.0 18.5 11.2 523.0 1368.0 780.5

Table 8

Ranking of selected Egyptian cities according to the Cycling Readiness Index.

Rank City Factor score Rank City Factor score

1 Cairo 436 22 Damietta -27

2 Alexandria 207 23 Zagazig -29

3 6th October 193 24 Damanhour -30

4 New Cairo 158 25 Khusus -36

5 10th of Ramadan 140 26 Fayoum -39

6 Giza 123 27 Bilbeis -50

7 Suez 25 28 Benha -53

8 Shubra el-Kheima 20 29 Girga -54

9 Ismailia 19 30 Luxor -55

10 Port Said 18 31 Shibin el-Kom -56

11 Hurghada 3 32 Minya -56

12 Asyut 0 33 Kafr el-Sheikh -58

13 Mansoura 0 34 Kafr ad-Dawwar -59

14 Mersa Matruh -7 35 Desouk -61

15 Qena -12 36 Abu Kabir -62

16 Mahalla el-Kubra -12 37 Mallawi -70

17 Arish -13 38 Mit Ghamr -73

18 Beni Suef -22 39 Hawamidiyah -76

19 Aswan -22 40 Qalyub -77

20 Sohag -23 41 Akhmim -85

21 Tanta -26 42 Matareya -99

Fig. 6. Locations of the positively scoring Egyptian cities (Set 1).

Cycling Readiness

Fig. 7. Scatter diagrams of the four key variables with the factor score of readiness index for the positively scoring cities (Set


In response to major environmental, socioeconomic and transport challenges in contemporary cities, the world has recognized the importance of reviving non-motorized transport means and, in particular, cycling. A sustainable transport framework that is mainly dependent on non-motorized means is now one of the principal goals of urban development programmes in many countries of the developed world. Utilitarian cycling offers many important benefits at the levels of urban mobility, environment, health and social life. Replacing motor vehicles with bicycles in daily urban mobility positively affects both the individual user and the community. The bicycle-friendly city, or BFC, is a well-established worldwide term that describes the existence of suitable infrastructure, transportation policies and societal awareness to prioritize and encourage the use of bicycles instead of other, motorized means. According to the Copenhagenize Index, the majority of BFCs are in Europe. Statistical analysis of the urban and environmental attributes of BFCs identifies a set of five primary indicators of city readiness to adopt cycling. These indicators are city population, road network length, city form, city area and motorized transport modal split. All of these indicators are positively correlated with cycling with the exception of city form, which is negatively correlated as linear urban forms result in longer trip distances that are harder to cycle. These five indicators together form an index of city readiness for cycling. When this index is applied to Egyptian cities they can be classified into three main sets. The first is cities with higher levels of readiness. Most of these cluster in a triangle-shaped region extending from the Suez Canal (base) to Greater Cairo (head) within a group of urban governorates. The second set consists of cities

Fig. 8. Scatter diagrams of the four key variables with the factor score of readiness index for the negatively scoring cities (Set 3).

with neutral cycling readiness. The third set, the largest one, represents cities with lower levels of readiness. Such cities are mainly scattered throughout the Nile Delta and Upper Egypt, and lie within rural governorates. Future research work in this field should spotlight the potential approaches, frameworks and plans for developing cities into BFCs according to their level of readiness.


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