Workers mode choice in the Netherlands:
The decision to cycle to work and the effect of work-related aspects
Eva Heinen e.heinen@tudelft.nl
Delft University of Technology
OTB Research Institute for Housing, Urban and Mobility Studies The Netherlands
Kees Maat c.maat@tudelft.nl
Delft University of Technology
OTB Research Institute for Housing, Urban and Mobility Studies and Faculty of Technology, Policy and Management
The Netherlands Bert van Wee
g.p.vanwee@tudelft.nl
Delft University of Technology
Faculty of Technology, Policy and Management The Netherlands
1. Introduction
Commuting is an important aspect of travel behavior for the society, because it contributes to economic prosperity. In the Netherlands, it covers about 30 per cent of the number of trips, which is more or less comparable with other western countries. Nevertheless, as commuting is mandatory and fixed in time and place for most employed people, commuting is concentrated and consequently sensitive for traffic congestion and a cause of environmental pollution. On shorter distances bicycle use would be a good alternative, and may contribute to reduce these negative aspects and may even help to improve health and to combat obesity. With this perspective in mind, governments in many countries encourage bicycle commuting. For example, recent changes in the US tax law (Bicycle Commuter Benefit Act, 2009), make it possible for employers to reimburse their workers who cycle to work for certain bicycle-related expenses. In some cities, such as Davis and Portland, a system of cycle tracks clearly changed the modal split for commuting in favor of the bicycle. In the Netherlands a tradition and positive attitude towards cycling, as well as good bicycle facilities, have led to the highest bicycle rate in the world. In addition, the national government encourages further bicycle commuting by providing tax benefits and enhanced facilities such as ‘bicycle highways’ (speech State Secretary Huizinga, 2007).
Much research has been conducted into commuting, in particular into the role of the car (e.g. Cervero, 2002; Dargay and Hanly, 2007; Susilo and Maat, 2007). However, bicycle commuting has so far got only limited attention. Available research found that the weather conditions and climate, socio-economic aspects, travel distance and attitudes towards cycling explain individual’s bicycle mode choice (e.g. Gatersleben and Appleton, 2007; Bergström and Magnussen, 2003; Dickinson et al., 2003; Parkin et al., 2008; Stinson and Bhat, 2004). In studying cycling to work, relatively limited academic research has been conducted into the effect of work-related factors on bicycle commuting. This is remarkable because it seems evident that work-related aspects may affect the choice of commuting transport mode.
To fill this gap, this paper addresses the question to what extent work-related aspects determine the choice to cycle to work. For this, we design a comprehensive model of bicycle commuting. We assume that bicycle commuting is not only determined by factors, such as the built environment, available infrastructure, socio-demographics and travel distance, but also that attitudes and expectations – not only of the cyclists themselves, but also by his or her social environment such as the employer – have their impacts on the decision to cycle. On the one hand, the employee may have a certain attitude to cycle for work, due to negative experienced aspects, such as the risk of sweating, the type of clothing worn or the risk of
getting wet due to the rain, or an unattractive built environment; or positive aspects, such as the opportunity to refresh from a day in the office. Also, the employee may not be able to cycle, because a car is needed during work hours or goods need to be carried. On the other hand, the decision is influenced by expectations and attitudes of the employer and co-workers. This includes norms at work, for example, the appearance to clients requires a suit and representative car. The culture on travelling also appears from the financial support for transport and the facilities at work (bicycle storage, showers) the employer provides – or conversely, a car-friendly policy which reduces the relative position of the bicycle.
Another aspect that needs more attention is the definition of bicycle commuter. Most travel surveys ask which mode is usually taken to work, or which mode has been taken today, so it is implicitly assumed that commuters always travel by the same mode. Although this is not true in general, it is even less true in the case of cyclists, who are much more dependent from a variety of aspects, such as the weather, carrying goods and so on. Therefore, we analyze the influence of work-related aspects for both full-time and part-time bicycle commuters. A full-time bicycle commuter is defined as someone who cycles every working day to work, while a part-time commuter cycles at least once a year to work. Finally, we limit the analyses to commuters who cycle the full trip from home to work; Commuters who use the bicycle for just a part of the journey, for example as access transport to the railway station, are not considered.
Research on work-related factors in bicycle commuting is aimed at contributing to policies for governments and employers to encourage bicycle commuting. Employers could adapt the incentives they provide for specific modes of commuting and benefit from healthier employees, the reduced need for parking places and lower commuting costs. Governments could develop targeted policies to help employers with adapting towards a more bicycle friendly insensitive and facilities, and develop policies to stimulate the commuter to transfer to cycling.
This paper is structured as follows. In the next section, a short literature review is provided, followed by a conceptual model. Then the data collection and research design are described. The last two sections present the research results and conclusions.
2. Literature review
Many factors have been assumed and found to influence bicycling and bicycle commuting. Some aspects like distance and weather are clearly related to bicycling and indeed always found to be significant (for example: Nankervis, 1999; Bergström and Magnussen, 2003;
Dickinson et al., 2003; Parkin et al., 2008). Other aspects as density, amount of bicycle pats and slope affects cycling as well (see for example Pucher and Buehler, 2006; Rodriquez and Joo, 2004; Rietveld and Daniel, 2004). As this paper aims at determining the influence of work-related aspects in general and specifically the softer aspects, this review will focus on these aspects. We will discuss facilities at work, attitudes, social norms and socio-economic aspects briefly. For a full literature overview see Heinen et al. (forthcoming).
2.1 Facilities at work
Cycling facilities at work include secure parking, the presence of showers and changing possibilities. These facilities reflect the attitude, preferences and expectations of the employer towards the commute mode choice. The effect of these facilities on bicycle behavior and the preferences of cyclists have been limited researched. Hunt and Abraham (2007) conducted a stated preference experiment among 1128 cyclists. All cyclists was asked to choose the preferred alternative, which differed in bicycle infrastructure facilities and more importantly facilities at work. They found that both the availability of secure parking and showers at the destination are important. Parking is more important than showers. Also for the youngest age groups (and particularly 16 year-olds) secure parking is more important than for other age groups. Interestingly, the level of experience does not influence the preferences: no effect was found on showers. As this research is a stated preference experiment, they real effect on bicycle mode choice is uncertain, but a similar effect could be expected. Abraham et al. (2002) investigated also with a stated preference survey the preferences of showers, change rooms, clothes lockers, individual bike lockers, bicycle enclosure and a standard bike rack. As expected the presence of all of these aspects enlarge the attractiveness of cycling. Storage is the most important facility and of all storage facilities an individual bike locker is preferred most, followed by bicycle enclosure. For commuting the facilities at the end of the trip were more important compared to other cycling purposes. Stinson and Bhat (2004) focused on commuting frequency specifically. They did not find an effect of the availability of showers or clothing lockers on the decision of bicycle commuters to cycle more frequently. Geus (2007) did, however, find cyclists to have more facilities at their work place compared to non-cyclists.
2.2 Attitudes and norms
The attitudes and opinions of employers are not only reflected in the availability of facilities, but individual attitudes and social norms towards cycling and its’ effect on bicycle use can
also be measured directly. Dill and Voros (2006), for example, investigated (among other things) the relationship between the perception of the environment, attitude and levels of cycling. Their results indicate a significant influence of attitude on cycling. Individuals with a more positive attitude towards cycling have a higher probability to be both regular and utilitarian cyclists. Additionally, they found that people who often see cyclists on the streets do more often want to cycle more than they do. The importance of the surrounding is also found by Geus (2007), who has researched the psychosocial factors associated with cycling. Cyclists compared to non-cyclists state that they get more support from their social environment to cycle. Moreover, they have more often a person to cycle with and perceive a higher social norm towards cycling, which means that the important individuals in his or her surroundings think more positive towards cycling. Other research shows a contrast in attitudes in different stages of change. Gaterslaben and Appleton (2007) investigated the differences between 5 stages of change of bicycle behavior and the reason in favor and against cycling. They found that in general individuals who already cycle for a long time, have just started or are prepared to cycle have a higher attitude towards cycling than individuals who do not consider cycling or have just started to consider cycling. Specifically, cyclists do in general like cycling, think it is healthy and environmental friendly. Gaterslaben and Appleton (2007) followed some of the respondents in an action study, in which the participants actually cycled to work. The impressions of the ‘new cyclists’ changed negatively on fitness improvements, having fun, being outside and inconvenience. However, their opinion about cycling improved on the aspect ‘flexible form of transport’ and ‘traffic safety’. Not only if a person changes his or her considerations about bicycle commuting this results in an attitudinal change, also other changes affect bicycle use. Bamberg et al. (2003), for example, conducted a longitudinal study into the effect of the introduction of a free public transport card on attitudes and mode choice. They found that the introduction effected the attitude and social norm towards bus use and increased the use of public transport. Although the introduction did not affect the attitude towards bicycling significantly, but the intention to use the bicycle decreased. A similar increase occurred in The Netherlands, where after the introduction of a free public transport card for students, cycling rates dropped (Rietveld, 2000).
2.3 Characteristics
The relation between cycling and socio-economic characteristics is often presented. The findings differ from research to research and the research location seems very relevant. Gerrard et al. (2008) conclude that gender, for example, is country dependent: in countries
with a low cycling rate mostly men cycle, whereas in countries with a high cycling rate, as in the Netherlands and Belgium, cycling is popular among women as well. The research findings of the effect of income and age are ambiguous. For example, Witlox & Tindemans (2004), Plaut (2005) and Guo et al., (2007) find an increase in income to have a negative effect on cycling. However, Parkin et al. (2008) conclude that the absence of high incomes in England and Wales is connected with a lower bicycle share for commuting. A reason for the uncertain influence of income could be two contrary trends. A higher income enables spending money on a bicycle which increases bicycle use, especially in countries where bicycle ownership is not obvious. However, a higher income also results in the availability of spending more money on transportation, and buying a car and car ownership decreases bicycle mode share (Witlox and Tindemans, 2004).
3. Research design 3.1 Conceptual model
As the commonly excepted influences on bicycle use, for example distance, from the literature review can be derived that bicycle facilities at work, personal attitudes and the expectations and attitudes of employers could effect bicycle commuting. From the literature review can also be concluded that socio-economic aspects are found to relate to cycling.
We expect that cycling is not only determined by ‘hard’ factors, but also the social environment and personal attitude impact on the decision to cycle. These social environment could be considered to be reflected in the provision of bicycle (un)friendly facilities or incentives.
Remarkably, only limited attention has been given to this social and work factors influencing cycling. It is plausible to assume that these aspects influence the decision to cycle to work, because the expectations and attitudes of the employer and co-workers affect not only the direct social norm at work, but also the office location, the culture on travelling, the office hours and thereby the hours during which is travelled, the financial support for transport and the facilities at work.
Therefore we assume that the work-related aspects together with socio-economic aspects and other aspects influence the individual decision to be a commuter cyclist (see figure 1). At a second analyzing level, a commuter cyclists can either cycle all days or irregularly. Thus the group of cyclists can be divided into two groups: part-time (pt) and full-time (ft) cyclists. So full-full-time cyclists are defined as workers, who cycle to work every working day, whereas part-time cyclists do cycle to work, but also use other means of
transport to commute. Therefore, part-time cyclists make a daily choice to cycle or not to cycle to work. In this study we expect an effect of work-related aspects together with the socio-economic aspects on both the division of being a cyclists or not and on being a pt-cyclist or ft-pt-cyclists.
Figure 1: Conceptual model
3.2 Survey location
This study was carried out in four municipalities in the Netherlands: the medium-sized towns of Delft (approximately 100,000 inhabitants) and Zwolle (approximately 115,000 inhabitants), and two more rural municipalities adjacent to Delft: Midden-Delfland (17,000 inhabitants) and Pijnacker-Nootdorp (38,000 inhabitants). These study areas were selected, based on three arguments. First, the municipalities were chosen for their high (commute) cycling percentage. Due to those high percentage the probability of a large number of cyclists as well as non-cyclists is high. Both Delft or Zwolle have high cycling rates, also for Dutch standards, 28.2% and 32.6% of all trips ending in Delft or Zwolle respectively. For commuting, the share of bicycle use is 22.1% and 27.5%, respectively, for all trips ending in those towns1. The second reason was the potential availability of possible interesting employers. This is defined as the presence of employers with over 100 employees in different branches within the city. Both Delft and Zwolle have large educational organizations, local and non-local governments, public services, industry and other business. The third criteria
1
was the university’s reputation in the cities. Particularly in the city of Delft the positive reputation of the university is an advantage, whereas in other university towns this could even be a disadvantage.
In all the selected municipalities separated bicycle infrastructure facilities are widely provided. Car ownership is 39 inhabitants per 100 cars in Delft, whereas in Zwolle this number is 49 and 57-58 in both Pijnacker-Nootdorp and Midden-Delfland.
3.3 Survey
In April/May 2008 we conducted an internet survey among (1) employees of several large employers (over 100 employees) in Delft and Zwolle, including Delft University of Technology, the Reinier de Graaf Hospital, housing authorities and a receivables management company, and among (2) the inhabitants of the municipalities mentioned above. Using local authority address data, a total of 22,000 letters were randomly sent to inhabitants within the working age of these municipalities as potential respondents, inviting them to participate (10,000 in Delft, 6,000 in Zwolle, 3,000 in Pijnacker-Nootdorp and Midden-Delfland). A reminder if no response was given, was sent a month later. To the companies’ employees invitation e-mails were sent (just under 3,500). The exact number of e-mails sent is uncertain, because the e-mails were sometimes distributed using internal mailing lists of the companies. These letters and e-mails asked the respondents to log into an internet questionnaire. A chance of in total 40 lottery tickets worth 12 Euros were offered as an incentive.
The questionnaire was presented as a survey on the commute mode of transport choice of the respondent for travelling to and from work, and the reasons behind this choice. The specific aim - to find out more about bicycle use for commuting - was kept unknown to the respondents in order to avoid a bias towards cyclists or people with a favorable opinion towards cycling. The questionnaire was placed on the internet and could be accessed using a special link. In total 4299 valid responses were collected. 2929 inhabitants fully cooperated of the 22000 send out letters, which results in a 13.3% response rate. 1370 employees responded of the approximately 3500 sent out requests, resulting in 39% response rate. The total response rate is 16.9 percent.
3.4 Representativeness survey
55.3% of the respondents are male and 44.7% female. The mean age of the respondents is 43. 53.9 percent of the respondents is high educated, 31.3 percent medium, and 14.8 percent is low educated. The educational level is above the national average.
Compared to the Dutch travel data (Ministerie van Verkeer en Waterstaat, 2007) from the Netherlands, we see some differences, but in general they show a similar image (Table 1). The main difference is that within the group of respondents stating that they do not use on particular mode to commute has a higher share of cyclists. Since there is no indication of ht percent of trips the respondents in this group use the bicycle instead of one of their other options, this number could still correspond with the Dutch average data.
Table 1: Modes choice in survey compared to Dutch travel data (MON 2007)
own data Dutch national data
always same
different
transport all trips
Frequency Valid Percent Frequency Valid Percent
main mode of transport
only car 1294 50,2 1031 61,7 58
only bicycle 648 25,1 1017 59,1 26
only public transport 133 5,2 447 26
only walking 61 2,4 113 6,6 4
combination pt and bicycle 161 6,2 231 13,4
combination car and bicycle 145 5,6 248 14,4
combination car and pt 42 1,6 93 5,4 13
other 95 3,7 261 15,2
Total 2579 100,0 1720
Missing 1720 2579
Within the dataset some differences exist between the collection methods (inhabitants or employees). Employees are compared to inhabitants more likely to be living in Delft, having a middle income, being older, being lower educated, having one or multiple cars in their household, having irregular working hours, at work having showers, changing facilities, inside bicycle storage and a highway within 1 kilometer. There is also a smaller change for employees to be working in some functions (2;4;5;7), sectors (2;5) and having a steady or temporary contract. Because of these differences we included a dummy variable each collection method, by which we see the influence on the results. We also expect some differences between municipalities. Therefore we also included a dummy variable for the municipality.
3.5 Variables
Table 2 provides an overview of the variables in the analyses. The selection of variables is to a large extent derived from the existing literature. In the scientific literature (see chapter 2) a relationship with cycling is found or discussed for gender, income, education level, ethnicity,
household structure, age, attitude, opinion of colleagues, vehicle availability, distance, drivers license bicycle facilities at work such as showers and storage availability. Dutch research and policy documents offer additional knowledge about variables that possible could stimulate cycling: limited car parking space, not allowing trips for work be made by car, a cycling compensation and no necessity to transport goods (Fietsberaad, 2005). The reasoning is as follows, in case of difficult or expensive car parking commuters are discourage in car use and may change their modes in favor of the bicycle. A similar financial stimulus is present if the employer does not refund car travel expenses, but does reimburse other travel expenses. Fietsberaad (2005) also claim that the transportation of goods needs to be discouraged or bundled on certain days. They reason that cycling while transportation goods is rather difficult and sometimes even impossible. If this need is avoided at all or limited to a limited number of days this is expected to result in higher bicycle use. Finally, since offering a free bicycle or a contribution towards a bicycle is considered to have a positive effect on cycling, it is reasonable to believe that provision of a free car or public transport card has a negative effect on cycling rates. Evidence for this argument is found under students in Germany and The Netherlands. Both countries introduced a free transport card and cycling rates among students dropped dramatically (Rietveld, 2000; Bamberg and Schmidt, 1994).
Some included variables are not directly derived from policy documents or scientific literature: (a) the amount of working hours, (b) amount of working locations, (c) amount of living locations, (d) need of transport mode for work, (e) clothing style, (f) function, (g) sector, (h) contract type and (i) employment status. We believe those aspects to affect bicycle commute mode choice, because of the following reasons. (a) We expect an influence of the amount of working hours on cycling, because of three reasons. First, if a person works more hours, he or she is more likely to cycle in the darkness. As darkness affects cycling negatively (Stinson and Bhat, 2004; Gatersleben and Appleton, 2007), specifically for women (Bergström and Magnussen, 2003; Cervero and Duncan, 2003), we expect longer working hours to have a negative effect on cycling rates. Second, if someone works eight hours a day, that person might prefer to exercise after office hours and therefore cycling more. Another possible effect could be that persons with fewer working hours have other responsibilities as well and these obligations may influence their commute mode choice, either in a favorable or negative way. (b) Secondly, we expect that the amount of working locations influences the commute mode. First, there is a possibility that the second work location is at greater distance. Second, the necessity of extra transport between these locations is also conceivable. Both are expected to result in a negative effect on bicycle use. (c) We expect a similar influence of
multiple living locations. (d) Fourthly, the need of a specific transport mode during office hours is assumed to affect the commute mode choice. Dutch popular literature, that aim at increasing commute cycling rates, states that companies should not pay for work trips in a is car use. This indicates that the use of specific transport mode for work may result in a increased use of the same transport mode for commuting. Habitual behavior and convenience are most likely to be the cause. (e) Fifthly, the clothing style at work is likely to be of influence on the commute mode choice. The need to wear a suit combined with cycling demands either changing clothes upon arrival or cycling with such a speed that no perspiration occurs. Because both options have their disadvantages, we expect that people wearing casual clothing are more often cyclists. (f-h) Sixth, although to our knowledge not defined in the literature, we also included the sector, function type and employment status. We expect that the sector and function influences the social-work-environment and thereby the cycling rate. For example, banks and law firms are known for their negative attitude towards cycling resulting in low cycling rates, whereas educational institutions and governments have generally higher rates. We expect a similar distinction for functions. People in functions that require a more professional appearance, for example finance, law consulting or sales, could have a lower probability of being a cyclist. (i) Finally, we consider the employment status to be of influence. Being in a steady job results could in a higher chance of being a cyclists compared to people in a temporary job and to detached people. The reasoning behind this idea is, that the latter two are more likely to have a shorter working history at their working location and therefore living further away and having a less established commute habit. Moreover, as individuals want to impress in the beginning of their job, they tend to wear ‘proper clothing’ and look representative, which is more easily done they would travel by car.
The two dependent variables for the analyses are ‘wwfietser’ and ‘wwfietser2’. Both dependent variables are dichotom. Therefore, for both analyses, a binary logit model is estimated using STATA 10.0.
Table 2: Variables
variable
name range description description values
wwfietser (0-1) being a cyclist 0=no; 1=yes
wwfietser2 (0-1) type of cyclist 0=part time cyclist; 1=full time cyclist
attitudefi~d (14-350)
attitude towards cycling; measured by asking people opinion of several aspects of bicycle commuting (mentally relaxing, cheap, quick etc) multiplied by the importance of these aspects werktij_1 (0-1) daily similar working hours wetijdA_1 (0-1) working hours 06:30-12:00 wetijdB_1 (0-1) working hours 12:00-19:00 wetijdC_1 (0-1) working hours 19:00-24:00 wetijdD_1 (0-1) working hours 0:00-06:30
spul (1-3) need to transport stuff to work 1=always, 2=sometimes, 3=never wevv2A_1 (0-1) needing a lease car during office hours
wevv2B_2 (0-1) needing own car during office hours wevv2C_3 (0-1) needing company car during office hours wevv2D_4 (0-1) needing a motor during office hours wevv2E_5 (0-1) needing a scooter during office hours wevv2F_6 (0-1) needing a bicycle during office hours wevv2G_7 (0-1) needing public transport during office
hours
wevv2H_8 (0-1) needing a taxi during office hours wevv2I_9 (0-1) needing another type of transport during office hours
a_worklo (1-4) amount of working locations 4=4 or more vzweC_1 (0-1) facility at work: bicycle storage inside
vzweD_1 (0-1) facility at work: showers vzweE_1 (0-1) facility at work: changing facility vzweH_1 (0-1) facility at work: free car parking vzweI_1 (0-1) facility at work: public transport within
500m
vzweJ_1 (0-1) facility at work: facility at work: train station within 1 kilometer
vzweK_1 (0-1) facility at work: highway entrance within 1 kilometer
vzweO_1 (0-1) facility at work: bicycle storage outside shops_1 (0-1) facility at work: shops at 500 m or
supermarket at 1km from work biccontr~1 (0-1) bicycle contribution from work freecar_1 (0-1) free car from work
freebike_1 (0-1) free bicycle from work freept_1 (0-1) free public transport from work
function (1-7) type of function
1=management, 2=technical, 3=organization, 4=finance, law, sales, consulting; 5= research education, health, 6=police, army; 7=other
sector4_1 (1-13) type of organization
1=agriculture and fishing & mineral producing; 3=industry; 4=public utility companies; 5=building and construction; 6=trade, hotel and restaurant; 7=transport, storage and communication companies; 8=bank and insurance; 9=education and research; 10=health
worktype_1 (1-4) type of work 1=employed; 2= own company; 3=volunteer work; 4=mix
aanst2_1 (1-6) type of employment
1=steady contract; 2=temporary contract; 3=interim base or detached; 4=temporary working; 5=own company or freelance; 6=other
sncol2_2 (1-4) expressed expected opinion of colleagues
how you should cycle to work 1=car; 2=bicycle; 3=other; 4=does not matter rijbew_1 (0-1) having a drivers license
op_au2_1 (1-3) having a car available for commuting
gender_1 (0-1) gender 0=male; 1=female
educatio~1 (1-3) education level 1=low; 2=medium; 3=high
bikeown_1 (0-2) owning a bicycle 0=none; 1=one bicycle; 2= 2 or more motorown~1 (0-1) owning a motor
scootown~1 (0-1) owning a scooter
student_2 (1-3) being a student 1=full time; 2=part time; 3=no kleding2_1 (1-6) personal clothing style at work
1= FT casual clothing; 2=FT suit; 3=PT suit; 4=FT neat clothing; 5=FT special working cloths; 6=other or other combination agegroup~1 (1-4) age in groups 1=<30; 2= 30-45; 3=45-60; 4=60+ ethnic_1 (1-3) ethnicity 1=Dutch; 2=western European; 3=other hhsam2_1 (1-4) household composition 1=single; 2=only with partner; 3=with children
or other family; 4=student house income2_2 (1-3) personal net income in groups 1=<1500€; 2=1500-3000€; 3=+3000€ multliva~1 (0-1) multiple living locations
dataverz_1 (0-1) location of survey 0=inhabitants; 1=employees stad2_2 (1-8) city where survey is conducted
1=Delft; 2=Zwolle; 3=Pijnacker; 4=Nootdorp; 5=Delfgauw; 6=Den Hoorn; 7=Maasland; 8=Schipluiden
Some variables had missing data, either because the respondents had indicated they could not answer the question or because the respondents did not wish to answer the question. We replaced missing data on the variables: werktijA-D, Income, age, ethnicity, education and function. The replacements are made after analyzing possibly related variables, using logical sense, and ‘compute’, multiple regression analysis and multi nominal logit-model. Cases with missing data on distance were excluded in the analysis. Missing data in the variables werktijA-D (44 missing data) have been appointed to the most frequent category, which was considering the amount of working hours, function and sector also the most appropriate.
The analyses below were both tested with the raw data and with the data with the replaced missings. Compared to the raw data, with final analysis the directions of the variables stayed similar and had a negligible higher explaining power.
4. Results
Table 3 shows the outcome of two binary logit models: the effect of the work-related aspects and socio-economic aspect on being a commuter cyclists compared to a non-cyclist (left) and on being a ft-cyclist compared to a pt-cyclist (right). Only the significant aspects and reference category are presented. The first analysis is based on 4171 observations and has a McKelvey and Zovoina’s-R2 of 0.832. According to Scott Long and Freese (2006) the McKelvey-Zavoina pseudo R-square is of the measures of fit are available the best approach
for binary logit models to explain the proportion of variation.The second analysis, the division between ft- and pt-cyclists, has a McKelvey and Zovoina’s -R2 of 0.550 and included 1660 cases. The odds ratio indicates the relative chance of a particular outcome if a certain factor is present. For example, if the odds ratio is +0.5 for age, then with every increase in years the chance that that person is a cyclist increases as well.
4.1 Cycling or not cycling to work
Table 3 shows the results of the first analysis. The often mentioned factor distance is found to be influential, but also as assumed attitudes and other social-work-environmental aspects affect the decision to be a commuter cyclist.
We can see in table 3 that the commute distance and the attitude towards commuting by bicycle are very influential on the choice to commute by bicycle. With every extra kilometer the change of being a commuter cyclist declines dramatically. This result is as expected and reported in all literature. Also attitudes towards cycling play an important role. With a more positive attitude towards bicycle commuting the chance of being a commuter cyclist increases. As both distance and attitude are the only two ratio variables, based on the odds ratios we can conclude that the effect of both aspects is large.
Many work-related aspects are important on the choice to be a commuter cyclist. First, not only one’s own opinion, but the opinion of colleagues is important as well. If your colleagues expect you to commute by car or another mode than a car or bicycle, you are less likely to be a cyclist than if your colleagues expect you to cycle to work. Second, the opinion of the employer is reflected in the financial travel support. Similar to the results found among students (Rietveld, 2000; Bamberg and Schmidt, 1994), if the employer offers the employees a financial insensitive for a transport mode this largely influences the commute mode choice. The provision of a free car or public transport card decreases the change of bicycle commuting. A contribution towards cycling, however, increases bicycle use. Third, the need of transport mode during working hours affects the bicycle commute mode choice. People who need a private car for commuting are less likely to cycle, and people who need a bicycle are more likely to cycle to work. Convenience and the possibility to travel directly from home to the other location instead of going first to work are probably the reasons. Fourth, a number of facilities at work increase the chance of being a cyclist: Bicycle storage inside, changing facilities and public transport stop within 500m. The first two facilities are consistent with the current literature (Noland and Kunreuther, 1995; Dickinson et al., 2003; Stinson and Bhat, 2004; Hunt and Abraham, 2007; Geus, 2007). The effect of the public transport stop is
somewhat surprising, but it might be explained by that a combination of public transport and cycling is more common, than the combination of car and bicycle. Another reason could be the employer’s attitude towards sustainable transport. If the employer stimulates cycling or public transport use, moneywise or in words, the employer is likely to be positive about all sustainable transport. Fifth, the working hours are influential. Respondents with daily similar working hours are less likely to cycle. This is an unexpected result. An explanation might be that those individuals are more aware of all the travel options at a specific time. This knowledge reduces waiting time for public transport commuting and may reduce time in traffic jams, resulting cycling to become a less favorable option. People working at night (19:00-24:00) have a lower probability to be a commuter cyclist. This is consistent with the found aversion of darkness by Stinson and Bhat (2004) and Gatersleben and Appleton (2007). Sixth, the need to transport goods for work has a negative influence on being a cyclist. Both people who always and sometimes need to transport goods are less often cyclists than people who do not have this necessity during working hours. Clustering the need to transport goods on a few days as policy document suggest (Fietsberaad, 2005) corresponds with this finding and could offer a solution.
Next to the aspects that reflect the opinion of the employer and can be adjusted to influence the commute mode choice, there are also work-related items that influence the choice of being a bicycle commuter, but cannot easily be changed by the employer. First, workers with two working locations instead of one are less likely to cycle. Second, people with their own company are less often cyclists than employed people. Finally, people working in the industrial sector are more likely to cycle than people working at public utility companies.
The results also indicate an effect of the personal aspects. Native Dutch people cycle more than foreigners. Additionally, the availability of transport modes is influential on the choice to cycle to work. If a household owns a bicycle, either one or more, people are more likely to cycle to work than if there is no bicycle available. If people own a scooter, however, they are less likely to cycle. The reason is that a scooter is often used for distances similar to bicycle distances. The availability of a car for the commute journey is important as well. People that have always a car available are less probable to be cyclists, which corresponds with the findings of Witlox and Tindemans (2004). No effect was found of people who sometimes have a car available.
The two variables, added to test for a difference in the data collection are significant. Employees are less likely to be bicycle commuters compared to the respondents from the
cooperating companies. This result shows that the data collections are not identical. Additionally, one city has a significant effect on the choice of cycling. People living in Delfgauw have a higher probability to be a commuter cyclist than inhabitants of Delft. One reason might be that Delfgauw is at a cycling distance of many places, but lack in proper public transport.
We included three interaction-effects in this analysis; the interactions between attitude and education, gender and distance and finally education and distance. Of these three interaction effects of the first two a significant effect is found. Higher educated people are with an increase of distance more likely to cycle compared to lower educated people. People being high educated work at further distance than medium and lower educated individuals. However, higher educated people are probable more aware of the advantages of the bicycle and more willing to make a positive contribution towards a more sustainable environment. The second significant interaction effect is the interaction between gender and distance. Women are less likely to cycle with an increase of distance than men. In our sample and in the Netherlands in general, women cycle more than men. However, women cycle over smaller distances. Thirdly, No significant effect is found of the interaction between education and attitude. We assumed that the attitude towards cycling relates with educational level. Lower educated people might think more negatively about bicycle commuting, because they might be less informed about the benefits of cycling or they might be less appealing compared to higher educated people.
Table 3: Logit models estimated results for being a commuter cyclist (left) and being a ft- or pt-cyclists (right)
wwfietser (being a cyclist) wwfietser2 (being a full-time cyclist)
Odds Ratio Std. Err. z P>|z| Odds RatioStd0, Err0, z P>|z|
afstand 0,859 0,023 -5,58 0,000 * * * 0,903 0,021 -4,28 0,000 * * *
attitudefi~d 1,007 0,002 3,36 0,001 * * * 1,010 0,001 7,24 0,000 * * *
eduXafst~3 1,055 0,029 1,95 0,051 * not included
genXafst~1 0,924 0,015 -4,94 0,000 * * * 0,862 0,036 -3,61 0,000 * * * werktij_1 0,822 0,093 -1,73 0,084 * wetijdB_1 0,583 0,177 -1,77 0,076 * wetijdC_1 0,719 0,115 -2,07 0,038 * * spul_1 reference 0,112 0,099 -2,48 0,013 * * spul_2 2,908 0,926 3,35 0,001 * * * 0,414 0,070 -5,19 0,000 * * * spul_3 3,197 1,017 3,65 0,000 * * * reference wevv2B_2 0,554 0,068 -4,83 0,000 * * * 0,251 0,055 -6,29 0,000 * * * wevv2D_4 0,140 0,161 -1,72 0,086 * wevv2F_6 1,825 0,212 5,19 0,000 * * * 1,627 0,278 2,85 0,004 * * * a_worklo~2 0,761 0,108 -1,92 0,055 * 0,633 0,139 -2,08 0,038 * * vzweC_1 1,400 0,162 2,92 0,004 * * * vzweE_1 1,383 0,175 2,57 0,010 * * vzweH_1 0,625 0,098 -3,01 0,003 * * * vzweI_1 1,336 0,136 2,84 0,005 * * * biccontr~1 1,374 0,144 3,04 0,002 * * * freecar_1 0,402 0,134 -2,73 0,006 * * * freept_1 0,563 0,095 -3,41 0,001 * * * 0,499 0,158 -2,19 0,028 * * sector4_3 1,936 0,647 1,98 0,048 * * sector4_5 0,199 0,153 -2,10 0,035 * * worktype_2 0,460 0,161 -2,22 0,027 * * 3,550 2,205 2,04 0,041 * * worktype_3 3,505 2,378 1,85 0,064 * wduurgro~1 2,878 1,113 2,73 0,006 * * * wduurgro~2 1,992 0,649 2,11 0,035 * * sncol2_1 0,499 0,080 -4,35 0,000 * * * sncol2_3 0,213 0,042 -7,76 0,000 * * * op_au2_1 0,609 0,088 -3,44 0,001 * * * 0,237 0,045 -7,59 0,000 * * * op_au2_3 0,317 0,064 -5,65 0,000 * * * bikeown_1 27,651 28,611 3,21 0,001 * * * dropped bikeown_2 56,916 58,793 3,91 0,000 * * * scootown~1 0,597 0,089 -3,45 0,001 * * * agegroup~3 1,876 0,677 1,75 0,081 * ethnic_1 1,867 0,407 2,86 0,004 * * * dataverz_1 0,793 0,095 -1,92 0,054 * stad2_5 1,816 0,441 2,46 0,014 * * significance: *** P<0,01; ** P<0,05; * P<0,1
4.2 Full time or part time cycling to work
The (social)-work situation has indeed an effect on the decision to cycle full-time or part-time. This indicates that employers and co-workers have an important influence on an individual, regarding his’ or hers commute mode choice.
First the commute distance affects the chance of being a ft-cyclist negatively, whereas the attitudes towards bicycle commuting results in a positive effect on this chance. Secondly, of all financial benefits offered by an employer only providing your employees with a free public transport card has an effect on being a ft-cyclist. In line with the findings from our first analysis and previous research, the possession of a free public transport card does not stimulate cycling. This is very important because many pro environmental employers offer both a free public transport card and bicycle benefits. These two fringe benefits are clashing instead of added something to each other. Third, some facilities offered by the employer influences to choice to cycle every day. Only one facility was found significant: the presence of free car parking. This presence has a negative effect on the probability of being a ft-cyclist, which is in line with the evidence of Dutch bicycle promoting literature (Fietsberaad, 2005). Fourth, the need to transport goods for work, either always or sometimes, has a negative effect on being a ft-cycling commuter. Since cycling while transporting goods is difficult, this result is expected (see also section 4.1). Fifth, the need of a transport mode during office hours influences the distinction to cycle every day. People who use a motor or private car during working hours are less likely to be ft-cyclists. This corresponds with our expectancies. The need of a bicycle results in a higher chance of being a ft-commuter cyclist.
In contrast to the first analysis, working hours do matter of the choice to cycle every day. Working between 12.00 and 19.00 lowers the chance to be a ft-cyclist is if a person It is uncertain what causes this effect. The amount of working hours influence being a ft-cyclists as well. People working 0 until 16 hours and 16 until 24 hours are more probable to be ft-cyclists than people working more than 40 hours. A reason might be that people working fewer hours have fewer commute trips. The chance that they decide to use another transport mode, because of an unforeseen occasion, is therefore smaller.
The only socio-economic aspect that has an effect is age: 45-60 year olds are more likely to cycle than people who are over 60 years old.
Another personal aspect that influence bicycle commuting is car availability. Having a car available, either always or only sometimes, lowers the chance of being a ft-cyclist. This findings corresponds with previous research (Witlox and Tindemans, 2004). The convenience
of a car seems very attractive. It is understandable that in case of bad weather that people who own a car would at least consider driving, whereas other workers do not have this option.
Finally, the sector and employment status of workers affects their bicycle commute decision. Compared to people working in the health care sector ft-cyclists can less often be found at building and construction companies. We do not have a solid explanation for this result. Moreover, compared to people who are employed, people with their own company or doing voluntary work have a higher chance to cycle every day to work. It is remarkable that people with their own company have a higher change to be a ft-cyclist, as they are less likely to be a cyclist (see 4.1). This contrast might be a result that if people with their own company decide to commute by bicycle, they have less difficulties to cycle every day, since they are better able to arrange their work according to their wishes than employed people. Voluntary workers might have less money to spend on transportation and choose therefore to cycle. Another reason could be that voluntary workers look for work in their direct surroundings, resulting in shorter distances.
In this analysis we included one interaction effect: the interaction between gender and distance. As explained in paragraph 4.1, women cycle smaller distances than men, despite the almost similar frequency. We therefore expect women to be less frequent fulltime cyclists with increasing distances. Indeed, the results indicate that women with an increase in distance are less likely than men to be full-time cyclists.
No effect was found of the included dummy-variables for the collection method or city. This indicates that the collection method did not influence the frequency with which is cycled to work.
5. Conclusion and discussion
This paper investigated the effect of work-related aspects on the choice to be a commuter cyclist, and on the frequency with which commuter-cyclists cycle to work. The data were collected by an internet survey in two cities, Delft and Zwolle, in the Netherlands among inhabitants and employees. We expected that cycling is not only determined by ‘hard’ factors, such as the built environment, available infrastructure and socio-demographics, but also that attitudes and expectations – not only of the cyclists themselves, but also by his social environment (such as the employer) – have their impacts on the decision to cycle. These expectations and attitudes are reflected in the provision of bicycle (un)friendly facilities or incentives, such as storage facilities and the provision of a transport mode. By estimating two
binary logit models, the first for being a cyclist and the second for being a ft- or pt-cyclists, insight was given in which work-related aspects influence the decision to cycle to work.
This research shows that the influence of bicycle facilities provided by the employer and the attitudes of the employer, co-worker and the worker him or herself are very influential on both the bicycle commute mode choice and the bicycle commute frequency. This was based on the following findings. First, the attitude towards bicycle commuting from the employee appeared to be important on his or her decision to cycle. In addition, the opinion of colleagues has an influence on being a cyclist. These results suggests that actively promoting cycling under employees by the employer and thereby changing the attitude towards bicycle commuting positively, may result in a higher number of bicycle commuters and a higher cycling frequency. Second, we also found the presence of bicycle storage inside, changing facilities and a public transport stop within 500m to increases the chance of being a bicycle commuter. Therefore by providing these facilities a employer could increase the number of bicycle commuters. This finding is in line with the transport plans of Dutch companies, providing bicycle facilities in order to stimulate cycling. Third, the presence of facilities for other transport modes have a negative effect on bicycle use. Results show that the presence of free car parking is connected with a lower number of ft-cyclists. This finding implies that, if increasing cycling is the main transport policy, free car parking should be limited. Fourth, individuals needing to transport goods are less likely to cycle. As for some workers the necessity to transport goods exist, it is difficult for them to commute by bicycle. The employer cannot change that, but could try to concentrate this necessity on certain days, enabling the employee to cycle to work on the remaining days. The same applies to the need of a car during office hours. Results show that workers needing a car for work have a higher chance of not commuting by bicycle. In order to increase bicycle commuting the employer could stimulate car use during for work on limited days. By doing this the worker is better able to commute by bicycle. Finally, a negative effect on cycling was found of providing employees with a free car or public transport card. Thus, if an employer specifically wishes to stimulate cycling a critical position towards public transport and car compensation is necessary. This is an important finding, as in the Netherlands bicycle encouragement is often combined with stimulating public transport use, as both are considered sustainable transport modes.
It was also found that similar to previous research distance is a very important factor on the decision to cycle to work and also the frequency with which is cycled. This means that in order to enable bicycle commuting the distance between the working and living location
should be reduced. Although, it is impossible for policy makers or employers to reduce this distance, they could try to reduce distance by offering compensation for moving closer to work or specifically recruit local employees.
This research provides evidence that the decision to cycle and the frequency with which workers cycle depends on different aspects. The presence of bicycle storage and changing facilities or offering a free car or a bicycle contribution stimulate to cycle, but do not have an influence on the frequency. Yet, the amount of working hours only affects the decision to be a ft-cyclist instead of a pt-cyclist. This finding strongly suggests that cycling to work is composed of multiple decisions, which are made after consideration of (partly) dissimilar aspects. In future research this distinction should be made in order to formulate better goal-oriented policies, to encourage either the amount of people cycling or the cycling frequency.
As mentioned in the introduction, our findings could be important for policy makers and employers. An increase in commuting by bicycle could benefit employers due to the reduced need for parking places, lower costs for commuting, company and lease cars, and healthier employees. Employers could encourage cycling by providing bicycles for (short distance) business trips for car commuters, providing company cars for business trips during working hours so that people would not need to commute by car simply because they need their car during working hours, developing an explicitly pro-cycling culture at work, and using financial stimuli to encourage bicycle commuting. Policy makers could use fiscal means to encourage bicycle commuting, develop employer-related policies (voluntary or compulsory), make business parks more cycle-friendly and demonstrate employer-related measures by implementing measures to encourage cycle themselves in their role as employers.
References
Abraham, J. E., S. McMillan, A.T. Brownlee and J.D. Hunt (2002) Investigation of cycling sensitivities, Transportation Research Board. Washington, D.C.
Bamberg, S. and P. Schmidt (1994) Auto oder Fahrrad? Empirischer Test einer Handlungstheorie zur Erklärung der Verkehrsmittelwahl, Kölner Zeitschrift für Soziologie und Sozialpsychologie, 46(1), 80-102.
Bergström, A. and R. Magnussen (2003) Potential of transferring car trips to bicycle during winter, Transportation Research Part A 37, 649-666.
Cervero, R. (2002) Built Environments and mode Choice: Toward a Normative Framework, Transportation Research Part D 7(4), 265-284.
Cervero, R. and M. Duncan (2003) Walking, bicycling, and urban landscapes: evidence from the San Francisco Bay Area, American Journal of Public Health, 93(9), 1478-1483.
Dargay, J.M. and M. Hanly (2007) Volatility of car ownership, commuting mode and time in the UK, Transportation Research. Part A: Policy & Practice 41(10), 934-948.
Dickinson, J. E., S. Kingham, S. Copsey and D.J.P. Hougie (2003) Employer travel plans, cycling and gender: will travel plan measures improve the outlook for cycling to work in the UK?, Transportation Research Part D, 8(1), 53-67.
Dill, J. and K. Voros (2007) Factors affecting bicycling demand: Initial survey findings from the Portland region, Transportation Research Board. Washington, D.C.
Fietsberaad (2005) Beleidswijzer fietsverkeer. Kennis voor fietsbeleid gebundeld. publicatie nummer 9, Fietsberaad: Ede.
Garrard, J., G. Rose and S.K. Lo (2008) Promoting transportation cycling for women: The role of bicycle infrastructure. Preventive medicine 46(1), 55-59.
Gatersleben, B. and K. M. Appleton (2007) Contemplating cycling to work: Attitudes and perceptions in different stages of change, Transportation Research Part A, 41(4), 302-312.
Geus, de B. (2007) Cycling to work. Psychosocial and environmental factors associated with cycling and the effect of cycling on fitness and health indexes in an untrained working population, doctoral dissertation, Department of human physiology and sports medicine, Brussel, Vrije Universiteit Brussel, 1-209.
Heinen, E., B. Van Wee and K. Maat (submitted in transport reviews) Commuting by Bicycle: An Overview of the Literature
Hunt, J. D. and J. E. Abraham (2007) Influences on bicycle use, Transportation, (34), 453-470.
Nankervis, M. (1999) The effect of weather and climate on bicycle commuting, Transportation Research Part A, 33, 417-431.
Noland, R. B. and H. Kunreuther (1995) Short-run and long-run policies for increasing bicycle transportation for daily commuter trips, Transport Policy, 2(1), 67-79.
Parkin, J., M. Wardman and M. Page (2008) Estimation of the determinants of bicycle mode share for the journey to work using census data, Transportation, 35(1), 93-109.
Pucher, J. and R. Buehler (2006) Why Canadians cycle more than Americans: A comparative analysis of bicycling trends and policies, Transport Policy, 13(3), 265-279.
Rietveld, P. (2000) Non-motorised modes in transport systems: a multimodal chain perspective for The Netherlands, Transportation Research Part D: Transport and Environment 5(1), 31-36.
Rietveld, P. and V. Daniel (2004) Determinants of bicycle use: do municipal policies matter?, Transportation Research Part A, 38, 531-550.
Rodríguez, D. A. and J. Joo (2004) The relationship between non-motorized mode choice and the local physical environment, Transportation Research Part D, 9(2), 151-173.
Scott Long, J. and J. Freese (2006) Regression Models for Categorical Dependent Variables Using Stata. College Station: Stata Press.
Stinson, M. A. and C. R. Bhat (2004) Frequency of bicycle commuting: internet-based survey analysis, Transportation Research Record (1878), 122-130.
Susilo, Y.O. and K. Maat (2007) The influence of built environment to the trends in commuting journeys in the Netherlands, Transportation, 34 (5), 589-609.
Witlox, F. and H. Tindemans (2004) Evaluating bicycle-car transport mode competitiveness in an urban environment. An activity-based approach, World Transport Policy & Practice, 10(4), 32-42.
Speech State Secretary of Ministry of Transport, Public Works and Water management Tineke Huizinga-Heringa, ‘Met de fiets minder File” (by bike fewer traffic jams) 11-07-2007, Delft, The Netherlands.
