To know where to provide for cycling, and what type of provision, it is important to have good information – such as how many people cycle or wish to cycle, where they wish to ride, for what purpose they ride, and how competent they are to handle a variety of conditions.

To help build this picture, this section describes:

  • the origins and destinations of cycling trips
  • methods for identifying the routes used by people on bicycles
  • the types and numbers of people who use these cycling routes or who may use them in the future.

The methods used will depend on the particular project focus (eg developing a totally new cycle network versus adding to an existing network), and what information is already available.

  • Common cycling origins and destinations

    Just like other travellers, people may wish to cycle anywhere. Common origins and destinations for cycling trips include:

    • residential areas
    • education establishments
    • areas with large employment
    • shopping areas
    • essential services (eg doctor, dentist, social welfare)
    • recreation (eg mountain bike parks and cycle trails) and entertainment facilities
    • public facilities (eg libraries, council offices)
    • public transport interchanges
    • tourist accommodation and food providers
    • historic and tourist sites.

    By mapping these locations, trip desire lines can then be plotted, permitting a qualitative assessment of where demand for cycling is likely to be significant.

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  • Identifying origins and destinations of cycling trips

    Methods for identifying origins and destinations, and the likely routes between them include:

    These items can be covered by questionnaires or focus groups.

    City/district planning information


    District planning documents map the existing land use and the hierarchy of roads. They also contain information about land use zones and growth areas, major residential subdivisions or commercial or community developments. They are a most useful source of primary data about likely origins and destinations of cycling trips. A higher concentration of people cycling can be expected near popular cycling destinations.


    Identify where cycle traffic could be expected by plotting significant trip origins and destinations on a map, alongside any existing cycle facilities and the road hierarchy.

    Census data


    The five-yearly census includes questions about the mode of travel to work on census day and the locations of the respondent’s residence and workplace.

    This data can identify the number and distribution of residents and employees in various age brackets and those who cycled to work on census day.


    If using this method, be aware of its limitations. Be mindful also to state the true typical percentage of travellers who cycle to work, by excluding in the total number of respondents those who did not travel to work that day or who worked at home.

    School cycle traffic


    School cycle traffic is localised and likely to be a significant proportion of the total cycling occurring in many areas. If there is a low rate of school children cycling, this could suggest that the cycling environment is not suitable for the interested but concerned group.

    Questionnaires and counting parked cycles are commonly used to assess cycle demand at schools.

    By obtaining the number of students attending school on a survey day, the percentage of students cycling to school can be calculated (Note: bear in mind how many staff also cycle).


    During network planning, count parked cycles to quantify existing school cycle use. To get a representative value, ensure that counting does not take place on ‘unusual’ days, eg cycle skills training week or weekly school sports day.

    During route planning, survey people to obtain detailed information on route choice and problem areas. Where possible, incorporate this survey into a neighbourhood accessibility plan or school travel plan.

    Visitor numbers


    This method uses the total number of visitors to particular locations, attractions or facilities to indicate their likely significance as cycling destinations, as a proportion of visitors will arrive by bike. These data could be obtained for example by the number of ticket sales at a venue.


    Use this method where the information is readily available. Note that cycle facilities such as urban bike parks are likely to attract a much higher proportion of visits by bike than non-cycling attractions.

    Counting parked bicycles

    Counting the number of bicycles parked at particular locations on a typical day can help determine the significance of those places as cyclist destinations.


    Develop a programme for counting parked bicycles at key destinations.

    Travel surveys


    Information on cycle demand can be gleaned from surveys conducted for transport planning and modelling, or from travel surveys, such as the Ministry of Transport New Zealand Household Travel Survey(external link).


    Use the household travel data to monitor overall changes in cycling, an indication of who is cycling, and a general picture about the cycling purposes other than the trips to employment, which are better from the census data.

    Work with council research teams to design effective surveys. These are powerful tools for planning and communicating cycleway projects at a local scale.

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  • Determining current demand for cycling

    There are a number of ways of evaluating the current demand for cycling along a particular route:

    Road hierarchy method


    District plans usually include maps of the road hierarchy in their areas (typically arterial, collector and local roads).

    A first assumption could be that the number of people wishing to cycle on a particular link in the road network will be in direct proportion to those using motor traffic on that link. So highly trafficked roads could be expected to carry relatively high volumes of cycle traffic, given appropriate cycling conditions.


    This method is a good way to begin assessing utilitarian cycle demand, especially for longer cycling trips. Other cycle demand assessment methods should be used to refine further the understanding of cycle travel patterns in an area.

    Cycle crash data


    Crashes involving people who cycle have been evaluated for the whole country to give an indication of the current actual safety issues for people who cycle in New Zealand. Cycle crashes are listed as an aspect to include in monitoring programmes.

    Cycle crash data covering a long period of time can indicate those routes that people have difficulty negotiating safely by cycle. Because crash numbers are dependent on exposure rates, they can also provide a proxy indicator of cycling usage.

    Useful crash data can be obtained from the NZ Transport Agency’s Crash Analysis System (CAS), ambulance services and individual road controlling authorities’(RCA) databases of locally reported crashes.


    Use this method as a supplement to volume-based methods, but be aware of its limitations.

    Start with CAS data. For a more complete picture, supplement this with ambulance and RCA data, and possibly even crowdsourced crash information, but remove any duplicate data from the combined database to avoid double counting.

    Include crash data in monitoring programmes.

    Location of existing cycle facilities


    This method involves plotting on a map the location of any existing cycle facilities along routes, at intersections and for trip ends. This may indicate where cycle demand is, or has been considered, significant.


    Use this method as it provides information needed for other purposes, including cycling promotion.

    Cycle counts

    Counting the people currently cycling on a particular route may give an indication for the level of demand for that route. The monitoring and reporting sectionoutlines the type of programme that should already be in place to monitor activity on a cycle network. But the specific route in question may not be already covered by the monitoring plan and therefore it may be necessary to add count stations, or to use data from other sites to estimate the level of cycling at the route in question.

    Manual cycle counts

    In this method, people at cycling sites record the numbers of people on bikes, their travel direction, and possibly their gender and/or whether each person cycling is assumed to be a primary school pupil, a secondary school student or an adult.

    At busy sites people on bikes should be counted separately rather than as part of a general traffic count, as they are easily overlooked. Counting is usually done during the morning or afternoon peak, but counts undertaken at other times can also be scaled up to give a representative peak period or average daily volume (see information on calibration and scaling cycle counts in Monitoring cycle throughput .

    The methods already mentioned above may provide a good indication of where to start counting to assess demand on particular cycle routes.

    Manual counts have the potential advantage of also collecting more detailed cycle user demographic/behaviour data, but they are also subject to more human error, especially at busy sites.

    Automated cycle counts

    Automatic mechanical counters can be used to count bicycles, even in conjunction with counting other traffic, however specialised cycle counters may be more accurate (ViaStrada, 2009).

    Bicycle detectors at traffic signals can also be used to regularly monitor the number and time pattern of people cycling. Beware of false counts that could be generated by cars driving in adjacent lanes or straying into the cycle lane.

    A number of different automatic counting technologies are available; a summary of cycle counting technologiesis given in the section on automatic count technologies in Monitoring and reporting. Each of the technologies has different operational constraints and advantages.


    Each local authority should carry out an annual programme of cycle counts to monitor cycle use trends and provide data to support funding applications.

    In addition to counting cycles using sections of routes soon to be investigated or designed in detail, it is recommended that some strategic counts be repeated annually. This could include counting cycles crossing a cordon around the central business district and or other key destinations, as well as on some outlying arterial routes.

    Cycle counting should not be performed only as a means of assessing demand for future provisions, but also as a way of monitoring existing and new facilities to gain a better understanding of cycle travel patterns and seasonal trends.

    See Monitoring and reportingfor a thorough guide to counting people on bikes.

    Crowdsourcing applications

    ‘Crowdsourcing’ entails gathering cycle use data from a large group of people, generally through use of GPS tracking of their routes on the internet. The rise of smartphones facilitates a greater availability of data. Applications are available that produce ‘heat maps’ of cycling activity – ie where people cycle within the network. Some applications allow users to provide additional data, such as demographics and trip purposes. It is also possible to filter data, for example it is possible to look for typical commuting periods and distances to filter out ‘training rides’ and the like.

    Generally, users of smartphone cycling applications subscribe voluntarily, therefore such applications generally only capture a certain sub-set of total cyclists and is considered biased. Data will only be gathered from cyclists who have a smartphone, the motivation to use the required application, and the dedication to do so consistently.

    However, this technology could be employed to gauge a specific subset of targeted users who have been solicited for a particular study, thus addressing the potential data bias.


    Crowdsourced GPS data provides extensive coverage across cycling networks, providing rich information on certain kinds of cycling activity. It supplements existing traffic surveys but does not replace them – especially if accuracy is important. Auckland Transport has purchased Strava data for 2013/14 and successfully used those for network planning and corridor optimisation projects (Norman and Kesha, 2015).

    Queensland Transport and Main Roads (Langdon 2015) have also evaluated the usefulness of Strava data and concluded that, where there is sufficient data recorded, crowdsourcing data could be used for:

    • cyclist route choice analysis
    • assessing the cycle and road network impacts of new cycleways
    • overview of cycle network usage, including connections between recreational and commuter or transport routes
    • identifying peak days/hours and indicative cyclist usage
    • planning to inform undertaking of a more detailed traffic survey
    • research using revealed preference (as distinct from stated preference data)
    • wayfinding and focal point mapping
    • route discovery and asset inventory
    • locating some route preferences between on-road and off road facilities
    • identifying gaps where facilities are unsuitable or not present
    • reviewing cyclist average speeds over particular links.

    Don’t use crowdsourced data for:

    • analysis at sites with low numbers of people cycling
    • calculating year to year growth
    • site specific volume comparisons
    • small distance analysis (less than 5metres) – such as whether on road or footpath
    • system wide scale up
    • intersection turning movement analysis.

    Consider also crowdsourcing in the form of data submitted by the public from their desktops, eg identifying routes taken, locations of specific problem areas (See Questioning people about their desire to cycle, below).

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  • Questioning people about their desire to cycle


    Questioning local bicycle users, or members of the public who do not currently cycle, can be useful to gather information on:

    • the types of people currently cycling
    • their origins and destinations
    • the routes where they currently cycle (and from this infer existing cycling volumes)
    • ‘desire lines’, ie the routes where they would like to cycle
    • barriers to cycling identified on their desire lines (ie in terms of the five main requirements for cycling – see Cycle trip types in People who cycle), which it could be useful to collate a map:
      • locations perceived to be hazardous, or where people have experienced crashes
      • physical features which sever route continuity, e.g. waterways, motorways, railways, large industrial areas etc
      • routes that are not suitably direct
      • routes that are not suitably attractive
      • lack of network cohesion, e.g. poor signage or inconsistent treatments.

    This type of investigation could be done by:

    • conducting a study or focus group
    • individual questionnaires, which could be conducted by:
      • intercepting people cycling on popular cycle routes and either conducting a roadside interview, or giving them a questionnaire to fill in and return later
      • on-line survey tools and crowdsourcing
      • via local cycle advocacy networks
      • newspapers
      • cycle shops, libraries or places that cyclists visit often
      • placing questionnaires on parked bicycles
      • in classrooms for school surveys, and at tertiary institutions and workplaces
      • at cycling-related events, eg Bike to Work breakfasts, ‘Ciclovia’ and ‘Open Streets’ days.

    Relevant information may also be available from previous initiatives, for example workplace and school travel plan projects and neighbourhood accessibility plans.

    People in the ‘no-way no-how’ group (see People who cycle) may have little or no interest in responding to a survey perceived to be solely about cycling, or they may have a response bias that will result in overly negative responses against cycling initiatives. When seeking information from people who do not currently cycle, it may be better to incorporate this in consultation on a wider range of issues, such as a city or district council’s annual citizens’ satisfaction survey.

    A sample questionnaire could also be used as the basis to develop questions for a study group.

    Note that many of the techniques listed above rely on ‘stated preference’ surveying, ie where the various cycling environments of interest are described to participants (either by words or images) without them actually having first-hand experience. People’s behaviour may not align with their answers given in a stated preference survey. For example, if a neighbourhood greenway situation is described, some people may think they’d be too scared of traffic to cycle there, but if accompanied to cycle on one, they may find they enjoy it. Similarly, someone might cite not having a bike as the reason they do not currently cycle, but, when given a free bike, still choose not to cycle. Giving people real cycling experiences is the best way to avoid inaccuracies from stated preference surveying.


    Consult with people who currently cycle and those who would like to cycle more, to obtain more information on their route preferences and existing barriers in the network. Consider whether it is most useful to do this via study groups or questionnaires.

    If there is no bicycle users’ group, consider convening one for the purpose of ongoing liaison during cycle planning and implementation. If the target audience includes the interested but concerned, ensure that the users’ group includes members who can represent the views of interested but concerned people. Bear in mind however that people who don’t currently cycle may not know the best cycling routes; typically they will default to trying the same routes they are used to driving.

    If questionnaires have not been used for network planning, they should still be considered for route planning.

    Developing and using a good questionnaire that will produce meaningful conclusions are not simple exercises; it may be wise to seek specialist survey design advice to ensure cost-effective and useful results.

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  • Combining data sources

    As discussed, there are advantages and disadvantages to each of the data collection methods outlined above.

    Many of the methods identified will produce large data sets relating to specific attributes, for example user volumes at a particular location, for a relatively low level of effort required to collect and analyse the data. However, these methods will not give a good understanding into the specific routes people take. It is difficult to get a large data set of actual routes cycled without either investing a lot of time and effort in choosing a representative cross-section of cyclists and tracking their routes, or by accepting the biases associated with crowdsourcing options. Finally, methods that provide insight into people’s preferences, such as questionnaires and study groups, require significant effort on the part of both the surveyors and the participants, which makes it difficult to include a large sample size.

    Using a variety of methods and combining the various data sets collected can give a more meaningful understanding of existing and potential cycling activity within a network or along a route, whilst reducing the effects of biases associated with individual methods. 

    For example, combining crowdsourced cycle route data (which has a widespread network coverage, but involves a sample of the general cycling population that is possibly skewed towards certain cyclist types) with specific spot counts (full count data but at a limited number of locations) can give a good overall estimate of complete network activity. This can be used to inform questions for questionnaires or study groups which will then reveal more about people’s current choices with respect to cycling and route choice.

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  • Estimating future demand

    Demand for cycling can increase over time as a simple function of population increase, but there are a lot of other factors that can influence the equation. In particular, the concept of latent demand describes potential new cycle trips that are currently suppressed, but that would be made if cycling conditions were improved. The Geller typology (see People who cycle) illustrates this principle of latent demand by showing that there is a large proportion of the general population who would choose to cycle if they felt safer (ie if provision was improved according to their needs). A fundamental aim of cycle network planning is to improve conditions for cycling and therefore the demand estimation undertaken in the planning process should consider the potential effects of releasing latent demand.

    Latent demand can be assessed in relation to specific route improvements or to the whole network, assuming it is fully developed and that complementary cycle promotion activities are undertaken. This is an often difficult but important task, and overseas experience has shown that significant problems can arise from not having foreseen the enormous demand that some cycling infrastructure may attract within a decade or two. For example, the VicRoads guideline on shared pathway dimensions (VicRoads, 2013) was commissioned following a coroner’s recommendation in a case where a pathway had become congested due to high use (Lloyd et al, 2008).

    Note that consultation with people regarding how much they would like to cycle if improvements were made gives a useful measure of latent demand. However, it may only be practical to undertake such consultation for a small population of people or on a specific route. Demand prediction models such as those listed below generally require some sort of consultation to inform their development.

    A wide range of methods have been proposed for forecasting cyclist travel demand, and those of relevance to New Zealand are listed below:

    • network models specific to cycling:
      • for example Roberts (2014), which details the development by QTP of the Christchurch Strategic Cycle Model (CSCM), based on the city’s existing traffic model, and taking account of changes in demographics, traffic congestion, fuel prices as well as people’s perceptions of the utility of cycling and attractiveness of various network improvement packages
      • the demand assessment of the Auckland Central Urban cycleways, by Flow Transportation Specialists (Jongoneel & Ormiston 2015), which has asimilar demand modelling approach
      • route choice models, such as the Abley Route Choice Metric (ARCM) outlined in Rendall et al (2012), which is based on a model developed for Portland and adapted to the New Zealand context (scaling factors are applied to base travel times to represent the desirability of different characteristics between and at intersection to enables evaluation of different route options)
      • Martin's (2015) examination of future cycle demand in Christchurch by a GIS analysis, which involved data on current patterns in employment and travel and predicted population growth to estimate demand that would be experienced on the planned Christchurch Major Cycleways Network (this analysis also employed the ARCM (Rendall et al, 2012) as an input to identify areas where improvements to the network were most needed; such an approach could therefore serve to inform route choice and prioritisation.
    • facility based models:
      • a tool for estimating demand for a new on-road cycle facility (without physical separation) to the existing road environment, which uses a step function to represent the change when the facility is introduced (the required inputs are existing cycle volumes and census mode share growth rate) (McDonald, et al., 2007)
      • a tool for estimating demand for a new off-road cycle facility parallel to an existing road. The required inputs are cycle AADT and motor vehicle volume on the parallel road; census cycle mode share; and the ratio of New Zealand average trip length by cycles to motor vehicles (from NZ Household Travel Survey) (McDonald, et al., 2007)
    • willingness to cycle models:
      • for example the model developed for Wellington City Council by Pettit and Dodge (2014) to assess willingness to cycle for people in different user type categories (different to the cyclist types defined by Geller (2009))
      • the Transport Agency’s Economic Evaluation Manual (2013b, worksheet A20.1) provides a simple method of calculating demand for new cycle facilities, based on existing use and population, which is designed to be used when cycle counts are unavailable or unreliable and is based on the assumption that the area surrounding the facility is residential in nature (i.e. demand for cycling is generated within a defined buffer) – where this constraint is not met, the method is unsuitable.

    Each of the methods presented have different advantages and disadvantages, depending on the inputs required. However, the following general observations can be made.


    While these methods require further research for application in New Zealand, and in terms of the interested but concerned audience in particular, the simpler methods may provide a useful starting point until this research can be done.

    A comprehensive monitoring programme of existing use at established locations (see Cycle counts, above) can provide useful inputs to produce meaningful demand estimations for future facilities for cycling.

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  • Using geographical information systems

    Geographical information systems (GIS) are well suited to analysing data to develop estimates of demand for cycling.  GIS then enables the present of the data and model outputs in an accessible, graphical manner. By presenting collected data as layers on common maps, many aspects can be considered together and a complete picture of cycle demand and obstacles developed. Sufficient work should be done to obtain a clear picture of where people wish to cycle, where they currently cycle and where the key network barriers to more cycling exist. The aim is to have usage information that is useful for project evaluation and prioritising improvements in cycle provision.  Martin (2015) gives a good example of how GIS has been used to estimate demand for cycling and assess the appropriateness of a planned cycling network.


    Note: Each spot represents a bicycle collision. Thickness of buffered line varies in proportion to the number of bicyclists surveyed.

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