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This section describes the monitoring required, particularly once the implementation of the cycle network plan has started.

For efficiency purposes, monitoring and surveys of cycling should be integrated with similar local authority or road controlling authority activities where possible.

  • Requirements for monitoring

    The NZ Transport Agency requires monitoring and reporting for ‘large’ cycling projects. In 2016 the definition of a ‘large’ cycling project is any that costs over $4 million or receives funding as part of the Urban Cycleways Programme. As part of the planning for investment in cycling requirements for performance measures should be checked on the Planning and Investment Knowledge Base (external link) . In addition to monitoring of individual projects road controlling authorities that have received funding through the Urban Cycleways Programme are also required to report on cycling uptake on the wider network through the annual achievement report (external link)  to the Transport Agency.

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  • Monitoring cycle throughput

    It is important to develop a monitoring programme for a network. Ultimately, the exact number and layout of counters depends on the specific network, but some guidance on how to structure a cycle counting programme is given here.

    This guidance includes the following topics:

    Uses of cycle throughput data

    Cycle throughput data can be used to:

    • understand changes in the number of people on bikes:
      • as a response to new and/or improved provision (ie evaluating the effects of investment – may be required by NZ Transport Agency)
      • that may affect cycle network implementation priorities
    • collect data to support funding applications
    • inform stakeholders and the public about cycling activity and trends
    • assess progress in achieving cycle mode share goals
    • improve understanding of current use and predictions of future use over the network:
      • including validation for any existing network models, or to develop new models
      • understanding of which parts of the network experience the highest utilisation
    • compare cycling levels at different sites with different types of provision/characteristics and inform network planning and facility design
    • measure exposure when assessing crash risk
    • long-term counts also allow shorter term counts to be calibrated and scaled to represent average volumes.
    Developing a cycle throughput monitoring plan

    Cycle counting programmes should consist of strategically placed permanent automatic count stations supplemented by other temporary count stations, which may involve either automatic counters or manual methods. Due to lower flows, relative to motor vehicles, counting people on bikes over short periods of time (i.e. less than two weeks) generally does not allow statistically robust trends to be measured.

    Intermittent manual counts also provide an opportunity to collect qualitative data and should be included in your monitoring programme.

     

    The planning of a throughput monitoring program is presented here as a sequence adapted from Ryus et al (2014).  In practice this can be an iterative process and requires a pragmatic approach.

    This guidance is developed in the context of monitoring of people on bikes across a network.  Where monitoring for a single investment is required, eg to satisfy NZ Transport Agency investment monitoring requirements, the principles still hold.

    The steps are:

    1. Specify the data collection purpose

    An understanding of why you are collecting the data will assist with developing the plan. Data needs are likely to change over time, this will result in the monitoring plan being added to and adapted. 

    A selection of possible purposes are provided in the section on uses of cycle throughput data. In addition to understanding numbers of people on bikes, strategically placed count stations can also allow trip lengths to be inferred. Especially where the network or facility is designed around a major attractor for people on bikes.

    2. Determine the number of count stations

    The more count stations included, the better the understanding of cycle traffic over the network. It should however be noted that too much data can become difficult to utilise.  Strong (2006) gives an empirically based model to suggest the number of automatic count stations to be used based on urban population (see Table 8 below). 

    Ultimately the number of count stations is likely to be dependent on available resource. This may include monitoring programme budget, availability of surveyors for manual counts and staff time to administer the programme.  As new infrastructure is implemented additional count stations may be required. 

    Table 8: Suggested level of provision of automatic counter stations (Strong, 2006)

    Population of discrete settlement

    Suggested number of automatic counter stations

    <25,000

    2–3

    25,000–50,000

    3–4

    50,000–100,000

    4–6

    100,000–150,000

    6–9

    150,000–200,000

    8–12

    200,000–250,000

    11–15

    250,000–300,000

    14–17

    300,000–400,000

    17–20

    Cities with populations exceeding 400,000 should have monitoring plans developed on a case by case basis to fit their data needs.

    3. Locate count stations and determine count duration

    There are many ways to approach the location of automatic count stations on a network. The most important criterion for site location is to position them in a way that adequately represents the coverage of the cycling network, not simply in terms of geographic range but is also in terms of the user and location characteristics. Automatic count station location principles are detailed in Table 9. Alternative methods for locating count stations are provided in Ryus et al (2014).

    Table 9: Count station location principles

     

    Principle

    Application guidance

    1

    Include key routes

    It is likely that the numbers of people cycling on key routes are of interest to many stakeholders. Permanent counters on key routes also have higher count numbers and therefore provide a good basis for scaling similar short term count sites.

    Use cycle network maps, existing count data and the Strava global heatmap (external link)  to assist with identification of key routes.

    2

    Cover the range of broad facility types included in the network

    Different facilities are likely to be used differently. This information provides insight into the attractiveness of implemented facilities. This information will also be useful to guide future investment, planning and design.

    Identify and categorise broad facility types across your network, for example:

    • shared paths
    • exclusive cycle paths and separated cycle facilities
    • on-road provisions such as cycle lanes.

    Allocate automatic count stations to each facility type from the most to the least predominant type.

    3

    Cover the range of user types and trip purposes on the network

    Peak periods for different user types occur at different times, seasons and on different days.  Therefore it is important that all user types are captured to allow appropriate scaling of non-permanent count sites.

    Consider and map the location of key destinations:

    • schools
    • tertiary institutions
    • dense employment areas (eg using workplace address information from census data)
    • public transport interchanges
    • other utility trip destinations eg shops
    • key recreational routes and facilities eg mountain bike tracks.

    Consider also a mixture of radial and circulatory routes where usage type may differ. 

    4

    Cover various adjacent land uses

    Due to the relatively short trip distances for people on bikes, adjacent land uses often provide an indication of trip purpose/user type.

    Similar to principle 3 overlay land use zoning to understand how the various zones are represented in the monitoring plan.

    5

    Locate count stations where cycle flows are highest

    This increases the robustness of recorded trends. 

    These locations may include:

    • where limited route choice is available and cyclists are channelled to use a single route eg underpasses, bridges and across railway lines
    • where two facilities converge into one
    • near key route destinations eg central business districts and outside schools, but before routes diverge
    • where cyclist position can be reliably predicted, ie away from intersections and on dedicated facilities.

    Specific locations should be chosen in conjunction with technology suppliers as these may be impacted by eg facility widths and topography. Approximate locations (+/- 500m) where flows are highest should be identified for the purposes of the monitoring plan.

    6

    Ensure that there is some spatial separation between count stations

    This allows an understanding of how patterns may differ across the network. This is a secondary consideration after applying principles 1–5.

    7

    A mixture of tidal directions  

    Counters on one-way facilities will only capture flow in one direction, where pairs of facilities are provided and tidal flows (eg for commuting traffic) exist, it is preferable to have counters on both facilities, to capture both the morning peak and the evening peak flows. 

    8

    Locate count stations at various distances from major trip attractors or along key routes

    This provides an understanding of how flows vary along a route and allows trip distances to be inferred.

    Following the allocation of count stations to strategic points on the network consider the provision of count stations at various distances from major trip attractors including: the central business district, business parks, education facilities and shopping centres.

    The number of count stations located in this way is likely to be dependent on the available resources for the count programme. Manual counts could also be used to collect this information where an automatic count station is located elsewhere on the route.

    9

    Prioritise identified count stations

    This allows the implementation of the monitoring plan to be staged where necessary and the number of count stations to be rationalised to fit the available resources.

    Using the data collection purpose(s) specified in step 1, prioritise the count stations identified using principles 1–8. 

    It may also be useful to include targeted locations, where sites or particular characteristics are selected because they are of special interest. For example it may be useful to closely monitor the performance of prominent projects (this may be required following significant NLTP investment), or locations where there are safety concerns (Ryus et al, 2014).

    Specific count station location should be determined with assistance from the technology supplier. Factors that may influence specific location include, but are not limited to:

    • cyclist speeds – counts are generally more accurate at lower/average speeds, i.e. not on steep downhill slopes
    • predictability of cyclist position on path/roadway
    • width of facility, narrower widths may reduce the cost of the technology required
    • location of cycle facility relative to general traffic and heavy vehicle numbers.
    4. Allocate count method and duration

    Counters can be automatic or manual.

    Automatic count stations can include:

    • permanent automatic count stations that operate continuously through the year
    • short-term automatic count stations that operate for at least two weeks per year

    Continuous count data from permanent automatic count stations can be specifically used to:

    • show long-term trends for a particular site
    • understand hourly, daily, weekly and seasonal fluctuations in cycle numbers on the network
    • derive scaling/calibration factors to be applied to short-term counts (either from manual counts or short-term automatic counters) so that average annual daily cycle volumes can also be estimated for these sites, and long-term changes can be evaluated.

    The permanent count stations should be chosen to give a good representation of the facility types and trip purposes on the network. 

    Short-term automatic counts are used to:

    • supplement the coverage of data from continuous automatic count stations (the number and locations of temporary automatic count stations is based on how well the continuous automatic count station placement covers the cycling network)
    • collect baseline data before a facility is installed and on parallel routes to assist with monitoring the success of the facility.

    Note that the nature of use of the existing route may mean that automatic count technology is unsuitable to collect baseline data, manual counts may be necessary.

    Manual counts are used to:

    • validate data from automatic count stations
    • provide information at intersections where there are multiple options for travel direction from each approach (this can also be derived from some automatic count technologies, e.g. ‘miovision’ products)
    • assess cycle use on routes parallel to key cycle routes, where people may be cycling if provision on the key route is particularly inadequate or as a baseline measure
    • collect baseline information before a facility is installed to understand cycling uptake post-implementation
    • provide supplementary information (e.g. gender, age etc) that cannot be obtained by automatic counters (the proportion of women cycling can be used as a benchmark for the ‘health’ of cycling provision (Bicycle Network, 2016), and the proportion of children cycling may also be another useful benchmark; monitoring changes in these demographics can be a useful way of measuring progress if the intention is to include a broader target audience)
    • supplement the coverage of data from continuous and temporary automatic count stations (note that automatic counting is preferred to gather cycle throughput data and manual counts should only be used for this purpose as a last resort, at sites where automatic count stations had not been planned and it is not financially viable to install a new site; the number and locations of manual count stations is based on how well the continuous count stations cover the cycling network and in order to reduce the variability of manual counts they should be undertaken during peak seasons for cycling and during fine weather)
    • understand the order of magnitude of the number of people on bikes at a location. 

    Manual counting is not recommended for sites where data is collected to understand trends over time. 

    Count duration

    When allocating count duration, consider:

    • reporting requirements for the site:
      • where a site has a high public or political profile or frequent (eg monthly) reporting is required it may be necessary to count permanently
      • investment monitoring requirements may also specify count durations.
    • the need to calibrate/scale short-term count sites – permanent count stations allow short-term counts to be scaled/calibrated to remove seasonal variance, while permanent count stations should cover a range of locations representative of all count station locations – this means that there will be an appropriate permanent count station to calibrate data from all short-term sites
    • the need for robust data trends – sites that experience higher flows of people on bikes provide a more robust measure of trends: where flows are low, a longer count period may be required (see Figure 93); it is recommended that short term counts are undertaken for a minimum of two weeks, however longer periods are preferable where possible
    • staff/contractor time to rotate counters between short-term count sites – some authorities with existing cycle count programmes choose to use permanent count stations only, which simplifies the requirements for staff/contractors and provides reliability of data collection. 
    • available counter technologies
    • budget.

    Having a mixture of permanent and short-term automatic count stations means that fewer counters need be purchased and therefore capital expenditure on counting is reduced. Counters can be transferred between the short-term stations. However, the ongoing resource involved in rotating counters should be acknowledged when considering the costs of your monitoring programme. Very short-term counts (‘temporary counts’ – less than one month) can be undertaken using pneumatic tubes or other similar temporary count technology.

    Preferably short-term counts should be undertaken during a busy period on the facility. Short-term count stations should be paired with permanent count stations for calibration and scaling.

    It should be noted that not all short-term count stations need to be monitored annually. Where the purpose of data collection does not require annual reporting the frequency of counting may be every three years for example. Permanent count stations are monitored continuously.

    The graph above is based on the assumption of measuring an annual change of 15% in cyclist volumes (count durations based generally on Figure 3 of Davies et al (1999)). Where annual changes are lower, the measured change may only be statistically robust over a longer period, eg three to five years, depending on variation in flows.

    All permanent count stations must be validated manually once per annum using a peak hour count to ensure data accuracy and identify a need to make changes to the count station. These manual validation checks are also an opportunity to collect additional qualitative data about the type of use of the facility.

    Automatic count Technologies 

    There are a range of technologies available for automatic count stations; an overview of those available in New Zealand is presented here. It should be noted that new products are constantly being developed. Therefore it is recommended that suppliers are contacted and the local context of the count programme discussed before making decisions on the appropriate count station technology for your network. This section focuses on technologies that count all cyclists.  Bluetooth and Wi-Fi detection technologies are available, and under development, however these do not reliably identify cyclists or currently collect data for all cyclists and are biased to exclude some entire groups, e.g. school children without smart phones. These technologies may be useful for some network metrics.  Furthermore apps such as Strava (external link)  sell throughput data.  However, this data does not collect a full dataset and is biased towards recreational users. This data does however provide some useful insight into route selection by people on bikes. 

    Counter requirements based on count duration

    Permanent and short-term count stations are subject to different requirements and may have different technology needs.

    Permanent count technologies utilise hardware that is permanently installed which communicates with a count unit to record cyclist numbers. Generally the permanent hardware (eg induction loops/in-ground pressure sensors) is not relocatable, however, the count unit (often the most expensive component of the count station) can be rotated between sites where permanent hardware has been installed. This means that permanent count technologies can be used for continuous count stations (eg calibration stations) and longer term (semi-permanent) count stations. 

    Note that allocation of a single count unit to multiple count stations should be done with input from the supplier. Site specific conditions may mean that the type of count unit required for each station is different and therefore the unit cannot be shared.

    Short-term count technologies are divided into ‘semi-permanent’ and ‘temporary’ (ie less than one month) for the purposes of this section. Semi-permanent count stations use permanent count technologies (see above) but one equipment unit may be shifted between several count stations. Pneumatic tubes are a common form of temporary count technology. Where these are used for periods of longer than two weeks, they must be maintained fortnightly to ensure data accuracy. Note that specialised tube counters have been developed specifically for counting cyclists, these are more sensitive than the tube counters used for monitoring general traffic. Some radar and infrared (does not distinguish between pedestrians and cyclists) technologies are relocatable and can also be used for temporary cyclist and/or pedestrian counts.

    Summary of available automatic counting technologies 

    Table 10 summarises available count technology and is intended as an initial guide to understand what may be appropriate for your network. The most appropriate cycle counting technology for a specific station is very dependent on local factors and individual data needs of RCAs, Table 10 is intended as a general guide only. Question marks (?) in Table 10 indicate where a technology may be appropriate but only in specific circumstances and this should be discussed with the supplier.

    Summary of cycle counting technologies [JPG, 1.7 MB]

    Table 10: Summary of cycle counting technologies

    Some technologies count total movements past a point only (ie both pedestrians and people on bikes). These may be appropriate in locations where other methods or nearby count stations are able to distinguish the mode share of each mode. Counting pedestrians on shared paths is recommended to better understand the overall use of the path and interaction between of these modes.

    A NZ Transport Agency trial of continuous cycle counting (ViaStrada, 2009) resulted in detailed information on the requirements, limitations, abilities and accuracies of two types of automatic inductive loop counting devices, and comparison with SCATs loops and pneumatic tubes.

    Calibration and scaling cycle counts

    The number of cyclists using a facility varies by time of day, day of year and variations in weather. The most accurate way to scale cycle counts is using a single day of year factor when data from a local permanent, continuous count station (or calibration station) is available (Ryus et al, 2014).  A Cycle count scaling spreadsheet tool [XLSM, 15 MB] allows permanent and short term count data to be scaled to average daily cyclist values (see the technical note that describes use of the tool [PDF, 458 KB]) .

    Based on some cycle counts from Christchurch and Auckland described below, the variation over an average weekday is shown in Figure A1. The variation in weekly flows across one year is shown in Figure A2.

    This information recommends a procedure for estimating the average annual daily flow of cyclists (cycling AADT) from cycle counts from time of day, day of week and week of year factors where no local calibration data is available. A formula for scaling up short period cycle counts is described below.

    Regional variations

    The method is split for determining AADTs for the Auckland region and anywhere else (based on Christchurch data). This is primarily because of the different profiles exhibited in Auckland characterised by an earlier morning peak and the lack of an afternoon school peak, as shown in Figure A1. Auckland scaling factors can be used where it is considered that the Auckland profile is more similar to the profile of the area for which cycle counts are being scaled.

     

    Scaling factors

    The scale factors in Tables 12 to 15 are based on year-round continuous cycle counts from 13 cycle loops around Christchurch and two-week counts from eight loops in Auckland. If an adequate set of continuous count data (see Cycle throughput under Aspects to monitor in Monitoring and reporting) is available for the local area concerned it should be used instead.

    The scale factors account for the time of day (H), day of the week (D), week of the year (W) and presence of rain at the time of the count (R). 

    Separate time of day (H) factors are provided for Auckland and non-Auckland sites. The daily pattern for Christchurch sites was found to vary depending on the presence of cyclists riding to and from school. The presence of school cyclists is shown by a peak after 3 pm (see Table 11) that is absent from work commuting. The amount of school cycling at the site also affects the extent of the drop in cycling during school holidays. For this reason there are two sets of factors in the tables to provide for situations with and without school cycle traffic. If using the non-Auckland H factors, the commuter category should be applied to sites with a proportion of school students roughly 30% or lower.  The ‘all’ category corresponds to sites with both school and commuting traffic.  It is unusual for a site to have school cyclists but no commuters, thus no such category is used.

    Separate day of week (D) factors are provided for Auckland and non-Auckland sites. Cycle traffic in Auckland was found to be relatively constant Monday through Saturday, with a peak on Sundays, whereas the Christchurch data involve more fluctuations during the week with less cycle traffic at the weekends.

    The week factor (W) varies with school holidays and season; this is illustrated in Figure A2 for non-Auckland sites with school and commuting traffic. At present the same factor is used for Auckland and non-Auckland sites, but it is intended that specific factors for Auckland will be provided

     

    A rain (R) factor can also be applied to adjust for counts taken when rain occurred during peak period commuting times, or rain occurring at other times during the day was considered to affect cyclists’ decisions to cycle. The rain factor is more critical for Auckland than non-Auckland sites.

    Calculation equation

    The following equation yields the best estimate of a cycling AADT:

    where Count = result of count period

    H = scale factor for time of day

    D = scale factor for day of week

    W = scale factor for week of year

    R = presence of rain (during peak commuting periods)

    If cycle count data for more than one day are available, then the calculation should be carried out for each day, and the results averaged.

    Worked example

    Suppose two counts (of 90 and 165 minutes respectively) have been undertaken at a Christchurch site on weekdays in May. The weather was fine on the first day but it rained on the second. The site is used by both school children and commuters. The count data and the coefficients to be used are shown in the table below, as well as the AADT estimates resulting from the two counts.

    Table 11: Worked example

     

    AM count

    PM count

    Time

    7:30–9:00

    3:00 to 5:45

    Cyclists

    125

    110

    Date

    29-May-03

    30-May-03

    Day

    Thursday

    Friday

    H

    2.0+3.1+3.0+4.9+7.8

    = 25.5%

    1.5+1.9+4.7+3.3+2.22.2+2.2+2.3+3.1+3.5+3.7

    = 30.6%

    D

    17%

    15%

    W

    1.0

    1.0

    R

    100%

    80%

    AADT estimate

     

    = 412

     

    = 427

    Averaging the estimates yields a cycling AADT of 420.

    Recommendations

    We recommend using the above equation for approximating the cycling AADT. As cycling volumes fluctuate from day to day depending on the weather, this method should be used with caution, and ideally the estimate should be achieved based on the average of the results of several counts. Individual counts should be for periods of no less than 60 minutes. Counts should be of cyclists in both directions and cover at least the morning peak period, the after school hour (where school cyclists are present) and the evening commuter peak. Counts during warmer months and school terms will provide the most reliable estimates. Also take note of tertiary calendars when planning counts. It is not appropriate to scale up counts from the Christmas/New Year holidays.

    The factors presented here should be used in the absence of better local information, but any demonstrable local factors should also be taken into account. Care should be taken to ensure any site specific data used in place of these factors are appropriate for the analysis. For example, a week-long count taken during a typical week will not give reliable D, W or R factors but can be used to determine appropriate H factors.

    While the data used in developing these factors have limitations, being from a limited number of sites in Christchurch and Auckland only, they make it possible to scale up cycle count data with some confidence.

    As more data is collected and the figures are refined, updated tables will be published. The data tables below are also available from that web page.

    Table 12: Time of day (H) factors showing typical daily profile

     

     

    Auckland

    Non-Auckland

     

     

    All sites

    All sites

    Commuter sites

     

     

    H (weekday)

    H (weekend)

    H (weekday)

    H (weekend)

    H (weekday)

    H (weekend)

    Period Starting

    Period Ending

    Mon to Fri

    Sat & Sun

    Mon to Fri

    Sat & Sun

    Mon to Fri

    Sat & Sun

    00:00

    06:30

    5.5%

    1.8%

    1.7%

    4.1%

    3.9%

    10.6%

    06:30

    06:45

    2.3%

    0.8%

    0.3%

    0.3%

    0.5%

    0.6%

    06:45

    07:00

    2.6%

    1.5%

    0.7%

    0.3%

    1.1%

    0.7%

    07:00

    07:15

    3.2%

    1.4%

    0.8%

    0.3%

    1.1%

    0.6%

    07:15

    07:30

    3.7%

    2.1%

    1.3%

    0.3%

    1.2%

    0.3%

    07:30

    07:45

    3.8%

    2.8%

    2.0%

    0.5%

    1.9%

    0.5%

    07:45

    08:00

    4.0%

    3.3%

    3.1%

    0.6%

    2.5%

    0.5%

    08:00

    08:15

    3.9%

    3.2%

    3.0%

    0.5%

    2.5%

    0.5%

    08:15

    08:30

    3.1%

    3.8%

    4.9%

    0.7%

    2.6%

    0.5%

    08:30

    08:45

    2.3%

    3.5%

    7.8%

    1.1%

    3.1%

    1.0%

    08:45

    09:00

    1.3%

    3.5%

    4.7%

    1.2%

    2.0%

    1.0%

    09:00

    10:00

    4.2%

    13.6%

    5.1%

    5.2%

    4.9%

    4.2%

    10:00

    11:00

    3.4%

    11.6%

    3.1%

    7.5%

    3.4%

    6.0%

    11:00

    12:00

    2.6%

    9.1%

    3.1%

    8.3%

    3.8%

    6.8%

    12:00

    13:00

    2.7%

    6.6%

    3.5%

    8.5%

    4.6%

    8.2%

    13:00

    14:00

    2.7%

    5.0%

    3.5%

    8.5%

    4.5%

    8.0%

    14:00

    14:15

    0.7%

    1.9%

    0.9%

    2.7%

    1.1%

    1.6%

    14:15

    14:30

    0.7%

    1.3%

    1.0%

    2.2%

    1.2%

    1.7%

    14:30

    14:45

    0.6%

    1.3%

    1.6%

    2.4%

    1.4%

    1.8%

    14:45

    15:00

    0.6%

    1.2%

    1.5%

    2.4%

    1.4%

    1.7%

    15:00

    15:15

    0.8%

    1.1%

    1.5%

    2.8%

    2.0%

    1.7%

    15:15

    15:30

    1.0%

    0.9%

    1.9%

    2.7%

    1.8%

    2.0%

    15:30

    15:45

    1.3%

    1.4%

    4.7%

    2.8%

    1.9%

    2.0%

    15:45

    16:00

    1.2%

    1.3%

    3.3%

    2.9%

    1.9%

    2.3%

    16:00

    16:15

    2.1%

    1.0%

    2.2%

    2.5%

    2.2%

    2.2%

    16:15

    16:30

    2.3%

    1.7%

    2.2%

    2.7%

    2.2%

    2.1%

    16:30

    16:45

    2.1%

    1.0%

    2.2%

    2.8%

    2.5%

    2.0%

    16:45

    17:00

    2.5%

    1.2%

    2.3%

    2.7%

    2.9%

    2.0%

    17:00

    17:15

    3.3%

    1.2%

    3.1%

    2.2%

    3.8%

    1.9%

    17:15

    17:30

    3.7%

    1.2%

    3.5%

    1.8%

    4.3%

    1.6%

    17:30

    17:45

    4.0%

    1.1%

    3.7%

    1.8%

    4.6%

    1.7%

    17:45

    18:00

    3.2%

    1.1%

    2.8%

    1.4%

    4.0%

    1.4%

    18:00

    18:15

    3.0%

    0.9%

    2.3%

    1.3%

    3.2%

    1.5%

    18:15

    18:30

    2.7%

    0.7%

    1.4%

    1.2%

    1.8%

    1.4%

    18:30

    18:45

    2.4%

    0.8%

    1.1%

    1.0%

    1.4%

    1.3%

    18:45

    19:00

    2.1%

    0.6%

    0.9%

    1.0%

    1.0%

    1.7%

    19:00

    20:00

    5.6%

    2.0%

    2.7%

    2.8%

    3.2%

    3.9%

    20:00

    00:00

    3.0%

    1.5%

    4.6%

    6.0%

    6.4%

    10.4%

     

    Table 13: Day of week (D) factors showing weekday usage percentages

     

    Auckland

    Non-Auckland

     

    All sites

    All sites

    Commuter sites

    Day

    D (all)

    D (all)

    D (comm)

    Monday

    14%

    17%

    16%

    Tuesday

    14%

    16%

    17%

    Wednesday

    14%

    16%

    17%

    Thursday

    14%

    17%

    17%

    Friday

    14%

    15%

    16%

    Saturday

    14%

    9%

    10%

    Sunday

    16%

    9%

    7%

     

    Table 14: Period adjustment (W) factors

     

    Auckland

    Non-Auckland

     

    All sites

    All sites

    Commuter sites

    Period (based on secondary schools)

    W (all)

    W (all)

    W (comm)

    Summer holidays

    1.02

    1.13

    1.02

    Term 1

    0.84

    0.78

    0.84

    April holidays

    0.97

    1.17

    0.97

    Term 2

    1.04

    0.98

    1.04

    July holidays

    1.40

    1.74

    1.40

    Term 3

    1.19

    1.22

    1.19

    Sep/Oct holidays

    1.24

    1.42

    1.24

    Term 4

    0.93

    0.91

    0.93

     

    Table 15: Rain (R) factors

     

    Auckland

    Non-Auckland

    Weather

    R

    R

    Fine

    100%

    100%

    Rain

    64%

    80%

    Investment monitoring considerations and baseline data

    For large cycling project investment monitoring the NZ Transport agency defines ‘reporting stations’ and ‘calibration stations’.  In this context a calibration station is a permanent count station used to scale data for investment monitoring.  A reporting station is a short-term count station where data will be reported to the  Transport Agency for investment monitoring.  A project may include multiple count stations, only data that minimises double counting should be reported to the Transport Agency as part of the investment monitoring unless otherwise requested.

    It is required that baseline data is collected before implementation of new facilities. Baseline data should be collected in a similar location to any proposed automatic count stations on the facility. Baseline data collection should also be considered for routes parallel to the proposed facility where there are currently high levels of cycling.  Data collected on parallel routes provides an understanding of diverted and new trips following the completion of the facility. The Strava Global Heatmap can be used to assist in identifying parallel routes. Baseline data is also a useful input for the design process where facility design is not in the advanced stages.

    Generally a large percentage increase cycling is anticipated following the implementation of new facilities. Therefore baseline data can be collected using manual count methodologies and relatively accurate comparisons formed.  However, where automatic counting is possible this will provide a more reliable measure and allow off peak periods to be captured.  Manual baseline counts should be collected during peak periods.  As a minimum a weekday at 7:00am–9:00am and a four-hour peak weekend period is recommended. Manual counts should be undertaken during fine weather. 

    Nationally coordinated wider network monitoring

    The NZ Transport Agency requires an annual survey of people on bikes in each of the main urban areas that have received Urban Cycleway Fund investment. The core component of this wider network monitoring is a cordon or screen line count(s) that measure the number of cycle trips to key cycle trip attractors in each network. For many local authorities this consists of a cordon surrounding the central business district, however, this is determined based on local context. The wider network monitoring is undertaken during the morning peak (7:00am–9:00am) on a single weekday during the first week of March each year. During census years, the cordon count should be undertaken during the census week. Cordon counts commenced in March 2016. Cordon count results are reported to the Transport Agency via the annual achievement reporting in July each year.

    The wider network monitoring aims to provide a consistent, reproducible, nearly absolute, number of cyclists crossing cordons or screen lines to key destinations on the network. These surveys are made up of a number of survey types including: cordon counts, screenline counts and destination specific surveys eg school bike shed counts or counts at public transport hubs. Cordon counts generally encompass an area (eg the CBD) while screenline counts are used to record the number of trips across a line and generally follow boundaries where there are a limited number of crossing points, eg railway lines or rivers.

    It is noted that it is impossible to capture all cyclists using the network, therefore this number is more useful as a tool to understand the relative number of cyclists in each district. The wider network monitoring requirements also require reporting of gender. It is also recommended that additional qualitative information is captured including travel direction, whether cyclists are travelling in groups, cyclist type and age and whether cyclists are using the cycle facility if one is provided. Where a cordon or screen line is already monitored with automatic cycle counts the Transport Agency requires that only 30% of cordon sites are surveyed manually to capture qualitative data.

    Case studies

    Lieswyn et al (2011) outline a method of developing a cycle count programme for Hamilton City, which was based on a similar programme developed for Christchurch City.

    Ryus et al (2014) provide a comprehensive guide to planning and implementing a data collection programme for bicycle traffic.

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  • Other aspects to monitor (in addition to throughput)

    As well as  throughput, the following aspects, relating to programmes/implementation plans, usage and satisfaction, should also be considered for monitoring:

    These aspects are described in the following sub-sections. 

    Physical works programmes

    As discussed in the section on Implementation, physical works programmes should be monitored to identify opportunities to include provisions for implementing sections of the cycle network, or for otherwise satisfying cyclists’ needs.

    Planned general or reactive maintenance works (including storm damage repair) should be monitored on a monthly, or as appropriate, basis. Meanwhile, NZ Transport Agency’s state highway infrastructure and maintenance work programmes and adjacent local authorities’ work programmes should be monitored at least annually.

    Cycle crashes

    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. Analysing cycle crash data is included as a possible method of gauging demand for cycling on a particular route (see Cycle crash data under Determining demand for cycling in Assessing cycle demand). On a broader level, cycle crash data should be monitored annually across the cycle network to identify:

    • any new or growing hazards that may require urgent attention, or an adjustment to the cycle network implementation priorities
    • any problems associated with recently completed cycle facilities
    • whether safety for people cycling (in terms of crashes per time or distanced cycled) is increasing or decreasing.

    It is important to pay attention to the crash rate (ie weighted by cycle volumes on a route, or distance or average or time cycled by individuals), as this reflects the individual risk faced by a person cycling. It is possible that a successful cycling network development programme may lead to an increase in total cycle crashes because the number of people cycling has significantly increased; but this should also reflect reduced crash rate per cyclist. Where the crash risk per person reduces as a result of more people cycling in a particular location this is known as a ‘safety in numbers’ effect (eg Jacobsen, 2003; Turner et al, 2006; Turner et al, 2009). 

    As cycle-related crashes are relatively infrequent, several years’ worth of data are required to derive meaningful conclusions and assess trends over time.  A minimum of five years’ of data is recommended. 

    Satisfaction levels

    A sample of all road users (including pedestrians) should be surveyed annually or biennially in order to identify the degree of satisfaction or dissatisfaction with provisions for cycling in the study area. This survey is probably best included in a local authority’s residents’ survey, if it has one. It is also desirable to include a more specific survey directed at people who cycle and those who are currently not cycling.

    Condition of cycle facilities

    The condition of existing cycle facilities should be monitored and any necessary maintenance programmed and carried out.

    A system for people to report hazards could be implemented, for example by freepost reply cards, email, the internet, social media, phone apps, or phone hotlines.

    Some road controlling authorities in European towns pay cycle advocacy groups to conduct regular condition surveys.

    Cycle network implementation

    It is important for cycle network planning and maintenance purposes to maintain an up-to-date plan and schedule of the sections of the cycle network that have been implemented. From these, the percentage of the ultimate network completed can be calculated and compared with the planned progress, and reported where appropriate. Positive news about network development may be useful marketing for encouraging both politicians and the general public.

    Level of service

    The level of service (LOS) of critical sections of the network can be monitored periodically to determine whether cycling conditions have deteriorated to an extent that upgrading should be given a higher priority (see Assessment methods under Evaluating cycle route options and facility type).

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  • Benchmarking

    Several towns in Europe participate in benchmarking surveys to assess the adequacy of road controlling authority policies and the performance of their networks in relation to the network attributes listed in Table 1 (see summary in People who cycle)

    These can be used to monitor progress in improving cycling conditions, and to compare network performance with other comparable centres that have taken part.

    Bicycle policy audits (BYPAD) are offered by specialist consultants throughout Europe. This process involves questionnaires completed by politicians, municipal officials and cyclists’ representatives. The auditor then facilitates the development of quality aims and measures for the future on the basis of the assessment results. More information is available at www.bypad.org (external link) .

    The Dutch Cyclists Union (Fietsersbond) also conducts benchmarking surveys called the Cycle Balance for the Dutch government. This involves surveying cyclist representatives and the local authority’s officers. An instrumented bicycle is used to ride a sample of routes between randomly selected homes and common cyclist destinations. These are compared with car travel for the same trip. Cities are rated on their directness, delays to cyclists, road surface quality, noise levels, competitiveness with the car, bicycle modal share (for trips under 7.5 kms), bicycle injury rates, cyclist satisfaction and documented cycling policies. The project is described in Borgman (2003) (external link) .

     

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  • Plan review

    The monitoring results should be assessed at least every three years and the cycle network plan and programme adjusted as appropriate. Whether the plan is yielding value for money should also be assessed, bearing in mind the difficulty of achieving significant growth in cycle numbers at an early stage of network development.

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