Cycle network and route planning guide

Print version: Appendix 2: Scaling cycle counts (PDF, 277 KB, 3 pages)

Appendix 2: Scaling cycle counts

Introduction

The number of cyclists using a facility varies by time of day, day of the week and week of the year. Based on some Christchurch cycle counts described below, the variation over an average weekday is shown in Figure A2.1. The variation in weekly flows across one year is shown in Figure A2.2. The purpose of this appendix is to recommend a procedure for estimating the average annual daily flow of cyclists (cycling AADT) from cycle counts conducted at one time. It is not normally practical to count cyclists over a whole year. A formula for scaling up short period cycle counts is described below.

Scaling factors

The scale factors in Tables A2.1 to A2.3 are based on year-round continuous cycle counts from 13 cycle loops around Christchurch. If an adequate set of continuous count data is available for the local area concerned it should be used instead. (A programme for collecting and updating such data for each area is recommended elsewhere in this guide.) The scale factors account for the time of day (H), day of the week (D), and week of the year (W). The week factor varies with school holidays and season. The pattern 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 Figure A2.1) 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.

Figure A2.1 Weekday daily cycling count profile corresponding to H-weekday for all sites in Table A2.1.

Figure A2.2 Profile of weekly cycling counts corresponding to W-all in Table A2.3.

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

If cycle count data for more than one day is 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 on weekdays in May. 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.

	AM COUNT	PM COUNT
TIME	7.30 TO 9.00	3.00 TO 5.45
CYCLISTS	125	127
DATE	29-May-03	30-May-03
DAY	Thursday	Friday
H	25.5%	30.6%
D	16.8%	15.2%
W	0.98	0.98
AADT ESTIMATE	410	382

Averaging the estimates yields a cycling AADT of 396.

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 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 Christmas/New Year holidays.

Use the Christchurch data in the absence of better local information, but take into account any demonstrable local factors. While the data has limitations, being from a limited number of sites in Christchurch only, it is now possible for the first time to scale up cycle count data with some confidence.

Acknowledgement

The method was developed by Axel Wilke of Christchurch City Council, building on work by Aaron Roozenburg (Beca Christchurch) in preparing data and undertaking some of the analysis. A fuller description of how the method was derived is available for Axel Wilke at Christchurch City Council. As more data is collected and the figures are refined, updated tables will be published.

Table A2.1 Typical daily cycling profile.

		ALL SITES		COMMUTER SITES
PERIOD STARTING	PERIOD ENDING	H weekday MON to FRI	H weekend SAT & SUN	H weekday MON to FRI	H weekend SAT & SUN
0.00	7:30	4.8%	5.3%	7.8%	12.7%
7:30	7:45	2.0%	0.5%	1.9%	0.5%
7:45	8:00	3.1%	0.6%	2.5%	0.5%
8:00	8.15	3.0%	0.5%	2.5%	0.5%
8:15	8.30	4.9%	0.7%	2.6%	0.5%
8:30	8.45	7.8%	1.1%	3.1%	1.0%
8:45	9.00	4.7%	1.2%	2.0%	1.0%
9.00	10.00	5.1%	5.2%	4.9%	4.2%
10.00	11.00	3.1%	7.5%	3.4%	6.0%
11.00	12.00	3.1%	8.3%	3.8%	6.8%
12.00	13.00	3.5%	8.5%	4.6%	8.2%
13.00	14.00	3.5%	8.5%	4.5%	8.0%
14.00	14.15	0.9%	2.7%	1.1%	1.6%
14.15	14.30	1.0%	2.2%	1.2%	1.7%
14.30	14.45	1.6%	2.4%	1.4%	1.8%
14.45	15.00	1.5%	2.4%	1.4%	1.7%
15.00	15.15	1.5%	2.8%	2.0%	1.7%
15.15	15.30	1.9%	2.7%	1.8%	2.0%
15.30	15.45	4.7%	2.8%	1.9%	2.0%
15.45	16.00	3.3%	2.9%	1.9%	2.3%
16.00	16.15	2.2%	2.5%	2.2%	2.2%
16.15	16.30	2.2%	2.7%	2.2%	2.1%
16.30	16.45	2.2%	2.8%	2.5%	2.0%
16.45	17.00	2.3%	2.7%	2.9%	2.0%
17.00	17.15	3.1%	2.2%	3.8%	1.9%
17.15	17.30	3.5%	1.8%	4.3%	1.6%
17.30	17.45	3.7%	1.8%	4.6%	1.7%
17.45	18.00	2.8%	1.4%	4.0%	1.4%
18.00	19.00	5.7%	4.5%	7.4%	5.9%
19.00	20.00	2.7%	2.85	3.2%	3.9%
20.00	0.00	4.6%	6.0%	6.4%	10.4%

TableA2.2 Weekday usage percentages

DAY	D all %	D commute %
MONDAY	17.1%	16.1%
TUESDAY	16.4%	16.6%
WEDNESDAY	16.5%	16.7%
THURSDAY	16.8%	17.0%
FRIDAY	15.2%	16.3%
SATURDAY	9.0%	9.9%
SUNDAY	9.0%	7.4%

Table A2.3 Period adjustment factors.

SECONDARY SCHOOL PERIOD	W all (factor)	W commute (factor)
SUMMER HOLIDAYS	1.13	1.02
TERM 1	0.78	0.84
APRIL HOLIDAYS	1.17	0.97
TERM 2	0.98	1.04
JULY HOLIDAYS	1.74	1.40
TERM 3	1.22	1.19
SEPT/OCT HOLIDYAS	1.42	1.24
TERM 4	0.91	0.93

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Page created: 4 October 2004