Impacts of Cycling Tool

Impacts of Cycling Tool

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Welcome to the Impacts of Cycling Tool (ICT)

The ICT is designed to do two things:

  1. As a data visualisation tool to look at how people travel in England and how active they are
  2. To look at how things would change under different scenarios in which the probability of being a cyclist increases

The starting point for the ICT is weekly data from the National Travel Survey for England from 2004 till 2014. This allows us to look at how different groups in the population travel. To estimate physical activity we use both travel activity and other leisure time physical activity. Our leisure time activity estimates come from combining the National Travel Survey data with data on similar people from the Active People Survey 2012-2015.

The scenarios are based around assuming that some non-cyclists become cyclists. By this we mean they have the same probability of cycling each trip as current cyclists. Note that if someone becomes a cyclist then that doesn't mean they cycle all or even most of their trips - people are more likely to cycle short trips than long ones, and our analysis indicates that the distance people are willing to cycle falls more quickly for women and older people. For this reason we probabilistically model which of the trips are cycled based on the trip distances and the person's age and gender. This process means that some people who become cyclists may not cycle any trips in a given week.

In modelling scenarios in which some non-cyclists become cyclists, we use three input parameters:

  1. The % of Population who are Potential Cyclists refers to the proportion of the total population in the area of interest who are cyclists. A value of 75% roughly corresponds to the current situation in the Netherlands.
  2. In Equity scenarios we assume that men and women and older and younger people are equally likely to become cyclists. Without Equity we assume current inequalities remain in the probability of becoming a cyclist.
  3. In Ebike scenarios we assume that new cyclists have got an ebike and so will be willing to cycle longer trips. We assume that with an ebike all population groups have the same probability of cycling a trip of a given length.

Thus by itself the Ebike scenario equalises the distances that people of different ages and genders are willing to cycle, but leaves unchanged age/gender differences in the probability of becoming a cyclist. By itself, the Equity scenario does the opposite, equalising the probability that people of different ages and genders become cyclsits, but leaving unchanged age/gender differences in the distance they are willing to cycle. In combination an Equity + Ebike scenario equalises both the probability of being a cyclist and the distance that people are willing to cycle.

The effects of people switching their trips to cycling are assessed across multiple domains including:

The impact on mortality risk of increasing activity is estimated based on this study here.















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Displays plots for mode share of trips based on main mode only. A scenario is selected by a combination of three inputs: % of Population who are Regular Cyclists, Equity and Ebike. Users can choose to compare mode share between selected sub-populations and the total population, and/or between selected scenarios and baseline.
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Displays plots of the change in journey time for trips that have been switched to cycling in a scenario, stratified by the previous main mode of the trip. A scenario is selected by a combination of three inputs: % of Population who are Regular Cyclists, Equity and Ebike. Users can choose to compare mode share between selected sub-populations and the total population, and/or between selected scenarios and baseline.
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Displays two plots for total miles cycled per cyclist per week, where a selected scenario is compared with the baseline. Note that the bar charts do not include a bar for people with zero cycling. In order to see the total number of cyclists in scenarios, please refer to the 'Number of Cyclists' in the Summary tab. Users can use the 'Denominator' option to switch between showing percentages relative to a) the total population or b) all cyclists. Users can choose to compare miles cycled between selected sub-populations and the total population, and/or between selected scenarios and baseline.
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Displays histogram of total physical activity and also the percentage of the population meeting the physical activity guidelines of the World Health Organization (WHO).

The WHO guidelines are for 150 minutes of moderate intensity or 75 minutes of vigorous intensity activity, with additional benefits by achieving 300 minutes of moderate intensity or 150 minutes of vigorous intensity activity. We have translated these guidelines into Marginal Metabolic Equivalent Task (MMET) hours per week. MMETs represent the body mass adjusted energy expenditure above resting. To do this we have assumed that moderate intensity activity is 3.5 MMETs, meaning that the lower target is 8.75 MMET hours per week, and the higher target is 17.5 MMET hours per week. We have assumed the MMET rates are 3.6 for walking, 5.4 for cycling, and 3.5 for ebikes (Costa et al., 2015 and Sperlich et al., 2012). Thus the lower target could be achieved by 145 minutes per week of walking, 97 minutes of cycling, or 150 minutes of ebiking.

Non-travel activity is estimated using self-reported data from probabilistically matched individuals of a similar age, gender, and ethnicity from the Health Survey for England 2012.

Users can choose to compare physical activity between selected sub-populations and the total population, and/or between selected scenarios and baseline.

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Displays two plots for health gains measured as Years of Life Lost (YLL) and Premature Deaths Averted. YLLs are taken from the Global Burden of Disease Study for the UK 2013. YLL is an estimate of the age specific life expectancy against an 'ideal' reference population. A scenario is selected by a combination of three inputs: % of Population who are Regular Cyclists, Equity and Ebike - this scenario can then be compared against baseline or against an alternative scenario. Results are presented by age and gender, or the display can be restricted to particular age and gender groups using the subpopulation option.
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Population distributions of car distance - for car miles reduced see Summary tab. Displays two plots for total Car Miles per week for the whole population in the selected scenario and baseline. Car Miles are calculated as the sum of all miles spent travelling as a car/van driver, a car/van passenger, by motorcycle or by taxi. Users can choose to compare car miles between selected sub-populations and the total population, and/or between selected scenarios and baseline.
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Population distributions of CO2 from car travel - for CO2 reduced see Summary tab. Displays two plots for CO2 produced during car travel, defined as travel as a car/van driver or car/van passenger. Users can choose to compare CO2 emissions from car travel between selected sub-populations and the total population, and/or between selected scenarios and baseline.

Welcome to the Impacts of Cycling Tool (ICT)

Impacts of Cycling Tool (ICT) is released under an Affero GPL and we accept no liability, included but not limited to loss or damages. The code of which is hosted on GitHub.

We would like to thank the Department for Transport for funding received as part of the Propensity to Cycle Tool (PCT) Project

The ICT has been created by CEDAR. The ICT academic project members include:

The Impacts of Cycling Tool (ICT) was designed to do two main things:

  1. As a data visualisation tool to look at how people travel in England and how active they are
  2. To look at how things would change under different scenarios in which the probability of being a cyclist increases

For more information or questions, please contact us at: jw745@cam.ac.uk