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Geographic analysis has long supported transport plans that are appropriate to local contexts. Many incumbent ‘tools of the trade’ are proprietary and were developed to support growth in motor traffic, limiting their utility for transport planners who have been tasked with twenty-first century objectives such as enabling citizen participation, reducing pollution, and increasing levels of physical activity by getting more people walking and cycling. Geographic techniques—such as route analysis, network editing, localised impact assessment and interactive map visualisation—have great potential to support modern transport planning priorities. The aim of this paper is to explore emerging open source tools for geographic analysis in transport planning, with reference to the literature and a review of open source tools that are already being used. A key finding is that a growing number of options exist, challenging the current landscape of proprietary tools. These can be classified as command-line interface, graphical user interface or web-based user interface tools and by the framework in which they were implemented, with numerous tools released as R, Python and JavaScript packages, and QGIS plugins. The review found a diverse and rapidly evolving ‘ecosystem’ tools, with 25 tools that were designed for geographic analysis to support transport planning outlined in terms of their popularity and functionality based on online documentation. They ranged in size from single-purpose tools such as the QGIS plugin AwaP to sophisticated stand-alone multi-modal traffic simulation software such as MATSim, SUMO and Veins. Building on their ability to re-use the most effective components from other open source projects, developers of open source transport planning tools can avoid ‘reinventing the wheel’ and focus on innovation, the ‘gamified’ A/B Street https://github.com/dabreegster/abstreet/#abstreet simulation software, based on OpenStreetMap, a case in point. The paper, the source code of which can be found at https://github.com/robinlovelace/open-gat, concludes that, although many of the tools reviewed are still evolving and further research is needed to understand their relative strengths and barriers to uptake, open source tools for geographic analysis in transport planning already hold great potential to help generate the strategic visions of change and evidence that is needed by transport planners in the twenty-first century.

The development and delivery of ICT-based persuasive strategies for the adoption of more environmental-friendly modes of transport is an emerging strategy to help tackle the problem of excessive us...

A large and growing body of evidence suggests fundamental changes are needed in transport systems, to tackle issues such as air pollution, physical inactivity and climate change. Transport models can play a major role in tackling these issues through the transport planning process, but they have historically been focussed on motorised modes (especially cars) and available only to professional transport planners working within the existing paradigm. Building on the principles of open access software, first developed in the context of geographic information systems, this paper develops and discusses the concept of open access transport models, which we define as models that are both developed using open source software and are available to be used by the public without the need for specialist training or the purchase of software licences. We explore the future potential of open access transport models to support the transition away from fossil fuels in the transport sector. We do this with reference to the literature on the use of tools in the planning process, and by exploring an example that is already in use: the ‘Propensity to Cycle Tool’. We conclude that open access transport models can be a leverage point in the planning process due to their ability to provide robust, transparent and actionable evidence that is available to a range of stakeholders, not just professional transport planners. Open access transport models represent a disruptive technology deserving further research and development, by planners, researchers and citizen scientists, including open source software developers and advocacy groups but, in order to fulfil their potential, they will require both financial and policy support from government bodies.

Pro-cycling interventions, and cycle hire schemes in particular, are often assumed to primarily benefit the privileged. This framing has played-out in academic research, with many papers exploring the relationship between cycling and existing inequalities. A growing body of evidence suggests that cycle hire schemes tend to serve wealthy areas and young, high income groups, mirroring inequalities in other types of cycling uptake, yet there has been little research into the ‘direction of travel’ and whether such inequalities are growing or ‘levelling up’ over time. This paper explores the uptake of the London Cycle Hire Scheme (LCHS), a large, early and prominent scheme that had the explicit aim of ‘normalising’ cycling. The method involved reproducible analysis (with code documented in the GitHub repo Robinlovelace/cycle-hire-inclusive) of 73.4 million cycle high records spanning 8 years from January 2012 to December 2019, using the geographic location of docking stations alongside official statistics to assess social and spatial inequalities in uptake. The method involved analysis of 73.4 million cycle high records spanning 8 years from January 2012 to December 2019, using the geographic location of docking stations alongside official statistics to assess social and spatial inequalities in uptake. We found that, contrary to the trend for increasing segregation and geographic inequalities, the usage of the LCHS have become increasingly geographically distributed across London over time, with AM peak usage in comparatively low-income areas seeing high levels of growth. Our study shows that cycle hire schemes can be designed and expanded in ways that benefit a wide range of people, including those from low income areas, and that new cycle hire docking stations in poorer areas can succeed.

Background Getting people cycling is an increasingly common objective in transport planning. A growing evidence base indicates that high quality infrastructure can boost cycling rates. Yet for measures to be effective, it is important to intervene in the right places. This creates the need for tools and methods to help answer the question ‘where to build?’ The Propensity to Cycle Tool (PCT) www.pct.bike is an open source, freely available, interactive tool to help prioritise cycling in England and Wales, covering both commuting and travel to school. In addition to the tool the data are available as download to allow more detailed GIS analysis. It was created by an academic team for the Department for Transport. Methods The PCT uses origin data from the 2011 Census on main mode of travel to work and the 2011 National School Census covering all state-schools in England. We modelled propensity to cycle as a function of route distance and hilliness between home and school or work. We generated scenarios, e.g. ‘Go Dutch’ – in which people in England were as likely to cycle as theDutch, accounting for trip distance and hilliness. We did this based on a synthetic microsimulation population, allowing flexible scenarios and more accurate impact calculations. We estimated changes in the level of cycling, walking, and driving, and associated impacts on physical activity and carbon emissions. For adults we estimated health economic benefits from reductions in premature mortality and sickness absence from increases in physical activity. Health outcomes were calculated using a bespoke and improved version of the UK Transport Appraisal Guidance (originally based on the WHO HEAT tool). Our improvements include using individual level rather than aggregate data, ebikes, integrating health gains from cycling with losses from less walking, and adjusting physical activity levels for route hilliness. Results The PCT scenarios show the substantial potential for increases in cycling and the large benefits that this could realise. For example in 2011, 1.8% of children cycled to school. Under the Go Dutch scenario, this would rise to 41.0%. This would increase physical activity from school travel among pupils by 57%, and reduce transport-related carbon emissions by 81 kilotonnes/year. These impacts would be substantially larger in secondary schools than primary schools (a 96% vs. 9% increase in physical activity, respectively). Conclusion The PCT is currently used by over 60 local authorities in England and Wales, and is contributing to the development of local policies as part of the Cycling and Walking Investment Strategy.

Background There is a paucity of population-based geospatial data about the association between active transport and myocardial infarction. We investigated the association between active transport to work and incidence of myocardial infarction. Design This ecological study of 325 local authorities in England included 43,077,039 employed individuals aged 25–74 years (UK Census, 2011), and 117,521 individuals with myocardial infarction (Myocardial Ischaemia National Audit Project, 2011–2013). Methods Bayesian negative binomial regression models were used to investigate the association of active transport to work and incidence of myocardial infarction adjusting for local levels of deprivation, obesity, smoking, diabetes and physical activity. Results In 2011, the prevalence of active transportation to work for people in employment in England aged 25–74 years was 11.4% (4,531,182 active transporters; 8.6% walking and 2.8% cycling). Active transport in 2011 was associated with a reduced incidence of myocardial infarction in 2012 amongst men cycling to work (incidence rate ratio (95% credible interval) 0.983 (0.967–0.999); and women walking to work (0.983 (0.967–0.999)) after full adjustments. However, the prevalence of active transport for men and women was not significantly associated with the combined incidence of myocardial infarction between 2011–2013 after adjusting for physical activity, smoking and diabetes. Conclusions In England, the prevalence of active transportation was associated with a reduced incidence of myocardial infarction for women walking and men cycling to work in corresponding local geographic areas. The overall association of active transport with myocardial infarction was, however, explained by local area levels of smoking, diabetes and physical activity.

Open access data, open source software and cloud computing are three parallel and mutually reinforcing drivers of change in the field of applied geographical information systems (GIS). While these developments create many new opportunities for GIS users and companies, they are also associated with under-explored risks. This chapter investigates cloud GIS, focusing on the Australian Urban Research Infrastructure Network (AURIN), an online, cloud-based data portal and GIS designed explicitly for Australian urban research and public-sector planning applications. AURIN provides direct access to more than 4000 data sets, over 100 spatial analysis tools and powerful computational resources that are typically available in desktop GIS. The experience of developing, communicating and using AURIN has provided insights into the opportunities unleashed by new models of GIS research. This chapter explores those opportunities, such as greater accessibility, transparency and consistency in decision support for public policy, and risks of cloud GIS and its potential uses for social benefit.

Car fleets across much of Europe have undergone a process of dieselisation over the past 20 years. Understanding the factors driving this process is therefore important for sustainable transport policy, with implications for how governments steer their national car fleets towards ultra-low emission vehicles in the future. At a general level, this paper contributes to this wider body of work which aims to understand the factors which led to the transition from petrol to diesel. Specifically, the paper investigates whether the availability of relatively cheap diesel fuel in the Republic of Ireland affected the rate of diesel car ownership in Northern Ireland. A geographic approach is used, which involves generating spatial variables measuring nearness to the Republic of Ireland and comparing these with the proportion of the local car stock that is fuelled by diesel. A series of spatial regression models are specified to determine if this association between nearness to the Republic and diesel ownership persists after accounting for the effect of socioeconomic, travel, and household characteristics. The results support the hypothesis that the availability of cheaper fuel in the Republic of Ireland is not only generating fuel-tourism, but is also affecting the structure of the car fleet registered in Northern Ireland. The findings are relevant beyond the case study and imply that the structure of a country’s car fleet is not only dependent on domestic policies, but is also affected by the policies of neighbouring countries.

The London Congestion Charge (LCC) is a transport policy with a precise spatial footprint. As such, its impact on the transport system can be expected to vary over space, providing an opportunity to explore the geographical reach of local transport interventions. This paper assesses whether the exemption of Hybrid Electric Vehicles (HEVs) from the LCC affected the registration rate of these vehicles in Greater London and the surrounding areas. The analysis uses official data on the number of HEVs registered across the local authorities of the United Kingdom. This dataset is assessed using [1] exploratory spatial analysis to determine the degree of spatial variation in HEV registrations, [2] area classifications to consider if HEV registrations diminish as nearness to the LCC recedes, and [3] spatial regression models to evaluate the association between distance to the LCC and HEV registrations, controlling for other area characteristics (i.e. socioeconomic, household, and transport system variables). The results clearly show that nearness to the LCC is positively associated with HEV registrations, implying that this form of transport policy is effective at promoting the adoption of low emission vehicles.

Large and unofficial data sets, for instance those gathered from social media, are increasingly being used in geographical research and explored as decision support tools for policy development. Social media data have the potential to provide new insight into phenomena about which there is little information from conventional sources. Within this context, this paper explores the potential of social media data to evaluate the aesthetic management of landscape. Specifically, this project utilises the perceptions of visitors to the Pennine Way National Trail, which passes through land managed under the Environmental Stewardship Scheme (ESS). The method analyses sentiment in trail users’ public Twitter messages (tweets) with the aim of assessing the extent to which the ESS maintains landscape character within the trail corridor. The method demonstrates the importance of filtering social media data to convert it into useful information. After filtering, the results are based on 161 messages directly related to the trail. Although small, this sample illustrates the potential for social media to be used as a cheap and increasingly abundant source of information. We suggest that social media data in this context should be seen as a resource that can complement, rather than replace, conventional data sources such as questionnaires and interviews. Furthermore, we provide guidance on how social media could be effectively used by conservation bodies, such as Natural England, which are charged with the management of areas of environmental value worldwide.

Factors associated with cycle safety, including international differences in injury and mortality rates, protective equipment and bicycle training, have been subject to increasing academic interest. Environmental variables associated with cycle safety have also been scrutinised, but few studies have focussed on geographical factors at the local level. This paper addresses this research gap by analysing a geo-referenced dataset of road traffic incidents, taken from the UK’s STATS19 dataset (2005–2012). We investigate incidents involving cyclists within West Yorkshire. This is an interesting case study area as it has an historically low cycling rate but very ambitions cycling plans following investment from the Department of Transport. West Yorkshire is found to be an unusually risky area for cyclists, with an estimated 53 deaths and 1372 serious injuries per billion kilometres cycled, based on census commuting statistics. This is roughly double the national average. This riskiness varies spatially and temporally, broadly in line with expectations from the previous literature. An unexpected result was that cycling seems to be disproportionately risky for young people in West Yorkshire compared with young people nationally. The case study raises the issue of potential negative health impacts of promoting cycling amongst vulnerable groups in dangerous areas. We conclude by highlighting opportunities for increasing cycling uptake via measures designed primarily to improve safety. The analysis underlying this research is reproducible, based on code stored at github.com/Robinlovelace/bikeR .

In many large Latin American urban areas such as the Sao Paulo Metropolitan Region (SPMR), growing social and economic inequalities are embedded through high spatial inequality in the provision of state schools and affordable public transport to these schools. This paper sheds light on the transport-education inequality nexus with reference to school accessibility by public transport in the SPMR. To assess school accessibility, we develop an accessibility index which combines information on the spatial distribution of adolescents, the location of existing schools, and the public transport provision serving the school catchment area into a single measure. The index is used to measure school accessibility locally across 633 areas within the SPMR. We use the index to simulate the impact of a policy aiming at increasing the centralisation of public secondary education provision, and find that it negatively affects public transport accessibility for students with the lowest levels of accessibility. These results illustrate how existing inequalities can be amplified by variable accessibility to schools across income groups and geographical space. The research suggests that educational inequality impacts of school agglomeration policies should be considered before centralisation takes place.

There has been much excitement amongst quantitative geographers about newly available datasets, characterised by high volume, velocity and variety. This phenomenon is often labelled as 'Big Data' and has contributed to methodological and empirical advances, particularly in the areas of visualisation and analysis of social networks. However, a fourth v - veracity (or lack thereof) - has been conspicuously lacking from the literature. This paper sets out to test the potential for verifying large datasets. It does this by cross-comparing three unrelated estimates of retail flows --- human movements from home locations to shopping centres --- derived from the following geo-coded sources: 1) a major mobile telephone service provider; 2) a commercial consumer survey; and 3) geotagged Twitter messages. Three spatial interaction models also provided estimates of flow: constrained and unconstrained versions of the 'gravity model' and the recently developed 'radiation model'. We found positive relationships between all data-based and theoretical sources of estimated retail flows. Based on the analysis, the mobile telephone data fitted the modelled flows and consumer survey data closely, while flows obtained directly from the Twitter data diverged from other sources. The research highlights the importance of verification in flow data derived from new sources and demonstrates methods for achieving this.

opentripplanner provides functions that enable multi-modal routing in R. It provides an interface to the OpenTripPlanner (OTP) Java routing service, which allows calculation of routes on large transport networks, locally or via calls to a remote server. The package contains three groups of functions for: (1) setting up and managing a local instance of OTP; (2) connecting to OTP locally or remotely; and (3) sending requests to the OTP API and importing the results and converting them into appropriate classes for further analysis.

Background The Propensity to Cycle Tool (PCT) is a publicly accessible planning support system and modelling project funded by the English Department for Transport (DfT). It calculates cycling potential at area, route and route network level currently, and under different scenarios of cycling uptake, with estimates of health and carbon reduction benefits. Led by an academic team, the PCT is in use by a range of local authorities across England. This talk presents results from case studies in which the PCT was used to assess cycling potential and inform interventions in a range of English urban and rural settings. Methods The PCT uses data on current travel patterns at the origin-destination level to generate scenarios, based on route distance and hilliness. Source data is currently the 2011 English Census on commuting desire lines, allocated to the transport network by CycleStreets.net. The PCT is open source code. The results are freely available online via an interactive web interface (www.pct.bike). Regional data can be downloaded for use by practitioners, campaigners and 'citizen scientists'. Results The results show that the PCT can provide a localised evidence base to improve cycle planning. A key finding is that the places and routes with the highest potential are not necessarily those that currently have the most cycling. This is illustrated at the area level in Bodmin, Cornwall, and at the route level in Ealing, London. The 'Ebike' scenario (representing uptake of electric cycles) highlights the importance of new technologies for boosting cycling potential in hilly and rural areas. Our Ebike scenario (which adds to Dutch cycling propensities an assumption that people use Ebikes for some longer and hillier trips, based on Swiss and Dutch data) indicates substantial additional potential in such contexts, for example in rural West Sussex. Analysis of a proposed bridge (since approved) in Rotherhithe, London, is used to show demonstrated the large additional cycling potential unlocked by replacing a (slow, expensive) ferry with a bridge, substantially shortening many journeys. Finally, case study results from Preston illustrate the health and environmental benefits that achieving scenario potential would provide. These results show that health gains and CO 2 reduction, while both substantial across the case study area, vary geographically and do not always correspond to absolute cycling potential. Conclusion The PCT represents a new approach to cycle planning. It is already influencing transport planning decisions, helping raise ambitions and identify priority areas and routes to prioritise investment in cycling.

Tools for transport planning should be flexible, scalable, and transparent. The stplanr package demonstrates and provides a home for such tools, with an emphasis on spatial transport data and non-motorized modes. The stplanr package facilitates common transport planning tasks including: downloading and cleaning transport datasets; creating geographic “desire lines” from origin-destination (OD) data; route assignment, locally and interfaces to routing services such as CycleStreets.net; calculation of route segment attributes such as bearing and aggregate flow; and ‘travel watershed’ analysis. This paper demonstrates this functionality using reproducible examples on real transport datasets. More broadly, the experience of developing and using R functions for transport applications shows that open source software can form the basis of a reproducible transport planning workflow. The stplanr package, alongside other packages and open source projects, could provide a more transparent and democratically accountable alternative to the current approach, which is heavily reliant on proprietary and relatively inaccessible software.

Iterative Proportional Fitting (IPF), also known as biproportional fitting, ‘raking’ or the RAS algorithm, is an established procedure used in a variety of applications across the social sciences. Primary amongst these for urban modelling has been its use in static spatial microsimulation to generate small area microdata — individual level data allocated to administrative zones. The technique is mature, widely used and relatively straight-forward. Although IPF is well described mathematically, accessible examples of the algorithm written in modern programming languages are rare. There is a tendency for researchers to ‘start from scratch’, resulting in a variety of ad hoc implementations and little evidence about the relative merits of differing approaches. These knowledge gaps mean that answers to certain methodological questions must be guessed: How can ‘empty cells’ be identified and how do they influence model fit? Can IPF be made more computationally efficient? This paper tackles these questions and more using a systematic methodology with publicly available code and data. The results demonstrate the sensitivity of the results to initial conditions, notably the presence of ‘empty cells’, and the dramatic impact of software decisions on computational efficiency. The paper concludes by proposing an agenda for robust and transparent future tests in the field.

Getting people cycling is an increasingly common objective in transport planning institutions worldwide. A growing evidence base indicates that high quality infrastructure can boost local cycling rates. Yet for infrastructure and other cycling measures to be effective, it is important to intervene in the right places, such as along 'desire lines' of high latent demand. This creates the need for tools and methods to help answer the question 'where to build?'. Following a brief review of the policy and research context related to this question, this paper describes the design, features and potential applications of such a tool. The Propensity to Cycle Tool (PCT) is an online, interactive planning support system which was initially developed to explore and map cycling potential across England (see www.pct.bike). Based on origin-destination data, it models and visualises cycling levels at area, desire line, route and route network levels, for current levels of cycling, and for scenario-based 'cycling futures'. Four scenarios are presented, including 'Go Dutch' and 'Ebikes', which explore what would happen if English people cycled as much as Dutch people and the potential impact of electric cycles on cycling uptake. The cost effectiveness of investment depends not only on the number of additional trips cycled, but on wider impacts such as health and carbon benefits. The PCT reports these at area, desire line, and route level for each scenario. The PCT is open source, facilitating the creation of additional scenarios and its deployment in new contexts. We conclude that the PCT illustrates the potential of online tools to inform transport decisions and raises the wider issue of how models should be used in transport planning., Comment: Paper submitted for peer review. 9 figures

Background In parallel with the growing evidence base supporting modal shift to walking and cycling for health and environmental reasons, tools for prioritizing interventions to encourage active travel have evolved. Increasingly detailed datasets provide researchers with insights into how transport systems operate and change, at levels of geographical and temporal resolution that were unavailable a few decades ago. Used in combination with new digital technologies and modelling techniques, such evidence can be made publicly accessible via interactive on-line 'web apps'. This paper explores the potential of such emerging tools to improve and democratize transport decision making for healthier outcomes, with a focus on the development of Propensity to Cycle analysis for Seville, Spain. Methods The National Propensity to Cycle Tool for England (www.pct.bike) is a freely-available online tool commissioned by the UK's Department for Transport to improve the effectiveness of interventions to get people cycling. This paper reports the application of some of the same methods at the city-level in a new context, with a case study of Seville. We report the preparation of Open Street Map (OSM) transport network data, the aggregation of geographical datasets into and origin-destination (OD) matrix of appropriate spatial resolution, and the estimation of current cycling levels and the potential for growth at desire line and route network levels. Results It was found that publicly accessible tools to help prioritize investment in walking and cycling are increasingly viable, even in contexts where detailed OD data (reporting mode split at the desire line level) is absent. The case study of Seville shows how open source software can be used to create reproducible transport planning workflows, the results of which can be made accessible online for public scrutiny and exploration. The results show that there is substantial cycling potential outside of the central area where cycling infrastructure has been constructed, providing an evidence-base for extending Seville’s cycling network into the city’s suburbs. Conclusions The case study of taking ‘propensity to cycle’ methods developed in England and applying them to Seville shows the potential for uptake of open source and open access tools for designing healthier transport system. New digital technologies, if used appropriately and building on the best available evidence, can empower researchers, planners and public with the evidence-based needed to design effective interventions for active travel.

Background Co-benefits are defined as the health and other benefits that can accompany greenhouse gas emission reductions. Modelling studies have identified the potential for large health co-benefits in the transport sector from a mode shift to physically active transport. Findings on emission reductions from active travel have been less clear. Cycling is usually an option for shorter trips, typically exhibiting a strong distance decay function, whilst in most settings the majority of emissions come from longer trips. Despite this there has been little systematic investigation of the circumstances under which active travel can achieve emission reductions and how such scenarios would relate to the size of the health benefits. Health and carbon benefits vary according to who is changing their behaviour and the kind of trips they make and policy goals may not always be aligned. Methods In this study we will fill this gap using a microsimulation model based on individual level travel survey data from England combined with the ITHIM health model. The ITHIM model estimates health impacts from transport scenarios using a comparative risk assessment approach and incorporates variation in disease risk and baseline physical activity levels both between and within age and gender groups. Changes to distributions of physical activity are applied to multiple diseases, using WHO Global Burden of Disease data. The microsimulation model has been implemented in R and parameterised with the English National Travel Survey. In the model four parameters are allowed to vary: 1) the cycling mode split, 2) the distribution of travel distances, 3) the distance decay function for cycling (based on electric bikes), 4) the proportion of multimodal trips amongst longer trips. The potential for trip distance reduction from changing destinations will be allowed to vary by trip purpose, with more individual level latitude for changing non-commuting trips. Results We will search the output of the model to identify those combinations of strategies that would achieve substantive emissions reductions and investigate how these correlate with health benefits. Using person level data we will investigate the top population groups to target for mode shift from a health, a carbon, and a co-benefits perspective. Conclusions The results of this study will guide policy on the extent on urban planning and inform development of cycling policies that can achieve multiple societal benefits.

Background Research indicates that overall, the health benefits of a modal shift to cycling greatly outweigh the risks. However, the extent of these benefits appears to vary across different contexts, in terms of both geography and demography. Within the UK cycling for everyday trips remains low, but we have seen an increase in general across the country despite a decrease in London. It is therefore important to understand the balance between the risks and benefits of cycling for different groups to effectively direct cycling policy. Few studies have focused on the geographical factors that affect cycling risk at a local level. Our approach was to examine the “where, when and who” in cycling road traffic casualties, which is appropriate for developing policies at a local scale. Methods We analysed secondary data from the UK’s STATS19 dataset, which reports road accident data from 2005 to 2012. We developed an equation to measure the underlying level of exposure and examined the spatial, temporal and demographic distribution of cyclist casualties within West Yorkshire. This region was selected due to its historically low cycling rates yet ambitious plans for improvements to its cycling infrastructure. A target of a 300% increase in the rate of cycling by 2026 has been set by the West Yorkshire Integrated Transport Authority, following investment from the Department of Transport. Results West Yorkshire was found to be an unusually risky area for cyclists, with an estimated 53 deaths and 1372 serious injuries per billion kilometres cycled, based on census commuting statistics; this is roughly double the national average. This riskiness varies spatially and temporally, broadly aligning with previous literature. Cyclists were disproportionately at risk on high traffic roads, junctions and roundabouts. The proportion of traffic casualties involving cyclists has increased over time, in line with the national trend. An unexpected result was that cycling appears to be disproportionately risky for young people in West Yorkshire compared with young people nationally. Conclusions This case study progresses our understanding that risk is context-specific and geographically and demographically variable. We have shown that risk to cyclists varies within West Yorkshire, which is more risky than national average for cyclists. We conclude that the identification of local hazardous areas using the methods presented can help to prioritise investment, and inform transport policy.

Cycling has a range of health, environmental and economic benefits compared with motorised forms of transport. There is a need to encourage more cycling, yet previous evaluations of cycling promotion schemes have been inconclusive about what works. A case study of a cycling promotion scheme at the University of Sheffield – the Cycle Challenge – is used in this paper to examine commuting behaviour and long-term behavioural shifts towards cycling in response to outside intervention at the organisational level. The Cycle Challenge was designed to encourage more people at the University to cycle through inter-departmental competition. Cycling behaviour was recorded before the Cycle Challenge and two years after the scheme’s completion. It was found that seventy five percent of participants who were not already regular cyclists reported increased cycling, yet the overall impact of this shift was limited because the majority of participants already cycled regularly. This failure to attract new cyclists suggests recruiting non-cyclists should be a priority in future schemes. Moreover, our study has methodological implications. Current strategies for evaluating the positive impact of cycle initiatives may overestimate the savings by neglecting the tendency of people to resume routine behaviour in the long run. Studies evaluating modal shift should therefore include provision for monitoring long-term behavioural change to provide input into estimated economic, environmental or health metrics of success.