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Zones are the building blocks of urban analysis. Fields ranging from demographics to transport planning routinely use zones - spatially contiguous areal units that break-up continuous space into discrete chunks - as the foundation for diverse analysis techniques. Key methods such as origin-destination analysis and choropleth mapping rely on zones with appropriate sizes, shapes and coverage. However, existing zoning systems are sub-optimal in many urban analysis contexts, for three main reasons: 1) administrative zoning systems are often based on somewhat arbitrary factors; 2) zoning systems that are evidence-based (e.g., based on equal population size) are often highly variable in size and shape, reducing their utility for inter-city comparison; and 3) official zoning systems in many places simply do not exist or are unavailable. We set out to develop a flexible, open and scalable solution to these problems. The result is the zonebuilder project (with R, Rust and Python implementations), which was used to create the ClockBoard zoning system. ClockBoard consists of 12 segments emanating from a central place and divided by concentric rings with radii that increase in line with the triangular number sequence (1, 3, 6 km etc). 'ClockBoards' thus create a consistent visual frame of reference for monocentric cities that is reminiscent of clocks and a dartboard. This paper outlines the design and potential uses of the ClockBoard zoning system in the historical context, and discusses future avenues for research into the design and assessment of zoning systems.

Contraflow cycling on one-way streets is a low cost intervention that research shows can improve the cycling experience and increase participation. Evidence from several studies suggest that cyclists on contraflows have a lower crash risk. However, implementing contraflow cycling is often controversial, including in the United Kingdom (UK). In this paper we examine whether contraflow cycling on one-way streets alters crash or casualty rates for pedal cyclists. Focusing on inner London boroughs between 1998 and 2019, we identified 508 road segments where contraflow cycling was introduced on one-way streets. We identified road traffic crashes occurring within 10 m of these segments and labelled them as pre-contraflow, contraflow or contraflow removed crashes. We calculated rates using the number of crashes or casualties divided by the time exposed and generated 95 % confidence intervals using bootstrap resampling. We adjusted the rates for changes in cordon cycling volume and injury severity reporting. There were 1498 crashes involving pedal cyclists: 788 pre-contraflow, 703 contraflow and 7 following contraflow removal. There was no change in adjusted overall pedal cyclist crash or casualty rates when contraflow cycling was introduced. Proximity to a junction doubled the crash rate. The crash rate when pedal cyclists were travelling contraflow was the same as those travelling with flow. We have found no evidence that introducing contraflow cycling increases the crash or casualty rate for pedal cyclists. It is possible that such rates may indeed fall when contraflow cycling is introduced if more accurate spatio-temporal cycling volume data was available. We recommend all one-way streets are evaluated for contraflow cycling but encourage judicious junction design and recommend UK legislative change for mandatory-two-way cycling on one-way streets unless exceptional circumstances exist.

Origin-Destination (OD) datasets provide vital information on how people travel between areas in many cities, regions and countries worldwide. OD datasets are usually represented geographically with straight lines or routes between zone centroids. For active travel, this geographic representation has substantial limitations, especially when zone origins and centroids are large: only using a single centroid origin/destination for each large zone results in sparse route networks covering only a small fraction of likely walking and cycling routes. This paper implements and explores the use of jittering and different routing options to overcome this limitation, thereby adding value to aggregate OD data to support investment in sustainable transport infrastructure. The route network results — generated from on an open dataset representing cycling trips in Lisbon, Portugal — were compared with a ground-truth dataset from 67 count locations distributed throughout the city. This approach enabled exploration of which jittering parameters and routing options lead to the most accurate route network results approximating the real geographic distribution of cycling trips in the study area. We found that jittering and disaggregating OD data, combined with routing using low level of traffic stress (quieter) preferences resulted in the most accurate route networks. We conclude that a combined approach involving 1) jittering with intermediate levels of disaggregation and 2) careful selection of routing options can lead to much more realistic route networks than using established OD processing techniques. The methods can be deployed to support evidence-based investment in strategic cycling and other sustainable transport networks in cities worldwide.

Origin-destination (OD) datasets are often represented as ‘desire lines’ between zone centroids. This paper presents a ‘jittering’ approach to pre-processing and conversion of OD data into geographic desire lines that (1) samples unique origin and destination locations for each OD pair, and (2) splits ‘large’ OD pairs into ‘sub-OD’ pairs. Reproducible findings, based on the open source odjitter Rust crate, show that route networks generated from jittered desire lines are more geographically diffuse than route networks generated by ‘unjittered’ data. We conclude that the approach is a computationally efficient and flexible way to simulate transport patterns, particularly relevant for modelling active modes. Further work is needed to validate the approach and to find optimal settings for sampling and disaggregation.

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.

Road traffic casualties represent a hidden global epidemic, demanding evidence-based interventions. This paper demonstrates a network lattice approach for identifying road segments of particular concern, based on a case study of a major city (Leeds, UK), in which 5,862 crashes of different severities were recorded over an eight-year period (2011-2018). We consider a family of Bayesian hierarchical models that include spatially structured and unstructured random effects, to capture the dependencies between the severity levels. Results highlight roads that are more prone to collisions, relative to estimated traffic volumes, in the northwest and south of city-centre. We analyse the Modifiable Areal Unit Problem (MAUP), proposing a novel procedure to investigate the presence of MAUP on a network lattice. We conclude that our methods enable a reliable estimation of road safety levels to help identify "hotspots" on the road network and to inform effective local interventions., 23 pages, 5 tables, 8 figures

Bikeability, the extent to which a route network enables cycling for everyday travel, is a frequently cited theme for increasing and diversifying cycling uptake and therefore one that attracts much research attention. Indexes designed to quantify bikeability typically generate a single bikeability value for a single locality. Important to transport planners making and evaluating infrastructure decisions, however, is how well-connected by bike are pairs of localities. For this, it is necessary to estimate the bikeability of plausible routes connecting different parts of a city. We approximate routes for all origin-destination trips cycled in the London Cycle Hire Scheme for 2018 and estimate the bikeability of each route, linking to the newly released London Cycle Infrastructure Database. We then divide the area of inner London covered by the bikeshare scheme into ‘villages’ and profile how bikeability varies for trips connecting those villages – we call this connected bikeability. Our bikeability scores vary geographically with certain localities in London better connected by bike than others. A key finding is that higher levels of connected bikeability are conferred to origin-destination village pairs of strategic importance, aligning with the stated ambition of recent cycling infrastructure interventions. The geography of connected bikeability maps to the commuting needs of London’s workers and we find some evidence that connected bikeability has a positive association with observed cycling activity, especially so when studying patterns of cycling to job-rich villages.

One of the main challenges for policymakers aiming to promote cycling in car-oriented transport systems is how to increase social diversity among its users. The under-representation of certain groups makes it difficult to normalise cycling and distribute its benefits throughout society. Many studies have explored cycling levels in relation to age and gender, and to a lesser extent disability and ethnicity. Less attention has been paid to bicycle use related to income and other socioeconomic factors associated with disadvantage. This study aims to better understand the relationship between income and other factors of socioeconomic disadvantage and cycling behaviour. We analysed data from the English National Travel Survey to estimate the likelihood of cycling participation (if individuals cycle at all), cycling frequency (‘how often’ they cycle), and cycling distance (‘how far’ they cycle) using regression models. We found that people in lower-income households cycle less for transport, particularly for commuting, than people in higher-income households. However, no income inequalities were found for leisure cycling. In addition, low-income leisure cyclists were found to cycle more often, but higher-income leisure cyclists further. Our findings may have important policy implications. Favouring a broader focus on non-commuting cycling and subsidising cycle access and maintenance could help to diversify cycling.

A lack of cycle parking is a known barrier to promoting the uptake of cycling in urban areas. Unlike cars that can be parked on the roadside with little additional infrastructure, bikes usually require dedicated parking facilities. The existing research and guidance on where cycle parking should be provided primarily focuses on key destinations such as train stations or schools. Thus, there is a gap in knowledge about the amount of general-purpose cycle parking required and how it should be distributed across a city. This paper presents a novel method for analysing and prioritising the spatial distribution of cycle parking. The method draws on established portfolio management techniques but applies them in a spatial context. Using the case study of London, we demonstrate that it is possible to identify areas that have a deficit of cycle parking as well as locations that have the most significant potential for increasing cycling uptake by providing additional cycle parking.

The location of new housing developments, and the provision of safe space for walking and cycling to key destinations around them, have major and long lasting impacts on travel behaviour, health, and environmental outcomes. Transit Oriented Development (TOD) is a well-recognised concept in urban planning, but systematic evidence is often lacking on the likely ‘active travel performance’ of new developments, making it hard for the planning process to support sustainable transport objectives. This paper articulates the concept of ‘Active Travel Oriented Development’ (ATOD) and describes methods for operationalising it. We demonstrate the use of a set of simple metrics to assess the active travel performance of new and proposed development sites. ATOD has the benefits of building on the established concept of TOD and being easy to assess. We conclude that ATOD, and tools for measuring it, are needed to ensure that transport and development policies work in harmony., European Journal of Transport and Infrastructure Research, Vol. 22 No. 4 (2022)

Cycling can be particularly beneficial for socioeconomically disadvantaged populations. First, because it can allow them to access opportunities that by other modes they may not afford. Secondly, because it can increase their physical activity levels and, consequently, improve their health. We analyse the extent to which socioeconomic disadvantage impacts on cycling participation in England, for both leisure and utility cycling. Then, we explore the extent to which this impact could be explained by the environment in which disadvantaged populations live. The study population includes 167,178 individuals, residing in 2,931 areas, and 326 Local Authorities. Data on individual factors were drawn from the Active Lives Survey, and data on environmental factors from several sources. Descriptive statistics and multilevel logistic models were estimated. The likelihood of cycling is lower among people living in deprived areas than among people living in non-deprived areas. This difference is significant for leisure, but also for utility cycling when controlling for individual and environmental factors. The study also found that cycling infrastructure and greater levels of cyclability are higher in deprived areas than in non-deprived areas. This suggests that infrastructure and cyclability are not enough to increase cycling levels among disadvantaged populations. Further research on other barriers to cycling among disadvantaged populations is required.

Introduction The Propensity to Cycle Tool (PCT) is a widely used free, open source and publicly available tool for modelling cycling uptake and corresponding health and carbon impacts in England and Wales. In this paper we present the methods for our new individual-level modelling representing all commuters in England and Wales. Methods Scenario commuter cycling potential in the PCT is modelled as a function of route distance and hilliness between home and work. Our new individual-level approach has allowed us to create an additional “Near Market” scenario where age, gender, ethnicity, car ownership and area level deprivation also affect an individual's likelihood of switching to cycling. For this and other scenarios, we calculate the carbon benefits of cycling uptake based on the trip distance and previous mode, while health benefits are additionally affected by hilliness and baseline average mortality risk. This allows the estimation of how health and carbon benefits differ by demographic group as well as by scenario. Results While cycle commuting in England and Wales is demographically skewed towards men and white people, women and people from ethnic minorities have greater cycling potential based on route distance and hilliness. Benefits from cycling uptake are distributed differently again. For example, while increasing female cycling mode share is good for equity, each additional female cyclist generates a smaller average health and carbon benefit than a male cyclist. This is based on women's lower baseline mortality risk, shorter commute travel distances, and lower propensity to commute by car than men. Conclusion We have demonstrated a new approach to modelling that allows for more sophisticated and nuanced assessment of cycling uptake and subsequent benefits, under different scenarios. Health and carbon are increasingly incorporated into appraisal of active travel schemes, valuing important outcomes. However, especially with better representation of demographic factors, this can act as a barrier to equity goals., Highlights • A sophisticated model underlies the Propensity to Cycle Tool (PCT) for England and Wales. • Cycling uptake-test and its health and carbon impacts are simulated under a range of scenarios. • Microsimulation allows analysis of demographic variation in uptake and benefits. • While women and ethnic minorities cycle less, they make shorter more ‘cyclable’ trips. • However, active mode appraisal methods risk placing lower value on their cycling uptake.

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.

Emerging datasets, new methods and shifting transport planning priorities have sparked a renewed interest in the analysis of street networks, linear geometries and attributes representing roads and other transport ways at local, city and regional scales. This paper outlines data structures, vital pre-processing steps and analysis methods using open source software. While the concepts and datasets presented are language agnostic, we implement them in R, an increasingly popular language for geographical and network data analysis. Two approaches are compared: one based on the stplanr package, which contains igraph and sf objects, and the other based on the dodgr package, which defines the dodgr_streetnet class, extending R’s mature data.frame class. A range of datasets, from a roundabout to an entire city network, are used to highlight the main features of each approach. We find that both enable street network analysis tasks, including shortest path and centrality measures. We conclude that the merits and potential pitfalls of the graph-based and data.frame-based approaches should be considered before embarking on street network analysis or software development. Finally, the discussion provides a basis for research using these data structures.

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.

Cycling has a range of benefits as is recognised by national and international policies aiming to increase cycling rates. Darkness acts as a barrier to people cycling, with fewer people cycling after-dark when seasonal and time-of-day factors are accounted for. This paper explores whether road lighting can reduce the negative impact of darkness on cycling rates. Changes in cycling rates between daylight and after-dark were quantified for 48 locations in Birmingham, United Kingdom, by calculating an odds ratio. These odds ratios were compared against two measures of road lighting at each location: 1) Density of road lighting lanterns; 2) Relative brightness as estimated from night-time aerial images. Locations with no road lighting showed a significantly greater reduction in cycling after-dark compared with locations that had some lighting. A nonlinear relationship was found between relative brightness at a location at night and the reduction in cyclists after-dark. Small initial increases in brightness resulted in large reductions in the difference between cyclist numbers in daylight and after-dark, but this effect reached a plateau as brightness increased. These results suggest only a minimal amount of lighting can promote cycling after-dark, making it an attractive mode of transport year-round.

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.

Background Planners and politicians in many countries seek to increase the proportion of trips made by cycling. However, this is often challenging. In England, a national target to double cycling by 2025 is likely to be missed: between 2001 and 2011 the proportion of commutes made by cycling barely grew. One important contributory factor is continued low investment in cycling infrastructure, by comparison to European leaders. Methods This paper examines barriers to cycling investment, considering that these need to be better understood to understand failures to increase cycling level. It is based on qualitative data from an online survey of over 400 stakeholders, alongside seven in-depth interviews. Results Many respondents reported that change continues to be blocked by chronic barriers including a lack of funding and leadership. Participants provided insights into how challenges develop along the life of a scheme. In authorities with little consideration given to cycling provision, media and public opposition were not reported as a major issue. However, where planning and implementation have begun, this can change quickly; although examples were given of schemes successfully proceeding, despite this. The research points to a growing gap between authorities that have overcome key challenges, and those that have not.