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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.

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.

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.

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.

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.

Established methods for synthesising a population from geographically aggregated data are robust and well understood. However, most rely on the potentially detrimental process of integerisation if a whole individual population is required, e.g. for use in agent-based modelling (ABM). This paper describes and investigates the use of quasirandom sequences to sample populations from known marginal constraints whilst preserving those marginal distributions. We call this technique Quasirandom Integer Without-replacement Sampling (QIWS) and show that the statistical properties of quasirandomly sampled populations to be superior to those of pseudorandomly sampled ones in that they tend to yield entropies much closer to populations generated using the entropy-maximising iterative proportional fitting (IPF) algorithm. The implementation is extremely efficient, easily outperforming common IPF implementations. It is freely available as an open source R package called humanleague. Finally, we suggest how the current limitations of the implementation can be overcome, providing a direction for future work.

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.

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

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.