Open access data for transport research: tools, modelling and simulation

Open access data for transport research: tools, modelling and simulation

Robin Lovelace (2021)

Robin Lovelace (2021)

Motor traffic-centric perspectives dominate road infrastructure planning (Parkin, 2018). Yet, other (active) travel modes, such as cycling and walking, have been found to bring an array of benefits: improved mental and physical health, decarbonisation, as well as being more efficient in terms of space (i.e., requires less space) (Parkin, 2018). Indeed, in the post-pandemic world, active travel will likely become even more important due to reduced public transport capacities, as highlighted by the Department for Transport’s £250m Active Travel Fund (ATF) and £2b allocated to walking and cycling over the next 5 years in the UK alone. Thus, new policies and investment programs, such as the ATF, have led to increased demand for local evidence to inform interventions ranging from new cycleways to improved pavement quality.

 

Therefore, acknowledging the increasing demand for local evidence to inform decision-making, the project will utilise two datasets – OpenStreetMap (OSM) and Ordnance Survey Open Roads (OSOR) – to explore how open datasets can be used to understand, prioritise, and design active travel infrastructure. The overall aim of the project is to understand the use of OSM data for transport planning, which has the potential to lead to more inclusive and accessible evidence-based decision-making due to its crowdsourced nature.

 

Methodologically, the project builds on geographic data science[1] and previous studies assessing open datasets for transport applications (Ferster et al., 2020; Haklay, 2010) and the experiences of lead supervisor (e.g., developing osmextract package in R) and PhD student Caroline Tait (who developed a CycleInfraLnd package in R) to develop a new package in R for downloading, processing, and adding value to transport datasets. To maximise the policy relevance and impact, the outputs of the project will be disseminated nationwide with the support of external partners (Department for Transport, The Open Innovation Team)[2].

 

Bibliography

Ferster, C., Fischer, J., Manaugh, K., Nelson, T., Winters, M., 2020. Using OpenStreetMap to inventory bicycle infrastructure: A comparison with open data from cities. International Journal of Sustainable Transportation 14, 64–73. https://doi.org/10.1080/15568318.2018.1519746

Gilardi, A., Lovelace, R., 2021. osmextract: Download and Import Open Stree tMap Data Extracts. R package version 0.3.1. https://CRAN.R-project.org/package=osmextract

Haklay, M., 2010. How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets. Environment and Planning B: Planning and Design 37, 682–703. https://doi.org/10.1068/b35097

Orozco, L.G.N., Battiston, F., Iñiguez, G., Szell, M., 2020. Data-driven strategies for optimal bicycle network growth. Royal Society Open Science 7, 201130. https://doi.org/10.1098/rsos.201130

Parkin, J., 2018. Designing for Cycle Traffic: International principles and practice. ICE Publishing, London.

Tait, C., Lovelace, R., 2021. CycleInfraLnd: Accesses and download data from the Transport for London Cycling Infrastructure Database into R as spatial data (Simple features). R package version 0.1.0. https://github.com/PublicHealthDataGeek/CycleInfraLnd/

 

[1] Importantly, even though active travel research is multi-disciplinary, it has received little attention from data science, with Orozco et al.’s (2020) paper on modelling cycle network growth providing a notable exception.

[2] If you wish to learn more about the project, visit project’s repository on GitHub: https://github.com/udsleeds/openinfra