tds.bib

@book{_python_a,
  title = {Python {{Polars}}: {{The Definitive Guide}}},
  shorttitle = {Python {{Polars}}},
  url = {https://learning.oreilly.com/library/view/python-polars-the/9781098156077/},
  urldate = {2025-01-22},
  abstract = {Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing....},
  isbn = {978-1-09-815607-7},
  langid = {english}
}

@software{abstreet_2022,
  title = {A/{{B Street}}},
  shorttitle = {A-b-Street/Abstreet},
  author = {Carlino, Dustin and Li, Yuwen and Kirk, Michael and Konieczny, Mateusz and Kott, Gedalia and Bruce and Nissar, Javed and Nederlof, Trevor and Steinberg, Vinzent and Lovelace, Robin and Dejean, Marcel and Orestis and Lauzier, Rémi and Sam and Wei, Andrew and Lewis, Brian and Schmidt, Christopher and Serdyuk, Dmitriy and Czaplicki, Filip and Fullstop000 and Raymakers, Jeff and Volker, Jeremias and Huston, Kyle and Schmid, Lorenz and Shenfield, Max and Schimek, Nick and Newsome, Tim and Tobias and Andi and {moo}},
  date = {2022-03-06},
  doi = {10.5281/zenodo.6331922},
  url = {https://zenodo.org/record/6331922},
  urldate = {2022-03-12},
  abstract = {overhaul the rat-run mode: tighten up things counting as shortcuts, keep the same path after adding a filter change the LTN route planning tool to be map-wide, not per-neighborhood place filters at non 4-way intersections shrink some cases of overlapping roads, improving geometry when OSM data is not great a first round of simplifying LTN panels when the LTN blockfinding would've crashed previously, just fallback to using more expensive blockfinding bugfix: detect existing filters on really short roads fix initial camera placement outside of the main A/B Street app},
  organization = {Zenodo}
}

@software{allaire_quarto_2024,
  title = {Quarto},
  author = {Allaire, J.J. and Teague, Charles and Scheidegger, Carlos and Xie, Yihui and Dervieux, Christophe and Woodhull, Gordon},
  date = {2024-11},
  doi = {10.5281/zenodo.5960048},
  url = {https://github.com/quarto-dev/quarto-cli},
  urldate = {2025-01-21},
  abstract = {Open-source scientific and technical publishing system built on Pandoc.},
  version = {1.6}
}

@article{arribas-bel_course_2019,
  title = {A Course on {{Geographic Data Science}}},
  author = {Arribas-Bel, Dani},
  date = {2019-04-26},
  journaltitle = {Journal of Open Source Education},
  shortjournal = {JOSE},
  volume = {2},
  number = {14},
  pages = {42},
  issn = {2577-3569},
  doi = {10.21105/jose.00042},
  url = {https://jose.theoj.org/papers/10.21105/jose.00042},
  urldate = {2025-01-22},
  annotation = {13 citations (Crossref) [2025-01-22]}
}

@article{boeing_osmnx_2017,
  title = {{{OSMnx}}: {{New Methods}} for {{Acquiring}}, {{Constructing}}, {{Analyzing}}, and {{Visualizing Complex Street Networks}}},
  shorttitle = {{{OSMnx}}},
  author = {Boeing, Geoff},
  date = {2017-09-01},
  journaltitle = {Computers, Environment and Urban Systems},
  shortjournal = {Computers, Environment and Urban Systems},
  volume = {65},
  pages = {126--139},
  issn = {0198-9715},
  doi = {10/gbvjxq},
  url = {http://www.sciencedirect.com/science/article/pii/S0198971516303970},
  urldate = {2017-07-13},
  abstract = {Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon.},
  keywords = {\nosource,Complex networks,GIS,nosource,OpenStreetMap,Python,Resilience,Street network,transportation,Urban design,Urban form,visualization}
}

@software{carlino_od2net_2024,
  title = {Od2net},
  author = {Carlino, Dustin},
  date = {2024-12-17T12:26:45Z},
  origdate = {2023-07-14T17:27:09Z},
  url = {https://github.com/Urban-Analytics-Technology-Platform/od2net},
  urldate = {2025-01-21},
  organization = {Urban-Analytics-Technology-Platform}
}

@software{carlino_osm2streets_2025,
  title = {Osm2streets},
  author = {Carlino, Dustin},
  date = {2025-01-17T05:28:51Z},
  origdate = {2022-05-01T18:01:10Z},
  url = {https://github.com/a-b-street/osm2streets},
  urldate = {2025-01-21},
  abstract = {Convert OSM to street networks with detailed geometry},
  organization = {A/B Street}
}

@book{dorman_geocomputation_2025,
  title = {Geocomputation with {{Python}}},
  author = {Dorman, Michael and Graser, Anita and Nowosad, Jakub and Lovelace, Robin},
  date = {2025-02-14},
  eprint = {Gl03EQAAQBAJ},
  eprinttype = {googlebooks},
  publisher = {CRC Press},
  url = {https://py.geocompx.org/},
  abstract = {Geocomputation with Python is a comprehensive resource for working with geographic data with the most popular programming language in the world. The book gives an overview of Python's capabilities for spatial data analysis, as well as dozens of worked-through examples covering the entire range of standard GIS operations. A unique selling point of the book is its cohesive and joined-up coverage of both vector and raster geographic data models and consistent learning curve. This book is an excellent starting point for those new to working with geographic data with Python, making it ideal for students and practitioners beginning their journey with Python.Key features: Showcases the integration of vector and raster datasets operations. Provides explanation of each line of code in the book to minimize surprises. Includes example datasets and meaningful operations to illustrate the applied nature of geographic research. Another unique feature is that this book is part of a wider community. Geocomputation with Python is a sister project of Geocomputation with R (Lovelace, Nowosad, and Muenchow 2019), a book on geographic data analysis, visualization, and modeling using the R programming language that has numerous contributors and an active community.The book teaches how to import, process, examine, transform, compute, and export spatial vector and raster datasets with Python, the most widely used language for data science and many other domains. Reading the book and running the reproducible code chunks within will make you a proficient user of key packages in the ecosystem, including shapely, geopandas, and rasterio. The book also demonstrates how to make use of dozens of additional packages for a wide range of tasks, from interactive map making to terrain modeling. Geocomputation with Python provides a firm foundation for more advanced topics, including spatial statistics, machine learning involving spatial data, and spatial network analysis, and a gateway into the vibrant and supportive community developing geographic tools in Python and beyond.},
  isbn = {978-1-04-030160-9},
  langid = {english},
  pagetotal = {309},
  keywords = {Computers / Mathematical & Statistical Software,Mathematics / Probability & Statistics / General,Psychology / Research & Methodology,Science / Earth Sciences / Geology,Science / Life Sciences / Biological Diversity,Science / Life Sciences / Botany,Social Science / Human Geography}
}

@inproceedings{fernandez_natural_2016,
  title = {Natural User Interfaces for Human-Drone Multi-Modal Interaction},
  booktitle = {2016 {{International Conference}} on {{Unmanned Aircraft Systems}} ({{ICUAS}})},
  author = {Fernandez, Ramon A. Suarez and Sanchez-Lopez, Jose Luis and Sampedro, Carlos and Bavle, Hriday and Molina, Martin and Campoy, Pascual},
  date = {2016-06},
  pages = {1013--1022},
  publisher = {IEEE},
  location = {Arlington, VA, USA},
  doi = {10/gkb5gt},
  url = {http://ieeexplore.ieee.org/document/7502665/},
  urldate = {2019-02-06},
  eventtitle = {2016 {{International Conference}} on {{Unmanned Aircraft Systems}} ({{ICUAS}})},
  isbn = {978-1-4673-9334-8},
  keywords = {\nosource,nosource}
}

@book{fox_data_2018,
  title = {Data {{Science}} for {{Transport}}: {{A Self-Study Guide}} with {{Computer Exercises}}},
  shorttitle = {Data {{Science}} for {{Transport}}},
  author = {Fox, Charles},
  date = {2018-03-04},
  edition = {1st ed. 2018 edition},
  publisher = {Springer},
  location = {New York, NY},
  abstract = {The quantity, diversity and availability of transport data is increasing rapidly, requiring new skills in the management and interrogation of data and databases. Recent years have seen a new wave of "big data", "Data Science", and "smart cities" changing the world, with the Harvard Business Review describing Data Science as the "sexiest job of the 21st century". Transportation professionals and researchers need to be able to use data and databases in order to establish quantitative, empirical facts, and to validate and challenge their mathematical models, whose axioms have traditionally often been assumed rather than rigorously tested against data. This book takes a highly practical approach to learning about Data Science tools and their application to investigating transport issues. The focus is principally on practical, professional work with real data and tools, including business and ethical issues."Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am not sure whether current professionals have these skills; and I am certainly not convinced that our current transport modeling tools will survive in a data rich environment. This is an exciting time to be a data scientist in the transport field. We are trying to get to grips with the opportunities that big data sources offer; but at the same time such data skills need to be fused with an understanding of transport, and of transport modeling. Those with these combined skills can be instrumental at providing better, faster, cheaper data for transport decision- making; and ultimately contribute to innovative, efficient, data driven modeling techniques of the future. It is not surprising that this course, this book, has been authored by the Institute for Transport Studies. To do this well, you need a blend of academic rigor and practical pragmatism. There are few educational or research establishments better equipped to do that than ITS Leeds". - Tom van Vuren, Divisional Director, Mott MacDonald"WSP is proud to be a thought leader in the world of transport modelling, planning and economics, and has a wide range of opportunities for people with skills in these areas. The evidence base and forecasts we deliver to effectively implement strategies and schemes are ever more data and technology focused a trend we have helped shape since the 1970's, but with particular disruption and opportunity in recent years. As a result of these trends, and to suitably skill the next generation of transport modellers, we asked the world-leading Institute for Transport Studies, to boost skills in these areas, and they have responded with a new MSc programme which you too can now study via this book." - Leighton Cardwell, Technical Director, WSP."From processing and analysing large datasets, to automation of modelling tasks sometimes requiring different software packages to "talk" to each other, to data visualization, SYSTRA employs a range of techniques and tools to provide our clients with deeper insights and effective solutions. This book does an excellent job in giving you the skills to manage, interrogate and analyse databases, and develop powerful presentations. Another important publication from ITS Leeds." - Fitsum Teklu, Associate Director (Modelling \& Appraisal) SYSTRA Ltd"Urban planning has relied for decades on statistical and computational practices that have little to do with mainstream data science. Information is still often used as evidence on the impact of new infrastructure even when it hardly contains any valid evidence. This book is an extremely welcome effort to provide young professionals with the skills needed to analyse how cities and transport networks actually work. The book is also highly relevant toanyone who will later want to build digital solutions to optimise urban travelbased on emerging data sources". - Yaron Hollander, author of "Transport Modelling for a Complete Beginner"},
  isbn = {978-3-319-72952-7},
  pagetotal = {206},
  keywords = {\nosource,nosource}
}

@book{gillespie_efficient_2016,
  title = {Efficient {{R Programming}}: {{A Practical Guide}} to {{Smarter Programming}}},
  author = {Gillespie, Colin and Lovelace, Robin},
  date = {2016},
  publisher = {O'Reilly Media},
  url = {https://csgillespie.github.io/efficientR/},
  isbn = {978-1-4919-5078-4},
  keywords = {\nosource,nosource}
}

@article{goodman_scenarios_2019,
  title = {Scenarios of Cycling to School in {{England}}, and Associated Health and Carbon Impacts: {{Application}} of the ‘{{Propensity}} to {{Cycle Tool}}’},
  shorttitle = {Scenarios of Cycling to School in {{England}}, and Associated Health and Carbon Impacts},
  author = {Goodman, Anna and Rojas, Ilan Fridman and Woodcock, James and Aldred, Rachel and Berkoff, Nikolai and Morgan, Malcolm and Abbas, Ali and Lovelace, Robin},
  date = {2019-03-01},
  journaltitle = {Journal of Transport \& Health},
  shortjournal = {Journal of Transport \& Health},
  volume = {12},
  pages = {263--278},
  issn = {2214-1405},
  doi = {10/ghdcfx},
  url = {http://www.sciencedirect.com/science/article/pii/S2214140518301257},
  urldate = {2019-03-04},
  abstract = {Background The Propensity to Cycle Tool (PCT) is a freely available, interactive tool help prioritise cycling initially launched in England in 2017 and based on adult commuting data. This paper applies the method to travel to school data, and assesses health and carbon benefits based on nationwide scenarios of cycling uptake. Methods The 2011 National School Census provides origin-destination data for all state-funded schools in England (N = 7,442,532 children aged 2–18 in 21,443 schools). Using this dataset, we modelled propensity to cycle as a function of route distance and hilliness between home and school. We generated scenarios, including ‘Go Dutch’ – in which English children were as likely to cycle as Dutch children, accounting for trip distance and hilliness. We estimated changes in the level of cycling, walking, and driving, and associated impacts on physical activity and carbon emissions. Results In 2011, 1.8\% of children cycled to school (1.0\% in primary school, 2.7\% in secondary school). If Dutch levels of cycling were reached, under the Go Dutch scenario, this would rise to 41.0\%, a 22-fold increase. This is larger than the 6-fold increase in Go Dutch for adult commuting. This would increase total physical activity 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 Cycling to school is uncommon in England compared with other Northern European countries. Trip distances and hilliness alone cannot explain the difference, suggesting substantial unmet potential. We show that policies resulting in substantial uptake of cycling to school would have important health and environmental benefits. At the level of road networks, the results can inform local investment in safe routes to school to help realise these potential benefits.},
  keywords = {Active travel,Carbon emissions,Cycling,Modelling,nosource,Physical activity,School}
}

@article{graells-garrido_adoptiondriven_2020,
  title = {Adoption-{{Driven Data Science}} for {{Transportation Planning}}: {{Methodology}}, {{Case Study}}, and {{Lessons Learned}}},
  shorttitle = {Adoption-{{Driven Data Science}} for {{Transportation Planning}}},
  author = {Graells-Garrido, Eduardo and Peña-Araya, Vanessa and Bravo, Loreto},
  date = {2020-01},
  journaltitle = {Sustainability},
  volume = {12},
  number = {15},
  pages = {6001},
  publisher = {Multidisciplinary Digital Publishing Institute},
  doi = {10/gmzkp5},
  url = {https://www.mdpi.com/2071-1050/12/15/6001},
  urldate = {2021-09-30},
  abstract = {The rising availability of digital traces provides a fertile ground for data-driven solutions to problems in cities. However, even though a massive data set analyzed with data science methods may provide a powerful and cost-effective solution to a problem, its adoption by relevant stakeholders is not guaranteed due to adoption barriers such as lack of interpretability and interoperability. In this context, this paper proposes a methodology toward bridging two disciplines, data science and transportation, to identify, understand, and solve transportation planning problems with data-driven solutions that are suitable for adoption by urban planners and policy makers. The methodology is defined by four steps where people from both disciplines go from algorithm and model definition to the development of a potentially adoptable solution with evaluated outputs. We describe how this methodology was applied to define a model to infer commuting trips with mode of transportation from mobile phone data, and we report the lessons learned during the process.},
  issue = {15},
  langid = {english},
  keywords = {data science,mobile phone data,transportation,urban mobility}
}

@article{gschwender_using_2016,
  title = {Using {{Smart Card}} and {{GPS Data}} for {{Policy}} and {{Planning}}: {{The Case}} of {{Transantiago}}},
  shorttitle = {Using {{Smart Card}} and {{GPS Data}} for {{Policy}} and {{Planning}}},
  author = {Gschwender, Antonio and Munizaga, Marcela and Simonetti, Carolina},
  date = {2016-11-01},
  journaltitle = {Research in Transportation Economics},
  shortjournal = {Research in Transportation Economics},
  series = {Competition and {{Ownership}} in {{Land Passenger Transport}} (Selected Papers from the {{Thredbo}} 14 Conference)},
  volume = {59},
  pages = {242--249},
  issn = {0739-8859},
  doi = {10/ghp5pr},
  url = {http://www.sciencedirect.com/science/article/pii/S0739885915300998},
  urldate = {2018-06-11},
  abstract = {The introduction in 2007 of a new public transport system in Santiago, Chile, brought to us an unexpected gift: the availability of Big Data; massive amounts of passive data obtained from technological devices installed to control the operation of buses and to administer the fare collection process. Many other cities in the world have experienced the same, and sooner or later, this is likely to happen everywhere. Seeing this opportunity, many researchers have developed tools to obtain valuable information from the available data. However, the case of Transantiago is particularly advantageous because all buses have GPS devices and the smart card presents an overall 97\% penetration rate. We describe a successful experience of collaboration between academia and the public transport authority to develop tools based on passive data processing. We include a brief description of the Transantiago system and the agreements made to develop the aforementioned tools. We also describe the methods developed to obtain valuable information like public transport trips origin-destination matrices, speed profiles of buses and service quality indicators, among others. Several examples of specific uses of the information for public transport policy and planning in Santiago are presented. The paper concludes with a discussion of what else can be obtained from this data and why we believe that this can change the way we do transport planning.},
  keywords = {\nosource,Automatic fare collection,Automatic vehicle location,nosource,Passive data,Public transport}
}

@book{heavey_modern_,
  title = {Modern {{Polars}}},
  author = {Heavey, Kevin},
  url = {https://kevinheavey.github.io/modern-polars/},
  urldate = {2025-01-22},
  abstract = {A side-by-side comparison of the Polars and Pandas libraries.},
  langid = {english}
}

@book{heer_visualization_2021,
  title = {Visualization {{Curriculum}}},
  author = {Heer, Jeffrey},
  date = {2021},
  url = {https://idl.uw.edu/visualization-curriculum/intro.html},
  urldate = {2025-01-22},
  abstract = {A data visualization curriculum of interactive notebooks, using Vega-Lite and Altair. This book contains a series of Python-based Jupyter notebooks, a corresponding set of JavaScript notebooks are available online on Observable.}
}

@software{heis_introduction_2025,
  title = {Introduction to {{GitHub}}},
  author = {Heis, Kevin},
  date = {2025-01-21T16:29:47Z},
  origdate = {2022-01-06T21:33:15Z},
  url = {https://github.com/skills/introduction-to-github},
  urldate = {2025-01-21},
  abstract = {Get started using GitHub in less than an hour.},
  organization = {GitHub Skills},
  keywords = {branches,commits,git,pull-requests,skills-course}
}

@book{lovelace_geocomputation_2025,
  title = {Geocomputation with {{R}}},
  author = {Lovelace, Robin and Nowosad, Jakub and Münchow, Jannes},
  date = {2025},
  eprint = {uRrl0AEACAAJ},
  eprinttype = {googlebooks},
  publisher = {CRC Press},
  url = {https://r.geocompx.org/},
  abstract = {"Geocomputation with R is for people who want to analyze, visualize, and model geographic data with open source software. The book provides a foundation for learning how to solve a wide range of geographic data analysis problems in a reproducible, and therefore scientifically sound and scalable way. The second edition features numerous updates, including the adoption of the high-performance terra package for all raster data processing, detailed coverage of the spherical geometry engine s2, updated information on coordinate reference systems and new content on openEO, STAC, COG, and gdalcubes. The data visualization chapter has been revamped around version 4 of the tmap package, providing a fresh perspective on creating publication-quality maps from the command line"--},
  isbn = {978-1-00-328056-9},
  langid = {english},
  pagetotal = {book},
  keywords = {Travel / General}
}

@article{lovelace_open_2020,
  ids = {lovelace_open_2020a},
  title = {Open Access Transport Models: {{A}} Leverage Point in Sustainable Transport Planning},
  shorttitle = {Open Access Transport Models},
  author = {Lovelace, Robin and Parkin, John and Cohen, Tom},
  date = {2020-10-01},
  journaltitle = {Transport Policy},
  shortjournal = {Transport Policy},
  volume = {97},
  pages = {47--54},
  issn = {0967-070X},
  doi = {10/ghfsg6},
  url = {http://www.sciencedirect.com/science/article/pii/S0967070X19302781},
  urldate = {2020-08-04},
  abstract = {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.},
  langid = {english},
  keywords = {Accessible models,Cycling,Demand modelling,Open access data,Open access models,Open access software,Sustainable transport,Transport planning}
}

@article{lovelace_open_2021,
  ids = {lovelace_open_2021a},
  title = {Open Source Tools for Geographic Analysis in Transport Planning},
  author = {Lovelace, Robin},
  date = {2021-01-16},
  journaltitle = {Journal of Geographical Systems},
  shortjournal = {J Geogr Syst},
  volume = {23},
  publisher = {{Springer Science and Business Media LLC}},
  issn = {1435-5949},
  doi = {10/ghtnrp},
  url = {https://doi.org/10.1007/s10109-020-00342-2},
  urldate = {2021-01-17},
  abstract = {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/\#abstreetsimulation 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.},
  langid = {english}
}

@article{lovelace_propensity_2017,
  title = {The {{Propensity}} to {{Cycle Tool}}: {{An}} Open Source Online System for Sustainable Transport Planning},
  shorttitle = {The {{Propensity}} to {{Cycle Tool}}},
  author = {Lovelace, Robin and Goodman, Anna and Aldred, Rachel and Berkoff, Nikolai and Abbas, Ali and Woodcock, James},
  date = {2017-01-01},
  journaltitle = {Journal of Transport and Land Use},
  shortjournal = {JTLU},
  volume = {10},
  number = {1},
  issn = {1938-7849},
  doi = {10.5198/jtlu.2016.862},
  url = {https://www.jtlu.org/index.php/jtlu/article/view/862},
  urldate = {2024-03-20},
  abstract = {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. is 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. e Propensity to Cycle Tool (PCT) is an online, interactive planning support system that was initially developed to explore and map cycling potential across England (see www.pct.bike). Based on origin-destination data it models 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 had the same propensity to cycle as Dutch people and the potential impact of electric cycles on cycling uptake. e cost effectiveness of investment depends not only on the number of additional trips cycled, but on wider impacts such as health and carbon benefits. e PCT reports these at area, desire line, and route level for each scenario. e PCT is open source, facilitating the creation of scenarios and 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.},
  langid = {english},
  keywords = {Cycling,modelling,Participatory,Planning},
  annotation = {73 citations (Crossref) [2024-06-12]}
}

@report{lovelace_reproducible_2020,
  title = {Reproducible Road Safety Research with {{R}}},
  author = {Lovelace, Robin},
  date = {2020},
  pages = {102},
  institution = {RAC Foundation},
  url = {https://www.racfoundation.org/wp-content/uploads/Reproducible_road_safety_research_with_R_Lovelace_December_2020.pdf},
  langid = {english}
}

@article{lovelace_stats19_2019,
  title = {Stats19: {{A}} Package for Working with Open Road Crash Data},
  author = {Lovelace, Robin and Morgan, Malcolm and Hama, Layik and Padgham, Mark},
  date = {2019},
  journaltitle = {Journal of Open Source Software},
  doi = {10/gkb498},
  url = {http://doi.org/10.21105/joss.01181},
  keywords = {\nosource,nosource}
}

@article{lovelace_stplanr_2018,
  title = {Stplanr: {{A Package}} for {{Transport Planning}}},
  author = {Lovelace, Robin and Ellison, Richard},
  date = {2018},
  journaltitle = {The R Journal},
  volume = {10},
  number = {2},
  pages = {7--23},
  doi = {10/gkb499},
  url = {https://doi.org/10.32614/RJ-2018-053},
  urldate = {2016-11-24},
  abstract = {stplanr - R package providing functions and data access for transport research},
  keywords = {\nosource,nosource}
}

@book{mckinney_python_2022,
  title = {Python for {{Data Analysis}}: {{Data Wrangling}} with Pandas, {{NumPy}}, and {{Jupyter}}},
  shorttitle = {Python for {{Data Analysis}}},
  author = {McKinney, Wes},
  date = {2022-09-20},
  edition = {3rd edition},
  publisher = {O'Reilly Media},
  location = {Beijing Boston Farnham Sebastopol Tokyo},
  url = {https://wesmckinney.com/book/},
  abstract = {Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.  Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the Jupyter notebook and IPython shell for exploratory computing Learn basic and advanced features in NumPy Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples},
  isbn = {978-1-09-810403-0},
  langid = {english},
  pagetotal = {579}
}

@article{morgan_opentripplanner_2019,
  title = {{{OpenTripPlanner}} for {{R}}},
  author = {Morgan, Malcolm and Young, Marcus and Lovelace, Robin and Hama, Layik},
  date = {2019-12-02},
  journaltitle = {Journal of Open Source Software},
  volume = {4},
  number = {44},
  pages = {1926},
  issn = {2475-9066},
  doi = {10/gkb5nh},
  url = {https://joss.theoj.org/papers/10.21105/joss.01926},
  urldate = {2020-01-29},
  abstract = {Morgan et al., (2019). OpenTripPlanner for R. Journal of Open Source Software, 4(44), 1926, https://doi.org/10.21105/joss.01926},
  langid = {english},
  annotation = {ZSCC: 0000000}
}

@article{nateraorozco_datadriven_2020,
  ids = {orozco_datadriven_2020},
  title = {Data-Driven Strategies for Optimal Bicycle Network Growth},
  author = {Natera Orozco, Luis Guillermo and Battiston, Federico and Iñiguez, Gerardo and Szell, Michael},
  date = {2020-12-16},
  journaltitle = {Royal Society Open Science},
  volume = {7},
  number = {12},
  pages = {201130},
  publisher = {Royal Society},
  doi = {10/gmf8dc},
  url = {https://royalsocietypublishing.org/doi/full/10.1098/rsos.201130},
  urldate = {2021-08-07},
  abstract = {Urban transportation networks, from pavements and bicycle paths to streets and railways, provide the backbone for movement and socioeconomic life in cities. To make urban transport sustainable, cities are increasingly investing to develop their bicycle networks. However, it is yet unclear how to extend them comprehensively and effectively given a limited budget. Here we investigate the structure of bicycle networks in cities around the world, and find that they consist of hundreds of disconnected patches, even in cycling-friendly cities like Copenhagen. To connect these patches, we develop and apply data-driven, algorithmic network growth strategies, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks. We introduce two greedy algorithms to add the most critical missing links in the bicycle network focusing on connectedness, and show that they outmatch both a random approach and a baseline minimum investment strategy. Our computational approach outlines novel pathways from car-centric towards sustainable cities by taking advantage of urban data available on a city-wide scale. It is a first step towards a quantitative consolidation of bicycle infrastructure development that can become valuable for urban planners and stakeholders.},
  keywords = {bicycle infrastructure,network growth,sustainable transport}
}

@article{obrien_mining_2014,
  title = {Mining Bicycle Sharing Data for Generating Insights into Sustainable Transport Systems},
  author = {O'Brien, Oliver and Cheshire, James and Batty, Michael and O’Brien, Oliver},
  date = {2014-07},
  journaltitle = {Journal of Transport Geography},
  volume = {34},
  pages = {262--273},
  issn = {09666923},
  doi = {10/gddh69},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S0966692313001178 http://dx.doi.org/10.1016/j.jtrangeo.2013.06.007},
  abstract = {Bicycle sharing systems (bike-shares) are becoming increasingly popular in towns and cities around the world. They are viewed as a cheap, efficient, and healthy means of navigating dense urban environments. This paper is the first to take a global view of bike-sharing characteristics by analysing data from 38 systems located in Europe, the Middle East, Asia, Australasia and the Americas. To achieve this, an extensive database depicting the geographical location and bicycle occupancy of each docking station within a particular system has been created over a number of years to chart the usage in the chosen systems (and others) and provide a consistent basis on which to compare and classify them. Analysis of the variation of occupancy rates over time, and comparison across the system's extent, infers the likely demographics and intentions of user groups. A classification of bike-shares, based on the geographical footprint and diurnal, day-of-week and spatial variations in occupancy rates, is proposed. The knowledge of such patterns and characteristics identifiable from the dataset has a range of applications, including informing operators and policymakers about the maintenance of a suitable balance of bicycles throughout the system area (a nontrivial problem for many bike-shares), the location of new docking stations and cycle lanes, and better targeting of promotional materials to encourage new users. Within the context of transport research, the systems utilised here are part of relatively small, closed environments that can be more easily modelled and validated. Such work lays foundations for the analysis of larger scale transport systems by creating a classification of the different systems and seeks to demonstrate that bike-shares have a lot to offer both as an effective method of transport and a rich source of data. ?? 2013 Elsevier Ltd.},
  keywords = {Bike-sharing,cities,Cities,Commuters,Cycling}
}

@article{olmos_data_2020,
  ids = {olmos_data_2020a},
  title = {A Data Science Framework for Planning the Growth of Bicycle Infrastructures},
  author = {Olmos, Luis E. and Tadeo, Maria Sol and Vlachogiannis, Dimitris and Alhasoun, Fahad and Espinet Alegre, Xavier and Ochoa, Catalina and Targa, Felipe and González, Marta C.},
  date = {2020-06-01},
  journaltitle = {Transportation Research Part C: Emerging Technologies},
  shortjournal = {Transportation Research Part C: Emerging Technologies},
  volume = {115},
  pages = {102640},
  publisher = {Elsevier},
  issn = {0968-090X},
  doi = {10/gh6f9s},
  url = {http://www.sciencedirect.com/science/article/pii/S0968090X19306436},
  urldate = {2020-06-18},
  abstract = {Cities around the world are turning to non-motorized transport alternatives to help solve congestion and pollution issues. This paradigm shift demands on new infrastructure that serves and boosts local cycling rates. This creates the need for novel data sources, tools, and methods that allow us to identify and prioritize locations where to intervene via properly planned cycling infrastructure. Here, we define potential demand as the total trips of the population that could be supported by bicycle paths. To that end, we use information from a phone-based travel demand and the trip distance distribution from bike apps. Next, we use percolation theory to prioritize paths with high potential demand that benefit overall connectivity if a bike path would be added. We use Bogotá as a case study to demonstrate our methods. The result is a data science framework that informs interventions and improvements to an urban cycling infrastructure.},
  langid = {english},
  keywords = {Bike infrastructure planning,Cycling facilities,GPS traces,Infrastructure planning,Mobile phone data,Network science,Percolation theory},
  annotation = {ZSCC: 0000000}
}

@book{ortuzars._modelling_2001,
  title = {Modelling Transport},
  author = {Ortúzar S., Juan de Dios and Willumsen, Luis G.},
  date = {2001},
  edition = {3rd ed},
  publisher = {J. Wiley},
  location = {Chichester New York},
  isbn = {978-0-471-86110-2},
  pagetotal = {499},
  keywords = {Choice of transportation,Mathematical models,Transportation,Trip generation}
}

@book{parkin_designing_2018,
  title = {Designing for {{Cycle Traffic}}: {{International Principles}} and {{Practice}}},
  shorttitle = {Designing for {{Cycle Traffic}}},
  author = {Parkin, John},
  date = {2018},
  publisher = {ICE Publishing},
  url = {https://www.icevirtuallibrary.com/isbn/9780727763495},
  abstract = {Designing for Cycle Traffic compares and evaluates international principles and practices for designing for cycle traffic. It sets design for cycling in the wider context of public realm design, traffic planning, traffic engineering and traffic management. Coverage includes: (1) principles for design to ensure inclusivity; (2) planning processes for cycle route networks; (3) design approaches, including capacity calculations for links and junctions, roundabouts and crossings, and signal control; and (4) modelling and level of service assessment approaches. Each chapter is extensively illustrated, provides a concise overview of the topic, and includes an introductory overview and summary of chapter highlights. Written in an accessible style by an established international authority, Designing for Cycle Traffic is essential reading for students, designers and planners in the fields of traffic and highway engineering, spatial and transport planning, architecture and urban design.},
  isbn = {978-0-7277-6349-5},
  langid = {english},
  pagetotal = {248}
}

@article{pebesma_r_2012,
  title = {The {{R}} Software Environment in Reproducible Geoscientific Research},
  author = {Pebesma, Edzer and Nüst, Daniel and Bivand, Roger},
  date = {2012-04-17},
  journaltitle = {Eos, Transactions American Geophysical Union},
  shortjournal = {Eos Trans. AGU},
  volume = {93},
  number = {16},
  pages = {163--163},
  issn = {2324-9250},
  doi = {10/gd8djc},
  url = {http://onlinelibrary.wiley.com/doi/10.1029/2012EO160003/abstract},
  urldate = {2017-10-25},
  abstract = {Reproducibility is an important aspect of scientific research, because the credibility of science is at stake when research is not reproducible. Like science, the development of good, reliable scientific software is a social process. A mature and growing community relies on the R software environment for carrying out geoscientific research. Here we describe why people use R and how it helps in communicating and reproducing research.},
  langid = {english},
  keywords = {\nosource,0520 Data analysis: algorithms and implementation,0530 Data presentation and visualization,1694 Instruments and techniques,1819 Hydrology: Geographic Information Systems (GIS),1978 Software re-use,nosource,R project,reproducible research}
}

@book{popper_logic_1959,
  title = {The {{Logic}} of Scientific Discovery},
  author = {Popper, Karl},
  date = {1959},
  publisher = {Hutchinson},
  url = {http://books.google.com/books?id=MdvaSAAACAAJ&pgis=1},
  pagetotal = {480},
  keywords = {\nosource,nosource}
}

@article{riggs_streetplan_2016,
  title = {Streetplan: Hacking {{Streetmix}} for Community-Based Outreach on the Future of Streets},
  shorttitle = {Streetplan},
  author = {Riggs, William W. and Boswell, Michael R. and Ross, Ryder},
  date = {2016},
  journaltitle = {Focus},
  volume = {13},
  number = {1},
  pages = {14},
  keywords = {⛔ No DOI found},
  annotation = {ZSCC: 0000004}
}

@book{rodrigue_geography_2013,
  title = {The {{Geography}} of {{Transport Systems}}},
  author = {Rodrigue, Jean-Paul and Comtois, Claude and Slack, Brian},
  date = {2013-06-20},
  edition = {Third},
  publisher = {Routledge},
  location = {London, New York},
  isbn = {978-0-415-82254-1},
  langid = {english},
  pagetotal = {432}
}

@book{rodrigues_building_2023,
  title = {Building Reproducible Analytical Pipelines with {{R}}},
  author = {Rodrigues, Bruno},
  date = {2023-10-03},
  url = {https://raps-with-r.dev/},
  urldate = {2025-01-21},
  langid = {english}
}

@software{szell_course_2025,
  title = {Course Materials for: {{Geospatial Data Science}}},
  author = {Szell, Michael},
  date = {2025-01-22T05:53:53Z},
  origdate = {2022-05-14T12:59:52Z},
  url = {https://github.com/mszell/geospatialdatascience},
  urldate = {2025-01-22},
  abstract = {Course materials for: Geospatial Data Science},
  keywords = {course-materials,data-science,geospatial,geospatial-analysis,geospatial-data,geospatial-visualization,gis,openstreetmap,osmnx,python,street-networks,teaching-materials}
}

@unpublished{szell_growing_2021,
  title = {Growing {{Urban Bicycle Networks}}},
  author = {Szell, Michael and Mimar, Sayat and Perlman, Tyler and Ghoshal, Gourab and Sinatra, Roberta},
  date = {2021-07-05},
  eprint = {2107.02185},
  eprinttype = {arXiv},
  eprintclass = {physics},
  url = {http://arxiv.org/abs/2107.02185},
  urldate = {2021-07-08},
  abstract = {Cycling is a promising solution to unsustainable car-centric urban transport systems. However, prevailing bicycle network development follows a slow and piecewise process, without taking into account the structural complexity of transportation networks. Here we explore systematically the topological limitations of urban bicycle network development. For 62 cities we study different variations of growing a synthetic bicycle network between an arbitrary set of points routed on the urban street network. We find initially decreasing returns on investment until a critical threshold, posing fundamental consequences to sustainable urban planning: Cities must invest into bicycle networks with the right growth strategy, and persistently, to surpass a critical mass. We also find pronounced overlaps of synthetically grown networks in cities with well-developed existing bicycle networks, showing that our model reflects reality. Growing networks from scratch makes our approach a generally applicable starting point for sustainable urban bicycle network planning with minimal data requirements.},
  keywords = {⛔ No DOI found,Computer Science - Computers and Society,Physics - Physics and Society}
}

@article{timms_imagineering_2014,
  title = {Imagineering Mobility: Constructing Utopias for Future Urban Transport},
  shorttitle = {Imagineering Mobility},
  author = {Timms, Paul and Tight, Miles and Watling, David},
  date = {2014},
  journaltitle = {Environment and Planning A},
  shortjournal = {Environ. Plan. A},
  volume = {46},
  number = {1},
  pages = {78--93},
  issn = {0308-518X},
  doi = {10/f542jt},
  abstract = {Over the past fifty years a growing body of work has sought to address the problem of planning for transportation in the long-term future through scenario building. Such thinking has generally been restricted to issues concerned with environmental sustainability and the 'images' of future transport so created are usually weak in terms of their social sustainability content, either treating social issues superficially, or ignoring them entirely, or even creating images that are socially undesirable. At the same time, there has generally been a marked decrease over the past twenty years in socially oriented utopian thinking. As a direct result of these two factors, hardly any consideration has been given recently to imagining socially sustainable views of transport in a future utopia. The key underlying aim of this paper is to provide some background thinking about how this lack might be addressed. To do so, it examines concepts about utopia in terms of their form, content, and function, and considers possible reasons for the recent decline in utopian thinking and their 'replacement' by a type of futures-thinking that is referred to as dystopian avoidance. It then examines transport characteristics of utopian thinking in urban planning in the 20th century and considers various 'antinomies of transport' with respect to future utopias. Based upon the insights gained, the paper comments on two existing 'practical' sets of transport-related scenarios in terms of their utopian and dystopian characteristics. One particular result is that the utopian aspects of these scenario sets in terms of their social content are relatively weak, in line with the hypothesised recent general decline in (social) utopian thinking. Various conclusions are made which emphasise the usefulness of utopian thinking in transport planning, particularly in participatory approaches. It is suggested that three elements of the transport system should be separately 'utopianised': the mobility of people and goods; physical aspects that facilitate or inhibit such mobility; and the system of governance with respect to formulating and implementing transport policy.},
  langid = {english},
  keywords = {\nosource,backcasting approach,emissions,environmental   sustainability,environmental sustainability,exploratory scenarios,forecasts,models,nosource,policy,projects,scenarios,social sustainability,system,transport planning,travel,utopia,visions},
  annotation = {WOS:000336258700006}
}

@report{transportsystemscatapult_transport_2015,
  type = {Government},
  title = {The {{Transport Data Revolution}}},
  author = {{Transport Systems Catapult}},
  date = {2015},
  pages = {108},
  institution = {Transport Systems Catapult},
  url = {https://ts.catapult.org.uk/wp-content/uploads/2016/04/The-Transport-Data-Revolution.pdf},
  langid = {english},
  keywords = {\nosource,nosource}
}

@book{tufte_visual_2001,
  title = {The {{Visual Display}} of {{Quantitative Information}}},
  author = {Tufte, Edward R.},
  date = {2001},
  edition = {2nd ed},
  publisher = {Graphics Press},
  location = {Cheshire, Conn},
  isbn = {978-0-9613921-4-7},
  pagetotal = {197},
  keywords = {\nosource,Graphic methods,nosource,Statistics}
}

@book{turrell_python_2025,
  title = {Python for {{Data Science}}},
  shorttitle = {Aeturrell/{{python4DS}}},
  author = {Turrell, Arthur and Pietro Monticone and Zeki Akyol and Josh Holman and Yiben Huang},
  date = {2025-01-07},
  publisher = {Zenodo},
  doi = {10.5281/ZENODO.10518241},
  url = {https://aeturrell.github.io/python4DS/},
  urldate = {2025-01-21}
}

@book{wickham_data_2023,
  title = {R for {{Data Science}}: {{Import}}, {{Tidy}}, {{Transform}}, {{Visualize}}, and {{Model Data}}},
  shorttitle = {R for {{Data Science}}},
  author = {Wickham, Hadley and Cetinkaya-Rundel, Mine and Grolemund, Garrett},
  date = {2023-07-18},
  edition = {2nd edition},
  publisher = {O'Reilly Media},
  location = {Beijing Boston Farnham Sebastopol Tokyo},
  url = {https://r4ds.hadley.nz/},
  abstract = {Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverseâ??a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto},
  isbn = {978-1-4920-9740-2},
  langid = {english},
  pagetotal = {576}
}

@article{yang_travel_2013,
  title = {The Travel–Obesity Connection: Discerning the Impacts of Commuting Trips with the Perspective of Individual Energy Expenditure and Time Use},
  author = {Yang, Jiawen and French, Steven},
  date = {2013},
  journaltitle = {Environment and Planning B: Planning and Design},
  volume = {40},
  number = {4},
  pages = {617--629},
  issn = {0265-8135},
  doi = {10/f46bbz},
  url = {http://www.envplan.com/abstract.cgi?id=b38076},
  keywords = {american time use,atus,Built environment,energy expenditure,Obesity,Physical activity,survey,Travel}
}