Hiruni Kegalle
Danula Hettiachchi
Mark Sanderson
ABSTRACT
Electric scooters (e-scooters), characterised by their small size and lightweight design, have revolutionised urban commuting experiences. Their adaptability to multiple mobility infrastructures introduces advantages for users, enhancing the efficiency and flexibility of urban transit. However, this versatility also causes potential challenges, including increased interactions and conflicts with other road users. Previous research has primarily focused on historical trip data, leaving a gap in our understanding of real-time e-scooter user behaviours and interactions. To bridge this gap, we propose a novel multi-modal data collection and integrated data analysis methodology, aimed at capturing the dynamic behaviours of e-scooter riders and their interactions with other road users in real-world settings. We present the study setup and the analysis approach we used for an in the wild study with 15 participants, each traversing a pre-determined route equipped with off-the-shelf commercially available devices (e.g., cameras, bike computers) and eye-tracking glasses.
- Ammar Al-Taie, Yasmeen Abdrabou, Shaun Alexander Macdonald, Frank Pollick, and Stephen Anthony Brewster. 2023. Keep It Real: Investigating Driver-Cyclist Interaction in Real-World Traffic. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 769, 15 pages. https://doi.org/10.1145/3544548.3581049Google Scholar
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- Michael V Angrosino. 2016. Naturalistic observation. Routledge, New York. 144 pages. https://doi.org/10.4324/9781315423616Google ScholarCross Ref
- Juliane Anke, Madlen Ringhand, Tibor Petzoldt, and Tina Gehlert. 2023. Micro-mobility and road safety: why do e-scooter riders use the sidewalk? Evidence from a German field study. European Transport Research Review 15, 1 (2023), 29.Google ScholarCross Ref
- Shunhua Bai and Junfeng Jiao. 2020. Dockless E-scooter usage patterns and urban built Environments: A comparison study of Austin, TX, and Minneapolis, MN. Travel Behaviour and Society 20 (2020), 264–272. https://doi.org/10.1016/j.tbs.2020.04.005Google ScholarCross Ref
- Barry Brown, Mathias Broth, and Erik Vinkhuyzen. 2023. The Halting Problem: Video Analysis of Self-Driving Cars in Traffic. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 12, 14 pages. https://doi.org/10.1145/3544548.3581045Google ScholarDigital Library
- Dorcas Gachomo. 2015. The Power of the Pruned Exact Linear Time(PELT) Test in Multiple Changepoint Detection. American Journal of Theoretical and Applied Statistics 4 (01 2015), 581. https://doi.org/10.11648/j.ajtas.20150406.30Google ScholarCross Ref
- Hebe Gibson, Angela Curl, and Lee Thompson. 2022. Blurred Boundaries: E-scooter Riders’ and Pedestrians’ Experiences of Sharing Space. Mobilities 17, 1 (2022), 69–84. https://doi.org/10.1080/17450101.2021.1967097Google ScholarCross Ref
- Christos Gioldasis, Zoi Christoforou, and Regine Seidowsky. 2021. Risk-taking Behaviors of E-scooter Users: A Survey in Paris. Accident Analysis and Prevention 163 (2021), 106427. https://doi.org/10.1016/j.aap.2021.106427Google ScholarCross Ref
- Stefan Gossling. 2020. Integrating E-scooters in Urban Transportation: Problems, Policies, and the Prospect of System Change. Transportation Research Part D: Transport and Environment 79 (2020), 102230. https://doi.org/10.1016/j.trd.2020.102230Google ScholarCross Ref
- Seung Woo Ham, Jung-Hoon Cho, Sangwoo Park, and Dong-Kyu Kim. 2021. Spatiotemporal Demand Prediction Model for E-Scooter Sharing Services with Latent Feature and Deep Learning. Transportation Research Record 2675, 11 (2021), 34–43. https://doi.org/10.1177/03611981211003896 arXiv:https://doi.org/10.1177/03611981211003896Google ScholarCross Ref
- J.A. Healey and R.W. Picard. 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems 6, 2 (2005), 156–166. https://doi.org/10.1109/TITS.2005.848368Google ScholarDigital Library
- Isaac Hooley. 2016. Ethical considerations for psychotherapy in natural settings. Ecopsychology 8, 4 (2016), 215–221.Google ScholarCross Ref
- Aryan Hosseinzadeh, Majeed Algomaiah, Robert Kluger, and Zhixia Li. 2021. Spatial analysis of shared e-scooter trips. Journal of Transport Geography 92 (2021), 103016. https://doi.org/10.1016/j.jtrangeo.2021.103016Google ScholarCross Ref
- Aryan Hosseinzadeh, Abolfazl Karimpour, and Robert Kluger. 2021. Factors influencing shared micromobility services: An analysis of e-scooters and bikeshare. Transportation Research Part D: Transport and Environment 100 (2021), 103047. https://doi.org/10.1016/j.trd.2021.103047Google ScholarCross Ref
- Barbara Humberstone and Carol Cutler Riddick. 2019. Ethical issues and practicalities in outdoor studies research. Research methods in outdoor studies - (2019), 21–32. https://doi.org/10.4324/9780429199004-3Google ScholarCross Ref
- Hye-Young Jo, Laurenz Seidel, Michel Pahud, Mike Sinclair, and Andrea Bianchi. 2023. FlowAR: How Different Augmented Reality Visualizations of Online Fitness Videos Support Flow for At-Home Yoga Exercises. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 469, 17 pages. https://doi.org/10.1145/3544548.3580897Google ScholarDigital Library
- Hiruni Kegalle, Danula Hettiachchi, Jeffrey Chan, Flora Salim, and Mark Sanderson. 2023. Are footpaths encroached by shared e-scooters? Spatio-temporal Analysis of Micro-mobility Services. In 2023 24th IEEE International Conference on Mobile Data Management (MDM). IEEE, Singapore, 255–264. https://doi.org/10.1109/MDM58254.2023.00049Google ScholarCross Ref
- Yoonji Kim, Youngkyung Choi, Daye Kang, Minkyeong Lee, Tek-Jin Nam, and Andrea Bianchi. 2020. HeyTeddy: Conversational Test-Driven Development for Physical Computing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 4, Article 139 (sep 2020), 21 pages. https://doi.org/10.1145/3369838Google ScholarDigital Library
- Maria Kjærup, Mikael B. Skov, and Niels van Berkel. 2021. E-Scooter Sustainability – A Clash of Needs, Perspectives, and Experiences. In Human-Computer Interaction – INTERACT 2021, Carmelo Ardito, Rosa Lanzilotti, Alessio Malizia, Helen Petrie, Antonio Piccinno, Giuseppe Desolda, and Kori Inkpen (Eds.). Springer International Publishing, Cham, 365–383.Google Scholar
- Holger Kleinertz, Dimitris Ntalos, Fabian Hennes, Jakob V. Nüchtern, Karl-Heinz Frosch, and Darius M. Thiesen. 2021. Accident mechanisms and injury patterns in E-scooter users. A retrospective analysis and comparison with cyclists. Deutsches Arzteblatt international 118, 8 (2021), 117–121.Google Scholar
- Barbara Laa and Ulrich Leth. 2020. Survey of E-scooter Users in Vienna: Who They Are and How They Ride. Journal of Transport Geography 89 (2020), 102874. https://doi.org/10.1016/j.jtrangeo.2020.102874Google ScholarCross Ref
- Petre Lameski, Ace Dimitrievski, Eftim Zdravevski, Vladimir Trajkovik, and Saso Koceski. 2019. Challenges in data collection in real-world environments for activity recognition. In IEEE EUROCON 2019 -18th International Conference on Smart Technologies. IEEE, Serbia, 1–5. https://doi.org/10.1109/EUROCON.2019.8861964Google ScholarCross Ref
- Kostas Mouratidis, Sebastian Peters, and Bert van Wee. 2021. Transportation Technologies, Sharing Economy, and Teleactivities: Implications for Built Environment and Travel. Transportation Research Part D: Transport and Environment 92 (2021), 102716. https://doi.org/10.1016/j.trd.2021.102716Google ScholarCross Ref
- Mohsin Mukhtar, Aiza Ashraf, Mark S. Frank, and Scott D. Steenburg. 2021. Injury incidence and patterns associated with electric scooter accidents in a major metropolitan city. Clinical Imaging 74 (2021), 163–168. https://doi.org/10.1016/j.clinimag.2021.02.005Google ScholarCross Ref
- Anton Pashkevich, Tomasz Burghardt, Sabina Pulawska-Obiedowska, and Matus Sucha. 2022. Visual Attention and Speeds of Pedestrians, Cyclists, and Electric Scooter Riders When Using Shared Road – A Field Eye Tracker Experiment. Case Studies on Transport Policy 10 (01 2022). https://doi.org/10.1016/j.cstp.2022.01.015Google ScholarCross Ref
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In Advances in Neural Information Processing Systems, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett (Eds.). Vol. 28. Curran Associates, Inc., Montreal, Quebec, Canada, 91–99.Google Scholar
- Jia-Cherng Song, I-Yun Lisa Hsieh, and Chuin-Shan Chen. 2023. Sparse trip demand prediction for shared E-scooter using spatio-temporal graph neural networks. Transportation Research Part D: Transport and Environment 125 (2023), 103962. https://doi.org/10.1016/j.trd.2023.103962Google ScholarCross Ref
- H. Stigson, I. Malakuti, and M. Klingegård. 2021. Electric scooters accidents: Analyses of two Swedish accident data sets. Accident Analysis and Prevention 163 (2021), 106466. https://doi.org/10.1016/j.aap.2021.106466Google ScholarCross Ref
- Sylvaine Tuncer and Barry Brown. 2020. E-Scooters on the Ground: Lessons for Redesigning Urban Micro-Mobility. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376499Google ScholarDigital Library
- Sylvaine Tuncer, Eric Laurier, Barry Brown, and Christian Licoppe. 2020. Notes on the Practices and Appearances of E-scooter Users in Public Space. Journal of Transport Geography 85 (2020), 102702. https://doi.org/10.1016/j.jtrangeo.2020.102702Google ScholarCross Ref
- [31] U.S. Department of Transportation. 2017. https://www.transportation.gov/research-and-technology/novel-transportation-modesAccessed: 2023-09-11.Google Scholar
- Pieter Vansteenkiste, Greet Cardon, Renaat Philippaerts, and Matthieu Lenoir. 2015. Measuring Dwell Time Percentage From Head-mounted Eye-tracking Data – Comparison of a Frame-by-frame and A Fixation-by-fixation Analysis. Ergonomics 58, 5 (2015), 712–721. https://doi.org/10.1080/00140139.2014.990524 PMID: 25529829.Google ScholarCross Ref
- Rick Wash, Emilee Rader, and Chris Fennell. 2017. Can People Self-Report Security Accurately? Agreement Between Self-Report and Behavioral Measures. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 2228–2232. https://doi.org/10.1145/3025453.3025911Google ScholarDigital Library
- Cara Waters. 2022. Hospitalisations involving e-scooter riders up 234 per cent in a year. https://www.theage.com.au/national/victoria/hospitalisations-involving-e-scooter-riders-up-234-per-cent-in-a-year-20221121-p5c02s.html Accessed: 2024-02-14.Google Scholar
- Hongtai Yang, Yongxing Bao, Jinghai Huo, Simon Hu, Linchuan Yang, and Lijun Sun. 2022. Impact of road features on shared e-scooter trip volume: A study based on multiple membership multilevel model. Travel Behaviour and Society 28 (2022), 204–213. https://doi.org/10.1016/j.tbs.2022.04.005Google ScholarCross Ref
- Wenwen Zhang, Ralph Buehler, Andrea Broaddus, and Ted Sweeney. 2021. What type of infrastructures do e-scooter riders prefer? A route choice model. Transportation Research Part D: Transport and Environment 94 (2021), 102761. https://doi.org/10.1016/j.trd.2021.102761Google ScholarCross Ref
- Natalia Zuniga-Garcia, Natalia Ruiz Juri, Kenneth A. Perrine, and Randy B. Machemehl. 2021. E-scooters in urban infrastructure: Understanding sidewalk, bike lane, and roadway usage from trajectory data. Case Studies on Transport Policy 9, 3 (2021), 983–994. https://doi.org/10.1016/j.cstp.2021.04.004Google ScholarCross Ref
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