Article

Deriving Knowledge from E-Scooter Riders’ Feedback at Pilot Study Stage: Case for a City in Ontario, Canada

Seun Daniel Oluwajana ORCID , Olubunmi Philip Oluwajana , Temitope Elizabeth Oguntelure , Crystal Mingyue Wang , Soha Saiyed
    Received
    15 Feb 2025
    Published
    21 May 2025

    Abstract

    This paper examines the sentiments and opinions of e-scooter riders in Windsor, Ontario, highlighting key issues and concerns they have expressed. It involved text mining of feedback collected over a six-month pilot program (May to October 2021) using dictionary-based analysis. Analysis of monthly word frequencies in rider feedback revealed fluctuations, with June, July, and August showing higher correlations with May—the initial pilot month—compared to September and October. This indicates a fading novelty associated with e-scooters in the city. Although monthly sentiments varied significantly, the overall sentiment in May and June remained positive. The most common words contributing to positive sentiment included fun, awesome, and nice, while negative sentiments were largely represented by words such as slow, broken, and throttle. Feedback reveals that riders primarily regard e-scooters as a source of leisure rather than functional transportation. Correlation analysis of words linked to negative sentiments identified terms like “flat-tire” and “broken throttle,” which emphasize significant concerns regarding e-scooter maintenance practices in Windsor. The findings underscore the need for a data-sharing policy and maintenance regulations while recommending a governance framework for e-scooters to ensure their sustainable benefits. It demonstrates that even with a limited feedback sample during the pilot phase of shared e-scooter implementation, dictionary-based opinion and sentiment analysis can yield valuable insights into rider concerns, guiding immediate policy needs and fostering the functional use of e-scooters as a transportation option.

    Keywords

    E-scooter, NPU, Opinion and Sentiment Analysis, Riders’ Feedback, Text Mining, User Generated Content, YOLOv11

    References
    1. [1] Latinopoulos, C., Patrier, A., Sivakumar, A., 2021. Planning for e-scooter use in metropolitan cities: A case study for Paris. Transportation Research Part D: Transport and Environment. 100, 103037. DOI: https://doi.org/10.1016/j.trd.2021.103037
    2. [2] Kazemzadeh, K., Sprei, F., 2022. Towards an electric scooter level of service: A review and framework. Travel Behaviour and Society. 29, 149–164. DOI: https://doi.org/10.1016/j.tbs.2022.06.005
    3. [3] Aman, J.J., Smith-Colin, J., Zhang, W., 2021. Listen to E-scooter riders: Mining rider satisfaction factors from app store reviews. Transportation Research Part D: Transport and Environment. 95, 102856. DOI: https://doi.org/10.1016/j.trd.2021.102856
    4. [4] Wang, K., Qian, X., Fitch, D.T., et al., 2023. What travel modes do shared e-scooters displace? A review of recent research findings. Transport Reviews. 43(1), 5–31.
    5. [5] Kong, V., Leszczynski, A., 2022. Dockless micromobility sharing in Calgary: A spatial equity comparison of e-bikes and e-scooters. Canadian Journal of Urban Research. 31(1), 97–110.
    6. [6] Evans-Cowley, J.S., Griffin, G., 2012. Microparticipation with social media for community engagement in transportation planning. Transportation Research Record. 2307(1), 90–98.
    7. [7] Dhini, A., Hardaya, I.S., Surjandari, I., 2017. Clustering and visualization of community complaints and proposals using text mining and geographic information system. Proceedings of the 2017 3rd International Conference on Science in Information Technology (ICSITech); 25 October 2017; Bandung, Indonesia. IEEE. pp. 132–137.
    8. [8] Gao, Y., Chen, Y., Mu, L., et al., 2022. Measuring urban sentiments from social media data: a dual-polarity metric approach. Journal of Geographical Systems. 24(2), 199–221.
    9. [9] Kapp, A., 2024. Collection, usage and privacy of mobility data in the enterprise and public administrations. Proceedings on Privacy Enhancing Technologies. 2022(4), 440–456. DOI: https://doi.org/10.48550/arXiv.2407.03732
    10. [10] Skiera, B., Yan, S., Daxenberger, J., et al., 2022. Using information-seeking argument mining to improve service. Journal of Service Research. 25(4), 537–548.
    11. [11] Field, C., Jon, I., 2021. E-scooters: A new smart mobility option? The case of Brisbane, Australia. Planning Theory & Practice. 22(3), 368–396.
    12. [12] Yang, H., Huo, J., Bao, Y., et al., 2021. Impact of e-scooter sharing on bike sharing in Chicago. Transportation Research Part A: Policy and Practice. 154, 23–36.
    13. [13] Hawa, L., Cui, B., Sun, L., et al., 2021. Scoot over: Determinants of shared electric scooter presence in Washington DC. Case Studies on Transport Policy. 9(2), 418–430.
    14. [14] Bozzi, A.D., Aguilera, A., 2021. Shared E-scooters: A review of uses, health and environmental impacts, and policy implications of a new micro-mobility service. Sustainability. 13(16), 8676. DOI: https://doi.org/10.3390/su13168676
    15. [15] Jiao, J., Bai, S., Choi, S.J., 2021. Understanding e-scooter incidents patterns in street network perspective: a case study of travis county, Texas. Sustainability. 13(19), 10583. DOI: https://doi.org/10.3390/su131910583
    16. [16] Dias, G., Arsenio, E., Ribeiro, P., 2021. The role of shared e-scooter systems in urban sustainability and resilience during the covid-19 mobility restrictions. Sustainability. 13(13), 7084. DOI: https://doi.org/10.3390/su13137084
    17. [17] Glavić, D., Trpković, A., Milenković, M., et al., 2021. The e-scooter potential to change urban mobility—Belgrade case study. Sustainability. 13(11), 5948. DOI: https://doi.org/10.3390/su13115948
    18. [18] Lo, D., Mintrom, C., Robinson, K., et al., 2020. Shared micromobility: The influence of regulation on travel mode choice. New Zealand Geographer. 76(2), 135–146.
    19. [19] Gebhardt, L., Wolf, C., Seiffert, R., 2021. "I'll take the E-scooter instead of my car"—The potential of E-scooters as a substitute for car trips in Germany. Sustainability. 13(13), 7361. DOI: https://doi.org/10.3390/su13137361
    20. [20] Reck, D.J., Haitao, H., Guidon, S., et al., 2021. Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland. Transportation Research Part C: Emerging Technologies. 124, 102947. DOI: https://doi.org/10.1016/j.trc.2020.102947
    21. [21] Almannaa, M.H., Alsahhaf, F.A., Ashqar, H.I., et al., 2021. Perception analysis of E-scooter riders and non-riders in Riyadh, Saudi Arabia: Survey outputs. Sustainability. 13(2), 863. DOI: https://doi.org/10.3390/su13020863
    22. [22] Sikka, N., Vila, C., Stratton, M., et al., 2019. Sharing the sidewalk: A case of E-scooter related pedestrian injury. The American Journal of Emergency Medicine. 37(9), 1807.e5–1807.e7.
    23. [23] Merlin, L.A., Yan, X., Xu, Y., et al., 2021. A segment-level model of shared, electric scooter origins and destinations. Transportation Research Part D: Transport and Environment. 92, 102709. DOI: https://doi.org/10.1016/j.trd.2021.102709
    24. [24] Bai, S., Jiao, J., 2022. From shared micro-mobility to shared responsibility: Using crowdsourcing to understand dockless vehicle violations in Austin, Texas. Journal of Urban Affairs. 44(9), 1341–1353.
    25. [25] Ali, F., Kwak, D., Khan, P., et al., 2019. Transportation sentiment analysis using word embedding and ontology-based topic modeling. Knowledge-Based Systems. 174, 27–42. DOI: https://doi.org/10.1016/j.knosys.2019.02.033
    26. [26] Hao, J., Zhu, J., Zhong, R., 2015. The rise of big data on urban studies and planning practices in China: Review and open research issues. Journal of Urban Management. 4(2), 92–124. DOI: https://doi.org/10.1016/j.jum.2015.11.002
    27. [27] Abbasi, A., Rashidi, T.H., Maghrebi, M., et al., 2015. Utilising location based social media in travel survey methods: bringing Twitter data into the play. Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks; 3 November 2015; Seattle, WA, USA. ACM. pp. 1–9.
    28. [28] Zhu, L., Yu, F.R., Wang, Y., et al., 2018. Big data analytics in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems. 20(1), 383–398. DOI: https://doi.org/10.1109/TITS.2018.2815678
    29. [29] Lin, C., Wang, G., 2017. Failure cause extraction of railway switches based on text mining. Proceedings of the 2017 International Conference on Computer Science and Artificial Intelligence; 5 December 2017; Shanghai, China. ACM. pp. 237–241. DOI: https://doi.org/10.1145/3168390.3168402
    30. [30] Choi, S., Ko, J., Kim, D., 2021. Investigating commuters' satisfaction with public transit: A latent class modeling approach. Transportation Research Part D: Transport and Environment. 99, 103015. DOI: https://doi.org/10.1016/j.trd.2021.103015
    31. [31] Sai, F., 2019. Survey of Sustainable Logistics Services via Text Mining. Journal of Mechanics Engineering and Automation. 9(3). DOI: https://doi.org/10.17265/2159-5275/2019.03.004
    32. [32] Shokouhyar, S., Shokoohyar, S., Sobhani, A., et al., 2021. Shared mobility in post-COVID era: New challenges and opportunities. Sustainable Cities and Society. 67, 102714. DOI: https://doi.org/10.1016/j.scs.2021.102714
    33. [33] Ridhwan, K.M., Hargreaves, C.A., 2021. Leveraging Twitter data to understand public sentiment for the COVID-19 outbreak in Singapore. International Journal of Information Management Data Insights. 1(2), 100021. DOI: https://doi.org/10.1016/j.jjimei.2021.100021
    34. [34] Yang, H., Ma, Q., Wang, Z., et al., 2020. Safety of micro-mobility: Analysis of E-Scooter crashes by mining news reports. Accident Analysis & Prevention. 143, 105608. DOI: https://doi.org/10.1016/j.aap.2020.105608
    35. [35] Silge, J., Robinson, D., 2017. Text Mining with R: A Tidy Approach. O'Reilly: Boston, MA, USA. pp. 1–193.
    36. [36] Lloyd, M., 2022. Weaving fun and functionality. A study of everyday urban e-scootering. Etnografia e Ricerca Qualitativa. 15(2), 241–262. DOI: https://doi.org/10.3240/103668
    37. [37] Krier, C., Chrétien, J., Lagadic, M., et al., 2021. How do shared dockless e-scooter services affect mobility practices in Paris? A survey-based estimation of modal shift. Transportation Research Record. 2675(11), 291–304.
    38. [38] Bieliński, T., Ważna, A., 2020. Electric scooter sharing and bike sharing user behaviour and characteristics. Sustainability. 12(22), 9640. DOI: https://doi.org/10.3390/su12229640
    39. [39] Kjærup, M., Skov, M.B., Van Berkel, N., 2021. E-scooter sustainability–a clash of needs, perspectives, and experiences. Proceedings of the IFIP Conference on Human-Computer Interaction; 26 August 2021; Bari, Italy. Springer: Cham, Switzerland. pp. 365–383.
    40. [40] Severengiz, S., Schelte, N., Bracke, S., 2021. Analysis of the environmental impact of e-scooter sharing services considering product reliability characteristics and durability. Procedia CIRP. 96, 181–188.
    41. [41] Kopplin, C.S., Brand, B.M., Reichenberger, Y., 2021. Consumer acceptance of shared e-scooters for urban and short-distance mobility. Transportation Research Part D: Transport and Environment. 91, 102680. DOI: https://doi.org/10.1016/j.trd.2020.102680
    42. [42] Dibaj, S., Hosseinzadeh, A., Mladenović, M.N., et al., 2021. Where have shared e-scooters taken us so far? A review of mobility patterns, usage frequency, and personas. Sustainability. 13(21), 11792. DOI: https://doi.org/10.3390/su132111792
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