| 研究生: |
李琦容 Li, Chi-Jung |
|---|---|
| 論文名稱: |
機車外送員破碎型UBI保險費關鍵因素之探討 The Study of Key Factors on the Fragmented Usage-Based Insurance Premium for Scooter Delivery Personnel |
| 指導教授: |
魏健宏
Wei, Chien-Hung |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 交通管理科學系 Department of Transportation and Communication Management Science |
| 論文出版年: | 2025 |
| 畢業學年度: | 112 |
| 語文別: | 英文 |
| 論文頁數: | 111 |
| 中文關鍵詞: | 機車外送員 、UBI保險 、保費因素 、駕駛行為 、運輸安全 、破碎型保單 |
| 外文關鍵詞: | Scooter Delivery Personnel, Usage-Based Insurance, Key Factors of Insurance Premium, Driving Behavior, Traffic Safety, Fragmented Insurance |
| 相關次數: | 點閱:17 下載:1 |
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現代人生活習慣改變,加上COVID-19爆發後,臺灣外送平台訂單數量迅速增長。審計部資料顯示,2022年12月全臺登錄機車外送員已有近17萬人。而眾多的訂餐時間與上下班高峰期重疊,外送員處於人車流量大、高風險之環境中,增加發生交通事故的可能性。然而,伴隨外送員人數增長以及發生事故可能性增加,外送員作為勞動者之保障比起多數運輸業與勞動人口卻顯不足。
儘管過去UBI (Usage-Based Insurance)在臺灣實際銷售狀況不佳,2023年金管會發布聲明,希望產險公司積極與金融科技專業人士合作促進加入駕駛行為因素之UBI,朝永續保險商品與永續經營發展。而破碎型保險係將傳統保險切隔成小單位來銷售的形式,為特殊時段或特殊風險量身制定之保險商品,適用於現行機車外送員與外送平台之狀況,使本研究訴求之機車外送員保險具有高度可行性。
為了全面考量各利益關係人之觀點,本研究蒐集海內外UBI保險商品之案例與文獻,邀請交通運輸、金融保險、勞工福利、社會安全等多元領域之專家,將「機車外送員破碎型UBI保險費率關鍵因素」作為目標,以層級分析法得出機車外送員破碎型UBI保費關鍵因素之權重與順序,並彙整來自各方領域專家、學者之意見以相輔相成。
本研究結果顯示,專家群以PHYD (Pay-How-You-Drive)為UBI保險中整體權重最高之項目,可見專家群對駕駛行為之重視。而評估準則的部分,專家群一致認為「超速比例」、「肇責賠款經驗」、「變換車道」為相對重要之關鍵因素。整體而言,機車外送員破碎型UBI保險的保費因子仍以PHYD、PAYD (Pay-As-You-Drive)、傳統保費綜合考量為佳。建議產險公司打造免費線上服務,提供使用者初步的駕駛行為與改善建議,透過降低保費、達成目標獎勵機制、完整的駕駛行為與改善建議報告書等作為誘因,增強外送員主動投保動機,提高其交通安全意識,減少危險駕駛行為。而此舉亦能作為產險業者朝向MHYD (Manage-How-You-Drive)保險發展之里程碑。
The rise of digital consumption habits, accelerated by the COVID-19 pandemic, has led to a surge in delivery platform usage in Taiwan. As of 2022, nearly 170,000 scooter couriers were registered nationwide, according to the National Audit Office. The overlap of delivery times with peak commuting hours places these workers in high-risk traffic conditions, increasing the possibility of accidents. Despite their growing numbers, scooter couriers remain insufficiently protected compared to other transportation and labor sectors.
Although Usage-Based Insurance (UBI) has faced limited adoption in Taiwan, the Financial Supervisory Commission (FSC) released a statement in 2023 promoting UBI development in collaboration with FinTech experts. The statement emphasized the incorporation of driving behavior into insurance design and the shift toward sustainable operations. In this context, fragmented insurance that aligns coverage with defined periods or operational conditions offers a viable solution tailored to the needs of scooter delivery personnel.
To explore premium-setting factors for fragmented UBI, this study examined both domestic and international literature and carried out an expert survey using the Analytic Hierarchy Process (AHP). Experts were selected from four key fields: traffic safety, social safety, finance and insurance, and labor welfare. Five main criteria and 20 sub-criteria were established for assessment.
Results indicate that experts identified Pay-How-You-Drive (PHYD) as the most critical dimension in premium determination, underscoring the importance of measurable driving behaviors. Key factors such as Ratio of speeding, Responsibility and compensation for accident, and Sudden lane change were consistently ranked highest. While PHYD factors were dominant, experts also recommended combining them with Pay-As-You-Drive (PAYD) and traditional insurance indicators to ensure comprehensive risk assessment.
Based on the findings, the study suggests that insurers introduce free digital tools to assess and improve riding behavior, offering incentives such as premium discounts and performance-based rewards. This approach may help raise delivery riders’ risk awareness and promote safer riding. It also lays the groundwork for future development of MHYD insurance in Taiwan.
Aarts, L., & Van Schagen, I. (2006). Driving speed and the risk of road crashes: A review. Accident Analysis & Prevention, 38(2), 215-224.
Abdulwahid, S. N., Mahmoud, M. A., Ibrahim, N., Zaidan, B. B., & Ameen, H. A. (2022). Modeling motorcyclists’ aggressive driving behavior using computational and statistical analysis of real-time driving data to improve road safety and reduce accidents. International journal of environmental research and public health, 19(13), 7704.
Åkerstedt, T., Kecklund, G., & Hörte, L. G. (2001). Night driving, season, and the risk of highway accidents. Sleep, 24(4), 401-406.
Arumugam, S. & Bhargavi, R. (2019). A survey on driving behavior analysis in usage based insurance using big data. Journal of Big Data, 6, 1-21.
Alessandrini, A., Cattivera, A., Filippi, F., & Ortenzi, F. (2012, August). Driving style influence on car CO2 emissions. In 2012 international emission inventory confer-ence (pp. 1-11).
Allianz. Insurance 2029: Outlook & Opportunities. 2019. Website: https://www.allianz.com/en/press/news/business/insurance/190522_Allianz-economic-research-insurance-2029-outlook.html.
AXA. The AXA Drive Coach app, an innovative contribution to safer driving, now on the Apple Watch. 2015. Website: https://www.axa.com/en/press/press-releases/drive-coach-apple-watch-en.
Ayuso, M., Guillen, M. & Pérez-Marín, A. M. (2016). Telematics and gender discrimina-tion: Some usage-based evidence on whether men’s risk of accidents differs from women’s. Risks, 4(2), 10.
Azmat, F., & Ha, H. (2013). Corporate social responsibility, customer trust, and loyalty—perspectives from a developing country. Thunderbird International Business Review, 55(3), 253-270.
Bagdadi, O., & Várhelyi, A. (2011). Jerky driving—an indicator of accident proneness?. Accident Analysis & Prevention, 43(4), 1359-1363.
BeRebel. BeRebel Pay per you. 2023. Website: https://www.berebel.it/it.
Bezerra, B. S. (2020). Road Safety and Sustainable Development. Good Health and Well-Being, 617-628.
Bian, Y., Yang, C., Zhao, J. L., & Liang, L. (2018). Good drivers pay less: A study of usage-based vehicle insurance models. Transportation research part A: policy and practice, 107, 20-34.
Bordoff, J. E., & Noel, P. J. (2008). Pay-as-you-drive Auto Insurance: A Simple Way to Reduce Driving-related Harms and Increase Equity. The Brookings Institution.
Butler, P. (2000). Why the Standard Automobile Insurance Market Breaks Down in Low-Income Zip Codes. Report to the Texas House Committee on Insurance.
Chang, H. L., & Yeh, T. H. (2007). Motorcyclist accident involvement by age, gender, and risky behaviors in Taipei, Taiwan. Transportation research part F: traffic psychology and behaviour, 10(2), 109-122.
Curry, A. E., Pfeiffer, M. R., Durbin, D. R., & Elliott, M. R. (2015). Young driver crash rates by licensing age, driving experience, and license phase. Accident Analysis & Prevention, 80, 243-250.
De Palma, A., & Lindsey, R. (2011). Traffic congestion pricing methodologies and technologies. Transportation Research Part C: Emerging Technologies, 19(6), 1377-1399.
DriveScore. Good drivers can save with DriveScore. 2024. Website: https://www.drivescore.com/
Dorweiler, P. (1929). Notes on Exposure and Premium Bases. PCAS XVI, 319.
Endsley, M. R. (2006). Expertise and situation awareness. The Cambridge handbook of expertise and expert performance, 633-651.
Fan, C. K., Wu, X., Zheng, D., & Lin, W. (2016). A market analysis of telematics-based UBI in Taiwan. Journal of Applied Finance and Banking, 6(6), 71.
Ford Newsroom. State Farm and Ford Team Up To Introduce Usage-Based Insurance To New Vehicle Owners. 2022. Website: https://media.ford.com/content/fordmedia/fna/us/en/news/2022/02/15/state-farm--and-ford-team-up-to-introduce-usage-based-insurance-.html.
Global Market Insight (2022). Usage-based Insurance Market Size By Package, By Technology (OBD-II, Smartphone, Blackbox, Embedded Telematics), By Vehicle (Passenger Vehicle, Commercial Vehicle) & Forecast, 2023-2032. Website: https://www.gminsights.com/
Gully, S. M., Whitney, D. J., & Vanosdall, F. E. (1995). Prediction of police officers' traffic accident involvement using behavioral observations. Accident Analysis & Prevention, 27(3), 355-362.
Insurance & Mobility Solutions. Usage-Based Insurance Accelerates In the United States. 2023. Website: https://ims.tech/knowledge-hub/usage-based-insurance-program-usa/.
Insurance Tekinsights (2014), Usage-based Insurance. Website: http://www.insurancetekinsights.com/definition/usage-based-insurance-ubi/
Jain, V., Sharma, A., & Subramanian, L. (2012, March). Road traffic congestion in the developing world. In Proceedings of the 2nd ACM Symposium on Computing for Development (pp. 1-10).
Kilpeläinen, M., & Summala, H. (2007). Effects of weather and weather forecasts on driver behaviour. Transportation research part F: traffic psychology and behaviour, 10(4), 288-299.
Kloeden, C. N., McLean, A. J., Moore, V. M., & Ponte, G. (1997). Travelling speed and the risk of crash involvement volume 2-case and reconstruction details. Adelaide: NHMRC Road Accident Research Unit, The University of Adelaide.
Lubkowski, S. D., Lewis, B. A., Gawron, V. J., Gaydos, T. L., Campbell, K. C., Kirkpat-rick, S. A., ... & Cicchino, J. B. (2021). Driver trust in and training for advanced driver assistance systems in Real-World driving. Transportation research part F: traffic psychology and behaviour, 81, 540-556.
Mahmud, S. S., Ferreira, L., Hoque, M. S., & Tavassoli, A. (2017). Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs. IATSS research, 41(4), 153-163.
Martin, J. L. (2002). Relationship between crash rate and hourly traffic flow on interurban motorways. Accident Analysis & Prevention, 34(5), 619-629.
Miller, M. J. (2009, March). Disparate impact and unfairly discriminatory insurance rates. In Casualty Actuarial Society E-Forum, Winter 2009 (Vol. 276).
Metromile. How does pay-per-mile insurance work. 2022. Website: https://www.metromile.com/pay-per-mile-car-insurance/.
Nai, W., Yang, Z., Wei, Y., Sang, J., Wang, J., Wang, Z. & Mo, P. (2022). A comprehen-sive review of driving style evaluation approaches and product designs applied to vehicle usage-based insurance. Sustainability, 14(13), 7705.
NAIC. (2015). Usage-Based Insurance and Telematics. The Center for Insurance Policy and Research. Website: http://www.naic.org/cipr_topics/topic_ysage_based_insurnce.htm
Paefgen, J., Staake, T., & Thiesse, F. (2013). Evaluation and aggregation of pay-as-you-drive insurance rate factors: A classification analysis approach. Decision Support Systems, 56, 192-201.
Parker, D., West, R., Stradling, S., & Manstead, A. S. (1995). Behavioural characteristics and involvement in different types of traffic accident. Accident Analysis & Prevention, 27(4), 571-581.
Pivato, S., Misani, N., & Tencati, A. (2008). The impact of corporate social responsibility on consumer trust: the case of organic food. Business ethics: A European review, 17(1), 3-12.
Progressive Casualty Insurance Company. Welcome to the Progressive Newsroom. 2023. Website: https://progressive.mediaroom.com/
Saaty, T. (1980, November). The analytic hierarchy process (AHP) for decision making. In Kobe, Japan (Vol. 1, p. 69).
Sasidhar, K., & Upasini, A. (2019, January). Two wheeler rash drive detection using smartphones. In 2019 11th International Conference on Communication Systems & Networks (COMSNETS) (pp. 754-758). IEEE.
Singh, H., & Kathuria, A. (2021). Analyzing driver behavior under naturalistic driving conditions: A review. Accident Analysis & Prevention, 150, 105908.
Shope, J. T., & Bingham, C. R. (2008). Teen driving: motor-vehicle crashes and factors that contribute. American journal of preventive medicine, 35(3), S261-S271.
Terzi, R., Sagiroglu, S., & Demirezen, M. U. (2018). Big data perspective for driver/driving behavior. IEEE Intelligent Transportation Systems Magazine, 12(2), 20-35.
Troncoso, C., Danezis, G., Kosta, E., & Preneel, B. (2007, October). Pripayd: privacy friendly pay-as-you-drive insurance. In Proceedings of the 2007 ACM workshop on Privacy in electronic society (pp. 99-107).
Tsiakis, T. (2009). Contribution of corporate social responsibility to information security management. Information Security Technical Report, 14(4), 217-222.
United Nations. (2023). Global Sustainable Development Report (GSDR). Website: https://sdgs.un.org/gsdr/gsdr2023.
Yang, J., & Lee, H. (1997). An AHP decision model for facility location selection. Facilities, 15(9/10), 241-254.
Zeier Röschmann, A., Erny, M. & Wagner, J. (2022). On the (future) role of on-demand insurance: market landscape, business model and customer perception. The Geneva Papers on Risk and Insurance-Issues and Practice, 47(3), 603-642.
內政部戶政司(2023),人口統計資料。
台灣勞工陣線(2021),平台外送人員從業狀況問卷調查,擷取日期:2023年9月12日,網站:https://labor.ngo.tw/issue/follow-topics/168-gig-economy-platform-economy/1032-2020questionnaire1。
外送作業安全衛生指引(2022),勞動部。
交通部運輸研究所(2020),運輸政策白皮書。
杜文苓、李翰林(2008),國際永續發展趨勢初探--以聯合國千禧年發展目標為例,臺灣國際研究季刊,4(2 ),頁211-237。
李威勳、盧冠宏、蕭至良、林章能、劉曜齊、蘇冠瑋、蘇浤諺、蔡以誠(2019),以深度學習方法分析駕駛風險並設計駕駛行為車險服務平台,科技部補助專題研究計畫報告。
金融監督管理委員會(2023),銀行局2018年至2022年金融統計指標信用卡業務統計。
金融監督管理委員會(2018),自用汽車定型化契約範本。
金融監督管理委員會(2014),保險業辦理微型保險業務應注意事項。
范姜肱、章明純、許伊婷(2016),臺灣保險業Next: UBI車聯網保險,財團法人保險事業發展中心。
徐暐(2019),臺灣UBI車險的困境與發展契機,國立政治大學風險管理與保險學系,碩士論文。
陳昱如(2019),知覺易用性、品牌忠誠度及產品複雜度對網路投保意願影響之研究-以汽車責任險為例,朝陽科技大學,碩士論文。
臺北市外送平台業者管理自治條例(2021),臺北市政府。
財團法人聯合信用卡處理中心(2023),跨世代消費大不同:各世代信用卡消費大數據分析,擷取日期:2023年9月9日,網站:https://www.nccc.com.tw/wps/wcm/connect/zh/home/openinformation/CaseAnalysisIntroduce/CNT_05_998_20230627102742。
許志誠、周文生、黃璽諭(2022),網路媒合外送平台外送員駕駛行為影響因素之研究,運輸學刊,第三十四卷,第三期,頁225-262 。
張海威(2023),基於深度學習辨識危險駕駛行為之機車外送員駕駛風險評估,國立成功大學交通管理科學系,碩士論文。
詹誼詔(2023),機車外送員創新保險制度之社會接受度探討,國立成功大學交通管理科學系,碩士論文。
機車外送交通安全指引(2022),交通部公路總局。