在宅經濟與疫情時代的環境下，外送產業迅速擴張，許多人選擇加入外送員的行列，提供運送美食的服務。然而外送服務的大幅增加也導致此產業對環境以及交通的負擔上升，外送機車產生的空氣污染、噪音污染、佔據道路容量、車流壅塞、肇事風險提升等問題容易被忽略，且由所有用路人共同在承擔。因此，本研究量化外送產業外部成本並且研擬相關政策。
政府欲追求外部成本最小，而外送企業欲追求利潤最大，本研究假設政府透過課稅以牽制企業的外部成本排放，企業再評估是否對外送機車數量進行管制，雙方所追求的目標不同但又會因彼此的決策而相互影響自身決策，因此本研究參考經濟學中的Stackelberg（領導者與跟隨者）博弈理論，利用雙層數學規劃的概念來建構兩方自主且衝突的決策者關係。上層以政府為優先決策者，對外部成本問題進行價值量化並計算稅值，下層以外送平台為次要決策者，在政府課稅的行為下管制外送車輛數進而實現利潤最大化。參考過去文獻使用基因演算法能同時考慮多個變數指標，有利於搜尋大範圍的可行解空間以避免落入局部最佳解，且能在短時間內求得近似解，符合本研究需求，因此採用基因演算法求解。
上層結果指出2020年疫情爆發後每多出一輛外送車輛相較於2019年額外產生191.23元的外部成本，與2006年使用機車的研究相比增加顯著，除了因10餘年的通膨及時間價值落差影響外，也與使用外送行為的差異有關，因此本研究針對疫情後實際狀況對外送企業所制定的課稅金額為96.66元。其中擁擠成本與2015年研究成長1.06倍，肇事成本與2015年研究成長1.28倍，可見外送機車肇事率是較為嚴重的議題。下層結果模擬政府允許外送企業外部成本年成長10%的狀況下，企業應將外送車輛管制在25,000輛，以達成企業利潤最大化。
本研究建立一套可供政府量化其外部成本的模型，透過稅收方式將其外部成本內部化，以減少外送車輛對社會產生之外部成本，有利政府及企業審視在疫情後外送狀況對社會造成的影響以及提供初步的解決方案。

With the rapid expansion of food delivery (FD) services, many external costs (ECs) to society have been incurred. To reduce these ECs, this study discusses relevant policies that can be developed between governments and FD enterprises, such as taxation and vehicle control. A bi-level planning model is proposed to solve conflict problems for stackelberg relationships between governments and FD enterprises. However, literatures about the introduction of ECs incurred in transportation were focused on the category of transport implements, lack of discussion on usage behavior. Therefore, in this study, we considered the behavior and characteristics of FD. Besides, we applied genetic algorithms to find the impact of these FD behavioral variables on ECs, quantify the ECs incurred before and after the-pandemic and make a comparative analysis to determine the appropriate amount of taxation and vehicle control.
The results show that the congestion problem has the most significant growth, and the problem of causing the accident has the most serious damage to society. Taxation policies can enable FD enterprises to regulate FD motorcycles to improve ECs emissions. In addition, based on the simulated countermeasure discussion, it is possible to effectively control ECs emissions and provide the government and FD enterprises with appropriate counter strategies.

1. 交通部運輸研究所（2002）。「永續運輸之量化指標研究」。
2. 交通部運輸研究所（2016）。「機車動態能耗與碳排放係數」。
3. 交通部運輸研究所（2020）。「道路交通事故成本推估之研究」。
4. 呂明蔘（2016）。都市道路使用成本與內部化之分析－以臺南市為例。國立成功大學交通管理科學所碩士學位論文。
5. 高婷婷、闫光辉、童敏勇（2007）。城市交通外部成本量化方法研究。天津工程師範學院學報，17(3)，52-54。
6. 高婷婷、邢艳云（2007）。哈尔滨市交通外部成本的量化分析。交通與計算機，25(2)，123-125。
7. 張亦寬（2004）以雙層次數學規劃建構旅客需求導向之票價設計模式-以台灣高鐵為例。國立成功大學交通管理科學所碩士學位論文，1-115。
8. 張凱羚（2006）。永續城際運輸之雙層規劃模式。國立交通大學交通運輸研究所碩士學位論文，79。
9. 郭瑜堅（2006）。都市旅次總成本模式構建之研究。運輸計劃季刊，36(2)，147-182。
10. 張有恆（2019）。運輸經濟學（第五版），臺北市：華泰文化。
11. Banfi, A., Muraglia, A., Dozin, B., Mastrogiacomo, M., Cancedda, R., & Quarto, R. (2000). Proliferation kinetics and differentiation potential of ex vivo expanded human bone marrow stromal cells: Implications for their use in cell therapy. Experimental Hematology, 28(6), 707-715. doi:https://doi.org/10.1016/S0301-472X(00)00160-0
12. Bard, J. F. (2013). Practical bilevel optimization: algorithms and applications (Vol. 30): Springer Science & Business Media.
13. Ben-Ayed, O., & Blair, C. E. (1990). Computational Difficulties of Bilevel Linear Programming. Operations Research, 38(3), 556-560. doi:10.1287/opre.38.3.556
14. Bialas, W. F., & Karwan, M. H. (1984). Two-level linear programming. Management science, 30(8), 1004-1020.
15. Brückner, M., & Scheffer, T. (2011). Stackelberg games for adversarial prediction problems.
16. Chen, Hsiao, Huang. (2019). discussing about defensive driving from the delivery accidents. Forum of Taiwan Police College, 33, 45-54.
17. Chung, Y., Song, T.-j., & Yoon, B.-J. (2013). Injury severity in delivery-motorcycle to vehicle crashes in the Seoul metropolitan area. Accident Analysis & Prevention, 62C, 79-86. doi:10.1016/j.aap.2013.08.024
18. Colson, B., Marcotte, P., & Savard, G. (2005). Bilevel programming: A survey. 4or, 3(2), 87-107.
19. Colson, B., Marcotte, P., & Savard, G. (2007). An overview of bilevel optimization. Annals of operations research, 153(1), 235-256.
20. da Silva, D. W., Andrade, S. M. d., Soares, D. F. P. d. P., Mathias, T. A. d. F., Matsuo, T., & de Souza, R. K. T. (2012). Factors Associated with Road Accidents among Brazilian Motorcycle Couriers. The Scientific World Journal, 2012, 605480. doi:10.1100/2012/605480
21. Demir, E., Huang, Y., Scholts, S., & Van Woensel, T. (2015). A selected review on the negative externalities of the freight transportation: Modeling and pricing. Transportation Research Part E: Logistics and Transportation Review, 77, 95-114. doi:https://doi.org/10.1016/j.tre.2015.02.020
22. Forkenbrock, D. (2001). Comparison of external costs of rail and truck freight transportation. Transportation Research Part A: Policy and Practice, 35, 321-337. doi:10.1016/S0965-8564(99)00061-0
23. Jeroslow, R. G. (1985). The polynomial hierarchy and a simple model for competitive analysis. Mathematical programming, 32(2), 146-164.
24. Havenga, J. H. (2015). Macro-logistics and externality cost trends in South Africa – underscoring the sustainability imperative. International Journal of Logistics Research and Applications, 18(2), 118-139. doi:10.1080/13675567.2015.1015509
25. Hejazi, S. R., Memariani, A., Jahanshahloo, G., & Sepehri, M. M. (2002). Linear bilevel programming solution by genetic algorithm. Computers & Operations Research, 29(13), 1913-1925.
26. Hong, C., Choi, H., Choi, E.-K., & Joung, H.-W. (2021). Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic. Journal of Hospitality and Tourism Management, 48, 509-518. doi:10.1016/j.jhtm.2021.08.012
27. Hook, W., & Replogle, M. (1996). Motorization and non-motorized transport in Asia: Transport system evolution in China, Japan and Indonesia. Land Use Policy, 13(1), 69-84. doi:https://doi.org/10.1016/0264-8377(95)00025-9
28. Kaddoura, I., Bischoff, J., & Nagel, K. (2020). Towards welfare optimal operation of innovative mobility concepts: External cost pricing in a world of shared autonomous vehicles. Transportation Research Part A: Policy and Practice, 136, 48-63. doi:10.1016/j.tra.2020.03.032
29. Kieling, R., Szobot, C., Matte, B., Coelho, R., Kieling, C., Pechansky, F., & Rohde, L. (2011). Mental disorders and delivery motorcycle drivers (motoboys): A dangerous association. European psychiatry : the journal of the Association of European Psychiatrists, 26, 23-27. doi:10.1016/j.eurpsy.2010.03.004
30. Klaus, P., & Müller, S. (2012). The roots of logistics: A reader of classical contributions to the history and conceptual foundations of the science of logistics: Springer-Verlag.
31. Kulanthayan, S., See, L. G., Kaviyarasu, Y., & Nor Afiah, M. Z. (2012). Prevalence and determinants of non-standard motorcycle safety helmets amongst food delivery workers in Selangor and Kuala Lumpur. Injury, 43(5), 653-659. doi:https://doi.org/10.1016/j.injury.2011.06.197
32. Leitmann, G. (1978). On generalized Stackelberg strategies. Journal of optimization theory and applications, 26(4), 637-643.
33. Levinson, D. M., & Gillen, D. (1998). The full cost of intercity highway transportation. Transportation Research Part D-Transport and Environment, 3(4), 207-223. doi:10.1016/s1361-9209(97)00037-0
34. Liu, B. (1998). Stackelberg-Nash equilibrium for multilevel programming with multiple followers using genetic algorithms. Computers & Mathematics with Applications, 36(7), 79-89.
35. McAuley, A., Stewart, B., Cormier, D., & Siemens, G. (2010). The MOOC model for digital practice. In.
36. Mitropoulos, L. K., Prevedouros, P. D., & Kopelias, P. (2017). Total cost of ownership and externalities of conventional, hybrid and electric vehicle. Transportation Research Procedia, 24, 267-274. doi:10.1016/j.trpro.2017.05.117
37. Mostert, M., & Limbourg, S. (2016). External Costs as Competitiveness Factors for Freight Transport — A State of the Art. Transport Reviews, 36, 1-21. doi:10.1080/01441647.2015.1137653
38. Oliveira, L. K. d., Morganti, E., Dablanc, L., & Oliveira, R. L. M. d. (2017). Analysis of the potential demand of automated delivery stations for e-commerce deliveries in Belo Horizonte, Brazil. Research in Transportation Economics, 65, 34-43. doi:https://doi.org/10.1016/j.retrec.2017.09.003
39. Ozbay, K., Bartin, B., Yanmaz-Tuzel, O., & Berechman, J. (2007). Alternative methods for estimating full marginal costs of highway transportation. Transportation Research Part A: Policy and Practice, 41, 768-786. doi:10.1016/j.tra.2006.12.004
40. Saad, A. T. (2020). Factors affecting online food delivery service in Bangladesh: an empirical study. British Food Journal, 123(2), 535-550. doi:10.1108/bfj-05-2020-044
41. Sakawa, M., & Nishizaki, I. (2009). Cooperative Decision Making in Hierarchical Organizations. In Cooperative and Noncooperative Multi-Level Programming (pp. 83-179). Springer, Boston, MA.
42. Shin, D., Byun, J., & Jeong, B. (2019). Crashes and Traffic Signal Violations Caused by Commercial Motorcycle Couriers. Safety and Health at Work, 10. doi:10.1016/j.shaw.2018.10.002
43. Sinha, D., & Pandit, D. (2021). A simulation-based study to determine the negative externalities of hyper-local food delivery. Transportation Research Part D: Transport and Environment, 100. doi:10.1016/j.trd.2021.103071
44. Truong, L., & Hang, N. (2019). Mobile phone related crashes among motorcycle taxi drivers. Accident Analysis & Prevention, 132. doi:10.1016/j.aap.2019.105288
45. Tsai, J.-F., Chu, C.-P., & Hu, S.-R. (2015). Road pricing for congestion and accident externalities for mixed traffic of motorcycles and automobiles. Transportation Research Part A: Policy and Practice, 71, 153-166. doi:https://doi.org/10.1016/j.tra.2014.10.020
46. Wang, Z., & He, S. Y. (2021). Impacts of food accessibility and built environment on on-demand food delivery usage. Transportation Research Part D: Transport and Environment, 100. doi:10.1016/j.trd.2021.103017
47. Wen, U.-P., & Hsu, S.-T. (1991). Linear bi-level programming problems—a review. Journal of the Operational Research Society, 42(2), 125-133.
48. Xi, G., Cao, X., & Zhen, F. (2021). How does same-day-delivery online shopping reshape social interactions among neighbors in Nanjing? Cities, 114, 103219. doi:https://doi.org/10.1016/j.cities.2021.103219
49. Yang, W., He, J., He, C., & Cai, M. (2020). Evaluation of urban traffic noise pollution based on noise maps. Transportation Research Part D: Transport and Environment, 87, 102516. doi:https://doi.org/10.1016/j.trd.2020.102516
50. Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665-U695. doi:10.1038/nmeth.1635
51. Zhang, H., Xue, L., Jiang, Y., Song, M., Wei, D., & Liu, G. (2022). Food delivery waste in Wuhan, China: Patterns, drivers, and implications. Resources, Conservation and Recycling, 177. doi:10.1016/j.resconrec.2021.105960
52. Zhang, J., & Zhang, Q. (2009). Stackelberg game for utility-based cooperative cognitive radio networks. In Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing (pp. 23-32).