近年來，外送產業隨著平台經濟（Platform Economics）快速發展，已由疫情期間的替代性服務，逐漸轉變為嵌入都市日常生活之常態化即時服務模式。外送員作為串聯平台運作與都市空間的重要行動者，其行為可區分為等單、取餐與送餐三個階段；其中，等單階段係指尚未接受任務前之閒置時間待命階段，外送員須自行選擇等待位置，並權衡接單效率、行動便利性、補給需求與環境條件，因此成為最能反映其空間需求與區位偏好的行為階段。實務上，等單行為常集中於商圈、路口及路側空間，呈現非隨機之空間分布特性，然而既有研究多著重於個案觀察或質性分析，較少於都市尺度系統性檢視其空間分布與形成機制。
本研究以臺南市外送平台服務範圍為研究區域，將外送員等單行為視為發生於都市道路網絡上的線性空間事件，透過問卷調查蒐集實際等單點位資料，並據以建構跨尺度分析架構。於路段層級，以道路路段為分析單元，整合空間型構法（Space Syntax）指標、活動暴露、補給設施可及性與路段環境條件，探討等單事件發生強度之影響因素；於路段內層級，則進一步分析微觀空間條件對實際停等位置選擇之影響。方法上，以線性網絡非齊次卜瓦松點過程（Linear Network Inhomogeneous Poisson Point Process, LN-IPPP）作為事件強度分析之概念架構，並考量資料具過度離散特性，採用負二項迴歸模型（Negative Binomial Regression, NBR）進行估計；另透過累積分布曲線（concentration curve）、命中率與 AUC 指標進行模型驗證。
研究結果顯示，外送員等單行為於都市道路網絡中呈現明顯之非隨機集中分布特性。於路段層級中，路網效率、活動暴露與補給條件對等單事件發生強度具有顯著正向影響；於路段內層級中，補給可及性、巷弄位置、活動暴露與綠視率對實際停等位置選擇具有顯著或邊際顯著影響。模型驗證結果顯示，本研究模型具有良好辨識能力，前20%高潛力路段可涵蓋約70 %之等單事件，AUC達0.84以上。綜合而言，外送員等單行為可被理解為由「路段選擇」至「路段內位置選擇」之跨尺度兩階段決策過程。本研究補充既有研究對外送員等待行為之都市尺度空間分析不足，並可作為未來都市空間管理、路側停等空間規劃及平台營運策略優化之實證參考。

This study examines food delivery riders’ idle-time standby behavior in Tainan City, Taiwan. Rather than treating standby behavior as ordinary parking or resting, this study conceptualizes it as a spatial event occurring along the urban road network. Based on questionnaire-based standby location data, Space Syntax indicators, platform restaurant POIs, amenity facilities, land-use data, and street-view-based environmental variables, this study develops a two-stage analytical framework. The road-segment-level model examines which road segments are more likely to attract standby events, while the within-segment-level model examines where riders actually stop within selected segments.
Methodologically, the study adopts the Linear Network Inhomogeneous Poisson Point Process as the conceptual framework and applies Negative Binomial Regression to address overdispersion in event count data. The results show that standby behavior is not randomly distributed. At the road-segment level, order activity exposure, amenity accessibility, and network integration are key factors shaping standby event intensity. At the within-segment level, amenity accessibility, alley-adjacent locations, fast-food restaurant adjacency, stopping feasibility, and order activity exposure further influence actual stopping locations. Model validation shows that the models have good discriminatory performance. Overall, the findings suggest that food delivery riders’ standby behavior is a cross-scale spatial decision-making process, from road-segment selection to within-segment location choice.

1.Aguilera, A., Dablanc, L., & Rallet, A. (2022). Digital work and urban delivery: Profile, activity and mobility practices of on-demand food delivery couriers in Paris (France). Information, 13(9), 433. https://doi.org/10.3390/info13090433
2.Atkinson, R., & Flint, J. (2001). Accessing hidden and hard-to-reach populations: Snowball research strategies. Social Research Update, (33).
3.Baddeley, A., Rubak, E., & Turner, R. (2015). Spatial point patterns: Methodology and applications with R. Chapman and Hall/CRC. https://doi.org/10.1201/b19708
4.Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139013567
5.Çamur, K. C., Roshani, M., & Pirouzi, S. (2017). Using space syntax to assess safety in public areas—Case study of Tarbiat pedestrian area, Tabriz-Iran. IOP Conference Series: Materials Science and Engineering, 245, 082002. https://doi.org/10.1088/1757-899X/245/8/082002
6.Colaço, R., & de Abreu e Silva, J. (2023). Exploring the role of accessibility in shaping retail location using space syntax measures: A panel-data analysis in Lisbon, 1995–2010. Environment and Planning B: Urban Analytics and City Science, 50(5), 1345–1360. https://doi.org/10.1177/23998083221138570
7.Diggle, P. J. (2013). Statistical analysis of spatial and spatio-temporal point patterns (3rd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/b15326
8.el Meouche, R., Ahmed, M., Anouar Abdelhakim, B., & Karas, I. (2024). Innovations in smart cities applications (Vol. 7). Springer. https://doi.org/10.1007/978-3-031-53824-7
9.Etikan, I., Musa, S. A., & Alkassim, R. S. (2015). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
10.Figueiredo, L., & Amorim, L. (2005). Continuity lines in the axial system. In A. van Nes (Ed.), Proceedings of the 5th International Space Syntax Symposium (pp. 161–174). TU Delft.
11.Gevaert, J., Doms, J., Vandevenne, E., & Van Aerden, K. (2025). Strategies for surveying platform workers: Lessons from a Belgian case study. Quality & Quantity, 59(1), 211–231. https://doi.org/10.1007/s11135-024-01935-5
12.Gu, Y., Cho, E., Oh, C., & Lee, G. (2025). Riding safety evaluation of food delivery motor scooters based on associating sensor-based riding behavior and road traffic characteristics. Accident Analysis & Prevention, 211, 107871. https://doi.org/10.1016/j.aap.2024.107871
13.Hillier, B., Burdett, R., & Peponis, J. (1987). Creating life: Or, does architecture determine anything? Architecture & Comportement / Architecture & Behaviour, 3(3), 233–250.
14.Hillier, B., & Hanson, J. (1989). The social logic of space. Cambridge University Press.
15.Hillier, B., Penn, A., Hanson, J., Grajewski, T., & Xu, J. (1993). Natural movement: Or, configuration and attraction in urban pedestrian movement. Environment and Planning B: Planning and Design, 20(1), 29–66. https://doi.org/10.1068/b200029
16.Hillier, B. (1996). Cities as movement economies. Urban Design International, 1(1), 41–60. https://doi.org/10.1057/udi.1996.5
17.Hillier, B., & Iida, S. (2005). Network and psychological effects in urban movement. In A. G. Cohn, & D. M. Mark (Eds.), Spatial information theory (pp. 475–490). Springer. https://doi.org/10.1007/11556114_30
18.Hsu, C.-K., Lee, T.-C., & Wong, K. I. (2024). Time is money: Effects of time-sensitive incentive on risky driving among on-demand food-delivery motorcyclists in Taiwan. Safety Science, 176, 106549. https://doi.org/10.1016/j.ssci.2024.106549
19.Igwegbe, E. (2025). The impact of digitalized food delivery systems on urban space: Exploring spatial implications of platform economies in Stockholm (Master’s thesis, Linköping University). DiVA Portal. https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-215191
20.Jayasinghe, A., Sano, K., Kasemsri, R., & Nishiuchi, H. (2016). Travelers’ route choice: Comparing relative importance of metric, topological and geometric distance. Procedia Engineering, 142, 18–25. https://doi.org/10.1016/j.proeng.2016.02.008
21.Jiang, B., & Claramunt, C. (2004). Topological analysis of urban street networks. Environment and Planning B: Planning and Design, 31(1), 151–162. https://doi.org/10.1068/b306
22.Jin, Y., Zhu, Y., Chen, Y., & Lin, Y. (2023). Identifying optimal rest spaces for food takeaway workers using digital marketing insights based on ArcGIS—A case study in Xujiahui Street, Shanghai. Computer-Aided Design and Applications, 135–149. https://doi.org/10.14733/cadaps.2024.S3.135-149
23.Lata, L. N., Copolov, A., & Azizi, R. (2025). The everyday lives of gig workers in Melbourne. Urban Geography. Advance online publication. https://doi.org/10.1080/02723638.2025.2535621
24.Lord, D., & Mannering, F. (2010). The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A: Policy and Practice, 44(5), 291–305. https://doi.org/10.1016/j.tra.2010.02.001
25.Ma, S., Bala, P., Nisi, V., Zimmerman, J., & Nunes, N. J. (2023). Uncovering gig worker-centered design opportunities in food delivery work. In Proceedings of the 2023 ACM Designing Interactive Systems Conference (pp. 688–701). https://doi.org/10.1145/3563657.3596123
26.Mohamed, A. A., & van der Laag Yamu, C. (2024). Space syntax has come of age: A bibliometric review from 1976 to 2023. Journal of Planning Literature, 39(2), 203–217. https://doi.org/10.1177/08854122231208018
27.Montello, D. R. (1991). The measurement of cognitive distance: Methods and construct validity. Journal of Environmental Psychology, 11(2), 101–122. https://doi.org/10.1016/S0272-4944(05)80071-4
28.Negrin, K. A., Slaughter, S. E., Dahlke, S., & Olson, J. (2022). Successful recruitment to qualitative research: A critical reflection. International Journal of Qualitative Methods, 21, 16094069221119576. https://doi.org/10.1177/16094069221119576
29.Noy, C. (2008). Sampling knowledge: The hermeneutics of snowball sampling in qualitative research. International Journal of Social Research Methodology, 11(4), 327–344. https://doi.org/10.1080/13645570701401305
30.Ojeda-Pereira, I. (2025). Securitizing the technoplatformized city: How delivery workers construct urban safe spaces in Chile’s gig economy. Digital Geography and Society, 9, 100136. https://doi.org/10.1016/j.diggeo.2025.100136
31.Okabe, A. (2015). Spatial analysis along networks. In S. Shekhar, H. Xiong, & X. Zhou (Eds.), Encyclopedia of GIS. Springer. https://doi.org/10.1007/978-3-319-23519-6_1648-1
32.Papakostopoulos, V., & Nathanael, D. (2021). The complex interrelationship of work-related factors underlying risky driving behavior of food delivery riders in Athens, Greece. Safety and Health at Work, 12(2), 147–153. https://doi.org/10.1016/j.shaw.2020.10.006
33.Peng, L., Shui, S., Li, Z., & Yang, J. (2022). Food delivery couriers and their interaction with urban public space: A case study of a typical “takeaway community” in the Wuhan Optics Valley Area. Sustainability, 14(10), 6238. https://doi.org/10.3390/su14106238
34.Penn, A. (2003). Space syntax and spatial cognition: Or why the axial line? Environment and Behavior, 35(1), 30–65. https://doi.org/10.1177/0013916502238864
35.Prakobkarn, P., Luangwilai, T., Prempree, P., & Kunno, J. (2024). Prevalence of motorcycle accidents among food delivery drivers and its relation to knowledge, attitudes, and practices in urban areas in Bangkok, Thailand. PLOS ONE, 19(5), e0303310. https://doi.org/10.1371/journal.pone.0303310
36.Putra, I. G. B., Kuo, P.-F., Chiu, C.-S., & Sulistyah, U. (2022). Analyze the spatial distribution of delivery motorcycle crashes and identify the related factors. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B4-2022, 163–169. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-163-2022
37.Putra, I. G. B., Kuo, P.-F., & Lord, D. (2023). Local spatial analysis of the crash frequency of food delivery motorcyclists vs. nondelivery motorcyclists in relation to points of interest. Journal of Advanced Transportation, 2023, 1–19. https://doi.org/10.1155/2023/6643649
38.Safira, F. (2025). Preliminary study on the dynamics of food delivery driver. Journal of Emerging Business Management and Entrepreneurship Studies, 5, 66–81. https://doi.org/10.34149/jebmes.v5i1.186
39.Streit, R. L. (2010). Poisson point processes: Imaging, tracking, and sensing. Springer. https://doi.org/10.1007/978-1-4419-6923-1
40.Sun, P. (2019). Your order, their labor: An exploration of algorithms and laboring on food delivery platforms in China. Chinese Journal of Communication, 12(3), 308–323. https://doi.org/10.1080/17544750.2019.1583676
41.Torres-Vanegas, J. (2024). The configuration of emerging cities: A review of spatial syntax. Sciéndo, 27(2), 295–301. https://doi.org/10.17268/sciendo.2024.043
42.Turner, A. (2004). Depthmap 4: A researcher’s handbook. University College London. https://discovery.ucl.ac.uk/id/eprint/2651/
43.Wood, A. J., Martindale, N., & Burchell, B. J. (2025). Beyond the ‘gig economy’: Towards variable experiences of job quality in platform work. Work, Employment and Society, Advance online publication. https://doi.org/10.1177/09500170251336947
44.Xie, Z., & Yan, J. (2008). Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5), 396–406. https://doi.org/10.1016/j.compenvurbsys.2008.05.001
45.Zaleckis, K., Kamičaitytė-Virbašienė, J., & Matijošaitienė, I. (2015). Using space syntax method and GIS-based analysis for the spatial allocation of roadside rest areas. Transport, 30(2), 182–193. https://doi.org/10.3846/16484142.2015.1045026
46.Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. arXiv. https://doi.org/10.48550/arXiv.1612.01105
47.Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2018). Semantic understanding of scenes through the ADE20K dataset. arXiv. https://doi.org/10.48550/arXiv.1608.05442
48.徐郁婷（2017）。比較廣義回歸模型和負二項回歸模型處理過度離散資料的問題 [碩士論文，國立陽明大學]。台灣博碩士論文知識加值系統。
49.連城（2025）。探討台南市次要道路中路側環境特徵對車速之影響 [碩士論文，國立成功大學]。台灣博碩士論文知識加值系統。
50.陳永奕（2019）。商圈機車停車位置選擇模式建構—以臺南市東寧路為例 [碩士論文，國立成功大學]。台灣博碩士論文知識加值系統。
51.蔡宛芸、王志弘（2023）。加速社會的補缺行動：臺北食物外送員研究。地理研究，（77），21–48。https://doi.org/10.6234/JGR.202305_(77).0002
52.鄭屹翔、李子璋（2023）。應用空間型構指標判斷道路層級之可行性。都市與計劃，50(3)，417–441。https://doi.org/10.6128/CP.202309_50(3).0003
53.羅章秀、林柏丞（2024）。結合深度學習與街景影像建構街道廣告招牌之空間聚集指標。航測及遙測學刊，29(1)，17–34。https://doi.org/10.6574/JPRS.202403_29(1).0002