機車事故長年以來都是困擾著臺灣的重要交通課題，自2012至2020年間，機車A1
及A2型事故總數由249,465件上升至324,604件，約增加30%，普通重型機車登記數量
在同期間增長了約100萬輛，提升了道路環境之機車密度，此外，國內外機車外送平
台藉助科技的進步自2012年開始投入臺灣市場，截至2020年營業額已超過100億臺幣
的規模，大幅增加機車騎士的道路曝光量，亦時常在外送途中發生交通事故。
本研究旨在探討機車事故件數與解釋變數的空間關聯性，並量化外送服務的興起
對機車事故件數在空間特徵上的影響，本研究包含三個研究對象，全臺灣總機車事故
件數、機車外送事故件數(FDMAs)以及非機車外送事故件數(NFDMAs)，本研究首先
檢驗機車事故在空間分布上是否有空間相依性，接著在考量時間序列的情況下其空間
分布是否群聚，以動態追蹤資料模型檢測其群聚位置變動原因，並以橫斷面及縱斷面
分析比較影響FDMAs與NFDMAs的解釋變數為何，同時測量FDMAs與NFDMAs之外
溢效應是否顯著，最後比較造成FDMAs與NFDMAs空間特徵不同的原因。
空間自相關結果顯示臺灣機車事故之空間型態為顯著的群聚分布，且臺南市的總
機車事故群聚地點自2012至2019年發生明顯改變，動態追蹤資料模型證明臺南市8個
行政區之機車事故件數亦具有時間相依性，當期事故件數顯著受前期機車事故件數以
及住宿及餐飲業密度所影響，且皆為正相關。
比較FDMAs和NFDMAs與解釋變數之橫斷面空間效應發現FDMAs被住宿及餐飲
業密度所解釋，而NFDMAs則與住宿及餐飲業密度、3岔路口數量以及人口密度有顯
著關連。追蹤資料分析FDMAs與解釋變數之空間及時間效應得知家戶數、住宿及餐
飲業密度以及運輸倉儲業密度皆與FDMAs呈現顯著正相關，而NFDMAs則僅能夠被
住宿及餐飲業密度所解釋。外溢效應分析在所有模型皆達到顯著水準，證明了影響一
行政區事故件數之因素除了來自該行政區內部外，亦受鄰近地區所影響，FDMAs模
型有顯著外溢效應可解釋為儘管餐飲外送服務存在服務距離限制，但仍然存在一些跨
區活動，因此，一行政區的FDMAs可被其相鄰地區中的住宿及餐飲業密度所解釋。
總結以上結果，考量空間效應之FDMAs模型、考量空間及時間效應之FDMAs模
型以及考量空間及時間效應之NFDMAs模型，皆能夠被住宿及餐飲業密度所解釋，由
此可知，住宿及餐飲業密度是影響台灣機車事故件數之關鍵因素，研究結果亦證明了
FDMAs與NFDMAs兩機車事故類別之影響因素存在差異，後續研究應將機車外送事
故獨立於機車事故進行探討。
本研究對機車事故空間型態及原因進行分析，希望能提升機車騎士風險意識、提
供保險公司區域定價差異化之參考，並對運輸規劃提出策略改善建議。

Motorcycle accidents have been a major occurrence in Taiwan for a long time. From 2012 to 2020, the total number of Types A1 and A2 motorcycle accidents rose from 249,465 to 324,604 cases, with an increase of about 30 percent. Ownership of ordinary heavy motorcycles also increased by about 1 million during the same period and increased the density of motorcycles in the road environment. In addition, since 2012, many domestic and foreign delivery platform companies have successively engaged the food-delivery market in Taiwan with the development of online technology. As of 2020, the turnover exceeded 10 billion Taiwan dollars, which significantly increased the road exposure of motorcyclists and often resulted in road accidents. This study examines whether the emergence of food delivery services affect the overall distribution patterns or the cause of motorcycle accidents.
First, we detect the spatial pattern of all motorcycle accidents, and implement the dynamic panel model for time series analysis to answer the research object 1. We then conduct cross-sectional analysis on the spatial pattern of food delivery motorcycle accidents (FDMAs) and non-food-delivery motorcycle accident (NFDMAs), which responds to the research object 2. Finally, we carry out the panel analysis on the spatial pattern of the FDMAs and the NFDMAs, thus exploring the research object 3.
Results show that the spatial pattern of motorcycle accidents in Taiwan is a significant clustered distribution, and the pattern has changed in the city of Tainan from 2012 to 2019, which is significantly affected by the number of motorcycle accidents in the previous year and the hotel and restaurant density within districts. The results of cross-sectional analysis reveal that the population density and the number of 3-legged intersections is more related to the NFDMAs. The results of the panel analysis show that household density and the transportation and warehousing service density is more related to the FDMAs. The hotel and restaurant density can explain the spatial effect on the FDMAs model, the spatial and temporal effect on the FDMAs model, and the spatial and temporal effect on the NFDMAs models It can be seen that the hotel and restaurant density is a key factor to the number of motorcycle accidents in Taiwan.
This study analyzes the spatial patterns and causes of motorcycle accidents, with aim to increase risk awareness of motorcyclists, provide insurance companies with a reference to differentiate their regional pricing, and propose strategic improvements in transportation planning.

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