應用手機信令軌跡資料，推估通勤道路之時空地震災害風險

Estimation of Space-Time Traffic Corridor Earthquake Risk Exposure Based on Cellular Trajectory Data

臺灣由於地理位置的特殊性，地震、颱風等自然災害頻傳。統計自民國47年至108年為止，平均每年發生3.6次颱風，地震為0.5次。儘管地震發生次數較少，但平均每次所造成的社會經濟損失卻最為慘重。身處如此的環境之中，更是突顯了災害風險評估的重要性。在評估災害風險時，往往忽略了風險的時間動態特性，無法在更細緻的時空尺度上提供災害防救決策。風險評估其中一個很重要的元素是暴露度，人流適合作為道路上暴露度的指標，於災害風險評估時，動態的因素如人流是影響暴露度最主要的變數，所產出之時空風險地圖於平時便能提升該地居民的風險知覺，也可幫助救災人員於事前制定應變措施，而手機信令資料能夠以較低的成本取得真實的人口動態暴露數據。本研究藉由此數據在風險評估中加入人口移動的時空動態特性，研究區設定為高雄市內，切分不同的時間區段，藉此挖掘並觀察通勤道路的地震災害風險時空模式，改善以往假定靜態災害風險的不足。研究成果顯示：道路暴露度與風險值皆由06:00急遽上升，12:00稍趨緩，直到接近17:00時再度上升並達到最高值，隨後逐漸下降至隔日凌晨。空間分布則是以中正路、中山路、國道10號為主要風險高峰道路。道路的風險時空分布以新興熱點分析後得知，凌晨至通勤尖峰與中午至通勤尖峰這兩個時段呈現較高風險熱點強度，但時間趨勢上前者熱點較晚出現，後者熱點則是逐步增強。災害風險的時空模式探勘結果，能夠在減災階段上提升風險知覺；在整備階段，能夠協助兵棋推演腳本的擬定、並且以更細緻的時空尺度規劃設備物資的調度、以及交通的規劃，增強地方的災害應對能力。

Due to the special geographical location of Taiwan, hazards such as earthquakes and typhoons occur very frequently. According to statistics, from 1948 to 2019, typhoons occurred 3.6 times, and earthquakes occurred 0.5 times on average per year. Although there are fewer earthquakes than typhoons, the earthquake's average negative social and economic impacts are extremely severe. Being in such a vulnerable environment highlights the importance of disaster risk assessment. When assessing disaster risk, the space-time dynamic characteristics of risk are often ignored, and it is impossible to provide disaster prevention decisions on a more accurate space-time scale. In order to include space-time characteristics of population movement to risk assessment, Call Detail Record (CDR) data were used because it can obtain more samples of real population dynamics at a lower cost. In this study, traffic corridor data were analyzed at different time windows to observe space-time patterns, so that the static disaster risk assessments in the past can be improved. The results of this research show that the corridor exposure and risk value both rose sharply from 06:00, until slowing down at 12:00. It rose again and reached the highest value near 17:00, and then gradually decreased to the early morning of the next day. Zhongzheng Road, Zhongshan Road, and National Freeway 10 were the main risk peak corridors. The space-time risk distribution of corridors was analyzed based on emerging hotspots. The two time windows, ''03:00 - 08:00'' and ''11:00 - 17:00'', showed higher risk hotspot intensity, but the former hotspot appeared later in the time trend, while the latter hotspot gradually intensified. The data mining results of the disaster risk space-time patterns can improve the risk perception in the mitigation phase, and assist in the preparation of simulation exercises in the preparedness phase. In addition, it can also plan the dispatchment of equipment and supplies on a more accurate space-time scale as well as improve transportation planning in order to enhance local capabilities when facing disasters.