利用旅客流模擬模式計算捷運系統人流服務指標之研究

Calculation of MRT Service Performance Indicators of Passenger Flow with a Simulation Model

10.6402/TPJ.201209.0279

鍾志成(Jyh-Cherng Jong)；林蓁(Jen Lin)；林志偉(Jyh-Wei Lin)；林誌銘(Chih-Ming Lin)

捷運系統 ； 旅客流 ； 旅客流模擬 ； Mass rapid transit system (MRT) ； Passenger flow ； Passenger flow simulation

運輸計劃季刊

41卷3期（2012 / 09 / 30）

279 - 310

繁體中文

大眾捷運系統（Mass Rapid Transit, MRT）提供都會區旅客方便快速的運輸服務，為了維持一定的服務水準，營業機構必須掌握系統的服務績效與旅客人潮的變化。由於捷運系統具有列車停站型態一致且無對號的特性，故旅客的搭乘方式非常自由，使得人潮在系統內的移動路徑與搭乘列車之掌握相當不易，有鑑於此，本研究利用自動收費系統（Automatic Fare Collection, AFC）的紀錄，採用旅客實際進入與離開系統之車站與時間，配合列車實際時刻表，以微觀的方式，逐一模擬旅客在捷運系統內的移動路徑並將旅客指派至列車上，並藉由全日旅客之模擬結果，計算列車乘載率、車站人潮變化、旅客候車時間與轉乘時間等各項人流服務指標，提供營運單位做為評估捷運系統目前的服務水準與績效之參考。本研究以高雄捷運系統之實際資料進行案例分析，從分析結果可知，本研究所發展的模式可正確且有效地進行旅客流的模擬，並計算出各項服務指標，有助於營運機構評估目前之營運績效。

Mass Rapid Transit (MRT) System provides its passengers with convenient and fast transportation services in the metropolitan area. To maintain a predetermined level of service, MRT operators should monitor service performance and the transition of passenger flow in the system. Since MRT trains typically have the same stopping pattern and no seat assignments, passengers can freely travel within the system. Consequently, recognizing passengers' traveling path and boarding trains becomes crucial. Therefore, this research developed a microscopic model to simulate passenger flow in MRT systems. This model first extracted the timestamps and station information of passengers entering and exiting an MRT system from the Automatic Fare Collection (AFC). With this information and actual timetables, the model then determined passengers' traveling paths and assigned each passenger to an appropriate train. Based on the simulated results of data from an entire day, performance indicators such as train load factor, flow fluctuations at stations, passenger waiting time, and transferring time can he calculated to help MRT operators evaluate their offered level of service. Empirical data from the Kaohsiung Rapid Transit Corporation (KRTC) was also collected for a case study. The results demonstrate that the model could effectively and efficiently simulate passenger flow for MRT systems. These resulting performance indicators could also help operators assess their current level of service.

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