摘要
近來各國對智慧城市的發展日漸重視，伴隨物聯網、大數據、雲端運算與行動應用等相關技術成熟，更加速各式智慧系統的發展。在公共運輸領域中，考量公車業者不易監測乘客上下車以外的行為，本研究以機會感知方法實踐行動群眾感知之概念，結合Beacon技術提出監測與分析乘客行為之架構，協助業者收集更完備的乘客行為資料作為其決策依據。於已蒐集的行為資料中分析數個衡量指標，包含乘客到站候車時間分布、班次載客量、乘車失敗情況與乘客的候車時間。考量初期大量部署系統的成本可觀，故自行開發模擬系統評估本架構之可行性與公車載運成效。模擬使用臺南市14號公車路線作為依據，其結果顯示本架構能夠更周全地監測乘客行為，使公車業者以更完備的行為資料進行分析，並結合模擬系統對公車班次進行規劃與調整，提供更優質的載運服務。
關鍵詞：行動群眾感知、智慧城市、智慧運輸、Beacon
Abstract
Over the past couple of years, the accelerating growth of some technologies, such as Mobile Computing, Cloud Computing, Big Data, and Internet of Things has laid a solid foundation for the development of smarter systems. These technologies let Smart city become a popular issue recently. With regard to public transportation, city buses need more information about passenger behavior for better service planning. However, how to monitor passenger behavior beyond getting on or getting off the bus (e.g. waiting time) is still a problem. Accordingly, this study uses Opportunistic sensing of Mobile Crowd Sensing with Beacon technology to propose a passenger behavior-monitoring model for city buses. Through the proposed model, it is easier to obtain more administrative information, including the distribution of passenger arrivals, passenger load in each bus, numbers of failure to get on the bus and passengers’ waiting time. A simulation system is based on the bus route of Tainan city bus No 14. to evaluate the feasibility of this proposed model and the efficiency of bus transportation. The result of this simulation shows that this model is feasible on monitoring passenger behavior to provide more behavior data for decision-making. Moreover, bus companies can use this model to figure out the demand of city buses and then adjust their bus schedules to improve their transport service
Keywords: Mobile Crowd Sensing, Smart City, Intelligent Transportation, Waiting Time, Beacon