近年來，共享運具的興起，不僅能夠使閒置資源被利用，更能促進社會的發展。而自駕車也是當今最熱門的話題之一，既可以提高道路安全性，又能緩解交通擁塞、節約能源等。隨著共享運具和自駕車的蓬勃發展，也出現了新的商業模式——共享自駕車（SAV），並推動了許多地區加入智慧城市的進程。例如: Google的自駕車Waymo，自2020年起，開放美國鳳凰城的一般民眾使用全自動駕駛叫車服務。透過共享經濟的應用、自駕車技術的進步；藉由車輛派遣演算法的研究、派遣策略的優化，城市的運營效率和生產力得以提升。
共享運具的概念和自駕車技術的結合可以提高社會運作的效率，因此，如何將旅客和共享自駕車進行有效率的分配，為值得探討的課題。考慮到需求的重要性，提供以需求為導向的服務——自動駕駛即時行動服務（AMoD），能夠提升服務品質。本研究中考慮了隨機需求模式下的共享自動駕駛汽車派遣問題，在自動駕駛即時行動服務的模式下，旅客可以在路網中的任意節點，透過行動裝置發送乘車需求，或者在物聯網叫車等候站中等待車輛。此物聯網車站能夠偵測到有乘客候車，並將自動將候車信息發送給車隊管理者。
本研究旨在解決AMoD服務的派遣問題。通過開發車輛派遣策略演算法，來極小化旅客等待時間，以提升服務品質，並利用Python建構車輛派遣策略演算法的程式。本研究使用台南市中心作為研究範圍，在該區域的路網中，實驗四種的最佳化派遣策略，並觀察不同的情境設定，會如何影響車輛派遣的效率。並使用行為導向的微觀交通模擬軟體（MATSim），與本研究的車輛派遣演算法進行比較和驗證。透過數值實驗分析，找出最適的營運方式，預期提供政府部門或運營商相關建議，提高共享自駕車派遣效率，以因應智慧城市的浪潮。

In recent years, the rise of shared mobility not only makes the use of idle resources but also promotes social development. The autonomous vehicle is also one of the hottest topics. It can improve road safety, relieve traffic jams, save energy. With the development of shared mobility and autonomous vehicle, a novel business model, shared autonomous vehicles (SAV) emerged. This promotes many cities joining in the process of the smart city. For example, Google's autonomous vehicle, Waymo, started to serve the public in Phoenix. Through the application of the sharing economy, the advancement of self-driving technology, the research of vehicle dispatching algorithms, and the development of dispatching strategies, the city's operational efficiency and productivity can be enhanced.
The integration of carsharing and self-driving technology is promising in social efficiency. Therefore, how to assign vehicles and travelers efficiently is an important issue. Concerning the importance of demand, Autonomous Mobility on Demand (AMoD), a demand-oriented service, must upgrade the service quality. This research considers the shared autonomous vehicles (SAVs) dispatching problem under stochastic demand. In the AMoD service, travelers can submit requests on their mobile devices. Travelers can also wait at the IoT SAV stops which can detect the queue and send the information automatically to the SAV operator.
This study aims to solve the dispatching problem for AMoD service. By developing formulations to minimize traveler's waiting time which represents service quality, and constructs the algorithm in python. This research conducts four dispatching strategies in the network of Tainan City Center and observes the efficiency under different scenarios. A microscopic, behavior-based traffic simulator (MATSim) is used to verify and compare with the dispatching algorithm of this study.
According to the experiments, the optimal management set can be found. The expected contribution of this research is to furnish some suggestions that public sectors or operators can refer to promote vehicle dispatching efficiency, likewise, to respond to the wave of the smart city.

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