共享交通模式是共享經濟重要的一環，不但能減緩空氣汙染，也促進智慧城市的發展。隨著人工智慧(AI)、V2X通訊技術發展愈趨成熟，以及5G時代的來臨，自動駕駛車輛在道路提供乘客接送服務指日可待。隨需自動駕駛服務為結合自駕車及隨需行動服務的概念，讓乘客享有最後一哩路的服務。除了解決乘車需求的時空不確定性，也能自主平衡城市中的供需問題，實現共享自駕車的服務理念。
研究指出，實施動態費率是叫車平台平衡供需的有效方法，一方面能增加司機進入市場的誘因，另一方面則減少價格敏感的乘客之搭車需求。在歐美國家，部分民眾考量私人汽車購置及維修成本過高，透過共乘的方式通勤，除了能減少在交通工具上的支出，也幫助城市減緩交通壅塞及降低空氣汙染。
基於上述研究背景，本研究建立了共乘機制的車輛指派模型，並在真實路網中進行數值實驗，比較五種不同因素對模型的影響。基於實驗結果的數據，本研究通過迴歸分析來預測低規模需求和供給下的服務效能與效率，並應用上述迴歸模型，建構共享自動駕駛汽車在 AMoD 系統下的乘車市場之供需模型。該模型使用動態定價策略和共乘機制，其中模型以社會福利最大化為目標，並以近似動態規劃求解每個決策階段的最佳費率。實驗結果發現，共乘模式下能提升乘車服務率，此外，本研究所建構乘車市場供需模型之結果顯示，在共乘機制下，動態費率透過平衡市場上的供需，獲得比固定費率還高的社會福利，也降低乘客的平均等待時間。

Shared transportation is a vital branch of sharing economy, which can lighten the air pollution problem and promote the development of a smart city. Artificial intelligence, V2X communication technologies, and 5G technology have become more and more mature, and using autonomous vehicles to provide pick-up and drop-off services on the road is just around the corner. Autonomous Mobility-on Demand (AMoD) combines self-driving and Mobility-on-Demand (MoD) services, allowing passengers to enjoy the last mile service. AMoD can solve the spatial-temporal uncertainty of ride demands and autonomously balances the supply and demand over time, which implements the concept of shared autonomous vehicle (SAV) service.
Studies point out that the implementation of dynamic pricing is an effective way for ride-hailing platforms to balance supply and demand. On the one hand, it can increase the incentives for drivers to enter the market; on the other hand, it can reduce the demand for rides from price-sensitive passengers. In European and American countries, some people consider that the cost of purchasing and maintaining private cars is too high so they take ridesharing services, which can not only reduce the expenditure on transportation but also help cities decrease traffic congestion and reduce air pollution.
Based on the research background, this research builds a ridesharing and dispatching model and conducts numerical experiments on the real road network to compare the effects of five different factors on the model. Based on the simulation data, this study performs linear regression to predict the average waiting time and meeting rate under small-scale demand and supply. Then, applying the above regression model, this study constructs a supply and demand model in the ridesourcing market with the application of SAV in AMoD systems, which considers dynamic pricing strategy and ridesharing. This model aims at maximizing social welfare and uses approximate dynamic programming to solve the optimal pricing for each stage. The experimental results reveal that ridesharing can improve the service rate of rides. Additionally, the demand and supply model results in the ridesharing market show that the dynamic pricing strategy achieves higher social welfare by balancing the supply and demand compared to fixed pricing under the ridesharing mechanism. It also reduces the average waiting time of passengers.

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