隨著科技與行動網路的發展，資訊傳播變得快速，人們追求更即時與便捷的服務。在傳統快遞物流中，快遞收件服務由專門物流車負責，在需求數量眾多的情況時，較難以滿足所有服務需求。本篇研究利用計程車載客時，軌跡涵蓋都會區域與即時運輸的特性，提出基於計程車的收件模式。使用者發送搭車與收件要求至派遣中心，經計算後指派合適的計程車載客與取貨，並在下班時將收集的包裹送至集貨站。本篇研究提出處理乘車與取貨服務的計程車派遣策略，系統首先評估乘客乘車與收件任務的權重，以此分配可用的計程車。系統並針對已派遣計程車，在避免貨物逾期的條件下調整其排程，以減少乘客的等待時間。在模擬的部分，使用曼哈頓的歷史計程車資料，模擬結果顯示在計程車數量較少時，相較簡單的貪婪算法，提出的方法能服務較多的乘客，與較低的包裹過期率。

With the development of technology and mobile networks, information dissemination becomes faster. People desire more immediate and convenient services. In the traditional express logistics, the package pickup services are handled by the logistics trucks. It is difficult to satisfy all customer requirements in the case of a large number of demands. A pickup service based on taxi fleets is proposed with the features that are taxi trajectory covering the metropolitan area and instant transportation in this thesis. The customers send ride or pickup requests to the dispatch center. The dispatch center assigns the appropriate taxi to the passenger or to the customer sending the pickup request. The collected packages will be delivered to the distribution station when taxi drivers are off work. This study develops a dispatching strategy which handles passenger and package requests. The dispatching system evaluates the weight of passenger rides and package pickup tasks to allocate available taxis; also, the system adjusts the schedule of working taxis for reducing passenger waiting time. In the simulation, the Manhattan's historical taxi data is used. The simulation results show that the method can achieve serving more passengers and lower expiration rate of packages than the greedy algorithms in the small number of taxis.

[1] 65 Billion Parcels Were Shipped in 2016. [Online]. Available:
https://www.statista.com/chart/10922/parcel-shipping-volume-and-parcelspend-in-selected-countries
[2] Breaking down the `Last-Mile Delivery': Challenges and Solutions. [Online]. Available: https://jungleworks.com/breaking-last-mile-delivery-challenges-solutions
[3] L. Faugere and B. Montreuil, Hyperconnected Pickup & Delivery Locker Networks, in 4th International Physical Internet Conference, July 2017.
[4] G. Cheng, D. Guo, J. Shi, and Y. Qin, Planning city-wide package distribution schemes using crowdsourced public transportation systems, IEEE Access, vol. 7, pp. 1234-1246, Dec. 2019.
[5] V. Carbone, A. Rouquet, and C. Roussat, The rise of crowd logistics: A new way to co-create logistics value, Journal of Business Logistics, vol. 38, no. 4, pp. 238-252, Dec. 2017.
[6] A. Mourad, J. Puchinger, and C. Chu, A survey of models and algorithms for optimizing shared mobility, Transportation Research Part B: Methodological, vol. 123, pp. 323-346, May 2019.
[7] NYC Taxi and Limousine Commission Trip Record Data. [Online]. Available: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page, 2019.
[8] X. Cheng, S. Liao, and Z. Hua, A policy of picking up parcels for express courier service in dynamic environments, International Journal of Production Research,
vol. 55, no. 9, pp. 2470-2488, Sep. 2017.
[9] D.-H. Lee, H. Wang, R. L. Cheu, and S. H. Teo, Taxi dispatch system based on current demands and real-time traffic conditions, Transportation Research Record, vol. 1882, no. 1, pp. 193-200, 2004.
[10] S. K. Verma and H. T. Vo, A predictive taxi dispatching system for improved user satisfaction and taxi utilization, in 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Dec. 2015.
[11] K. T. Seow, N. H. Dang, and D. Lee, A collaborative multiagent taxi-dispatch system, IEEE Transactions on Automation Science and Engineering, vol. 7, no. 3, pp. 607-616, July 2010.
[12] M. Maciejewski, J. Bischoff, and K. Nagel, An assignment-based approach to efficient real-time city-scale taxi dispatching," IEEE Intelligent Systems, vol. 31, no. 1, pp. 68-77, Jan. 2016.
[13] M. Hyland and H. S. Mahmassani, Dynamic autonomous vehicle fleet operations: Optimization-based strategies to assign AVs to immediate traveler demand requests, Transportation Research Part C: Emerging Technologies, vol. 92, pp. 278-297, July 2018.
[14] J. P. Hanna, M. Albert, D. Chen, and P. Stone, Minimum Cost Matching for Autonomous Carsharing, in IFAC Symposium on Intelligent Autonomous Vehicles, June 2016.
[15] G. Gao, M. Xiao, and Z. Zhao, Optimal multi-taxi dispatch for mobile taxi-hailing systems, in 2016 45th International Conference on Parallel Processing (ICPP), Aug. 2016, pp. 294-303.
[16] G. Dai, J. Huang, S. M. Wambura, and H. Sun, A balanced assignment mechanism for online taxi recommendation," in 2017 18th IEEE International Conference on Mobile Data Management (MDM), May 2017, pp. 102-111.
[17] H. Zheng and J. Wu, Online to offline business: Urban taxi dispatching with
passenger-driver matching stability, in 2017 IEEE 37th International Conference
on Distributed Computing Systems (ICDCS), June 2017, pp. 816-825.
[18] J. Xu, R. Rahmatizadeh, L. Bölöni, and D. Turgut, Real-time prediction of taxi demand using recurrent neural networks," IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 8, pp. 2572-2581, Aug. 2018.
[19] F. Miao, S. Han, S. Lin, J. A. Stankovic, D. Zhang, S. Munir, H. Huang, T. He, and G. J. Pappas, Taxi dispatch with real-time sensing data in metropolitan areas: A receding horizon control approach, IEEE Transactions on Automation Science and
Engineering, vol. 13, no. 2, pp. 463-478, Apr. 2016.
[20] J. Xu, R. Rahmatizadeh, L. Bölöni, and D. Turgut, Taxi dispatch planning via demand and destination modeling, in 2018 IEEE 43rd Conference on Local Computer Networks (LCN), Oct. 2018, pp. 377-384.
[21] B. Li, D. Krushinsky, H. A. Reijers, and T. Van Woensel, The share-a-ride problem: People and parcels sharing taxis, European Journal of Operational Research, vol. 238, no. 1, pp. 31-40, Oct. 2014.
[22] S. Pan, C. Chen, and R. Y. Zhong, A crowdsourcing solution to collect e-commerce reverse flows in metropolitan areas, IFAC-PapersOnLine, vol. 48, no. 3, pp. 1984-1989, 2015.
[23] C. Chen, D. Zhang, X. Ma, B. Guo, L. Wang, Y. Wang, and E. Sha, crowddeliver: Planning city-wide package delivery paths leveraging the crowd of taxis," IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 6, pp. 1478-1496, June 2017.
[24] J. Munkres, Algorithms for the assignment and transportation problems, Journal of the society for industrial and applied mathematics, vol. 5, no. 1, pp. 32-38, Mar. 1957.
[25] F. Bourgeois and J.-C. Lassalle, An extension of the munkres algorithm for the assignment problem to rectangular matrices, Communications of the ACM, vol. 14, no. 12, pp. 802-804, Dec. 1971.
[26] P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. WieBner, Microscopic traffic simulation using sumo, in The 21st IEEE International Conference on Intelligent Transportation Systems, 2018.
[27] A. Wegener, M. Piórkowski, M. Raya, H. Hellbrück, S. Fischer, and J.-P. Hubaux, Traci: An interface for coupling road traffic and network simulators," in Proceedings of the 11th Communications and Networking Simulation Symposium, Apr. 2008, pp. 155-163.
[28] OpenStreetMap contributors. [Online]. Available: https://www.openstreetmap.org, 2019.
[29] E. W. Dijkstra, A note on two problems in connexion with graphs, Numerische Mathematiks, vol. 1, no. 1, pp. 269-271, Dec. 1959.