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Our latest Kufiya restock has completely sold out within less than 12 hours! Pre-orders for January are now closed. For those who missed out, don’t worry! We are working on opening pre-orders for the upcoming months. This will give you the opportunity to buy your Hirbawi Kufiya in advance, making sure that you receive it in the month specified on your pre-order. Each Kufiya takes approximately half an hour to make. Our machines are continuously running to meet the demand, and we assure you that everyone will have the chance to own their very own Hirbawi Kufiya. With each restock, we are reaching out to tens of thousands of you who were earlier to join our "notify me" list, particularly those who have been patiently waiting for a long time. If you were not emailed about the restock this time, we assure you that you are still on our list and will be contacted in an upcoming restock. We are very grateful for the overwhelming love and support the Hirbawi factory has received. You keep us going, even in the face of all the ongoing challenges. We are working hard to continue to improve our factory production and online customer experience so that we can satisfy all of you. Thank you for being a part of the Hirbawi family❤️ #hirbawi #kufiya #keffiyeh #kuffiyeh #keffiye

Autonomous mobility on demand systems, though still in their infancy, have very promising prospects in providing urban population with sustainable and safe personal mobility in the near future. While much research has been conducted on both autonomous vehicles and mobility on demand systems, to the best of our knowledge, this is the first work that shows how to manage autonomous mobility on demand systems for better passenger experience. We introduce the Expand and Target algorithm which can be easily integrated with three different scheduling strategies for dispatching autonomous vehicles. We implement an agent-based simulation platform and empirically evaluate the proposed approaches with the New York City taxi data. Experimental results demonstrate that the algorithm significantly improve passengers' experience by reducing the average passenger waiting time by up to 29.82% and increasing the trip success rate by up to 7.65%.