Delivery and Taxi Transportation Platform with Gig-Economy Marketplace eBook | February 27, 2021

Designing a Delivery and Taxi Software Platform

The sharing economy has given rise to a wave of new online multi-sided marketplace apps and business models, where consumers and sellers are matched across a unique, differentiated set of products and services. In this series, we will explore the applications, domains, and software subsystems applicable to a wide variety of transportation use cases – from restaurant and grocery delivery (e.g. DoorDash, Instacart), to taxi and rideshare services (e.g. Uber, Lyft), to third-party deliveries and shipments (e.g. Shipt, Convoy, Uber Freight).

Specifically, we will design a three-sided gig-economy marketplace and transportation software platform that matches:

• Customers, making purchases and requesting rides
• Third-party vendors, including merchants and taxi services
• Gig-economy drivers, delivering goods and people

A range of transport timeframes will be supported – from same-hour to several days out. Drivers can choose to schedule work in advance to guarantee sufficient working hours or work on-demand for maximum flexibility, obtaining work in real time. Multiple competing goals must be balanced: low-latency, cost-efficient fulfillment with accurate ETAs for customers; consistent, high-availability delivery and transport for vendors; and fairness, transparency, and flexibility for drivers.

We will explore the platform’s end-to-end architecture, including:

• Product flows across web and mobile apps
• Backend systems, APIs, and event-driven services
• Core domain models, such as order management, route planning, dynamic pricing, ETA prediction, driver-route targeting and matching, forecasting, and payments
• Infrastructure strategies, such as horizontal scaling, observability, reliability, multi-region fault isolation, service discovery, networking, and security
• Machine learning services for forecasting and optimization
• Technical analysis of design patterns, consistency models, data modeling, and technology choices

The Economics of Multi-sided Market Design and Matching Theory | January 11, 2021

Market Design is the economic study of designing marketplaces to achieve specific desired outcomes using different mechanisms or rules. Auction Theory, Matching Theory, and Game Theory are related areas of study, with Auction Theory focusing on using prices to influence market outcomes, Matching Theory focusing on finding the maximal outcome of pairs of mutually-interested participants in a market, and Game Theory focusing on optimizing decision-making of independent competing actors. Marketplaces are distinct from centralized planning in that the market participants themselves make the decisions as opposed to a centralized planning system making the decisions. Marketplaces can be seen everywhere in our lives from in nature in how animals mate, to the traditional stock exchange clearinghouse where a buyer and seller are matched at an optimal price point, to auctions like that of radio spectrum to telecom providers, to modern online job boards, websites like Ebay, Amazon, Craigslist, and Airbnb, and gig-economy apps likes DoorDash and Uber.

We’ll go over Market Design and Matching Theory, four historic examples, what causes markets to fail, and finally the principles of good Market Design.

Software Architecture - Mapping, Navigation, and ETA System | January 2, 2021

Mapping and navigation functionality is all around us today, from dedicated applications such as Google Maps and Apple Maps to applications with built-in mapping capabilities like Uber, Lyft, Yelp, and DoorDash. These applications provide users the ability to see where they are located, their surroundings, geographical search functionality, and often both the route to a given location and an estimated time of arrival (ETA). The technology to support this geospatial functionality is complex and specialized. Here we will design our very own mapping, navigation, and ETA software solution supporting billions of users and hundreds of millions of points of interest.

Graph Theory and Shortest-Path Routing Algorithms | December 17, 2020

There are multiple classes of geospatial and routing algorithms within Graph Theory, each designed for different use-cases from finding the shortest-path between a single start and end location, to generating permutations of optimal routes covering all nodes in the network, to optimizing graphs through graph transformation, and many more. For example, one of the most famous problems in graph theory is the Four-Color-Problem, determining the possibility of coloring a map with just four colors where no region borders another region with colored the same color. In this article, we will focus on the shortest-path class of algorithms with a single start location as it is fundamental to the mapping navigation and ETA system we will build in a future architecture series article.