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

The sharing economy has introduced a new wave of online marketplace apps and business models, where consumers and sellers are matched across a unique differentiated set of product inventory. This contrasts with traditional e-commerce platforms, where inventory is standardized and sellers are few. Online multi-sided marketplaces range from Uber and Lyft in gig-economy transportation of people with unique routes to DoorDash and Postmates in restaurant delivery with unique orders, Instacart and GoPuff in grocery delivery with unique pick-up and drop-off locations, TaskRabbit and Amazon Mechanical Turk with unique ad-hoc jobs, Turo and Boatsetter for individual car and boat rentals, Airbnb with unique one-of-a-kind accommodations, and Rover for dog care.

Each of these businesses has successfully created a marketplace that matches customers with a wide variety of sellers offering differentiated products or services, rather than standardized items. This creates a more complex system than traditional retail as it presents a complex optimization problem where we must balance the multiple needs across a diverse set of users - times/dates, locations, qualifications / features / amenities, availability, regional regulations, and so forth. Additionally, the rules and functioning of the marketplace need to be considered carefully to prevent gaming and ensure a fair and transparent marketplace for all parties.

In this 8-part e-book, we will design a multi-sided online marketplace encompassing multiple domains, subsystems, and applications, with principles which can be applied to a wide variety of marketplace and gig-economy use cases from taxi services (e.g. Uber, Lyft), to restaurant delivery (e.g. DoorDash, Postmates), to third-party deliveries (e.g. Instacart, Shipt, Convoy). We’ll focus on a complex three-sided marketplace use-case involving three distinct sets of users: customers making purchases, third-party vendors, and gig-economy drivers. By the end we will have designed a global transportation platform with multi-sided marketplace where (i) consumers can shop through a variety of vendors (3rd-party merchants or taxi services), (ii) have their goods or themselves delivered quickly – as quick as same-hour, (iii) by gig-economy drivers, (iv) either scheduled-ahead or on-demand, (v) all without merchants or taxi companies managing any of the logistical complexity.

We must balance: speed, low-cost, and accurate-ETA’s for customers; consistent, highly-available taxi & delivery services for third-party merchants and taxi companies; and fairness, transparency, and consistency for gig-economy drivers. The platform will support a wide range of transport speeds starting as quick as same-hour with low latency APIs to optimize the checkout flow for maximum conversion. We will support multiple pick-up and drop-off locations, given products and customers will be distributed. Finally, we will support multiple mechanisms for gig-economy drivers to obtain work, either scheduling work ahead to guarantee sufficient working hours or working on-demand with maximal flexibility, obtaining work in real time.

1. Part 1: Introduction, Requirements, Mobile and Web Applications
2. Part 2: Backend Infrastructure and Service Architecture
3. Part 3: Vendor Management and 3rd Party Customer Shopping & Checkout
4. Part 4: Transport Supply & Demand Management and Forecasting with Machine Learning
5. Part 5: Route Planning, ETAs, and Dynamic Pricing with Machine Learning
6. Part 6: Driver Onboarding, Preferences, Standings, Rewards, and Payments
7. Part 7: Driver-Route Targeting, Pushed On-Demand Routes, and Precomputed Eligibility
8. Part 8: Gig-Economy Route Marketplace for Schedule-Ahead Routes with Deferred Route Matching

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.