On-demand mobility services continue to evolve fast. New solutions are introduced constantly to address changes in consumer urban transportation tastes, or address shortcomings of existing offerings. Consumers demand for personalized transportation solutions that are affordable, convenient, and safe has led to the rapid growth of ride-hailing. But in cities where it is most popular, single passenger ride-hailing is a major contributor to traffic congestion and lengthening travel times leading to deteriorating passenger experience. We need to find solutions that increase the passenger throughput per mile and permanently remove vehicles from streets. Micromobility and new forms of ridesharing emerged in part to address these problems but also to provide lower cost transportation alternatives. They are now being combined with ride-hailing to offer multimodal transportation. Multimodal and shared on-demand mobility will have seven implications that will require careful analysis.
The congressional and European Parliament testimonies of Facebook’s CEO focused attention on Internet and ecommerce corporations and startups whose business models rely on the collection and exploitation of big data, with personal data being a major component. Legislators and the public at large came to realize a) the leverage such companies now possess through the dominant positions of the free and frequently personalized services they offer in exchange for the data they collect, b) the risks associated with not properly safeguarding this data, c) the legislators lack of detailed understanding about how the data is collected and used by these companies and their partners, and d) how difficult it will be to regulate the collection, processing, AI-based exploitation, and use of this data in a way that is agreeable to both consumers and businesses. These issues are re-emerging as more connected vehicles are shipped and will become more critical as companies using autonomous vehicles in a variety of services start to employ big data in insights-enabled business models. As we consider the monetization of transportation-related data it is necessary to understand who the main generators and users of this data are, who owns each type of generated data, the risks that may arise from mishandling the collected data, and whether existing and proposed regulations relating to autonomous vehicles and more broadly next-generation mobility suffice or need to be augmented.
On-Demand Mobility Services, and particularly ride-hailing, have emerged as a strong option for consumer urban transportation. In the process, ride-hailing has disrupted the taxi and limo industries and could next disrupt public transportation and last-mile package delivery. Nowhere is this more evident than in cities such as New York and San Francisco. Other mobility services such as shared ride-hailing, and micromobility, as well as various forms of microtransit and car sharing are also showing robust growth. In a previous post I organized automotive OEMs into five categories. I’ve tried to create a similar structure with a small set of categories for the companies offering on-demand mobility services, focusing particularly on ride-hailing services. It proved to be a harder task because of the complex interactions among the dimensions I used. In this post I present my first attempt to organize these companies into five categories based on how they approach next-generation mobility and the value they offer to their customers.
In early November 2017, Waymo announced that while it will continue its tests in Washington, California, and Texas, it was ready to start ferrying consumers in its fleet of driverless minivans in Chandler, Arizona. Later the same month GM presented their roadmap for autonomous vehicles and details about the mobility services it intends to offer using such vehicles starting in 2019. These larger scale efforts follow a year during which incumbent OEMs, automotive suppliers, global ride-hailing companies, large technology companies, and startups have been demonstrating autonomous vehicles of many form factors targeting a variety of next-generation mobility applications. Automotive OEMs are making important decisions about the role they want to play in next-generation mobility. These decisions will organize automotive OEMs to five categories.
In my book and previous posts I build a broad case for the key role big data and AI play in next-generation mobility, and provide several examples from transportation and logistics. Next-generation mobility is about intelligent, connected vehicles that utilize some form of electrified propulsion, and on-demand shared transport services of people and goods that will be offered through such vehicles. Many of these vehicles will be capable of autonomous movement. Next-generation mobility will help us address some of our biggest challenges, such as pollution and climate change, urbanization and congestion, aging population, and traffic fatalities, while enabling us to maintain economic prosperity by operating highly optimized supply chains that span the globe. It will give rise to a new value chain where big data and AI will play a key role. It is therefore important to identify the new monetization opportunities enabled by big data and AI in the context of this value chain.