Love it or hate it, on-demand mobility is here to stay, but where should we go from here? How do we realize its potential without devastating entire industries or exacerbating urban congestion and pollution? These are just some of the challenges discussed in Rethinking on-demand mobility - Turning roadblocks into opportunities from management consultancy Arthur D. Little’s Future of Mobility Lab.
The report identifies three eras of on-demand mobility, starting with 1.0—traditional taxis and private vehicles for hire. Asset-heavy and reliant on human driving skills and traditional forms of sales and dispatch, these saw little innovation for over half a century. Circa 2009, however, the digital revolution and the arrival of ride-hailing ushered in a new era: 2.0. New providers now have the edge through innovations like smartphone booking, easy payment options, pick-up and drop-off visualization, and by leveraging data analytics, smart technology and AI to understand customer needs and match demand and supply in real time.
Interestingly, while “e-hailing” only accounts for 1% of all km traveled globally, it’s growing faster than other forms of shared mobility like carpooling, car- or bike-sharing. The Little report predicts growth in trips per year from 6 to 83 billion and market value increase from 61 (in 2017) to 285 billion USD to 2030.
2.0 signifies a shift from collective public transport and private vehicles towards shared individual mobility. Incumbents and authorities now face significant challenges and tough decisions. For example, taxi companies must choose between partnering with ride-hailing platforms or trying to compete through costly internal reinvention and leveraging their local knowledge.
Similarly, public authorities must prevent single- or low-occupancy ride-hailing substituting public transport. While ride-sharing options exist, 2018 US data released by certain ride-hail companies suggest the vast majority of rides are still unshared. Authorities must regulate to incentivize ride-pooling and/or partner to provide first-mile, last-mile connectivity and on-demand services where traditional fixed-route/schedule transport may be impractical. At Shotl we firmly believe in the potential of demand-responsive public transport and are proud to be featured as an example in the Little report.
Authorities also walk a regulatory tightrope: Too heavy a hand may stifle beneficial competition and innovation; too liberal could wipe out traditional industries. The extent to which cities were unprepared for 2.0 is evident in huge global disparities in ride-hail legislation and operating conditions, which has prevented these scaling to achieve global presence.
This is a concern for ride-hails since despite aggressive “get big quick” strategies, their asset-light-cash-heavy model remains unprofitable. Therefore, they, too, must choose whether to compete or collaborate, to personalize or diversify their offering and introduce innovations like smart pricing or subscriptions.
Whatever the response, appetite for on-demand mobility will only increase, driven by greater connectivity, lower rates of car ownership among young people and the integration of ride-hailing into other shared mobility solutions or Mobility-as-a-Service (MaaS) platforms. All players must rise to the challenge fast since an even bigger game-changer looms on the horizon: driverless vehicles. While still some years off, “3.0” will further blur the boundaries between public transport and private on-demand mobility, and bring increased profits for ride-hails by eliminating drivers’ salaries.
On-demand mobility represents one of the greatest mobility challenges and opportunities of our age. Striking the right balance between the interests of all stakeholders, and creating the conditions for innovation that benefits customers, will be key to managing the transition in the years to come.
Machine Learning (ML) is a field of Artificial Intelligence (AI) which focuses on the development of techniques that allow computers to learn how to perform a specific task.
Located in the Alta Segarra region in the rural interior of Catalonia, Spain, Calaf has 3,000 inhabitants and is the main town in an area with 13 villages with fewer than 600 inhabitants each.