Original article was published on Artificial Intelligence on Medium
Mobility Systems Are Evolving
Future mobility has been a ‘buzz’ industry for several years now. The promise of self-driving vehicles and subscription-based transport services have long been hyped as the future of moving people and goods. Many hundreds of reports have been written on the subject, highlighting the opportunities, risks and timelines for the deployment of ‘game-changing’ transport technologies and business models.
It’s true, the opportunity is there. A behavioural shift towards on-demand, subscription services, plus new autonomous capabilities, 5G communications, vehicle electrification and data availability can support clean, efficient, accessible and equitable mobility.
But transport systems remain complex and traveller behaviour remains unpredictable.
But Getting Services ‘Right’ is Hard…
Whilst transport technology is currently ‘sexy’, service profitability remains tough. Over the last 12 months, we’ve seen high profile new service deployments struggle to compete and survive.
In January 2019, Teo Taxi closed their all-electric taxi operations in Montreal. The ‘societal good’ business model of high hourly wages, the operational challenges of EV fleets and the lack of joined up charging infrastructure all contributed to a lack of profitability.
In February 2019, Chariot ended their on-demand minibus service across the globe, 2.5 years after a $65million acquisition by Ford. Early deployments were limited to fixed routes, and therefore competed with public bus services, whilst also competing on price and convenience with ride-share services. A good day in New York saw an average of 9 riders per vehicle. Long-term profitability was ultimately hard to come by.
More recently, in September 2019, Lime did not proceed with their LimePod free-floating car-share service in Seattle (there are now 500 white and green Fiat 500’s up for grabs…). They had an ambitious (and commendable) commitment to e-mobility but struggled to partner to make electric car share a reality.
These examples highlight some of the complexities involved in deploying new mobility services and challenges are encountered throughout the deployment life-cycle.
…With Important Decisions Required at Every Step
At every step in the implementation of new mobility services, big questions remain, and important decisions are required.
The strategic team need to understand the most appropriate deployment location, which business model to adopt, how many vehicles are required and what type of vehicles are suitable — all with the intention of deploying a highly utilised, profitable service.
Once deployed, the operational team need to understand where demand is high across the day, where the inefficiencies lie in their service, what happens if demand increases and how network disruption affects operations — all with the intention of sustaining a highly utilised, profitable service.
In an evolving and complex mobility world, the answers to these questions change over time and an operator’s ability to prepare and react must be improved — or they may evolve into the next Teo Taxi, Chariot or LimePod.
Let’s Test and Train Mobility Services
If we are genuinely going to change the way the world moves, then we need to train and develop world-class mobility solutions that don’t just launch and defend in the real world first. The finest, sustaining mobility services in the world today use simulation and data analytics to continually benchmark, train and refine their operations to improve performance, resilience and efficiency — ultimately leading to sustainable business operations delivering great customer service.
Like our athletes, this training must be consistent and measured to ensure progress. We cannot afford to be sporadic and unstructured, or we risk failing to compete.
Our idea of a mobility ‘gym’ is the digital representation of a services’ operating environment — be that London, Sydney or Singapore. We provide access to simulation — the digital imitation of the operation of a real-world system over time and space — to benchmark and compare operating strategies against observed and synthetic data.
Simulation’s key advantage over data-driven methods is that it allows us to forecast things that have never happened before and to run scenarios outside of historical bounds — including crisis scenarios. Mobility service operators are at the mercy of disruption including rail strikes, emergency utility works, and major sporting events — all with varying levels of notice period but significantly affect fleet productivity, travel time confidence and customer satisfaction. Traditionally, this would be partially resolved ‘locally’ by driver intuition or ‘globally’ by the fleet operator in the control room. Unfortunately, individuals’ knowledge and experience are suppressed when facing severe disruptions that have never previously occurred. Using simulation, hundreds of thousands of future scenarios for service operation in cities can be run to ‘train’ the existing fleet planning, allocation and dispatch algorithms to ensure a future proof service that can fulfil the promise of on-demand mobility services. We can ‘fast-forward’ the next 30–60 minutes in a disrupted ‘world’ to guide vehicle rerouting, allocation and dispatch in anticipation of the future network state. Disrupted mass transit passenger services will also yield new trip opportunities and our simulation tools enable fleets to effectively preposition services in anticipation of future demand.
So, how do we continuously train profitable mobility services in a complex city environment? Going forward, all operators need their ‘gym membership’ in order to compete and survive. We cannot afford to keep launching and defending new services. The time has come to effectively design new services to meet the needs of our citizens.
Who are Immense?
We are a growing team of transport modellers, software developers, data scientists and transport enthusiasts focused on improving decision making capability across the transport ecosystem. It is not that our decision making in transport is bad, necessarily. But the process for making good decisions is slow, expensive and usually based on poor data, leaving us frustrated and lacking confidence in the choice adopted. The time is right to employ new technologies and data to empower fast, confident decision making in order to effectively deploy world-changing infrastructure and mobility assets.
Our decision support platform is trusted by global partners across automotive, transport and energy sectors that are dealing with complex and uncertain mobility futures. Backed by Amadeus Capital Partners and Global Brain Corporation, Immense is growing rapidly and currently have offices in the UK, Europe and North America.
To find out more about our mobility decision-support platform, feel free to reach out to me: email@example.com