Modern mobility. The steam engines of tomorrow
The effect of data science on the mobility industry has been nothing like an unexpected lightning strike or a sudden ‘Eureka’ moment. On the contrary, we’ve seen several milestones along the way of mobility progression. Let’s take an example of the GPS and one of the data-related technology developments that forever changed how people move and commute.
The Global Positioning System technology has its roots in the military, but entered public use internationally in the early 90s. Before that, the first GPS-enabled devices like portable receivers or GPS phones were available, but their prices remained prohibitive. As a result, the technology did not see widespread use until a few years later, when new satellites allowed for a dedicated civilian GPS channel to launch, accompanied by unprecedented developments in software and hardware.
This might seem like ancient history today, when most of us can hardly imagine a drive to the corner store without getting traffic and route suggestions from the navigation software. But behind every trip lies immense work of data scientists and technologists delving deep into data from vehicles, satellites, mobile apps, and other sources to make our trips safer, more cost-efficient, and more convenient
The same goes for other mobility industry trends and innovations, like autonomous driving, self-diagnose platforms, EV solutions, ride-hailing services, and many more. All of them may have appeared to be too futuristic a few years ago. Today, they play a key role in increasing the efficiency and safety of mobility on an individual, commercial, and mass level. Let’s review some of these innovations.
Mobility as a service (MaaS)
MaaS denotes a data-driven solution that allows people to organize and fund trips independently under a coherent, comprehensive, and on-demand mobility service. Using that service, we can plan every trip, choose the means of transport, book it, and pay for it online, sometimes in a manner as simple as scanning our finger on the phone or identifying via Face ID advanced technology (biometric payment).
Depending on the provider, mobility as a service can be subscription-based or leverage the pay-as-you-go model. It is widely applied in smart cities, such as London (the Oyster smartcard) or Singapore (the EZ-Link card). Using it, citizens and tourists can access all places cashless and hassle-free: they renew the funds on the card whenever they want, without needing to buy tickets for every trip. Another example includes flexible public transport services that don’t operate on a fixed timetable, such as Palo Alto’s on-demand shuttles or pre-booked shared transport services in Queensland, Australia.
Because of the immense convenience and cost-efficiency it offers, MaaS has been gaining traction worldwide. Enough to say, in 2021, its value stood at $187.31 billion, with predictions that it may reach over four times as much by 2029.
Ride-hailing and car-sharing services have become so common that they seem to have always existed. Meanwhile, Uber, the uncontested leader of ride-hailing services (with 72% of the market share in the US), only launched in 2009! There are several reasons why rideshare has taken off so rapidly, one being the fact that it has reinvented the approach to modern, data-based transportation.
Data science underlies every single Uber or Lyft trip. A lot is happening behind the scenes as you take a few seconds to set the pickup location and request a ride. Sophisticated algorithms process enormous amounts of data in record time to ensure you reach your destination safely, cost-efficiently, and on time. First, the app records each user’s history, preferences, location, and demands. Then, it pairs this information with drivers’ availability and geo-position, route, traffic, and navigation data to predict the pickup time. Finally, it applies dynamic pricing to provide the fee for the ride even before the driver is confirmed.
Low-emission electric vehicles (EVs)
Even though the investments in electric fleets are growing worldwide, EVs remain a niche mobility solution, merely covering 9% of global car sales. Still, car manufacturers and transportation planners are constantly working on making the charging infrastructure more convenient and minimizing its impact on the electrical grid. Data science is essential in solving these challenges and opening the doors to widespread global EV adoption.
Integrating data analytics in the EV design and production processes improves battery efficiency and operational reliability. Based on massive volumes of structured and unstructured data, manufacturers significantly enhance their vehicle performance and optimize production. Moreover, smart analytics helps them better align industry and consumer requirements with the vehicles they make. Finally, data-driven technologies such as AI and machine learning help optimize EVs’ performance, coordinate their charging, and evaluate the infrastructure needs in particular neighborhoods.
Transportation is responsible for 25% of global CO2 emissions; road transportation contributes to 80% of that. To revert this catastrophic trend, countries worldwide are pushing sustainable mobility (green mobility) initiatives incorporating electric vehicles while embracing other eco-friendly means of air, water, and road transport. The goal is to reduce the environmental impact of mobility by “significantly increasing the sale, share, and uptake of zero-emission light duty vehicles, including zero-emission public transport and public vehicle fleets” (same source).
As in the EVs’ case, data and connectivity play a crucial role in facilitating the development and adoption of green mobility solutions. First, acting on data insights underpins strategic planning and manufacturing of energy-efficient vehicles. Secondly, mobility services prompt eco-friendly trends like car sharing, smart public transport, and hail-riding. Connected car platforms and vehicle networks help optimize road utilization, reduce fuel and energy consumption, and improve maintenance and vehicle use. All of these data-driven benefits together power more sustainable mobility solutions.
Road safety improvements
While most of the abovementioned trends concentrate on cost-efficiency and convenience, travel safety is another crucial element of data-driven mobility. Especially as car crash fatalities are rising globally. In 2021, US traffic deaths hit a 16-year-high; an astonishing 42,915 people died in road accidents, up by 10.5 percent compared to the year before. In Europe, both the scale and the year-by-year increase have been lower—an estimated 19,800 people were killed in road crashes in 2021, +5 percent from 2020. Still, this tragic trend continues upwards, and data can hold an answer to reversing it.
Actionable insights that may contribute to improved roadway safety can be pulled from various sources: