Picture yourself in 19th-century Britain. You’re doomed to the ordeal of commuting in a horse-drawn-wheeled carriage, accompanied by a dozen of sweaty co-passengers whose heads bob religiously to the rhythm of the road bumps and cracks. Then, one day, rail reaches your town, allowing you to venture out hundreds of miles away in an amazingly short time. The steam locomotive’s design is revolutionary, making the impossible real and massively enhancing travel comfort. This innovation feels like a breakthrough in the history of mobility, not only for you. As the mobility revolution is picking up, the percentage of citizens in your country’s urban areas grows from 15% to an enormous 85%. With new means of transport, people can move further and faster, and space and distance have gradually gained a new meaning.
Now, let’s go back to the future as Marty McFly does. Today, when horses are mostly used for recreation, and steam locomotives excite only railway enthusiasts, data is the new thing in mobility. The use of data science in the mobility industry has reached an unprecedented level, and it underpins the car industry’s future, which manifests itself in one acronym — CASE.
Vehicles of the next decades are bound to be Connected, Autonomous, Shared, and Electric, and we would add another letter to this list—’o’ like Optimized. As many auto vendors and fleet companies already benefit from data-driven connectivity, we can see the mobility revolution unfold. It wouldn’t be an overstatement to call it the Industrial Revolution of our times. And its impact may be comparable to the one of upgrading from horses to the railway.
Mobility Technology
What it is and where it takes data from
Vehicle Telematics
This is a well-checked way of monitoring vehicles, such as cars, buses, and trucks. The collected data include speed, location, maintenance and servicing insights, and harsh braking, acceleration, and cornering. The technology relies on GPS and OBD (onboard diagnostics) to gather the information.
Mobile Apps
Mobile applications are popular among carpooling and ride-hailing providers like Uber, Lyft, Car2go, or Zipcar. They track the time and mileage for each trip, allowing them to calculate the cost of travel for the provided route. Additionally, traffic, landmarks, navigation, and other data can be sourced from third-party providers and sent over API to optimize the ride for drivers and passengers.
GPS Readings
GPS lets us know our coordinates when driving or riding a vehicle. The receivers directly obtain geospatial data from satellites in medium Earth orbit and decode it into a machine-readable binary format. This data is then represented as latitude and longitude.
IOT Sensors
These smart sensors track and collect real-time (or near real-time) data from devices connected to IoT (Internet of Things). Depending on the transmitter device, the sensors can gather basic, raw status data about the state of the device, automation data from smart systems and devices (like smart thermostats, vehicle cameras, automated lighting, etc.), and location data.
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.
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.
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.
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:
This data together can not only help understand and improve the behavior of individual drivers. Given appropriate AI algorithms, it can also help identify the root cause of near misses, pinpoint blackspots (risky road zones), and motivate law enforcement authorities to implement additional safety precautions, where required. Additionally, the increasing volume of road accident data also helps car manufacturers improve safety features in their vehicles, further contributing to lowering the risk of fatal injuries.
Also called self-driving cars, autonomous cars are equipped with multiple features and devices based on data science, such as GPS, thermographic cameras, radars, and more. Depending on their level of sophistication and automation, there are six levels (called SAE levels) of car automatization: level 0 stands for no automation, while level 5 signifies vehicles with no human input required.
Self-driving cars are entirely reliant on data used by algorithms to inform every action. Features like auto braking, blind spot detection, lane departure alerts, and self-parking capabilities all use enormous volumes of data processed instantaneously to enable smooth and safe driving without human intervention. A combination of data collection sensors and deep learning and AI capabilities allow driverless cars to:
BENEFITS OF USING DATA IN MOBILITY
Enhanced Vehicle Manufacturing Process
Greater Fuel and Energy Efficiency
Increased Safety Thanks To Risk Assessment
Efficient, Cost-effective Fleet Management
Traffic And Route Optimization
Improved Vehicle Health And Maintenance
The invention of the steam engine marked a new era in transportation solutions. Today, data is revolutionizing modern transport similarly, resolving complex challenges like traffic congestion, public transport crowding and inadequacy, parking issues, or transport emissions. And although we can’t know how the future of mobility will unfold, certain trends are taking shape more prominently than others. Some still need to bend the cost curve or boost efficiency to experience mass adoption, but all have one thing in common—they feed on data.
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