Envisioning Transportation of the Future
The Future of Transportation and Where Computer Vision Fits
Transportation is the backbone of any city looking to keep its status as a major urban center. After all, efficient movement from one location to the next is what allows for economies to grow. However, while one might think population growth would mean more hands and brains to work and innovate, without adequate infrastructure, population growth could spell the exact opposite fate for cities.
In a city ruled by corporations and small businesses working alongside one another to propel the economy, people need to get from one place to the next as quickly and efficiently as possible. As older demographics phase out of city workforces and younger generations come in, new transportation demands and norms will emerge. Since space in cities is a limited resource, transportation, as we know it today, will become a luxury few can afford.
The solution to the transportation problem isn’t to think bigger but smarter. The answer lies in clean transportation, safe mass transit, and autonomous vehicles.
The Future of Urban Transportation
Luckily, transportation systems of the future don’t have to start from scratch. Most of us know the benefits and pitfalls of common transportation options in cities such as trams, busses, cars, etc. These technologies will still exist, but their utility and value will change, and some new options will emerge that will shake up the way we all get around.
Transportation of the future can be described in three words; clean, accessible, and safe.
Further, more than 85% of companies that report using customer analytics extensively claim their company has experienced a significant value contribution from customer analytics. These include customer loyalty, reduced marketing costs, better in-store experience, and deeper customer understanding.
Electric Mass Transit
The first step towards achieving cleaner transportation is eliminating the use of fossil fuels and non-renewable energy sources. 95% of transportation energy today comes from fossil fuels. While not all currently available electric sources are clean, investment towards electric public transit makes the shift to renewable energy much smoother and cheaper later on. Additionally, providing public transportation that covers the majority of an urban area makes transport more accessible.
Often, people opt for individual modes of travel, such as cars and cabs, because of the lack of first and last-mile transportation. Simply having a direct line of transit from one end of an urban center to the next isn’t enough to accommodate anyone living further than 20 minutes from the train or bus station. Micromobility is a concept that includes a range of small and lightweight vehicles that allow riders to reach the broader transportation network. Building cities, streets, and sidewalks with micromobility in mind could save a lot of space that would’ve, otherwise, been occupied by single-occupancy drivers.
In the United States alone, cars take up 40-50% of urban spaces. Land that could otherwise be developed is often occupied by vehicles just sitting in parking lots while their owners work or live in the city.
Shared mobility is the process of turning transportation from an individual act to a service offered by companies big and small. TaaS, or Transportation as a Service, would result in fewer cars in every city and fewer licensed drivers. This tactic ensures all vehicles in the city at any given time are used to their fullest. Instead of being stowed away in a garage or parking lot at night and during work and school hours, cars would only be used when needed, shaving hundreds of dollars a month in car payments for city inhabitants.
Cross-Country High-Speed Rail
Long-distance travel is essential to sustaining the economy. Whether it’s people traveling on business trips every other week, or people going on vacation, long-distance travel has become a part of modern life. However, as the first industrial revolution witnessed more people traveling long distances for work, family, school, and leisure, growth in the world's population and the economy will have the same effects all over again.
Cross-country long-distance travel can be a clean solution that replaces air travel and car and bus-based journeys. Maglev trains can even stretch over a distance of 1,000 miles, making travel more accessible, safer, and cleaner than ever before.
While all the previously mentioned solutions are essential to the future of urban transportation, they pale in comparison to the futuristic concept of autonomous vehicles. After all, they embody all three descriptives of future urban transport.
They are clean as they can optimize their drive for the least amount of fuel necessary. And not only are they accessible to individuals who cannot drive due to age, or a cognitive or physical disability, but they’re also optimized for safety. Free from human distractions, autonomous vehicles can adhere to traffic laws at all times. But in order to successfully implement autonomy into transportation, the cars need to see and understand the road ahead of them in real-time.
Autonomous Transportation - Where Computer Vision Fits In
As the name suggests, Computer Vision is a field of computer science that focuses on developing models that allow computers to gain a high level of understanding from digital images and videos. In other words, Computer Vision is the science of mimicking human vision in computers.
But Computer Vision is unique in that computer engineers do not entirely create it; they just pave the way and provide the necessary tools. While it’s more or less plausible to create a Computer Vision system from scratch and develop every algorithm individually, that’s not the best approach to Computer Vision — or any type of AI, really.
Engineers instead rely on machine learning — self-supervised learning in particular. This machine learning approach lets the machine itself categorize and label various elements in images and videos, allowing it to reach its own conclusions with little human intervention. Not only is this approach time-efficient, but it also ensures the engineer’s biases don’t taint the results. Since the software works purely off user and customer data, it can reveal what users want, when they want it, and where they want it.
Computer Vision has the capability to revolutionize the ways all types of vehicles, from cars to shuttles, trains to ferries, move passengers. CV models are trained specifically to adapt to the challenges presented by distinct transportation methods. For example, busses will have to be trained to detect and adhere to sidewalks whereas ferries and boats will be more cognizant of speed regulation in bays versus open water. For Computer Vision and the new world of transportation to succeed, computer vision needs to be adapted to all forms of transportation.
Computer Vision Outside the Vehicle
While autonomous vehicles are still in their infancy and can’t replace the current need for a human driver, they can help enhance users’ experience and increase safety levels. Outside the vehicle is where the majority of driving happens. Whether it’s a car, a motorbike, or an electric scooter, computer vision can help by providing real-time data analysis and navigation.
Safer Transportation for Riders, Drivers, and Pedestrians
Instead of relying on vague location data from an external source, an autonomous vehicle would scan the road for identifiable marks. For instance, if it’s a bike or scooter, the software could help drivers stay in their assigned lanes and off streets and sidewalks. For larger vehicles, like cars and public buses, the technology can help drivers follow their lane lines even if the paint on the road is a little faded.
Additionally, Computer Vision succeeds where most human drivers fail; multi-tasking and poor visibility. It can be challenging for drivers to focus on the road ahead of them while paying attention to their surroundings, especially in low light and poor visibility. Self-driving vehicles could come equipped with object detection and tracking, person detection, and person counting capabilities.
Moreover, as autonomous mass transit becomes more commonplace, cities must ensure our most vulnerable citizens maintain the same levels of care they require. ADA requirements can be monitored through computer vision models to ensure assistance equipment and accessible features are operating properly. For example, object tracking models can position a bus perfectly against a curb to allow for ADA ramps to extend to accommodate wheelchairs. No human assistance required. Additionally, Computer Vision can use person detection models to ensure that all disabled riders have adequate time to board the vehicle.
It’s the little things that are going to make the biggest difference in the future of autonomous transportation. With the help of computer vision, city planners can ensure their population will receive the same, if not a better experience for all regardless of individual needs.
Computer Vision Inside the Cabin
While the outside is where the driving happens, the people inside the vehicle — from privately-owned cars to public transit — are your customers and target audience. Why optimize the transportation experience for people outside and forget the people inside?
With Edge CV, manufacturers have the tools to gain deeper insights than ever into the in-cabin experience for public and private transit. So whether you want to optimize rider safety, traffic flow, or something else entirely, Edge CV applications exist to implement cost-effective solutions at scale.
The power of Computer Vision enables a cabin to “perceive” the occupants through sensors like cameras, to see far more than the human eye and act on that information in a split-second with low latency and high accuracy. CV will result in next-generation transportation that can better protect drivers and passengers, increase profitability, and improve the transportation experience for all.
Support for Smart Cities
68% of the world’s population will be city dwellers by 2050. Intelligent transportation will be a critical component of building the world’s future smart cities in which so many of us will live and thrive.
As populations in cities grow, so too will the need for smarter mass transit. With models capable of counting people to determine the prevalence of empty routes on buses, for example, Computer Vision is a key component to ensure public transit is efficient and profitable.
Transform Rider Experience
Inside the vehicle is a rich world full of variables just as unpredictable as the outside. While less complex, there are still safety, comfort, and convenience elements to consider when designing an intelligent vehicle. For instance, the AI system could monitor how people enter the cabin and analyze their timing and movement for the best routes possible, especially on crowded vessels like buses, trains, and ferries. You can examine whether people can find their seats right away or bump into other passengers often.
Mass transit and passenger vehicles transport millions of people every day. Yet, there are dark spots on the reputation of mass transit. According to the Transportation Research Board, "violent crime is perceived as pandemic .... Personal security affects many peoples' decisions to use public transportation."
With Computer Vision, stakeholders can add a crucial level of security to their cabin and further protect their riders on board from other passengers. For example, activity detection models can be trained to identify criminal behavior like theft or assault before drivers or fellow passengers ever notice. Systems can be put in place to alert authorities in real-time when these events take place.
Real-Time Safety Enhancements
Real-time analysis of the conditions inside the cabin isn’t only for optimizing passenger comfort and convenience. That same data and monitoring system could be used to enhance accessibility and safety.
Smart scanners can identify disabled individuals and elderly passengers and automatically activate hidden accessibility features such as ramps and wheelchair lifts. Simultaneously, the monitors could alert drivers if they'd been distracted or driving for too long and recommend a nearby location where they could rest. They also could spot medical emergencies, such as heart attacks and seizures, stop the car at a safe place, and alert authorities.
Making Micromobility a Profitable Business Model
Shared transportation and micromobility hold the key to a cleaner and more accessible form of transportation. In the beginning, projects and businesses faced many difficulties and threats to their growth and revenue as they explored uncharted territory. And since such companies work in the risky industry of transportation where people’s lives are on the line, they have also faced countless regulatory issues from governments and concerned locals. But today, micromobility is a billion-dollar industry that’s only expected to grow in the upcoming years.
Micromobility’s Biggest Challenges
The road to widespread adoption of micromobility is long, and several challenges are standing in the way.
Manufacturers need to achieve a sustained level of profitability to be able to continuously optimize operations to fit the expectations of future city dwellers. However, a huge facet of a profitable micromobility company is the extent to which they mitigate risk and address challenges in various areas.
The main issue with micromobility is the heightened risk of accidents. After all, cars, buses, and motorcycles have strict traffic laws set by the local government. Not to mention, one would need licensing to operate any of those vehicles in a public area. However, since the same doesn’t apply to bikes and scooters — electric or not — riders could pose a danger to themselves, nearby pedestrians, and even heavy vehicle drivers in the streets.
There’s also a matter of regulation and trust when it comes to consumers. Those who are not very familiar with improvements being done can remain wary of utilizing scooters or e-bikes at their full potential, and this lack of understanding can also translate into improper laws and regulations being adopted that hinder progress even further.
Equipment Damage and Theft
Lastly, manufacturers also need to consider the issues of damage and theft, two big problems that hurt their operations and threaten to lower their revenue. Both Bird and Lime have stated that their electric scooters tend to last one to two months before being replaced — if even that. A dataset released by Bird found that the average life span was just 28.8 days.
Go Full Throttle With Computer Vision
By implementing Computer Vision into your first- and last-mile transportation solutions, you could add a second ‘pair of eyes’ to the bike or scooter. The software would check the rider’s surroundings and ensure they don’t leave their designated area or hit someone. Also, thanks to real-time insights, you could monitor the state of the equipment and constantly evaluate them for increased risk factors such as:
- Rider negligence
- Rider aggressiveness
Data informing the extent to which your user base is putting themselves, others, and your equipment at risk can all be assembled into a regular report. Additionally, TaaS providers can compile reports that spot trends in common part failures and route insights like hot spots for equipment pickup, average rider speed, and more that can help optimize fleet management.
All in all, the micromobility market is predicted to be worth between $200B and $300B by 2030. But as demand increases, so too do some of the risks and challenges associated. Computer Vision can be a powerful tool to reduce risk and thereby increase profitability.
Micromobility producers are already leveraging CV to:
- Optimize fleet management
- Extend equipment longevity
- Take control of your liability
- Improve rider safety
With Computer Vision, bike and scooter manufacturers can add a deeper, real-time level of analytics to their operations. This robust and real-time data is poised to deliver a more effective rider experience and profitable future for all stakeholders.
Building Trust With Computer Vision for Transportation
Like any new technology, autonomous and semi-autonomous vehicles don’t have people’s trust just yet. Providing tangible solutions with solid evidence is the only way to get people on board towards a cleaner and safer transportation system for urban centers. Computer Vision is one of the few technologies currently available that show great promise, allowing for real-time insights into a future where private and public transportation at all scales adapts and molds to each persons’ individual needs and daily routine.
But due to its tricky nature, building public trust with computer vision is a sensitive and time-consuming procedure. After all, it’d only take one accident or malfunction in a real-world application for people to become weary of the tech as a whole. And while some people might still invest their time and money into improving their transportation experience and reduce their carbon footprint, haphazardly implementing Computer Vision could be detrimental to the entire field of study and development.
What to Look for In a Computer Vision Provider?
As a stakeholder in the transportation industry, making the right decision regarding the industry’s future is essential. Machine learning and artificial intelligence are sensitive mechanisms that need careful planning and considerate and candid execution. One of the vital qualities you should look for in a Computer Vision provider is their experience. What projects have they worked on before? What was the outcome? How much did it cost, and were they considerate with their spending?
But while the previous points are critical, they’re mostly logistical in nature. Implementing Computer Vision into your vehicles and products is going into uncharted territory. You need a partner that shares your vision and values and has the ambition to match yours.
Scalability should also play a primary role when picking your business partners for the long run. While investing your time, money, and energy into a startup project with great promise might be tempting, thinking long-term is the way to go. How much time and energy will this model cost on a bigger scale? Is it resource-intensive? Is it environmentally sustainable?
Edge Computing vs. Cloud Solutions
When it’s time to build your infrastructure for your AI, you need to settle on a type of foundational technology, and while cloud-based services are the best option for many large-scale tech projects, Computer Vision is the exception. In cloud computing, there are two endpoints; your physical devices and the cloud servers. Visual data would have to travel from your devices and into the cloud to be fully processed before the results are sent back to you. Not only is this process time-consuming, but it can also lead to latencies in the system and affect the quality of your vision system.
Alternatively, Edge computing is a type of computing where data is processed outside the cloud and as close as possible to the source device. Visual data stays at the ‘Edge’ of a centralized cloud system, which significantly improves response time, reduces latency, and saves bandwidth.
Edge computing can reach far and wide as it doesn’t depend on a data center that always needs to be within reach. This allows vehicles and devices utilizing Computer Vision to work from a variety of remote locations, without worrying about the lack of infrastructure or network capacity. This level of flexibility allows Edge computing tech to be the more affordable option, making it a suitable option for both startups with not much to invest and large corporations not looking to risk it all.
Still, one factor that often goes unnoticed is security. Computer vision data plays a major role in the safety of riders, drivers, passengers, and pedestrians on the street. Any threat to the integrity or security of the data can be detrimental to the future of alternative transportation. Fortunately, Edge computing disperses data among multiple devices, allowing for mock-network segmentation where visual data is isolated and secure even if one access point is breached.
alwaysAI For Transportation Alternatives
alwaysAI can help you get started with Computer Vision models explicitly made for Edge systems, allowing you to streamline your Computer Vision projects and make advanced AI systems with ease. Additionally, you can quickly implement Computer Vision for in-cabin monitoring, where trained applications track the vehicle’s status and driver for warning signs.
Leading the revolution in Computer Vision and based in San Diego, CA, alwaysAI is operated by tech veterans who are passionate about what they do. Our team of world-class engineers is always available to support your development and ensure you meet all of your milestones on time. alwaysAI is on a mission to help businesses lead and gracefully ride the Computer Vision revolution worldwide.