AI in Transportation All Set to Redefine Urban Mobility

AI in Transportation

The hypotheses and developments in artificial intelligence stirred all kinds of hypes around this field of study. Research is underway for decades but it was only in the recent years that developments managed to hit consumer markets with integration in a range of products and services. AI in transportation is making a huge impact as computer vision software systems and machine learning algorithms are entirely redefining urban mobility.

Many new concepts would make to the development phase while a number of extensively tested features would be available for commercialization. This articles exclusively states the AI innovations that have the potential to revolutionize the industry in 2020.

Use of AI in Transportation for Autonomous Vehicles

Autopilot in aerial and marine vehicles is around for over a century now. Initially, the technology assisted human pilots in a very limited selection of tasks but improved over the time. We have witnessed a number of entirely autonomous aircraft models popularly known as unmanned aerial vehicle (UAV).

However, it was not until early 1990s that we started having autonomy in vehicles – that too on an intensely limited scale. Mitsubishi is arguably regarded as the first manufacturer to introduce and commercialize cruise control in vehicles. Mercedes-Benz is also one of the earliest automobile makers to invest in autonomy.

Society of Automobile Engineers (SAE) introduced a standard to classify the autonomy levels in commercial automobiles. Cruise control – the ability of a vehicle to maintain its lane and issue warning when a nearby vehicle or another object approaches dangerously near – defines the level 1 of autonomy. The standard also defines level 0 where vehicles are only capable of issuing warning and have no other control whatsoever.

The subsequent three levels further classify AI in transportation. Level 2 vehicles are capable of switching lanes and tackling nearby objects without human assistance but driver is required to keep hands on the wheel at all times. Level 3 allows hands off the vehicle but signals to intervene as needed. At level 5, a vehicle is fully autonomous eliminating human driver’s assistance.

SAE Autonomy Levels
Where does the autonomy in transportation stand today?

The challenges in autonomy are more of a regulatory framework than technical limitations. A number of manufacturers including BMW, Nissan, Tesla, Alphabet, Mercedes-Benz among others claim to have developed sufficient technology for self-driving cars. However, each of them is testing the vehicles under a wide range of circumstances. Besides, they are also ensuring to test the vehicles for a predetermined number of miles to achieve more certainty.

Waymo – a subsidiary of Google’s parent Alphabet – is offering level 4 autonomous taxi rides in Arizona. Tesla aims to introduce fully autonomous vehicles by the end of 2020 indicating a giant leap for artificial intelligence in transportation industry. Uber is testing level 3 and level 4 autonomous vehicles manufactured by Volvo. The ridesharing giant in partnership with some automobile manufacturers is aiming to introduce its flagship self-driving car in 2020.

Learn about some of the case studies at Mob Inspire

The year ahead is set to yield the outcome of the efforts building up for the past couple of decades regarding the association of AI and transportation. Although the large-scale commercialization of self-driving cars is still unlikely, yet technology would see widespread acceptance in various parts of the world.

Mobility as a Service (MaaS) to Enhance Ride-hailing Operations

Mobility as a Service

On-demand taxi service providers have redefined the ride-hailing industry by enabling passengers to instantly book a ride with only a few taps on smartphones. Although radio taxi services prior to the advent of on-demand economy were also providing instant booking over phone calls, yet the on-demand model carries its significance in that it allowed passengers and administrators to track ride in real time.

Besides, customers have the power to pay e-wallet and share feedback as soon as ride ends. Similarly, the apps on driver’s end provides them with the best route to destination. Many ride-hailing businesses allow drivers to find the nearby drivers from same service so that they may relocate to a nearby location where they have greater chances of getting a ride request.

Also read: Use Cases of IoT in Logistics

Ride-hailing is not immune of challenges though. Use of AI in transportation is on its way to address those challenges. A number of problems some of which are stated below have emerged in the decade down the road since the start of Uber era.

  1. Number of vehicles and gig workers registered with ride-hailing businesses is growing rapidly.
  2. Number of privately owned vehicles is not reducing as previously aimed.
  3. Service providers are ending up increasing fares to make their businesses sustainable.
  4. The amount of traffic congestion is increasing with dozens of ride-hailing businesses operating.
  5. Bus-pooling is an alternative but most passengers have to walk excessively to hop on a bus.
  6. Ridesharing or bus-pooling also takes more time than on-demand taxi services.
  7. There are very sharp tradeoffs when deciding between on-demand taxi and ridesharing.
How MaaS tackles the challenges of ridesharing with application of AI in transportation

MaaS also referred to as shared mobility network is a concept that has been widely regarded to define the future of AI in transportation. Instead of providing separate services for on-demand taxi and ridesharing, MaaS providers aim to combine these services so that passengers have more seamless experience.

For instance, a passenger looking to travel from point A to B would be offered the most optimized options that would include on-demand taxi, rideshare, and micromobility services. The resultant ride plan would reduce the walking time to almost minimal and may include one or more types of vehicles. A passenger may get a set of options like the one below.

MaaS AI in Transportation

It is notable that passengers would not require to book each ride separately. Instead, the artificial intelligence in transport system would enable passengers to get the entire ride plan by scheduling only once.

Also read: Technology in Logistics 2020

The transport departments in many developed countries are facilitating the efforts to phase out private vehicles, standalone services for on-demand taxi, public transit and micromobility and replace them with shared mobility. The regulatory framework for this transforming is under development stages for the past few years. It is highly likely that MaaS services would attract a large-scale adoption in 2020 in various metropolises.

Facial Recognition for Gig Driver Verification

Facial Recognition Software AI in Transportation

Uber recently faced a ban from operating in London – one of its biggest markets – on grounds of failure to prevent unregistered drivers from using accounts of other drivers. Uber, Lyft and other major ride-hailing businesses previously introduced a number of measures when the city’s transport authority warned ride-hailing giant of a potential ban in case of failure to meet the standards.

The measures include:

  1. Snap selfies at various times during the course of operations to ensure that the driver is a verified one.
  2. Panic button that allows passengers to call 911 with only a couple of clicks.
  3. Sharing ride-location in real-time with one of the contacts.
  4. Reminders to confirm number plate and driver as the one appearing in app.
  5. Automatic alert system to alarm passenger if ride goes excessively off track.
  6. Extensive background checks and driver education before registering them.

Despite all these measures, the ban of Uber indicates that transport authorities are all set to come down hard on ride-hailing service providers. Artificial intelligence solutions in transportation are enabling ride-hailing startups to overcome this challenge. All ride-hailing businesses will aim to add facial recognition feature to verify driver by at most end of the Q2 in 2020.

Mob Inspire is one of the pioneers in commercial development of facial recognition for gig drivers thereby expanding use of AI in transportation. One of our clients in Australia recently requested this feature. We developed and delivered a comprehensive system that recognizes drivers based on Computer Vision technology.

AI for Vehicle Surveillance and Traffic Forecast

Vehicle surveillance with AI in Transportation

Surveillance cameras are being used in hundreds of cities exclusively for surveillance of vehicles. The authorities retrieve the pictures from a particular time of the day to identify crime suspects.

Computer Vision technology is an impressive application of AI in transportation that identifies license plate in a picture to allow characters reading. Mob Inspire uses a combination of Computer Vision algorithms along with image processing to find a license plate in an image that may have any number of objects apart from vehicles.

Once the license plat is found, the algorithms of optical character recognition (OCR) are used to read the characters on plate. The outcome is compared with the database of vehicle registration departments to determine the origin and owner of vehicle. This way, authorities keep a track of each vehicle that passes through any junction in the city.

Artificial intelligence in transportation industry is also enabling transport departments to craft effective projections for traffic. Intelligent systems can identify growing traffic at one road and reducing at another in real-time. They use this information to attach bias in traffic signals so that each driver may get more or less equal wait time.

The use of big data with AI augments the capabilities of intelligent software systems. Traffic management units perform predictive analytics to identify the capacities of roads at usual times and during blockades. The proposed shared mobility network would also integrate passengers by sending them real-time notifications on traffic and recommendations for their route plan.

Drone delivery and AI powered network control system

Drone Delivery Network

Ground based on-demand delivery has been a huge source of relief to consumers’ lives who can deliver and receive parcels at their doorstep within an hour of placing the order. However, the excess of delivery businesses is going to make the space saturated in future. We already witness some cities reeling from traffic congestion.

Research in the past couple of decades indicates that mini drones are highly effective for delivery in urban areas due to growing congestion. Some drone delivery startups have emerged over the past few years mainly in San Francisco and Los Angeles. The operations from these businesses are so far so good.

However, there would be challenges in future as the number of drone traffic would increase. In a conventional case, the administrators will have to define speed, height and path for each drone in a spatial region.

Also read: Impact of Blockchain in Transportation

This is where Artificial intelligence solutions would be vital. Drone based delivery would serve little purpose if operators have to control each drone manually. AI enables you to make the entire delivery process automated from the time a drone is loaded with a parcel, delivered to destination, and returns. This is one of the most notable cases of future of AI in transportation.

The developers at Mob Inspire take their inspiration for drone delivery network from air traffic control systems. The difference lies in the fact that airplane pilots and UAVs set their path as per the directions from ground-based controller whereas drones in an AI network make the route plan independently unless explicitly tasked. A drone can set the height, speed and all notable parameters itself by identifying all other drones in the network.

Drones based on-demand delivery services are emerging for the past few years and 2020 would witness their expansion on a significantly large scale. The year would also possibly bring a regulatory framework for this industry.

Prepare to Live Ahead

The industries associated to transportation including logistics, delivery, ridesharing and automobile are all set to incorporate large-scale changes. Businesses are testing innovative features to enhance operations and optimize costs for years. The year 2020 will transform these industries with the innovations shared above as administrations are finalizing the corresponding regulatory framework.

It is about time you plan ahead of competitors and become of the pioneers in providing these features of AI in transportation. This investment is not only going to facilitate your customers but enable you to minimize costs and time consumption significantly.

Mob Inspire carries an experience spanning over a decade in developing intelligent systems. Our widespread clientele leads their respective industries by utilizing our transportation solutions.

What are you planning to accomplish? Do you intend to optimize your business with matchless software infrastructure or redefine business model right from scratch? Mob inspire can assist you in crafting a business model and developing a highly efficient software solution customized to your business needs. Contact us today so that our experts can take you further.

A data science and marketing professional with an insane passion to explore AI, Cyber Security, Quantum Computing, and future of mobility. Also carries an incredible amount of flare to write about things that he barely knows.