AI in Transportation and Logistics: Making the Industry Intelligent

The dawn of the third millennium witnessed an outburst of many disruptive technologies (virtual and augmented reality, machine learning, the Internet of Things, blockchain, etc.) that are changing the face of numerous industries – from manufacturing and education to retail and healthcare. Artificial Intelligence is on top of the list of such innovations, bolstering almost all current sectors of the economy, and the transportation realm is no exception. Moreover, AI in transportation is seen as one of the major drivers that are likely to propel its growth and development. So, how does harnessing AI benefit this realm? Read this article to get the answer.

The Boons of the Application of AI in Transportation

There are several improvements that the introduction of this know-how can usher into the transportation industry.

1. Optimization of traffic patterns

Modern cities are notorious for their jammed streets where drivers waste hours of their precious time. And when they do reach their destination, they may spend another eternity scanning the neighborhood for a vacant place to leave their car or looking for an unoccupied space in the parking lot. Artificial Intelligence in transportation can help relieve such problems via smart traffic light management and specialized mobile apps that will notify the driver about the availability of a slot to park their vehicle.

2. Improvement of ecology

The traffic congestion scourge leads to a greater amount of exhaust gases a car emits while standing in a jam since drivers don't shut off the engine, hoping to resume movement soon. With the growth of the urban population, such situations turn into a global environmental problem, threatening to double the emission of carbon dioxide by 2050. More efficient traffic management with the help of Artificial Intelligence in transportation industry will curtail such harmful impact of vehicles on the atmosphere of the planet and the health of all its residents.

3. Increasing safety

We all make mistakes in our daily activities because to err is human. But mistakes on the roads cost both pedestrians and drivers too dear, resulting in the death or injuries of people and damage to their property. And according to statistics, human error accounts for 90% of accidents that occur in the EU. Thanks to AI transportation domain can become much safer with the mass advent of autonomous cars and a significant degree of operations automation in conventional cars.

4. Upgrading fleet management

The developed delivery framework is the blood circulation system that keeps the economy going 24/7. Considering the number of vehicles used in logistic ventures all over the world, AI can be highly useful not only for municipal authorities or average road users. Artificial Intelligence in transportation market is leveraged for a plethora of commercial purposes, including cargo vehicle route optimization, fleet schedule management, and vehicle tracking. The application of this technology will enable a logistic company’s personnel to plot the optimal route, avoid adverse weather conditions, provide the safety of the cargo, and exercise round-the-clock monitoring and control of the vehicle wherever it is.

5. Predictive fleet maintenance

The efficient functioning of any logistic organization vitally depends on the satisfactory operation of the machinery it employs. The use of AI in transportation industry can ensure the adequate technical condition of vehicles, help pinpoint issues, and prevent malfunctions in advance via timely inspections.

6. Enhancing inventory management

A logistic business is impossible without warehouses where goods are kept before being delivered to consumers. AI-based software can be honed to streamline inventory management and keep the stock to the minimum necessary for the uninterrupted operation of the enterprise. In this way, the products to be shipped will sit on the shelves in the shortest possible time, so the company will be able to reduce renting costs and utilize the storage space effectively.

7. Detecting niche trends

Business in any field is doomed to defeat if it builds its development strategies on a hunch. AI in transportation industry can furnish insights into the efficiency of the organization’s pipeline, consumer behavior, marketing trends, and other summands of business success. Having such metrics at their fingertips, stakeholders will make data-driven decisions that would steer their company ship to the bonanza shore.

8. Stepping up cost-efficiency

Having optimized all previously mentioned aspects of the functioning of a transport industry venture, you will be able to cut down drastically the operational costs. If your enterprise can choose the shortest routes for its drivers, ensure non-stop running of the fleet, minimize storage expenditures, and guarantee timely delivery of goods, the increase in revenues will not be late in coming. Now you know what the assets of employing Artificial Intelligence in the industry are. But how is AI used in transportation in real-life?

Use Cases for AI in Transportation

AI-driven solutions have numerous application fields in logistics and transportation. Let’s take a closer look at them.

1. Traffic signs detection

This function is indispensable for driverless cars that are getting traction nowadays, but it also comes in handy when there is a human at the wheel. Children learn to recognize traffic light signals at an early age, yet in the USA alone, the annual number of people who are killed after a vehicle runs a red light equals 1,000. It happens because of different reasons, such as driver fatigue, pedestrian/driver distraction (by being engrossed in smartphones as often as not), attempts to reach the destination in the time squeeze, etc.

AI can drastically reduce the number of casualties and injuries if the car doesn't budge until its computer brain "sees" the green or if the brake system stops the car when it approaches a red traffic light. Road sign detection can be programmed to operate in much similar way. More sophisticated solutions are trained to work in various weather conditions, poor ambient lighting, and occlusions (say, billboards or streetlamps).

2. Pedestrian detection

Pedestrians (both humans and animals) are no less unpredictable than drivers in their behavior, so traffic safety can be greatly augmented if the AI-based vehicle computer recognizes them and differentiates them from trees or other roadside objects. To do that, various motion, texture, gradient, and shape recognition technologies are harnessed, giving close to 100% accuracy to the procedure.

The major challenge for such systems is to identify a human who is carrying bags, walking a dog, using a wheelchair – in a word, when their shape is too unusual or complicated to analyze. Another problem for the AI is to predict the pedestrian’s intention (whether they are going to cross the road or are just walking along). Human pose estimation technology relying on the skeleton- and contour-based models can do much to solve the latter difficulty.

3. Traffic flow analysis

In the 21st century, not only cars but also cities are getting intelligent. The concept of smart cities embraces a network of cameras (both drone-mounted and stationary ones) connected to in-vehicle sensors that monitor the traffic and environmental conditions to warn drivers about congestions or accidents on their way, road repairs, and detours to avoid them, the expected duration of the waiting time in queues.

Another perk of such IoT-powered solutions is the data it provides that, once accumulated and analyzed, can be utilized in urban infrastructure planning to relieve bottlenecks clogging the city’s transport arteries.

4. Vehicle movement analysis

AI-fueled smart city software can be instrumental on a micro-scale level as well. Its mechanisms enable an analysis of the trajectory and speed of vehicles entering street bends and turns, estimate the risk of conflicts between road users (cyclists, pedestrians, and cars), or assess the waiting time at crossroads and traffic lights. In this way, taking care of a certain section of the road paves the way for a greater degree of convenience for urban dwellers.

5. Parking management

One more use case of smart city solutions is parking system optimization. The complex of sensors placed at parking lots detect vacant slots and inform drivers via smartphones that there is one for their car to occupy. This technology is especially welcomed in places where vehicles tend to crowd in large quantities (sports facilities, airports, railway stations, shopping malls, etc.).

6. License plate recognition

This know-how is typically leveraged for security purposes in places of restricted access. Being located at the entrance to tunnels, ferries, or drives, AI-powered cameras scan the license plate and decide whether the vehicle should be allowed to proceed or signal that an entrance fee must be charged.

7. Road condition surveillance

The poor condition of the road surface leads to vehicle damage on a prodigious scale. AI algorithms can be utilized to provide real-time information to drivers about potholes on their way as well as about wet or slippery road sections, debris, fallen trees, or other obstacles posing a threat to travelers. And relevant local authorities will use this information to (hopefully) react promptly and eliminate the cause of inconvenience.

8. Traffic incident detection

If an accident occurs, human life depends on how fast the emergency services learn about it and dispatch their personnel to the scene. The AI-powered Intelligent Transportation System (ITS) exercises permanent monitoring of roads in a given area through the network of cameras and alerts rescue team operators who will deal with the problem on short notice.

9. Driver monitoring

Falling asleep at the wheel causes more than 50,000 accidents a year in the USA, resulting in 1,500 deaths. Such casualties can be prevented if people aren’t allowed to nod while driving. AI-based in-vehicle computer vision systems are now employed on a large scale to estimate face expression and head posture and recognize telltale signs of drowsiness. Once it happens, a sound signal makes the driver stay awake.

Another possible application of this technology is distraction detection. In case the driver gets busy with their mobile phone or starts talking to a backseat passenger, the system reminds them of their direct responsibilities instantly.

Our Case Study

The goal of our project was to create a predictive model that could accurately forecast the demand for transportation services. The model aimed to consider various factors such as seasonality, national holidays, and weather conditions, enabling transport companies to plan the rational placement of vehicles and improve service efficiency.

In tackling the challenge of accurately predicting transportation service demand, our approach was multi-faceted and data-driven. We focused on three key areas.

Data collection and analysis

Historical data on transportation service demand was meticulously analyzed, focusing on patterns influenced by seasonality, national holidays, and weather conditions.

Model development

• An autoregression model was developed to capture the time-dependent nature of transportation demand.
• Random Forest regression, known for its effectiveness in handling complex, non-linear data patterns, was employed to enhance the model's accuracy.

Integration of weather data

A third-party API was utilized to integrate real-time weather data into the model, acknowledging the significant impact of weather conditions on transportation demand.

AI in Transportation: Glimpses of the Future

As Industry 4.0 continues to conquer an ever-growing number of spheres, entrepreneurs all over the world realize its revenue-generating potential and invest heavily into its implementation. The sector of transportation and logistics is witnessing a considerable spike in the application of AI-driven technologies, whose market is expected to display a three-fold increase between 2013 and 2023.

Such a CAGR of 14.5% is explained by the widespread harnessing of Artificial Intelligence for city infrastructure design and management, cargo and public transport demand modeling, and travel behavioral patterns. With autonomous cars and aircraft taxis gathering momentum, we are likely to see an avalanche of AI solutions penetrating various domains of the transportation industry. The principal challenge for their across-the-board advent is the need to update the existing legal regulations according to the novel IT-driven developments.

Conclusion

Nowadays, Artificial Intelligence has stopped being a futuristic notion and entered multiple spheres of our everyday life. In the field of transportation, its application can optimize urban traffic, improve safety on the roads, and increase the efficiency of transportation and logistic enterprises. Requestum as a seasoned software vendor can develop an AI solution for your business that will impress you with top-notch quality and affordable price.