Transportation, the industry that deals with the movement of commodities and passengers from one place to another, has gone through several studies, researches, trials, and refinements to reach where it is now. Technological advancements have helped the transportation sector progress in its journey of innovation and evolution. One such new-age technology that has contributed to the sector is AI. Leveraging AI in transportation helps the sector increase passenger safety, reduce traffic congestion and accidents, lessen carbon emissions, and also minimize the overall financial expenses.
The safety of passengers, pedestrians, and drivers has always been the number one concern for the transportation industry. Taking advantage of AI models does far more than decrease the number of human errors; transportation analytics assists in minimizing effects of driving hazards in crowded urban areas, while also monitoring safety regulation compliance and vehicle maintenance reports.
The problems of intermodal logistics are always relevant to businesses with a sizable fleet, complex infrastructures, and numerous links in a cooperation chain. State-of-the-art modeling technology can address these problems and improve operational efficiency: optimal route scheduling with minimum wait times, traffic detection to adjust the route in real time, on-time regulation compliance, etc. Using data analytics in logistics provides a data-driven view on routes and driver behavior, upgrades the transportation planning process, saves resources, and increases safety.
The road freight transport system can utilize accurate prediction methods to forecast their volume using AI methods, which simplifies transportation company planning. Additionally, several decision-making tools for transport can be designed and run by AI. This will affect investments made by companies in the future in a productive way.
Traffic, the prime transportation disruptor, causes delays, accidents, and wasted fuel. Prediction techniques, however, allow you to perform traffic condition forecasting using traffic monitoring data, information about sporting events or construction in the city, and even automatically calculate alternative routes.
Do smarter route planning by considering historical and real-time data on road condition, weather, traffic, wait time, maintenance stops, and more.
Employ telematics solutions to care for your vehicles. Receive timely alerts and track vehicle condition so that hazards don’t catch you off quard.
Manage your workflow more effectively with minimal manual effort. Empower your supply chain professionals with intelligent tools trained on big data to balance supply and demand, scrub staff schedules, and automate customer service with voicebots & chatbots.
Forecast traffic congestion and incidents, adjust your route on the go, and allocate extra time for instances when traffic jams are unavoidable.
Implement driver behavior monitoring to improve safety and increase productivity. Analyze sensor data and monitor your fleet via data-driven dashboards for smart trains, trucks, and ships connectivity.
Safety is arguably the most important consideration for those working within the travel or transportation industries. In order for services to be successful in any shape or form, passengers and customers need to know that they or their belongings are in safe hands.
Technology has made increasing safety levels much easier over the years and now, with the advent of AI technologies that are becoming increasingly adopted by businesses and enterprises operating within the transportation arena, safety levels could about to reach even higher peaks.
Another top consideration of many businesses or enterprises operating within travel or transportation is the reliability of their services or vehicles. Passengers are much less likely to travel with operators or in vehicles that look or that are known to be unreliable. Use of Artificial Intelligence in Public Transportation to enhance the reliability of the service is one of the key drivers of its adoption within the industry.
Using artificial intelligence technologies, it is hoped that the ability to process and predict data and outcomes in much larger quantities than humans are capable of will allow travel and transport operators, as well as eventually the public themselves, the ability to schedule public and private transportation services in a significantly improved manner.
Being energy efficient is an increasingly important aspect of travel and transport as our journeys and commutes become ever more integrated with technology. While this undoubtedly has its benefits, it also means new technologies will need to manage their power supplies much more efficiently.
Artificial intelligence technologies will undoubtedly enhance the efficiency of the systems it integrates with; however, power will need to be used much more intelligently by all of the systems in play in order to truly utilize the potential of newer technologies.
With a large percentage of the world becoming increasingly environmentally focused as the effects of climate change are seen across the world, drastic reduction of polluting substances within the travel and transportation industries is required in order to secure their long-term sustainability.
Artificial intelligence could play a big role in developing and deploying new and innovative ways in which to deal with pollution as well as helping to enable scientists and engineers to come up with much more environmentally friendly methods to power and run vehicles and machinery for travel and transportation.
Nowadays, there really is an app for everything. This includes AI-powered real-time traffic updates through services such as Google Maps. By using location data collected from users smartphones, these apps are able to predict and analyse traffic conditions in your local area so as to better inform your travel plans.
We are now entering a new era in which AI capabilities are reaching heights that will have a major impact on how aviation business is conducted. The use of AI in air traffic operations is very much in its infancy. Progress in automation and computing power, utilising technologies associated with machine learning and data analytics models, are being used to improve the management of increasing air traffic volumes. The IATA report points out that the development of unmanned aircraft systems (UAS) and UAS traffic management systems, using enhanced computing capabilities, will create new opportunities for improving existing traffic management systems, separation standards and airspace planning design. What is known as advanced business intelligence can substantially modify the way airlines run their business in marketing and sales, distribution, pricing, and fleet management.
A high potential application of machine learning is the translation of historical and real-time insights about customers' behaviour into real-time tactical changes (adjusting the website content presented to the customer).
Other uses include social media sentiment analytics, which implies predicting customers' needs based on their social media behaviour. Another area where AI can make a difference, whether in terms of processes or speed, is ground handling. Some examples of high potential use cases include safety checks, aircraft movement operations (pushback and towing), aircraft turnaround operations (fuelling, catering, loading and unloading, de-icing and anti-icing), and ground transportation on the ramp (passengers, baggage, cargo and mail). AI can also facilitate a shift towards seamless airport security, as it is able to digest large amounts of data both in historical and real-time and to detect anomalies.
Railways were one of the most innovative sectors of the economy and a major actor of the industrial revolution. With the subsequent rapid growth of road and air transport, rail lost its leading position in innovation.
Since the 1990s, the emergence and development of the internet, the internet of things and big data have provided the rail sector with an opportunity to embark upon a new phase of technological innovation. Indeed, the vast quantity of data generated by these digital technologies can be a useful instrument, enabling rail companies to modify their organisational structure, improve their performance and create new added value.
To reap the full benefits of digitalisation, railways can rely on AI.AI can improve manufacturing, operations and maintenance for rail operators and infrastructure managers. It can consequently be perceived as a lever to improve management, lower costs and enhance competitiveness with respect to direct competitors or to other transport modes.
Over the past twenty years, sea and inland waterway transport has undergone important developments. To name just a few of the trends that have shaped it, ship traffic has grown denser, which has raised the stakes of maritime safety and called for advances in maritime surveillance. The further increase of container traffic has called for adaptations to port terminals and better connections with their hinterland.
Ever-growing vessel sizes have amplified the pressure that ships exert on ports and their cities. The raising awareness of environmental issues has brought the obligation to adapt to greener rules in the context of fierce international competition within the global maritime industry.
To this landscape, the technologies of digitalisation, the internet of things, big data and automation are a game-changer.
Having penetrated various parts of the sector to a varied degree, the one feature common to these technologies is the generation of data. Building on that data, new tools, including AI, make it possible to analyse the information and gain insights which facilitate decision-making, in particular helping to improve safety, energy efficiency and optimise logistics. The several types of AI applications already used or tested only affirm the sector's focus on the coordinated introduction of such enabling technologies.
Maritime operations typically require swift adaptation to changing conditions and decisions taken based on many parameters. With the more advanced navigation systems, a growing amount of ship performance and navigation data is being generated. Data comes for example from radar, electronic navigation charts, auto-pilot systems and other related sensors. Special purpose vessels also need wave radars, oil spill detectors and high accuracy sensors.
Automatic identification systems (AIS) transfer data such as the ship's identification number, position, course, speed and destination. Insights gained from analysing such data can be used to carry out technical operations and maintenance, make a ship more energy-efficient and help it to meet emission control standards, for instance. Detection of anomalies in marine operations can improve safety at sea and facilitate the management of accidents and environmental risks from shipping. Thanks to the combination of recorded ship movements and advanced image recognition, ships can be identified even if they turn their AIS transmitters off.
Machine learning techniques can provide predictions of delays due to bad weather or traffic congestion, required maintenance, estimates of future demand and oil prices. However, decisions that need to be taken to adjust the navigation system to the new situation are complex.
The ship uses the autonomous operation only during deep-sea voyage, and not in congested waters or during its port approach. There is also potential for using autonomous ships in short-sea shipping and on inland waterways.
Making the port and logistics supply more predictable, it also allows for more just-in-time operations and transparency regarding the available dock space. Using this information, ships can adjust their speed, while also improving their energy efficiency. Data collected during container port operations is stored and analysed as the basis for future AI-assisted tools, which are expected one day to manage the entire delivery cycle and further optimise terminal operations. These technological advances are understood as a part of a wider transformation of the supply chain.
We’ve worked extensively in the transportation industry, providing robust security camera solutions for mass transit systems, ports, subways, city buses, and train stations. As veteran security integrators we understand the common security threats within the transportation sector include petty crime, harassment, liability suits, and vandalism. Our solutions are designed to insulate transportation systems from security threats and become safer environments for passengers.
Our project consulting is just the beginning; the services we offer are customized to every client and span installation support, configuration, wireless network design, end-user training, network monitoring, backup & recovery, cloud and complete site security evaluations to ensure nothing is overlooked. Dovetailed together, our comprehensive security services provide a trustworthy security solution.
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