Artificial intelligence in transportation industry<\/span>: why it\u2019s a promising but complex relationship<\/span><\/h2>\n<\/div>\nAs we\u2019ve written before, AI in urban mobility<\/a>, logistics<\/a>, and fleet management<\/a> is highly anticipated and already deployed to an extent.<\/p>\nFor instance, 65% of logistics<\/a> company leaders name AI as an important technology for the next three to five years. Also, as of 2018, one in four<\/a> public transport managers already use AI for real-time operations management and customer analytics.<\/p>\nMoreover, many governments are doling out funds and support for an array of AI for transportation projects. <\/span>Canada has an ambitious ACATS Program<\/a>, offering up to $2.9 million in grant and contribution funding to companies to Advance Connectivity and Automation in the Transportation System.<\/div>\nSingapore has a national AI strategy<\/a> promoting the creation of \u201cintelligent freight planning\u201d by 2030.<\/p>\nIntelligent freight planning<\/b>
\n![\"AI](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2021\/10\/intelligent-freight-planning.png\")
<\/p>\n
Source: Smart Nation Singapore \u2014 National Artificial Intelligence Summary<\/a><\/em><\/p>\nSuccessful machine learning in transportation system pilots have been done across countries, too \u2014 mainly demonstrating pilot runs of self-driving electric pods, AI-regulated traffic light scheduling, and smart road infrastructure.<\/p>\n
Yet despite successful reports for years, the number of successful pilots drastically outweighs the number of commercial AI for transportation solutions.<\/p>\n
Why is that? Because implementing and scaling AI deployments is a difficult balancing act of benefits vs concerns and tradeoffs.
\n![\"AI](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2021\/10\/ai-benefits-on-transportation-1.png\")
<\/p>\n
Examples of the use of AI in transportation<\/h2>\n
Some people think that AI in the transportation industry is still the stuff of sci-fi movies and the distant future. This is probably because some of the most radical transformations cannot be seen with the naked eye.<\/p>\n
The fact is that AI has already changed the transportation industry. Let\u2019s look at the most illustrative and important use cases that prove this.<\/p>\n
Self-driving vehicles<\/h3>\n
Technology allowing vehicles to make trips without drivers got a phenomenal boost in the past decade. IoT sensors collect and transmit large volumes of data, and that data is instantly processed and aligned with other telematics and geolocation data. Simultaneously, data-based commands are sent to a vehicle\u2019s receiver in real time. This is what a simplified machine learning for transportation workflow for a self-driving car looks like.<\/p>\n
In Tokyo, autonomous vehicles are already allowed to operate<\/a> throughout the city, though drivers are still required so they can intervene in an emergency. But the biggest potential for AI-powered autonomous driving lies in the commercial sector and the public transportation industry.<\/p>\nJust think of the 65% of all goods that are transported by truck globally<\/a>. Bringing AI technologies to trucks can not only revolutionize the logistics and transportation industry but become a game-changer for the entire global trade system.<\/p>\nLegal restrictions, safety concerns, and a lack of user trust remain the major obstacles to the mass adoption of self-driving cars. The question, however, is not whether autonomous cars will<\/em> invade our streets but rather how quickly<\/em>they will do so.<\/p>\n\n\t\t\t![\"Digitally](\"https:\/\/intellias.com\/wp-content\/uploads\/2022\/07\/Digitally-Mature-Logistics.jpg\")
<\/div>\n\t\t\t\n\t\t\t\t\n\t\t\t\t\tDIGITALLY MATURE LOGISTICS WHITEPAPER<\/div>\n\t\t\t\t\tConquering the Waves of Technological Disruption<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t Download now <\/span>\n\t\t\t<\/div>\n\t\t<\/a><\/div>\nTraffic management<\/h3>\n
Traffic management \u2014 in particular, dealing with congestion \u2014 is another good illustration of how machine learning in transportation systems is transforming the industry.<\/p>\n
Once again, large volumes of data are collected via cameras, sensors, and other IoT devices and transmitted to the cloud, where AI-driven algorithms analyze the data and identify the risk of particular traffic issues before they occur. Afterwards, actionable insights are sent to both centralized traffic management systems<\/a> (e.g. for controlling traffic lights) and to individual users (e.g. route suggestions or accident notifications).<\/p>\nAI-driven traffic management also brings sustainability to the industry. For example, the SurTac AI solution<\/a> by Rapid Flow Technologies has enabled the city of Pittsburgh not only to reduce average travel times by 25% but also to cut emissions by 20%.<\/p>\nPredictive maintenance<\/h3>\n
Predictive maintenance technology<\/a> powered by AI helps to predict vehicle breakdowns before they occur. Here\u2019s how it works: The performance of automotive parts and key indicators are tracked in real time, and when a deviation from a safe range is identified, the AI-based system sends a signal to the vehicle owner or manager responsible for fleet maintenance.<\/p>\nThe more data is received and processed by the machine learning in transportation industry, the more accurate and timely the predictions. AI-based anomaly detection is already helping individual and corporate vehicle owners improve their fleet performance, cut repair costs, and ensure the reliability of transportation services.<\/p>\n
Drone taxis<\/h3>\n
A drone taxi<\/a> is a stunning example of AI for transportation, probably even more exciting than self-driving cars. This year, the first airport for air taxis<\/a> was opened in England, confirming that flying taxis are no longer a sci-fi fantasy.<\/p>\nPilotless aerial vehicles are not only a reminder that the Fifth Element<\/em>-like world is closer than we might think but also a sustainable solution to several challenges.<\/p>\nDrone taxis operated with the help of AI in transportation industry can substantially mitigate carbon emissions, resolve traffic congestion, and save costs on future infrastructure development and public transportation. And that\u2019s without mentioning the benefits for drone taxi passengers, who can save hours per week by reducing commute times.<\/p>\n
\nHow to safely infuse benefits of AI in transportation: a roadmap<\/span><\/h2>\n<\/div>\nAI has the potential to solve pressing transportation problems. But as usual, execution and delivery is the real bane.<\/p>\n
Collectively, the industry is actively exploring how machine learning in transportation industry, intelligent automation, neural networks, and deep learning can be used to make transportation safer, greener, cheaper, and more efficient.<\/p>\n
But given the complexity of the domain itself and the algorithm, there\u2019s still no one-size-fits-all path to success. Yet there are a few proven steps that could help refine your product development vector.<\/p>\n
Develop a data management strategy<\/h3>\n
Road sensors, connected car dashboards, floating cellular data, location-based services<\/a> \u2014 the transportation industry has overwhelming access to data sources and big data use cases.<\/p>\n\n \n Discover viable big data use cases in transportation<\/p>\n
\n <\/div>\n <\/div>\n <\/div>\n Read more<\/span>\n\t\t <\/a><\/div>\nFar fewer players have mature DataOps<\/a>. Or even a formalized long-term data management and governance process \u2014 a cadence for processing, securing, standardizing, and operationalizing incoming insights.
\n![\"AI](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2021\/10\/data-governance-iin-transportation.png\")
<\/p>\nYet AI systems are fickle when it comes to the quantity and quality of data. Scalable transportation algorithms require better<\/b>, faster<\/b>, and cheaper<\/b> data processing. And in this case, you can\u2019t choose just two options \u2014 it\u2019s all or nothing.<\/p>\n
When assessing a specific AI for transportation use case, think if your company can:<\/p>\n
\n- Obtain sufficient, variable, and labeled data sets for model training and validation.<\/li>\n
- Establish an ethical, anonymized, representative, and secure data collection process for passenger and vehicle data.<\/li>\n
- Enable low-latency data processing between all connected systems \u2014 IoT and edge devices, cloud data repositories, cloud-based or on-premises analytics systems.<\/li>\n
- Apply data fusion techniques to consolidate, pre-process, and operationalize records obtained from different sources and in different formats.<\/li>\n<\/ul>\n
The intricacies of collecting and processing data for algorithms is one of the core reasons why AI transportation use cases are cautious small-scale pilots rather than city-wide deployments.<\/p>\n
Getting sufficient training data is arguably one of the biggest issues. Transportation companies solve it in different ways. Tesla, for example, built a patented system<\/a> for sourcing self-driving training data from its customers\u2019 vehicles.<\/p>\nIEEE recently published a classification study<\/a> that ranked different types of transportation data sources by accuracy, reliability, and cost of use.<\/p>\nComparison of traffic data sources<\/b>
\n![\"AI](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2021\/10\/traffic-data-sources.png\")
<\/p>\n
Source: IEEE \u2014 Data Sources for Urban Traffic Prediction<\/a>: A Review on Classification, Comparison and Technologies<\/em><\/p>\nAnd even when you have obtained all the data you need, matters can still go awry if that data is skewed in some way. Take it from Volvo. When the company tested its AI-powered Large Animal Detection system in Australia several years ago, it was defeated by a kangaroo. Why? Because their system was designed in Sweden and originally trained and tested on elk, deer, and caribou \u2014 the native species \u2014 and wasn\u2019t quite ready for a marsupial suddenly interfering in a road test.<\/p>\n
The takeaway: Effective pattern recognition in the transportation industry requires varied, representative data. Obtaining it, however, may not always be immediately feasible or cost-effective.<\/p>\n
But you are not alone in your battle for better transportation data. Private companies and legislators worldwide are working hard to provide access to better public data sources, standardize data formatting, and open data API integrations<\/a>.<\/p>\nFor example, the US Department of Transportation<\/a> has several cool initiatives underway:<\/p>\n\n- Safety Data Initiative<\/b> \u2014 Aimed at integrating available data on car crashes and highway designs with anonymous data from GPS devices to develop better speed regulation practices<\/li>\n
- Waze Pilot<\/b> \u2014 Tests the feasibility of using crowdsourced data from the Waze app to predict crash risks and other types of road accidents<\/li>\n
- Computer vision tools<\/b> applied to naturalistic driving data \u2014 The feds are building a set of computer vision tools for collecting and analyzing naturalistic driving data so that everyone can get access to better datasets<\/li>\n<\/ul>\n\n \n
Read also: Best Computer Vision Applications<\/a><\/p>\n <\/div> \n <\/div>\nUnderstand the advantages and limitations of different AI methods<\/h3>\n
When we talk about artificial intelligence in transportation industry, people with a technical background often think of algorithmic methods, whereas most other people imagine use cases.<\/p>\n
AI as a term often lacks substance. Is Google autocomplete AI? Yeah, it is as of late. Is it the same AI that powers Alphabet\u2019s Waymo self-driving vehicles? Nope; the methods are different.<\/p>\n
Artificial intelligence in transport system is a collection of supervised, unsupervised, and reinforcement learning methods for developing (self-)learning systems:<\/p>\n
![\"AI](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2021\/10\/ai-and-machine-learning-in-transportation.png\")
<\/p>\n
The common machine learning techniques in the transportation industry are:<\/p>\n
\n- Artificial neural networks (ANN)<\/li>\n
- Genetic algorithms (GA)<\/li>\n
- Simulated annealing (SA)<\/li>\n
- Fuzzy logic model (FLM)<\/li>\n
- Ant colony optimizer (ACO)<\/li>\n<\/ul>\n
The use cases of neural networks in transportation engineering alone are plentiful \u2014 transportation demand forecasting, predictive maintenance of transport infrastructure, driver behavior monitoring, and more.<\/p>\n
\n \n Transportation software development<\/p>\n
\n Securely integrate intelligence into your core transportation, logistics, and supply chain products<\/div>\n <\/div>\n <\/div>\n Learn more<\/span>\n\t\t <\/a><\/div>\nThat\u2019s a lot of choices, right? But every option comes with some inherent limitations. Let\u2019s take ant colony optimizer algorithms. These were found to be effective for developing better routing of public transport and improving passenger pick-ups on popular ride-sharing platforms.<\/p>\n
But scaling \u201cintelligence\u201d in transportation is hard. Researchers have found<\/a> that ACO algorithm speed drops significantly when the number of iterations (e.g. the number of optimized routes) grows.<\/p>\n
For instance, 65% of logistics<\/a> company leaders name AI as an important technology for the next three to five years. Also, as of 2018, one in four<\/a> public transport managers already use AI for real-time operations management and customer analytics.<\/p>\n Singapore has a national AI strategy<\/a> promoting the creation of \u201cintelligent freight planning\u201d by 2030.<\/p>\n Intelligent freight planning<\/b> Source: Smart Nation Singapore \u2014 National Artificial Intelligence Summary<\/a><\/em><\/p>\n Successful machine learning in transportation system pilots have been done across countries, too \u2014 mainly demonstrating pilot runs of self-driving electric pods, AI-regulated traffic light scheduling, and smart road infrastructure.<\/p>\n Yet despite successful reports for years, the number of successful pilots drastically outweighs the number of commercial AI for transportation solutions.<\/p>\n Why is that? Because implementing and scaling AI deployments is a difficult balancing act of benefits vs concerns and tradeoffs. Some people think that AI in the transportation industry is still the stuff of sci-fi movies and the distant future. This is probably because some of the most radical transformations cannot be seen with the naked eye.<\/p>\n The fact is that AI has already changed the transportation industry. Let\u2019s look at the most illustrative and important use cases that prove this.<\/p>\n Technology allowing vehicles to make trips without drivers got a phenomenal boost in the past decade. IoT sensors collect and transmit large volumes of data, and that data is instantly processed and aligned with other telematics and geolocation data. Simultaneously, data-based commands are sent to a vehicle\u2019s receiver in real time. This is what a simplified machine learning for transportation workflow for a self-driving car looks like.<\/p>\n In Tokyo, autonomous vehicles are already allowed to operate<\/a> throughout the city, though drivers are still required so they can intervene in an emergency. But the biggest potential for AI-powered autonomous driving lies in the commercial sector and the public transportation industry.<\/p>\n Just think of the 65% of all goods that are transported by truck globally<\/a>. Bringing AI technologies to trucks can not only revolutionize the logistics and transportation industry but become a game-changer for the entire global trade system.<\/p>\n Legal restrictions, safety concerns, and a lack of user trust remain the major obstacles to the mass adoption of self-driving cars. The question, however, is not whether autonomous cars will<\/em> invade our streets but rather how quickly<\/em>they will do so.<\/p>\n Traffic management \u2014 in particular, dealing with congestion \u2014 is another good illustration of how machine learning in transportation systems is transforming the industry.<\/p>\n Once again, large volumes of data are collected via cameras, sensors, and other IoT devices and transmitted to the cloud, where AI-driven algorithms analyze the data and identify the risk of particular traffic issues before they occur. Afterwards, actionable insights are sent to both centralized traffic management systems<\/a> (e.g. for controlling traffic lights) and to individual users (e.g. route suggestions or accident notifications).<\/p>\n AI-driven traffic management also brings sustainability to the industry. For example, the SurTac AI solution<\/a> by Rapid Flow Technologies has enabled the city of Pittsburgh not only to reduce average travel times by 25% but also to cut emissions by 20%.<\/p>\n Predictive maintenance technology<\/a> powered by AI helps to predict vehicle breakdowns before they occur. Here\u2019s how it works: The performance of automotive parts and key indicators are tracked in real time, and when a deviation from a safe range is identified, the AI-based system sends a signal to the vehicle owner or manager responsible for fleet maintenance.<\/p>\n The more data is received and processed by the machine learning in transportation industry, the more accurate and timely the predictions. AI-based anomaly detection is already helping individual and corporate vehicle owners improve their fleet performance, cut repair costs, and ensure the reliability of transportation services.<\/p>\n A drone taxi<\/a> is a stunning example of AI for transportation, probably even more exciting than self-driving cars. This year, the first airport for air taxis<\/a> was opened in England, confirming that flying taxis are no longer a sci-fi fantasy.<\/p>\n Pilotless aerial vehicles are not only a reminder that the Fifth Element<\/em>-like world is closer than we might think but also a sustainable solution to several challenges.<\/p>\n Drone taxis operated with the help of AI in transportation industry can substantially mitigate carbon emissions, resolve traffic congestion, and save costs on future infrastructure development and public transportation. And that\u2019s without mentioning the benefits for drone taxi passengers, who can save hours per week by reducing commute times.<\/p>\n AI has the potential to solve pressing transportation problems. But as usual, execution and delivery is the real bane.<\/p>\n Collectively, the industry is actively exploring how machine learning in transportation industry, intelligent automation, neural networks, and deep learning can be used to make transportation safer, greener, cheaper, and more efficient.<\/p>\n But given the complexity of the domain itself and the algorithm, there\u2019s still no one-size-fits-all path to success. Yet there are a few proven steps that could help refine your product development vector.<\/p>\n Road sensors, connected car dashboards, floating cellular data, location-based services<\/a> \u2014 the transportation industry has overwhelming access to data sources and big data use cases.<\/p>\n Discover viable big data use cases in transportation<\/p>\n Far fewer players have mature DataOps<\/a>. Or even a formalized long-term data management and governance process \u2014 a cadence for processing, securing, standardizing, and operationalizing incoming insights. Yet AI systems are fickle when it comes to the quantity and quality of data. Scalable transportation algorithms require better<\/b>, faster<\/b>, and cheaper<\/b> data processing. And in this case, you can\u2019t choose just two options \u2014 it\u2019s all or nothing.<\/p>\n When assessing a specific AI for transportation use case, think if your company can:<\/p>\n The intricacies of collecting and processing data for algorithms is one of the core reasons why AI transportation use cases are cautious small-scale pilots rather than city-wide deployments.<\/p>\n Getting sufficient training data is arguably one of the biggest issues. Transportation companies solve it in different ways. Tesla, for example, built a patented system<\/a> for sourcing self-driving training data from its customers\u2019 vehicles.<\/p>\n IEEE recently published a classification study<\/a> that ranked different types of transportation data sources by accuracy, reliability, and cost of use.<\/p>\n Comparison of traffic data sources<\/b> Source: IEEE \u2014 Data Sources for Urban Traffic Prediction<\/a>: A Review on Classification, Comparison and Technologies<\/em><\/p>\n And even when you have obtained all the data you need, matters can still go awry if that data is skewed in some way. Take it from Volvo. When the company tested its AI-powered Large Animal Detection system in Australia several years ago, it was defeated by a kangaroo. Why? Because their system was designed in Sweden and originally trained and tested on elk, deer, and caribou \u2014 the native species \u2014 and wasn\u2019t quite ready for a marsupial suddenly interfering in a road test.<\/p>\n The takeaway: Effective pattern recognition in the transportation industry requires varied, representative data. Obtaining it, however, may not always be immediately feasible or cost-effective.<\/p>\n But you are not alone in your battle for better transportation data. Private companies and legislators worldwide are working hard to provide access to better public data sources, standardize data formatting, and open data API integrations<\/a>.<\/p>\n For example, the US Department of Transportation<\/a> has several cool initiatives underway:<\/p>\n Read also: Best Computer Vision Applications<\/a><\/p>\n <\/div> \n <\/div>\n When we talk about artificial intelligence in transportation industry, people with a technical background often think of algorithmic methods, whereas most other people imagine use cases.<\/p>\n AI as a term often lacks substance. Is Google autocomplete AI? Yeah, it is as of late. Is it the same AI that powers Alphabet\u2019s Waymo self-driving vehicles? Nope; the methods are different.<\/p>\n Artificial intelligence in transport system is a collection of supervised, unsupervised, and reinforcement learning methods for developing (self-)learning systems:<\/p>\n The common machine learning techniques in the transportation industry are:<\/p>\n The use cases of neural networks in transportation engineering alone are plentiful \u2014 transportation demand forecasting, predictive maintenance of transport infrastructure, driver behavior monitoring, and more.<\/p>\n Transportation software development<\/p>\n That\u2019s a lot of choices, right? But every option comes with some inherent limitations. Let\u2019s take ant colony optimizer algorithms. These were found to be effective for developing better routing of public transport and improving passenger pick-ups on popular ride-sharing platforms.<\/p>\n But scaling \u201cintelligence\u201d in transportation is hard. Researchers have found<\/a> that ACO algorithm speed drops significantly when the number of iterations (e.g. the number of optimized routes) grows.<\/p>\n
\n<\/p>\n
\n<\/p>\nExamples of the use of AI in transportation<\/h2>\n
Self-driving vehicles<\/h3>\n
<\/div>\n\t\t\tTraffic management<\/h3>\n
Predictive maintenance<\/h3>\n
Drone taxis<\/h3>\n
How to safely infuse benefits of AI in transportation: a roadmap<\/span><\/h2>\n<\/div>\n
Develop a data management strategy<\/h3>\n
\n<\/p>\n\n
\n<\/p>\n\n
Understand the advantages and limitations of different AI methods<\/h3>\n
<\/p>\n\n