It may sound odd, but I was once called an AI in transportation skeptic, despite being a rather vocal advocate for this technology. But let’s be honest: it does feel that there’s more AI aspiration than action in our industry. Yes, self-driving vehicle pilots are being held all over the world. But does it mean I get to ride a self-driving shuttle to work next year? Probably not.
When it comes to self-driving vehicles, we have a clear ranking system of Level 1 to Level 5 autonomy. But what about other transportation systems and software solutions? How do you define “intelligent” for them, and what does it take to apply machine learning solutions for transportation? Let’s strategize.
Artificial intelligence in transportation industry: why it’s a promising but complex relationship
As we’ve written before, AI in urban mobility, logistics, and fleet management is highly anticipated and already deployed to an extent.
For instance, 65% of logistics company leaders name AI as an important technology for the next three to five years. Also, as of 2018, one in four public transport managers already use AI for real-time operations management and customer analytics.
Singapore has a national AI strategy promoting the creation of “intelligent freight planning” by 2030.
Intelligent freight planning
Source: Smart Nation Singapore — National Artificial Intelligence Summary
Successful machine learning in transportation system pilots have been done across countries, too — mainly demonstrating pilot runs of self-driving electric pods, AI-regulated traffic light scheduling, and smart road infrastructure.
Yet despite successful reports for years, the number of successful pilots drastically outweighs the number of commercial AI for transportation solutions.
Why is that? Because implementing and scaling AI deployments is a difficult balancing act of benefits vs concerns and tradeoffs.
Examples of the use of AI in transportation
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.
The fact is that AI has already changed the transportation industry. Let’s look at the most illustrative and important use cases that prove this.
Self-driving vehicles
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’s receiver in real time. This is what a simplified machine learning for transportation workflow for a self-driving car looks like.
In Tokyo, autonomous vehicles are already allowed to operate 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.
Just think of the 65% of all goods that are transported by truck globally. 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.
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 invade our streets but rather how quicklythey will do so.
Traffic management
Traffic management — in particular, dealing with congestion — is another good illustration of how machine learning in transportation systems is transforming the industry.
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 (e.g. for controlling traffic lights) and to individual users (e.g. route suggestions or accident notifications).
AI-driven traffic management also brings sustainability to the industry. For example, the SurTac AI solution 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%.
Predictive maintenance
Predictive maintenance technology powered by AI helps to predict vehicle breakdowns before they occur. Here’s 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.
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.
Drone taxis
A drone taxi is a stunning example of AI for transportation, probably even more exciting than self-driving cars. This year, the first airport for air taxis was opened in England, confirming that flying taxis are no longer a sci-fi fantasy.
Pilotless aerial vehicles are not only a reminder that the Fifth Element-like world is closer than we might think but also a sustainable solution to several challenges.
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’s without mentioning the benefits for drone taxi passengers, who can save hours per week by reducing commute times.
How to safely infuse benefits of AI in transportation: a roadmap
AI has the potential to solve pressing transportation problems. But as usual, execution and delivery is the real bane.
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.
But given the complexity of the domain itself and the algorithm, there’s still no one-size-fits-all path to success. Yet there are a few proven steps that could help refine your product development vector.
Develop a data management strategy
Road sensors, connected car dashboards, floating cellular data, location-based services — the transportation industry has overwhelming access to data sources and big data use cases.
Far fewer players have mature DataOps. Or even a formalized long-term data management and governance process — 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, faster, and cheaper data processing. And in this case, you can’t choose just two options — it’s all or nothing.
When assessing a specific AI for transportation use case, think if your company can:
- Obtain sufficient, variable, and labeled data sets for model training and validation.
- Establish an ethical, anonymized, representative, and secure data collection process for passenger and vehicle data.
- Enable low-latency data processing between all connected systems — IoT and edge devices, cloud data repositories, cloud-based or on-premises analytics systems.
- Apply data fusion techniques to consolidate, pre-process, and operationalize records obtained from different sources and in different formats.
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.
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 for sourcing self-driving training data from its customers’ vehicles.
IEEE recently published a classification study that ranked different types of transportation data sources by accuracy, reliability, and cost of use.
Comparison of traffic data sources
Source: IEEE — Data Sources for Urban Traffic Prediction: A Review on Classification, Comparison and Technologies
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 — the native species — and wasn’t quite ready for a marsupial suddenly interfering in a road test.
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.
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.
For example, the US Department of Transportation has several cool initiatives underway:
- Safety Data Initiative — Aimed at integrating available data on car crashes and highway designs with anonymous data from GPS devices to develop better speed regulation practices
- Waze Pilot — Tests the feasibility of using crowdsourced data from the Waze app to predict crash risks and other types of road accidents
- Computer vision tools applied to naturalistic driving data — 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
Understand the advantages and limitations of different AI methods
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.
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’s Waymo self-driving vehicles? Nope; the methods are different.
Artificial intelligence in transport system is a collection of supervised, unsupervised, and reinforcement learning methods for developing (self-)learning systems:
The common machine learning techniques in the transportation industry are:
- Artificial neural networks (ANN)
- Genetic algorithms (GA)
- Simulated annealing (SA)
- Fuzzy logic model (FLM)
- Ant colony optimizer (ACO)
The use cases of neural networks in transportation engineering alone are plentiful — transportation demand forecasting, predictive maintenance of transport infrastructure, driver behavior monitoring, and more.
That’s a lot of choices, right? But every option comes with some inherent limitations. Let’s 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.
But scaling “intelligence” in transportation is hard. Researchers have found that ACO algorithm speed drops significantly when the number of iterations (e.g. the number of optimized routes) grows.
The use of neural networks in transportation is also ubiquitous. For instance, Uber uses such algorithms to provide accurate ETA predictions for journeys, and many customer analytics solutions are powered by artificial neural networks. Yet working out the optimal combination of weights, hidden layers, and training parameters for a neural network to perform efficiently is a tedious trial-and-error process. Oftentimes, the solution ends up performing well on a small dataset, but the performance drifts and flops in the pre-production stage, forcing your ML transportation division back to square one to try something else.
The bottom line: Scaled machine learning solutions for transportation are hard to implement since no single method provides a universally plausible result. Thus, the development timeline is long and assumes some tradeoffs along the route to fast, cheap, and high-quality performance.
Focus on model explainability
One of the major constraints of AI in the transportation industry is that algorithms may be forced to make life or death decisions in dire circumstances (e.g. killing one passenger to save ten pedestrians). That’s why the question of model explainability and AI ethics is rather acute.
Before we allow our cars to make ethical decisions, we need to have a global conversation to express our preferences to the companies that will design moral algorithms, and to the policymakers that will regulate them.
The current concerns are mostly related to black box algorithms such as neural networks and reinforcement learning techniques, where the algorithms are given full rein in determining the optimal course of action.
In some cases, the algorithm may decide that imposing lower speeds in an area would be good for preventing casualties, but at the same time would lead to longer cruising for parking and subsequent traffic bottlenecks. Likewise, having an algorithm decide on the moral dilemma of letting an ambulance pass to save one patient versus keeping the road blocked for the fire crew to pass isn’t something most of us would feel comfortable with.
What adds to the complexity is that humans are controversial creatures too. A major study around the ethics of autonomous vehicles found that people in surveys said they wanted an autonomous vehicle to protect pedestrians over passengers. Yet most respondents also said they wouldn’t purchase a vehicle that was programmed this way!
One of the likely paths to ethical and sustainable AI adoption in transportation could be explainable artificial intelligence (XAI) — a methodology aimed at building descriptive AI models where developers can explain and moderate the model’s accuracy, fairness, and outcomes. In other words, XAI conveys the rationale behind the AI decision-making.
Ways to manifest and convey the reasoning behind AI decisions
Source: Accenture — Understanding Machine: Explainable AI
Many other big data analytics models are actually explainable since they are powered by programmatic instructions and statistical methods for modeling different outcomes. Data science and machine learning models belonging to the “explainable” family include:
- Linear models
- Decision trees
- Generalized additive models
- Rulesets
However, because such systems are not self-learning, they cannot propose novel (and perhaps better) solutions that an ML/DL algorithm might devise. However, the cost of poor decisions in the transportation industry can be too high. Thus, tradeoffs in the levels of black box thinking may be required to ensure that your solution is ready for the market.
Collaborate with other ecosystem partners
The transportation industry has many private and public players. Plus regulators overseeing their actions. Oftentimes, market participants choose a strategy of competition over community. And this decision leads to a host of issues:
- Low interoperability between different transportation systems
- Lack of data format, quality, and integration standardization
- Disparate and hard-to-integrate systems
- Poor access to underlying infrastructure (e.g. road sensors, 4G/5G connectivity)
But without systematic, rigorous, and joint planning efforts, scaled implementations of new technologies are hardly possible.
System interoperability is not being advanced because the market evolution is not clear. If market norms could be agreed upon, then questions such as ‘who is liable if the service fails?’ will become clearer and investment decisions potentially easier to make. In addition, Future of Transport (FoT) is advancing new collaborative approaches to working with data as both a commodity and as an infrastructure.
Interoperability between different software systems for the transportation industry — from city-wide traffic management software to individual fleet management systems — is crucial for ensuring that all vehicles and devices can effectively exchange data. Such dynamic data can then be used for optimizing transport system operations, leading to greater safety, predictability, and efficiency of the mobility experience for all end users.
Specifically, to realize the advantages of machine learning solutions for transportation, more joint effort should be aimed at standardizing data quality so that most participants agree to specifications on:
- Data structure, content, and exchange protocols
- Definitions and classification of data types
- Organizational practices for creating, exchanging, and using data
The good news is that many regulators already have working or approved frameworks in place. For example:
- ISO/TC 204 was developed for regulating the system and infrastructure aspects of intelligent transport systems (ITS).
- TC 241 specifies regulations for road traffic safety and management standards.
- ISO/IEC 27001 is an IT systems security standard.
- INSPIRE-MMTIS is an EU directive aimed at opening access to static and dynamic data categories by Member States to improve multimodal travel information services.
- Auto-CITS is an EU body promoting unified standards for autonomous driving and V2X technology adoption.
Apart from achieving compliance, a greater degree of interoperability also makes transportation industry players more competitive. By pursuing a platform business model, you can tap into new pockets of growth without launching new features. Instead, you can gain extra revenue through API monetization, new sales channels (e.g. in-car commerce or digital freight matching), or launching “as a service” offerings (e.g. a mobility as a service solution).
As you assess different examples of transportation technology, think from the partnership perspective: will investing in this use case benefit just my business, or can it open up interesting new partnerships and cross-integrations?
Treading ahead with benefits of AI in transportation, safely and confidently
AI has a future in the transportation sector. The technology’s potential has already been shown in many amazing pilots, use cases, and market-ready solutions such as Rapid Flow real-time adaptive traffic light controls, TuSimple’s autonomous trucking, and Sensible 4 self-driving buses.
However, to see more scaled deployments of AI-powered transport solutions, more groundwork should be laid today, from tech architecture standardization and operating safety protocols to fine-tuning ML/DL methods and realigning business models. AI is a complex technology to implement. So how do you plan to turn it into an ROI-backed investment rather than an ongoing expense?
Contact Intellias for a personal consultation on incorporating AI into your transportation software solution.
