Investor Presentation • Jun 17, 2021
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INDEPENDENT EQUITY RESEARCH
June 17, 2021
Delay in closing deals amidst the global COVID-19 crisis overshadowed the company's progress; an Increase in the company's R&D spending; We update the price target to NIS 15.8
Axilion is an artificial intelligence (AI) software company that develops AIbased systems to better manage traffic mobility in cities, thereby reducing their carbon footprint and improving urban traffic safety.
Market—2020 was a breakthrough year for cloud-based AI. We anticipate 2021 being the year in which AI systems will be deployed globally, utilizing the new infrastructure built into data centers. This will attract more investments in the coming years.
According to Frost & Sullivan, the global AI market is expected to grow to US\$386.1 billion by 2025, at a staggering CAGR of 19.7%. The rapid growth in the volume of data being generated, along with the increasing deployment of cloud-based computing platforms, is fueling AI adoption across various industry verticals, including automotive, healthcare, retail, telecoms, transportation, etc. Axilion is well-placed to address the emerging needs of smart mobility management systems.
Strategy—Axilion plans to leverage its AI capabilities to reduce the carbon emissions of the transportation sector within cities and charge cities to manage its traffic patterns. Axilion's solution utilizes Microsoft Azure as a strategic partner for its products. Axilion is currently in the stage of technological feasibility and building the business model. Also, there is a certain risk associated with the commercialization of its solutions; At this stage, we examine the company's value according to similar transactions and the company's state.
We update the price target and estimate it to be in the range of NIS 14.2 to NIS 17.4, with a mean of NIS 15.8.
Axilion 17.06.2021
We identify three focal points that might have hampered the company's progress in recent months:
In conclusion, in the past months, the company's technological and business progress has not met the forecast.
In light of the above we update the price target of the stock to NIS 15.8.
A significant share of global fossil fuel-based energy generation goes to the transportation sector. The combustion of fossil fuels, such as gasoline and diesel, releases carbon dioxide and other greenhouse gases (GHG), causing adverse environmental impacts, such as global warming and air pollution, that can result in respiratory illness in humans. Climate change due to global warming also has other, far more serious, consequences, such as disrupted monsoons (threatening the global food supply chain) and increased occurrences of natural disasters, such as drought. In 2020, in the United States alone, GHG emissions from the transportation sector accounted for 28% of total U.S. GHG emissions1 , making it the largest contributor to U.S. GHG emissions.
Driven by stringent environmental norms and increasing environmental awareness, industry participants across the globe have begun adopting technology solutions that enable them to adhere to zero-emission protocols. Electrification of the transportation sector is considered to be an important stepping stone towards a sustainable transportation sector; however, the electrification process entails its own cost and infrastructure-related challenges. Another approach towards reducing the carbon footprint of transportation networks is establishing congestion-free road networks and increasing public transportation utilization, as the carbon footprint per person traveling via public transportation is much lower than that per person traveling via private vehicle.
The traditional method of building more flyovers, highways, roads, and underpasses is costly and timeconsuming. An alternative solution, which is derived from the idea of Industry 4.0, or the fourth industrial revolution, is the increased automation of traditional industrial practices using smart and modern technology solutions. This entails developing highly adaptive and AI-powered smart traffic management systems capable of autonomously managing the traffic flow and prioritizing fixed-schedule vehicles (primarily public transportation vehicles). There is an urgent need for a traffic-management system capable of accurately predicting traffic patterns to determine the optimum traffic light sequence. AI and deep reinforcement learning technology are ideal candidates to enable such smart traffic management systems.
1 Source: United States Environmental Protection Agency
Technology for Autonomous Mobility Optimization Saving time and resources, transforming the transportation network without costly infrastructure changes
Deep Reinforcement Learning
Utilizing an AI Mobile Edge Camera, Axilion's technology solution (X Way Suite) is able to capture the roadtraffic network and convert the collected data into actionable insights via X Way Suite's AI cloud services. The idea is to leverage the data collected from AI-based cameras via the proprietary trained neural network to determine the optimum traffic signal schedule across the network.
X Way Suite's advanced algorithms continuously analyze the incoming data from dashboard cameras and, in parallel, simulate the entire city's transportation network via a digital twin, where solutions such as deep reinforcement learning AI technology are used to run multiple tests and determine the most efficient traffic signal schedule for multiple intersections. Data collected from the cameras are streamed through Microsoft Azure's IoT hub, where Azure Edge's encryption technology is utilized for data protection and enhanced cyber security.
In addition to the above, the developed system leverages the fixed route of the public transportation system and onboard cameras to dynamically track the traffic pattern on a real-time basis and to change traffic light signals, prioritizing the movement of public transportation to reduce travel time.
The digitization of public transportation schedules and coordination with traffic signals creates a far more efficient public transportation network, where users can track the entire schedule from mobile apps or screens at bus stops, and plan their travel accordingly. In the long run, faster and more efficient public transportation networks aid in changing commuter preferences toward public transportation over private vehicles, thereby directly reducing the carbon footprint of the transportation network.
We view Axilion as a great opportunity for those seeking to invest in sustainable and smart mobility and specifically in a primary element of traffic flow management.
Axilion (TLV: AILN) hereafter "the Company" and/or "Axilion" is a publicly-traded AI software company headquartered in Israel and has offices in Israel, US, UAE, and Europe. The company focuses on developing AIbased software systems to better manage traffic mobility in cities, thereby reducing the carbon footprint and improved safety. In recent years, the company has been successful in implementing as well as in piloting its software solutions across multiple geographies: Israel, France, Switzerland, the United Arab Emirates, the city of Jerusalem, and the United States. In Israel, US, and Europe alone Axilion's solutions have been deployed at more than 1,000 traffic intersections.
The company's vision has primarily been towards leveraging AI capabilities to reduce the carbon emissions of the transportation sector. Traffic congestion is a challenge that contributes to millions of dollars in lost time and waste fuel across the globe. Further, the issue of traffic congestion is more frequent in urban centres than in rural settings primarily due to a larger number of private vehicles, commercial vehicles, and sometimes also due to heavyweight vehicles. Axilion has developed its X Way Suite specifically to address the challenges faced by urban centres with minimum required investment. The X Way cloud services are based on more than 10 years of the company's experience in developing and implementing traffic light program planning software - TransEm (the company's previous generation of products), which is used by more than 100 traffic engineers in Israel and around the world.
The company deals via Microsoft's existing agreement with government bodies where the Axilion X Way suite is provided as an add-on solution. For every camera installed, Axilion generates about USD 150-600 per month of Azure credit depending upon data requirements. Further, Axilion charges the city for managing the traffic patterns, whereas it takes a share of 50% of the cost incurred to the city for utilizing Microsoft Azure.
Additionally, the developed system is hardware agnostic and in the coming years is likely to be integrated with multiple types of sensors, which might be already present in a city's transportation infrastructure (stand-alone pedestrian traffic signals, speed gun detectors, and others) to ensure that at the least all the vehicles with a predefined route faces as fewer red lights as possible. For instance, the X Way Suite has ensured that the Light Rail in Jerusalem always passes through the green light, all the while taking pedestrian safety under
consideration. The end result was that the Jerusalem average commute time drastically dropped to 42 minutes from the previous 80 minutes, headway reduced to 6 minutes and the ridership increased by as much as 387.4%.
As sensors and cameras continue to enhance their data collection capabilities, AI has taken a central role in understanding patterns and enabling data driven decisions to maximize operational efficiency and accuracy. With innumerable successful trials, pilots and growing number of commercial implementations, AI based smart traffic management systems are being seen as an inseparable element of the urban environment. In fact, futuristic algorithms such as RL (reinforcement learning) are also finding adoption in smart traffic management systems, imparting higher autonomy and adaptability to these systems.
Axi's Deep Learning AI-based traffic management system has been extensively researched and refined by testing in multiple geographies.
In addition, the company has been actively collaborating with research institutes such as Technion, Tel Aviv University, and the Innovation Authority for continuous development in the X Way Suite.
Unlike autonomous vehicles which analyse the data from multiple cost intensive and in-vehicle hardware units such as LIDAR, RADAR, and other processing units, the X Way suite optimizes the traffic flow pattern without physical infrastructure changes and without any expensive hardware installation. Only a dashboard camera (equipped with GPS and a wireless connection) installation is required which allows for real time video analysis. In general, about 40 – 50 operational cameras are required for mid-sized city intersections (about 24 traffic signal arterials).
Axilion technology decreases average commute times, improves pedestrian safety, reduces air pollution, decreases stoppage times at red lights, drives public transit use, improving quality of life.
Deep Reinforcement Learning AI Technology
Maximize Existing Traffic
Infrastructure
Real-Time, Adaptive Control
Smart & Scalable Hardware Agnostic Efficient Simulation via Digital Twin
The company has three SaaS service offerings:
Identification and analysis of the traffic light network and its efficiency by collecting and analysing video footage captured by cameras installed on moving vehicles as well as cameras installed on intersections. X Way Pulse analyses the information via deep learning networks designed to understand the video for an in-depth understanding of the driving behaviour from the driver's perspective.
In order to effectively manage the large amount of incoming data and to analyse it with foremost efficiency, Axilion has smartly optimized the incoming data flow by reducing both the frame rates as well as image resolution of incoming video feeds making the data collection and analysis process more efficient.
X Way Pulse generates actionable insights for not only traffic intersection points but also for entire routes and transportation networks, enabling granular visibility into network activity and easy identification of root causes behind traffic issues.
By utilizing all the data gathered by X Way Pulse, X Way Twin creates a digital twin or a digital replica of the entire city's transportation network in the cloud. The digital twin model is then used to run multiple simulations or test multiple "what-if" scenarios for deciding the optimum traffic light schedule with the best possible outcome. The digital twin utilizes the origin-destination matrix or the OD matrix which allows the digital twin to identify where vehicles are going and where they are coming from, to identify heavy load junctions so that most critical traffic intersections can be tweaked for optimization.
Further, the X Way Twin also provides granular insights about the city on a real-time basis by leveraging the data collected by X Way Pulse. Data points such as traffic signal progression (queue length, queue time, average vehicular speed through intersections), pedestrian density, two-wheeler density, and abstract data points such as pedestrian face mask usage is also available.
X Way Neural utilizes an AI-driven algorithm for automating the traffic light schedule as per several parameters: traffic volume, vehicle types, road capacity, pedestrian presence, driving patterns, and weather conditions amongst other factors.
X Way Neural is essentially an addition to the X Way Pulse Monitoring system and X Way twin digital modelling system. X Way Neural uses deep reinforcement learning techniques to identify the most optimum traffic light pattern across multiple city intersections to identify the one with the highest average traffic intersection vehicular speed, further the traffic pattern is beforehand tested on the digital twin and the performance is evaluated for proposing a new mobility plan.
X Way Pulse Monitoring system uses proprietary AI video technology to capture safety hotspots and probable traffic issues and performs a root cause analysis. The data is then sent across Microsoft Azure Cloud on a realtime basis, where the incoming data feed is redacted (faces and number plates are blurred) per General Data Protection Regulation (GDPR) compliance. X Way Neural constantly alters the digital Twin Model parameter and proposes the most optimum changes required for the best possible outcome.
Further, the system is also equipped with deep reinforcement learning AI, which means the system is continuously improving and is always learning. For instance, for a blocked intersection the suite captures and understands the issue, recommends an optimized plan, and then analyses the result, until finally a similar situation arises and the suite leverages the previous analysis to generate more valuable suggestions.
Haifa was one of the first cities to adopt Axilion's X Way Suite, and the city upgraded its Metronit bus rapid transport (BRT) network by adopting Axilion's X Way Suite for 200 traffic lights along the bus route, for continuous and automated optimization of the traffic flow. In a short period, the BRT network witnessed roughly a two-fold increase in ridership (115,000 travellers from a previous 60,000 travellers per day), faster commute (average traveling speed of buses increased from 20 kilometres per hour or kmph to 26 kmph, whereas the average travel time decreased from 73 minutes to 58 minutes), less congestion, and enhanced pedestrian safety.
In addition to the above, there was a drop of 11% in the private vehicle utilization, an estimated annual savings of USD 7 million in operations and maintenance, along with an estimated 140,000 tons of annual CO2 emissions avoided.
The tram network in the city of Jerusalem updated its 273 intersection points by Axilion X Way suite for enhanced traffic management.
| Before | After | Benefits | |
|---|---|---|---|
| Travel time | 80 min | 42 min | 47% |
| Number of trams required | 32 | 21 | 34% |
| Tram frequency (headway) | 15 min | 5 min | 66% |
| Number of Passengers | 40,000 | 200,000 | 400% |
Artificial Intelligence has a strong potential to overcome the deficiencies in the automotive sector and provide significant benefits in terms of improved productivity and added revenue. AI acts as a crucial technology enabler in transforming every aspect of the automotive value chain starting from research and development to enhancing the car driving experience. In the automotive sector, a
major chunk of the innovations is focused at streamlining the in-vehicle driving experience, whereas just a few are focused at the overall scenario of smart traffic management.
Traffic congestion is a problem faced by both the developing as well as developed economies, where AI powered systems for managing traffic lights is expected to be the most ideal solution due to its massive data handling and analysis capabilities. AI is considered to be a promising technology solution for transport authorities to achieve rapid improvements in relieving traffic congestion, improved travel time, and improved utilization of their assets for enhanced revenue generation, productivity, and lower carbon footprint. Among the prominent players specifically catering towards technology solutions streamlining traffic flow across the city are: Nexar (Israel), Mobileye (Israel), Moovit (Israel), C3 AI (United States), Google WAZE (Israel), NoTraffic (United States). Rapid Flow Technologies LLC (United States), FLIR Systems Inc. (United States), Alibaba Cloud City Brain (China), Telefonaktiebolaget LM Ericsson (Sweden), and AlndraLabs (India) amongst others.
The global artificial intelligence (AI) market is expected to grow from US\$157.2 billion in 2020 to US\$386.1 billion by 2025, at a CAGR of 19.7%. The rapid growth in the volume of data being generated along with the increasing deployment of cloud-based computing platforms is fueling AI adoption across various industry verticals including automotive, transportation, retail, telecom, healthcare, etc.
Axilion is well placed to address the emerging need of smart traffic management systems. The company's X Way Suite combines data analytics (data gathered via AI based cameras), digital twin, and a deep learning AI technology platform, to achieve a highly autonomous and adaptive traffic management system.
The AI market includes revenue from three technology sub-segments – Software, Hardware, Services. Software is the largest AI technology segment delivering close to 80% of all AI revenue.
The global computer vision market is expected to grow from US\$14.1 billion in 2020 to US\$20.2 billion by 2025, at a CAGR of 7.5%. The increasing application of computer vision technology in the automotive and transportation industry is one of the key growth drivers. The emergence of self-driving cars equipped with advanced cameras and LiDAR sensors leverages computer vision to have a safe ride.
Axilion's proprietary dashboard cameras are equipped with GPS and wireless, which enables a real-time data transfer. Through Microsoft azure, Axilion uses its video analysis tool for data collection and analysis for an adaptive and autonomous traffic management system.
The global AI software market in transportation industry is expected to grow from US\$2.1 billion in 2020 to US\$4.7 billion by 2025, at a CAGR of 17.5%.
Deep learning holds the highest share of around 67% in the global AI transportation market 2020. Deep learning algorithms analyze the hidden patterns effectively in huge volume of data and assist the transportation industry to overcome the traffic issues.
Further, deep learning is a fully automated technique which offers more accuracy when compared with the traditional methods. Computer vision accounts for 19% share of the global AI transportation market and it is primarily used to enable the traffic management system to accurately capture images and analyze them under a wide variety of conditions such as bad weather and lighting, tracking vehicles at high speed and extremely congested traffic jams.
There is a great demand on global markets to reduce carbon emission/carbon capture solutions. All pathways that limit global warming to 1.5°C project the use of carbon dioxide removal on the order of 100-1000 Gigatons over the 21st century. It is projected that CO2 removal with the right policy support will become the world's biggest market. The climate math propounded by various agencies suggests a need for 10-20 Gt CO2 per year. At an average cost of US\$50-100/ton for capture and removal, that creates an industry at least thrice as large as the current size of the Oil and Gas industry.
Tackling the carbon challenge is complex. The world emits an average of 52 Gt CO2e/yr. However, the cost of capturing that carbon is enormous
To achieve the hypothetical net-zero, viz. to remove almost the entirety of 50+ Gt CO2 of annual emissions, the world has to spend at least \$85 trillion annually (i.e., ~6% of the world GDP). If the world achieves a sub \$50/t cost of carbon capture, the world will still ~2.5% of its GDP but makes the entire process more viable.
As discussed earlier, Axilion's solutions help to reduce carbon emissions significantly. In our view, the company could receive a premium for its services due to its carbon-reducing effect.
We based our valuation on a top-down, market benchmark analysis. Observing Axilion market positioning we identified 89 similar companies in terms of activity (AI & ML) and growth stage and excluded outliers from our sample. The average post-money valuation for these similar deals is \$453.7M (See appendix 1 of our initiation of coverage report for the full data set). Below we present a sample of the 10 top AI deals:
| Company Name |
Description | Deal Date | Deal Type | Pre money Valuation (million, USD) |
Post Valuation (million, USD) |
Country |
|---|---|---|---|---|---|---|
| Pony.ai | Developer of an autonomous driving technology intended to facilitate manufacturing of automated vehicles. |
07-Feb-2021 | Later Stage VC |
4,933 | 5,300 | United States |
| C3.ai | C3.ai Inc is an enterprise artificial intelligence company. | 09-Dec-2020 | IPO | 3,375 | 4,026 | United States |
| Luminar | Luminar Technologies Inc is an autonomous vehicle sensor and software company. |
02-Dec-2020 | Reverse Merger |
2,994 | 3,400 | United States |
| SentinelOne | Developer of automated cybersecurity software designed to protect devices and servers against malware and threats. |
11-Nov-2020 | Later Stage VC |
2,733 | 3,000 | United States |
| Verafin | Developer of cloud-based fraud detection and anti-money laundering software. |
11-Feb-2021 | Merger/ Acquisition |
2,750 | Canada | |
| Sumo Logic | Sumo Logic Inc is a software company. | 17-Sep-2020 | IPO | 1,846 | 2,171 | United States |
| Farmer's Business Network |
Farmers Business Network is a provider of a farmer-to farmer agronomic information network. |
03-Aug-2020 | Later Stage VC |
1,600 | 1,800 | United States |
| Lookout | Developer of cloud-based security software designed to detect mobile threats and improve mobile security. |
01-Mar-2020 | Later Stage VC |
1,701 | 1,751 | United States |
| Harness | Developer of a delivery-as-a-service platform created to simplify the software delivery process. |
06-Jan-2021 | Later Stage VC |
1,615 | 1,700 | United States |
| Verkada | Manufacturer of enterprise security cameras designed to offer enterprise physical security services. |
29-Jan-2020 | Later Stage VC |
1,520 | 1,600 | United States |
We also view AI and Mobility tech firms since 2020 and found that the median post money valuation is \$526.2M, as we present below some of the companies2 :
| Post Valuation (Median): \$526.20M Technology Services |
|||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| POON. WSIMPIO ANDREWS CONDUCT CONDUCT OF A CALICAR PARAMENT ZONE AND THE REAL PRIMER PARAMENT PRODUCT OF THE CONDUCT OF THE VISIT OF THE VISITS BRAIN SALES OF A CONDUCT OF THE VISITS OF A CONDUCT OF THE VISITS OF A CONDU | |||||||||||||||||||
| DE 2011 V 900 SOME SERVICE DE 10 CENT ANNO 1999 NO CONSTITUTION OF STRAIGHT AND SERVICE SERVICE SERVICE SERVICE |
Axilion is now after the technological feasibility phase and before the significant sales phase of projects such as those it closed in Haifa and Jerusalem. Therefore, at this stage, we examine the company's value according to similar deals. Based on the aforementioned data and analysis, we evaluate the company's equity value at NIS XX million; we estimate Axilion's price target to be in the range of NIS XX and NIS XX, with a mean of NIS XX.
2 Source: Pitchbook (showing 38 of 468 companies).
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