Jun 13, 2024

Reinventing the Wheel: How Transport-tech is Revolutionizing Travel and Mobility

Michael Sable

ometimes, driving just plain sucks. In a country like the United States with vast distances and decrepit infrastructure, the general process of transporting goods and people is surprisingly inefficient, expensive, exasperating and all too often dangerous as myriad human foibles combine to make the experience one that is increasingly dreaded by many Americans. 

That so many of them must travel long distances due to the suburbanized model of life that was adopted after World War II makes the problems intrinsic to transportation even more acute and compelling. Indeed, after housing expenses, transportation is the second greatest cost to consumers as even though cars are only driven 4% of the time, the average annual bill for US drivers is $8,000.

Researchers at the Rocky Mountain Institute estimate that finding more effective ways to consume transportation services could result in approximately 50% to 80% less driving and tremendous benefits in terms in terms of reduced energy cost and impact on the environment. Implicitly aware of this reality, burdened with student loan debt, and tired of navigating the congestion required to live in the suburbs, more and more young Americans are abandoning the long-held dream of owning a car in favor of alternative technology-enabled transportation models like Uber, ZipCar, and the like. 

Innovation in these new businesses is driven by entrepreneurs with the support of venture capitalists. A new industry—transporttech—has emerged that is radically altering how people and goods move around the country and around the world. While it won’t be a complete salvation, artificial intelligence and related technologies are the disrupting and enabling force that is making it possible to reinvent transportation in powerful ways with palpable benefits for everyone. Just as buildings and the systems within them are becoming part of the Internet of Things, transportation systems and their component agents—cars, trains, and planes—are the vanguard for an emerging Internet of Moving Things that will make travel a more context-aware and personal experience than ever. The implications are global and financially lucrative given the vast size of the transportation market.

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Transporttech is the use of technologies such as artificial intelligence, software, sensors, 5G, the Internet of Things, etc.—to make transportation faster, safer, more efficient and less costly. By the end of Quarter 4, 2021, venture capital investors had poured $97.7 billion into transporttech startups which was a record that represented an 81.9% year-over-year increase. Part of the attraction of the transporttech market is that there are so many different lucrative investment opportunities within it. 

For example, technologies that can enhance the efficiency of transport operations are in high demand so software and related technologies which facilitate improved route planning, load balancing, asset utilization, fleet management and the real-time tracking and monitoring of both goods and transportation vehicles can generate enormous returns on investment. A single digit enhancement in efficiency can be enormously beneficial to users. Likewise, safety and security are critical. 

Driving can be dangerous. Tools to facilitate driver monitoring, improve vehicle maintenance and inspection, prevent accidents, and ensure compliance with safety standards and regulations are also highly sought after in the transportation market. These tools can even be sold to car insurance companies that need data to assess individual driving behavior so they can generate customized risk profiles. Transportation is a leading cause of greenhouse gas emissions so technologies that reduce fuel emissions and enable smart traffic management as well as alternative fuel adoption will have an enormous public benefit while reducing costs for end users. 

This has been further catalyzed by the growth of the electric vehicle market. Few of these manufacturers are completed vertically integrated so they often work with component suppliers in the transporttech space to acquire all of the related technologies required to build their vehicles. Another factor is that cars and other vehicles are rapidly upgrading the quality of the digital experiences within them. This has generated demand for audio and haptic tools, mobile booking and payment options and the same personalized digital technologies that we use in our domiciles. 

We now talk to our cars and an entire ecosystem of transporttech companies is emerging to provide these technologies. In addition, through the mass deployment of artificial intelligence, our vehicles are learning about our behaviors and adapting to us so as to improve how we use them. It is a brave new world and it is all being driven by the maturation of the various technologies intrinsic to transporttech. Artificial intelligence is the most important of the technologies that are attracting venture capital interest in transporttech so a deep dive into its impact is essential.


The first aspect of the transportation industry to be thoroughly disrupted by technology is taxi cabs which are the most archaic model of mobility on demand. Getting a taxi can be a genuinely frustrating experience. Cab drivers won’t necessarily be in your vicinity or be willing to drive to your neighborhood; they play games with fares by deliberately choosing the longest route; they may not know where they are going; and all too often, they may not be willing to pick you up because of the color of your skin. 

In short, taxi cabs are an industry rife with the kinds of inefficiencies and poor customer experience that information technology has been ruthlessly effective in taking advantage of. It is therefore not a surprise that just as Blockbuster, which made a disproportionate share of its profits from late fees, was upended by Netflix’s ability to eliminate those unnecessary costs while enhancing the customer experience through vast selection and personalization, so too companies like Uber and Lyft are using AI to mitigate the aforementioned inefficiencies and create a more personalized model of mobility on demand. 

The company at the leading edge of disrupting mobility on demand is Uber. Uber is not merely a ride sharing app. It is an artificial intelligence company. Its business model, growth and long-term viability are predicated on monetization of the information that it gathers about where people and things are transported. Uber uses artificial intelligence algorithms and its database of drivers to match passengers asking for a ride with the driver best suited to provide one based upon their location and rating. 

It also uses those algorithms and predictive modeling along with GPS and street data to calculate fares automatically based upon the estimated time that the journey should take not the distance covered. This is an important part of why the service is so popular: you know you will get a cab and you know what it will cost. Pricing is dynamic as Uber’s “surge pricing” technology infrastructure monitors traffic conditions and journey times so that the price of a journey changes in real time in response to real world conditions; and importantly, drivers are in a position to know when demand for transportation is high—so they have a much better chance of obtaining a fare—versus when demand is low so it is better to do something else.


Artificial intelligence is also playing a leading role in the evolution of the electric car industry that is poised to become a larger share of the automobile market in coming years. Tesla, the leading electric car manufacturer, is using AI to provide its customers with the personalized customer experience that it regards as critical to its long-term competitive advantage as more players enter the market.  Since the company has no dealer network, it interacts with its customers directly. 

To make sure that there is no barrier between itself and its customers, and to immediately gather as much data on them as possible, each car itself is fully instrumented with an array of sensors and analytics tools by default so that data is constantly being mined and wirelessly transmitted back to Tesla for analysis which the company then uses to iteratively analyze, anticipate and correct problems in real-time and predictively maintain its complex product. 

The Tesla S is the most connected car on the market and the vehicle is like a mobile device as the company’s engineers are able to collect data and then send software updates and auto fixes to the car over the air in real time. There is no need for AAA, Tesla does it for you. The implications are profound. As noted earlier, with upkeep, fuel, and insurance, the annual cost of a vehicle is considerable. 

Despite the higher initial cost of its cars, Tesla’s harnessing of the power of artificial intelligence is giving its customers the ability to reduce the first two costs which may add up to an overall lower cost of ownership for the lifetime of the vehicle. This interactive relationship also means that Tesla’s customers are becoming its partners in each successive design of its vehicles as their aggregated data which is collected while they are using the product is leveraged to identify flaws and improve each version of the car.


Self-driving cars or autonomous vehicles are the next big thing in transportation. As consumers become more comfortable with the joint models of ownership intrinsic to the sharing economy and the dream of owning a car is outweighed by the hassles and costs of doing so, the advent of self-driving vehicles owned by manufacturers or mobility on demand companies could reduce vehicle ownership by 50% and annual auto demand by 40% over the next 25 years. 

This is why GM has invested $500 million in Lyft to build an “autonomous on-demand network” of vehicles. To survive in an era of reduced demand for car purchases, auto manufacturers will have to enter the mobility on demand market by helping to develop autonomous vehicles as a revenue-generating service. Aside from their potential as a financial boost to the bottom lines of corporations, self-driving cars have a lot to offer. Most aspects of why car crashes occur are rooted in the flaws of the human beings that drive them. Not every driver is sufficiently skilled or experienced. 

Human beings get tired, they age and their capacity to control the vehicle declines accordingly. And far too many people drive under the influence of drugs and alcohol. A self-driving vehicle has none of those problems but it does have a major problem: it will operate in a context in which the vast majority of vehicles are operated by human beings. 

Ironically, this is why self-driving vehicles have experienced a crash rate that is twice that of those manned by human beings. Self-driving vehicles obey all traffic laws and play it safe but in the real world sometimes the most efficient, best course of action is to violate the rules because if you don’t, you create congestion or can become a victim in contexts in which human drivers are prone to behave aggressively in spite of the law. 

Despite the advantages of automation, for self-driving cars to become a viable technology, they must be able to use artificial intelligence to be personalized—to be able to interpret and adapt to likely human behavior in a particular situation based upon a huge, iteratively updated body of knowledge about context, environmental conditions, as well as norms of conduct for a specific route or location. This is a massive AI challenge as it highlights a key issue: What use is a car that always obeys the rules if the result is more crashes? 

The companies that succeed in this space will likely not be those that are the most successful at automation but rather those that use AI to understand how people actually behave and what the personal preferences of their passengers and those around them are. Relatedly, autonomous vehicles will likely have to be supported by an elaborate data infrastructure that the public may have to invest in. 

Aside from the sensors and wireless infrastructure within the vehicle, self-driving cars will have to be able to harness data from cameras and other IOT devices throughout the city so that they can anticipate, adapt and react to pedestrians, accidents, weather, traffic, road conditions, and the like. Without that real-time data, self-driving vehicles won’t have the intelligence required to maximize their utility. Increasingly, data infrastructure will be as important to urban transportation as physical infrastructure. This will create opportunities for the startups that can develop and provide that data infrastructure and these entrepreneurs will need the financial support of venture capitalists.


One of the reasons that the transporttech is attracting so much investment is that how we travel, particularly on the ground, is so unsafe. 94% of car crashes are related to human error. Particularly in the rapidly growing markets of the developing world, road traffic deaths are a major concern.

A number of transporttech startup are working to address this problem. One of the most prominent is Mobileye which develops vision-based advanced driver assistance systems that provide warnings to help prevent or mitigate collisions. The company’s technology is being used by Tesla in its Model S cars. Specifically, Mobileye’s technology gives drivers an alert before a collision when someone enters their blind spot. Increasingly, the company is focused on using the data that it gathers about where collisions are occurring or are likely to occur and then sending those alerts or near alerts back to cloud. 

The data is then analyzed and a map is created that demonstrates where all the alerts are happening. This can serve as a tool for urban designers and infrastructure planners so that they can identify the changes that need to be made in their cities to reduce collisions in the future. According to Prof. Amnon Shashua, CTO of Mobileye, “You are moving to design that is not based on trying to pre-think what will happen but is based on real data.” That data is not static but is organic as it is being consistently updated in real time as more and more is constantly being gathered from the vehicles that are out in the real world engaging with the physical environment.

Another threat to public safety is distracted driving. Amazingly, distracted driving, especially from those trying to multi-task with a cellphone, accounts for 10% of all traffic fatalities each year. A startup called Driversiti is endeavoring to use AI to ameliorate this problem. The Driversiti app uses sensors in a mobile phone to gather data about driving performance and conditions and then analyze that data along with specific events and behavioral anomalies to create a record of crashes. Uniquely, the company maintains a database of road conditions and the app tracks car telematics—speed, acceleration, deceleration, swerving, etc. 

This data can be used to warn drivers of dangerous routes and suggest that they reroute; as well as sense the proximity of unsafe drivers and those evaluated as driving distractedly. Those drivers deemed to be engaging in unsafe behaviors can be held accountable through higher insurance premiums or warned via the Driversiti app. The company has found a way to use cellphones, which are a major cause of accidents, as part of the solution to the problem. However, the leading AI solution to distracted driving is TrueMotion’s behavior-based insurance platform which has been embraced by leading insurance companies such as Progressive and Traveller’s. 

The company’s software and smartphone sensor solution which runs in the background without user interaction leverages machine learning and signal processing techniques to assess both trip analytics (mileage, acceleration, hard brakes, weather and high-risk areas, etc.); and if a driver is distracted—how the cellphone is being used during driving so that a personalized score can be generated that assesses the driver’s risk profile along with an insurance discount recommendation. The company asserts that it can lower claim costs by 50% and the fact that Progressive has chosen TrueMotion to power Snapshot, its industry leading mobile usage-based insurance platform suggests that there is some validity to its arguments.


The movement of goods whether via ground, rail or in the air is also poised to be dramatically improved by transporttech. Freight is a critical part of the customer experience. How soon we can get something is a fundamental part of the decision to order it online and the companies that transport freight are as much a part of greenhouse gas emissions as passenger vehicles. 

The implications of improving freight are therefore multi-dimensional and profound. According to GE’s Paul Rogers, the strategic use of data can save $27 billion over 15 years by eliminating system inefficiencies in freight rail operations. The biggest of these system inefficiencies is waiting time as 2/3 of the typical time in a railcar trip is spent doing nothing except waiting for a clear track. 

What a tremendous waste of energy, time and manpower! By using data about track conditions and congestion bottlenecks from satellites and sensors as well as analytics designed specifically for freight, it becomes possible to dramatically reduce that downtime and perfect the routing of trains and the products they transport. Significant bottlenecks like changes in address or preferred pickup location can be conveyed in real time so that the not insignificant time spent identifying where the goods should be delivered can be reduced. 

Just as consumers increasingly use data to make decisions on whether it is best to walk, bike or drive to a location, it is becoming possible for freight companies to decide the best mode of transport and whether or not to consolidate shipments for that last leg of the journey to the customer. The savings to companies and the environment as well as the benefits in terms of customer satisfaction are immense. 

In addition, there are important risks to be mitigated. In-transit stock is still the inventory of the producing entity until it is delivered to the consumer so using sensors to track it is important to avoid losses due to theft or accident. Freight companies are increasingly aware of the benefits of artificial intelligence and are embracing it. For example, US Xpress has 1,000 sensors in each of its trucks that are used to monitor speed, driver behavior, and provide data for preventive maintenance. In 2011, through the use of data from their operational delivery systems, UPS was able to reduce the distance traveled by its drivers by 30 million miles and the carbon dioxide emissions produced by 30,000 metric tons. A reduction of one mile per day per driver can amount to a savings of $50 million annually in fuel for a company like UPS. 

The impact on safety and risk reduction was also great as the company was able to avoid accidents by limiting turns at intersections and other locations likely to cause mechanical problems for its vehicles. Similar benefits can be reaped from the application of AI to air transport. According to GE’s Beth Comstock, the hundreds of sensors on a jet engine generate 1 terabyte of data in an average flight. That data can be used to help create a flight plan that uses the least amount of fuel or better navigates the increasingly volatile weather that will characterize air travel more and more as the impacts of climate change become more pronounced.

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