Shared Mobility is the Precursor to Autonomous Vehicle Networks
Who will you meet?
Cities are innovating, companies are pivoting, and start-ups are growing. Like you, every urban practitioner has a remarkable story of insight and challenge from the past year.
Meet these peers and discuss the future of cities in the new Meeting of the Minds Executive Cohort Program. Replace boring virtual summits with facilitated, online, small-group discussions where you can make real connections with extraordinary, like-minded people.
The current hype about autonomous vehicle is accompanied by a surge of interest from shared mobility operators. Ridesharing providers such as Uber, Lyft and Didi are investing heavily into AV technology. Earlier this year, Uber announced its partnership with Daimler to bring self-driving technology to the market. Didi has opened up an artificial intelligence lab in Mountain View, the backyard of many autonomous vehicle competitors. Lyft’s collaboration with GM is well known and this month they announced an investment from Jaguar Landrover to bring autonomous connected vehicles on the road.
The buzz clearly indicates that the autonomous revolution is imminent. The engineering communities are excited about solving some of the technological challenges, which will ensure data sharing and interoperatability. Governments and cities are trying to grasp the implications of AVs on the road and provide the right regulatory frameworks. Amidst all of this excitement, we shouldn’t forget the impacts this revolution will have on people and that we will have to solve some real operational challenges.
There are three areas that define a carsharing future, in fact any shared mobility venture, and they all have to be addressed when launching and operating a service. They are the three cornerstones of what I call the “Shared Mobility Bermuda Triangle”:
- The physical space where AVs will drive or park
- The vehicles, which will be connected to the cloud, and also need to be safe, clean, and in the right location at the right time
- The members of the carsharing network who will have to subscribe to the services and become part of the program
These operational areas impact autonomous vehicle networks the same way they impact shared mobility services. This article will highlight one key challenges for each of the three areas as well as provide examples from different cities and how the local shared mobility operators have already solved them. I strongly believe that AV network providers should look to shared mobility solutions and learn from them in order to introduce AV networks in a more consumer friendly and sustainable way.
Optimizing Utilization of Physical Space: The case of ReachNow
Currently our cars are the most underutilized assets in our households. It is estimated that a private car remains unused 90% to 95% of the time. Free-floating carsharing models such as ReachNow or car2go have a higher rate of utilization and are parked at a lesser rate of about 88% to 93%. The AV revolution promises that vehicles will be driving at any given time unless they are being cleaned or repaired. Yet how do you make sure the AVs are in the right place at the right time?
ReachNow is BMW’s North American mobility service, currently offered in Seattle, Portland, and Brooklyn. ReachNow offers a free-floating carsharing service, like car2go, and it also offers a ridesharing service. Members of a carsharing service will not always want to, or be able to drive. For example, Friday and Saturday nights, after a few drinks, is a good time to use the ridesharing service. Traditionally, the vehicles of a free-floating carshare service would sit idle or be driven by drunk drivers.
Instead of adding more vehicles to city roads to offer a new taxi or ridehailing service, it makes more sense to utilize an existing fleet in a more effective way. ReachNow in Seattle is currently piloting exactly such a flexible scheme; they use their carsharing fleet to run a ridesharing service. ReachNow’s combined fleet not only increases utilization rates of their current fleet, it also learns the travel patterns of Seattleites at different times. This data will be incredibly valuable for any autonomous vehicle network because they will have to apply machine learning over large sets of travel data in order to get vehicles to the right location at the right time. Shared mobility vehicles today are already learning these patterns.
Managing a Dispersed Vehicle Fleet: The case of Eco-Service
The simple promise of AVs is similar to carsharing: you have the convenience of getting around town without any of the hassles of car ownership. The car is always available at the click of an app; no cleaning is required, no maintenance is required, and you’ll never have to find a gas station again. This sounds simple in theory, but the reality is not quite so easy. How do we make sure that shared AVs are in safe and clean conditions and at the right place at all time?
Vancouver has four different carshare providers. Two are station-based: Zipcar and Modo; and two are free-floating: Evo and car2go. When I was working as the Regional Director for car2go, we would see many issues that required someone from the fleet team to seek out a vehicle. For instance, there would never be enough vehicles in the Westend, a densely populated residential neighborhood with a lack of parking. For that reason, many residents of that neighborhood don’t own vehicles, and instead they rely on carsharing. The area is what we call a ‘hotzone’, an area where there is more demand than supply of vehicles for members of the network. Other areas always had too many parked vehicles and not enough demand, which leads to lack of parking and illegally parked vehicles. Not to mention the mundane operational challenges of dealing with cleanliness issues; members leave empty food containers or Starbucks cups, or leave behind a car full of dog hair.
How do carsharing organisations deal with all of this? Most free-floating providers such as Evo, ReachNow or car2go outsource this part. A company that is a pioneer in this field is Eco-Service. They are a service company based in Vancouver and deal with the infield fleet operations of Evo, ReachNow and Gig (AAA’s free-floating service in Oakland, CA). Eco-Service cleans the vehicles parked on the streets with a waterless solution, relocates them to hotzones, and refuels or recharges them to provide the ultimate convenience to customers. With a fleet dispersed all over the city, their work seems a bit like a Sisyphean task, especially if it also should be done in an efficient and timely way. To be more effective, Eco-Service has built special software that manages their staff in the field. Their teams are equipped with smart phones and receive tasks through a ticketing system based on the operators and customers needs.
David Welch recently published an article in Bloomberg Technology claiming that nobody has thought through the operational challenges of self-driving fleets. I disagree with that statement because infield fleet management companies such as Eco-Service already have the knowledge of how many times vehicles need to be refueled, cleaned, and to which ‘hotzones’ they need to be relocated to. Using that data or existing algorithms would provide a useful baseline for driver-less vehicle networks of the future.
Gaining Member Trust: The case of Blablacar
The most crucial element for successful shared mobility or AV services are the members. Shared mobility operators must convince the residents of their city that their convenient, safe and affordable service is trustworthy. Research by Maru/Matchbox, a global consumer intelligence firm, shows that 28% of surveyed Americans consider sharing a vehicle through Uber or car2go as unsafe. Uber is a prime example of what happens when you have to compromise on quality standards in exchange for a lower price and higher availability. The once-high standards are eroded by an increase of scary stories about Uber drivers, which erode trust in the service.
One of the key findings of Maru/Matchbox’s survey is that these incidents lead to a general lack of trust in the sharing economy The sharing economy consists of several layers, all of which need to earn the trust of the consumer: the product, the platform or brand, and – if it is a peer-to-peer service – the individual offering the service. That’s a much more complex set of relationships than in traditional industries such as retail or brokerages. How will self-driving vehicle operators ensure that passengers in their AVs are not exposed to harm or danger from others inside the vehicle?
One big player in the sharing economy in Europe is BlaBlaCar, a carpooling operator that is considered the online version of hitchhiking. BlaBlaCar has done extensive research to understand the challenges around trust and to develop trust building tools. They have come up with a trust framework called D.R.E.A.M.S which is focused on providing relevant information on prospective drivers and riders. Their research has focused on finding which pieces of information on a peer are most relevant and build the greatest level of trust.
D.R.E.A.M.S. stands for:
- Declared (photo and name)
- Rated (ratings)
- Engaged (booking box)
- Active (information on past activities)
- Moderated (evidence of verification of information)
- Social (Facebook and LinkedIn connections)
Blablacar has not come up with this framework and the technological implementation overnight. Building a trusted environment is a continuous process. With each year that went by, BlaBlaCar tested several different trust tools and developed a better understanding of the building blocks of trust based on continuous feedback from ridesharers. They were able to increase trust significantly implementing new product features such as ratings. Today 88% of their own members say that they highly trust other BlaBlaCar members that have completed full profiles.
When self-driving vehicles are introduced to the market, the operators of these fleets will have to answer how to provide a safe service and start building trusted environments for their passengers. Trust will be the new gold. I believe those who will win the battle for trust are those who learn from the existing shared mobility operators and implement their lessons learnt.
Self-driving vehicle networks share many of the same operational challenges as today’s shared mobility services. That industry already has an understanding of what people care about, how they travel in cities, and what their biggest fears are. Today’s successful mobility companies have created operational solutions for these problems. I believe that integrating the lessons learned from shared mobility services is the only way to introduce AVs in a smart, human-centric, and efficient way that will truly solve some of today’s biggest urban transportation challenges.
Leave your comment below, or reply to others.
Please note that this comment section is for thoughtful, on-topic discussions. Admin approval is required for all comments. Your comment may be edited if it contains grammatical errors. Low effort, self-promotional, or impolite comments will be deleted.
Read more from MeetingoftheMinds.org
Spotlighting innovations in urban sustainability and connected technology
In my business, we’d rather not be right. What gets a climate change expert out of bed in the morning is the desire to provide decision-makers with the best available science, and at the end of the day we go to bed hoping things won’t actually get as bad as our science tells us. That’s true whether you’re a physical or a social scientist.
Well, I’m one of the latter and Meeting of the Minds thought it would be valuable to republish an article I penned in January 2020. In that ancient past, only the most studious of news observers had heard of a virus in Wuhan, China, that was causing a lethal disease. Two months later we were in lockdown, all over the world, and while things have improved a lot in the US since November 2020, in many cities and nations around the world this is not the case. India is living through a COVID nightmare of untold proportions as we speak, and many nations have gone through wave after wave of this pandemic. The end is not in sight. It is not over. Not by a longshot.
And while the pandemic is raging, sea level continues to rise, heatwaves are killing people in one hemisphere or the other, droughts have devastated farmers, floods sent people fleeing to disaster shelters that are not the save havens we once thought them to be, wildfires consumed forests and all too many homes, and emissions dipped temporarily only to shoot up again as we try to go “back to normal.”
So, I’ll say another one of those things I wish I’ll be wrong about, but probably won’t: there is no “back to normal.” Not with climate change in an interdependent world.
I caught up with Steph Stoppenhagen from Black & Veatch the other day about their work on critical infrastructure in Las Vegas. In particular, we talked about the new Bleutech Park project which touts itself as an eco-entertainment park. They are deploying new technologies and materials to integrate water, energy, mobility, housing, and climate-smart solutions as they anticipate full-time residents and park visitors. Hear more from Steph about this new $7.5B high-tech biome in the desert.
Planning for new, shared modes of transit that will rival private vehicles in access and convenience requires a paradigm shift in the planning process. Rather than using traditional methods, we need to capture individual behavior while interacting with the systems in questions. An increasing number of studies show that combining agent-based simulation with activity-based travel demand modeling is a good approach. This approach creates a digital twin of the population of the city, with similar characteristics as their real-world counterparts. These synthetic individuals have activities to perform through the course of the day, and need to make mobility decisions to travel between activity locations. The entire transportation infrastructure of the city is replicated on a virtual platform that simulates real life scenarios. If individual behavior and the governing laws of the digital reality are accurately reproduced, large-scale mobility demand emerges from the bottom-up, reflecting the real-world incidences.