Using Mobility Pricing for Relocation of Carsharing Fleets
We are at a pivotal moment in the evolution of car sharing. Cities around the world are integrating car sharing into people’s day-to-day lives. In fact, Morgan Stanley predicts that shared cars could account for 26 percent of global miles traveled by 2030, up from four percent in 2015.
This revolution is occurring because, according to respondents who participated in our recent survey, car sharing is a convenient way of getting around town; is cheaper than owning a car or a second car; minimizes usage of owned cars; and is more environmentally friendly.
While there are several commercial applications for sharing cars, free-floating car sharing systems, such as car2go, ReachNow or Gig offer the ultimate flexibility and freedom. That’s because this model allows members to drop off a shared car anywhere, which is the number one car sharing feature that convinces people to give it a try, even if they are strong believers in car ownership.
Supply and Demand is the Biggest Challenge in Carsharing
In free floating car sharing systems, the freedom that members enjoy represents the biggest challenge for operators. While operators focus on maximum utilization of their assets, car drop offs don’t always flow as they should, and there are often popular areas where cars are routinely unavailable. A main contributor to this problem is that cars are often parked in neighborhoods where they sit idle for hours or even days.
Ultimately, the goal of system operators is to balance supply and demand. Ideally, this is done by placing cars in locations that are optimum for both operators and members. It’s a deceptively simple issue of supply and demand. Yet to this date, it is one of the most expensive operational tasks in the carsharing industry, as industry experts have shared with movmi in a recent study. The better balanced the system, the more cars are utilized, the better members are served, and operators earn more revenue and have lower operational costs.
It’s not easy for operators to create an optimum balance between supply and demand throughout their service area. The problem of imbalance is illustrated by the recent experience of car2go in Vancouver. Originally, car2go offered members the opportunity to drive cars to the Horseshoe Bay ferry terminal just outside of Vancouver. It was a very popular location for cars because car2go served the needs of commuters from the island as well as local tourists and vacation home residents visiting the Sunshine Coast.
When people left Vancouver headed for Horseshoe Bay, they preferred to take a car2go instead of relying on the bus service. That’s because riding the bus doesn’t always fit the member’s schedule, and the bus takes longer to get them to their destination.
The problem for car2go was that members were perfectly happy to take a bus back into town because it was much cheaper than renting a car. After almost six years of trying to optimize the ebb and flow of cars - even using a van full of relocation drivers to bring cars back into the city - car2go shut down service to Horseshoe Bay.
The reality is that any operator in North America can have the same experience as car2go. So, how can this be avoided?
Software, Data, and Insights
System operators often lack the necessary insights into current locations of cars and where the cars need to be to best serve system members.There are two sides to these insights. On one hand, operators need to know when a member requires a car but there isn’t one available; these areas are called ‘hot zones.’ On the other hand, operators need to know where cars are sitting idle because no one needs the car; these are called ‘cold zones.’ Without sufficient technology to supply this real-time knowledge, the system will remain unbalanced.
Having the right software that provides the necessary insights is critical. Platform players that are specialized in free-floating carsharing such as EcoMobix, Vulog or Ridecell, provide some support. These platforms can identify demand based on historical data, as well as general trends and insights into the way a particular operating territory works. Even with these insights, vendors will still face the problem of predicting real time demand, and incentivizing users to distribute cars according to the identified model of supply and demand.
Crowdsourcing Relocation through Mobility Pricing
In the movmi study referenced above, we surveyed Vancouver and Washington carsharing members to understand their free-floating experience regarding car availability and their reactions to a range of possible solutions to the relocation problem. The study tested four conceptual situations in which members would be enlisted to support the efforts through financial incentives.
Three of the four concepts were inspired by mobility pricing, a concept widely accepted by transportation demand managers to control traffic movements. Cities like Stockholm and London have used mobility pricing successfully in the past for transportation demand management. While these cities aimed charges at downtown core areas in an effort to reduce traffic clogs, a similar concept could be used to support the carsharing relocation problem. We tested two variations of this dynamic pricing concept for carsharing. In the first, the carshare operator offers a discounted rate if the member drops off their vehicle at a specific location. In the second, members would be charged less during certain times of day if they pick up their vehicle at a particular location.
The first situation was more preferable to the survey participants from Vancouver and Washington. In the mind of the consumer, it might be worth the savings to walk an additional few blocks to their desired location or hop on a secondary form of transportation to reach their destination. In fact, this concept had the strongest preference of the four.
The third situation addresses a delivery fee concept in which members would pay more to have a car delivered to their preferred location. This yielded very polarizing results and interest seems to be based on income levels of users. Washington participants generally had a higher income and were more favorable to this concept than Vancouverites.
The last concept we tested was incentivized shuttle groups, in which members of the service would relocate cars to hot zones, and receive system credits in return. This concept has been tested with students by Catch-a-Car in Switzerland, a daughter of the oldest carsharing organization in the world: Mobility Carsharing. This ideas was very popular among the Swiss students, but overall was not a concept that North Americans were interested in.
There is no silver bullet for carsharing relocation issues.
In terms of optimizing relocation, we know that operators want software tools that integrate the latest trends in big data analysis, artificial intelligence, and machine learning. This will help them to determine in detail where most rentals start and end. Operators will also be able to arrange the city into smaller areas that make sense from a traffic, parking, and time-of-day perspective. Additionally, if there is a big weekend event in the downtown core, the operator should be able to block an entire area for redistribution. Once the parameters are set, the system would highlight areas that are in need of cars and neighborhoods that have too many, and then automatically send the relocation tasks to the relocation team.
Even with a software system that provides these insights, mobility pricing would need to be tailored to specific markets. While every car sharing member values convenience, what is perceived as convenient is different in various markets. Operators may need to modify how they incentivize members based on demographics, or offer a menu of incentives to meet different preferences. Based on our findings, I don’t believe that there is one solution that fits all circumstances.
Leave your comment below, or reply to others.
Read more from the Meeting of the Minds Blog
Spotlighting innovations in urban sustainability and connected technology
For almost a year, our team has been working on a toolkit to help readers navigate the nuanced, complicated conversations that surround algorithms and the data that they consume. The project came about after a small workshop held in the city of San Francisco in February of 2018. The conversation around data science and transparency for laypeople brought us to the idea that a new resource was needed to bridge the gap between data scientists and non-data scientists.
A mid-sized city’s demonstration corridor for innovation in safety, sustainability, and multimodal mobility.
Has the future of mobility arrived yet? Of course, we haven’t reached our final destination, but there are reasons to feel good about our overall progress. A couple cities have made great strides toward the end goal of MaaS, and their successes should serve as examples to other urban areas and regions considering their own next steps.