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April 10 2012

Carsharing saves U.S. city governments millions in operating costs

One of the most dynamic sectors of the sharing economy is the trend in large cities toward more collaborative consumption — and the entrepreneurs have followed, from Airbnb to Getable to Freecycle. Whether it's co-working, bike sharing, exchanging books and videos, or cohabiting hackerspaces and community garden spaces, there are green shoots throughout the economy that suggest the way we work, play and learn is changing due to the impact of connection technologies and the Great Recession.

This isn't just about the classic dilemma of "buy vs. rent." It's about whether people or organizations can pool limited resources to more efficiently access tools or services as needed and then pass them back into a commons, if appropriate.

Speaking to TechCrunch last year, Lauren Anderson floated the idea that a collaborative consumption revolution might be as "significant as the Industrial Revolution." We'll see about that. The new sharing economy is clearly a powerful force, as a recent report (PDF) by Latitude Research and Shareable Magazine highlighted, but it's not clear yet if it's going to transform society and production in the same way that industrialized mass production did in the 19th and 20th centuries.

Opportunity Infographic - The New Sharing Economy Study by latddotcom, on Flickr
Infographic from "The New Sharing Economy" study. Read the report (PDF) and see a larger version of this image.

Carsharing is saving

What is clear is that, after years of spreading through the private sector, collaborative consumption is coming to government, and it's making a difference. A specific example: Carsharing via Zipcar in city car fleets is saving money and enabling government to increase its efficacy and decrease its use of natural resources.

After finally making inroads into cities, Zipcar is saving taxpayers real money in the public sector. Technology developed by the car-sharing startup is being used in 10 cities and municipalities in 2012. If data from a pilot with the United States General Services Agency fleet pans out, the technology could be also adopted across the sprawling federal agency's vehicles, saving tens of millions of dollars of operating expenses though smarter use of new technology.

"Now the politics are past, the data are there," said Michael Serafino, general manager for Zipcar's university and FastFleet programs, in a phone interview. "Collaborative consumption isn't so difficult from other technology. We're all used to networked laser printers. The car is just a tool to do business. People are starting to come around to the idea that it can be shared."

As with many other city needs, vehicle fleet management in the public sector shares commonalities across all cities. In every case, municipal governments need to find a way to use the vehicles that the city owns more efficiently to save scarce funds.

The FastFleet product has been around for a little more than three years, said Serafino. Zipcar started it in beta and then took a "methodical approach" to rolling it out.

FastFleet uses the same mechanism that's used throughout thousands of cars in the Zipcar fleet: a magnetized smartcard paired with a card reader in the windshield that can communicate with a central web-based reservation system.

There's a one-time setup charge to get a car wired for the system and then a per-month charge for the FastFleet service. The cost of that installation varies, predicated upon the make of vehicles, type of vehicles and tech that goes into them. Zipcar earns its revenue in a model quite similar to cloud computing and software-as-a-service, where operational costs are billed based upon usage.

Currently, Washington, D.C., Chicago, Santa Cruz, Calif., Boston, New York and Wilmington, Del. are all using FastFleet to add carsharing capabilities to their fleets, with more cities on the way. (Zipcar's representative declined to identify which municipalities are next.)

Boston's pilot cut its fleet in half

"Lots of cities have departments where someone occasionally needs a car," said Matthew Mayrl, chief of staff in the Boston Public Works department, during a phone interview.

"They buy one and then use it semi-frequently, maybe one to two times per week. But they do need it, so they can't give up the car. That means it's not being used for highest utilization."

The utilization issue is the key pain point, in terms of both efficiency and cost. Depending on the make and model, it generally costs between $3,000 and $7,000 on average for a municipality to operate a vehicle, said Serafino. "Utilization is about 30% in most municipal fleets," he said.

That's where collaborative consumption became to relevant to Boston. Mayrl said Boston's Public Works Department talked to Zipcar representatives with two goals in mind: get out of a manual reservation system and reduce the number of cars the city uses, which would reduce costs in the process. "Our public works was, for a long time, administered by a city motor pool," Mayrl said. "It was pretty old school: stop by, get keys, borrow a car."

While Boston did decide to join up with Zipcar, public sector workers aren't using actual Zipcars. The city has licensed Zipcar's FastFleet technology and is adding it to the existing fleet.

One benefit to using just the tech is that it can be integrated with cars that are already branded with the "City of Boston," pointed out Mayrl. That's crucial when the assessing office is visiting a household, he said: In that context, it's important to be identified.

Boston started a pilot in February that was rolled out to existing users of public works vehicles, along with two pilots in assessing and the Department of Motor Vehicles. The program started by taking the oldest cars off the road and training the relevant potential drivers. Using carsharing, the city of Boston was able to reduce the number of vehicles in the pilot by over 50%.

"Previously, there were 28 cars between DPW [the Public Works department] and those elsewhere in the department," said Mayrl. "That's been cut in half. Now we have 12 to 14 cars without any missed reservations. This holds a lot of promise, only a month in. We don't have to worry about maintenance or whether someone is parked in the wrong place or cleaning snow off a car. We hope that if this is successful, we can roll it out to other departments."

The District's fleet gets leaner

While a 50% reduction in fleet size looks like significant cost savings, Serafino said that a 2:1 ratio is actually a conservative number.

"We strive for 3:1," Serafino said. "The one thing we have is data. We capture and gather data from every single use of every single vehicle by every single driver, at a very granular level, including whenever a driver gets in and out. That allows a city to measure real utilization and efficiency. Using those numbers, officials can drive policy and other things. You can take effective utilization and real utilization and say, 'we're taking away these four cars from this area.' You can use hard data gathered by the system to make financial and efficiency decisions."

Based upon the results to date, Serafino said he expects Washington, DC, to triple its investment in the system. "The original pilot was started by a mandated reduction by [former DC Mayor Adrian] Fenty, who said 'make this goal,' and 'get it done by this date.' Overall, DC went from 365 to 80 vehicles by consolidating and cooperating."

Serafino estimated the reduction represents about 50% of the opportunity for DC to save money. "The leader of the DC Department of Public Works wants to do more," he said. "The final plans are to get to a couple of hundred vehicles under management, resulting in another reduction by at least 200 cars." Serafino estimated potential net cost savings would be north of $1 million per year.

There is a floor, however, for how lean a city's car fleet can become — and a ceiling for optimal utilization as well.

"The more you reduce, the harder it gets," said Serafino. "DC may have gone too far, by going down to 80 [vehicles]. It has hurt mobility." If you cut into fat deep enough, in other words, eventually you hit muscle and bone.

"DC is passing 70% utilization on a per-day basis," said Serafino. "They have three to four people using each of the cars every day. The trip profile, in the government sense, is different from other customers. We don't expect to go over 80%. There is a point where you can get too lean. DC has kind of gotten there now."

In Boston, Mayrl said they did a financial analysis of how to reduce costs from their car fleet. "It was cheaper to better manage the cars we have than to buy new ones. Technology helps us do that. [Carsharing] had already been done in a couple of other cities. Chicago does it. The city of DC does it. We went to a competitive bid for an online vehicle fleet management software system. [Zipcar] was the only respondent."

Given that FastFleet has been around for more than three years and there's a strong business case for employing the technology, the rate of adoption by American cities might seem to be a little slow to outside observers. What would be missing from that analysis are the barriers to entry for startups that want to compete in the market for government services.

"What hit us was the sales cycle," said Zipcar's Serafino. "The average is about 18 months to two years on city deals. That's why they're all popping now, with more announcements to come soon."

The problem, Serafino mused, was not making the case for potential cost savings. "Cities will only act as sensitive as politics will allow," said Serafino.

"Boston, San Francisco, New York and Chicago are trying. The problem is the automotive and vehicle culture," Serafino said. "That, combined with the financial aspects of decentralized budgeting for fleets, is the bane of fleet managers. Most automotive fleet managers in cities don't control their own destinies. Chicago is one of the very few cities where they can control the entire fleet.

Cities do have other options to use technology to manage their car fleets, from telematics providers to GPS devices to web-based reservation systems, each of which may be comparatively less expensive to buy off the shelf.

One place that Zipcar will continue to face competition at the local level is from companies that provide key vending machines, which are essentially automated devices on garage walls.

"You go get a key and go to a car," said Serafino. "If you have 20 cars in one location, it's not as likely to make sense to choose our system. If you have 50 cars in three locations, that's a different context. You can't just pick up a keybox and move it."

Collaborative consumption goes federal?

Zipcar is continuing along the long on-ramp to working with government. The next step for the company may be to help Uncle Sam with the federal government's car fleet.

As noted previously, the U.S. General Services Agency (GSA) has already done a collaborative consumption pilot using part of its immense vehicle fleet. Serafino says the GSA is now using that data to prepare a broader procurement action for a request for proposals.

The scale for potential cost savings is significant: The GSA manages some 210,000 vehicles, including a small but growing number of electric vehicles.

Given congressional pressure to find cost savings in the federal budget, if the GSA can increase the utilization of its fleet in a way that's even vaguely comparable to the savings that cities are finding, collaborative consumption could become quite popular in Congress.

If carsharing at the federal level succeeded similarly well at scale, members of Congress and staff that became familiar with collaborative consumption through the wildly popular Capital bike sharing program may well see the sharing economy in a new light.

"There's a broader international trend to work to share resources more efficiently, from energy to physical infrastructure," said Mayrl. "Like every good city, we're copying the successful stuff elsewhere."

Related:

April 06 2012

Top Stories: April 2-6, 2012

Here's a look at the top stories published across O'Reilly sites this week.

Privacy, contexts and Girls Around Me
The application of user data is pushing at the edges of cultural norms. That can be a positive, but finding "the line" requires adherence to a few simple and clear guidelines.

Data as seeds of content
Visualizations are one way to make sense of data, but they aren't the only way. Robbie Allen reveals six additional outputs that help users derive meaningful insights from data.


State of the Computer Book Market 2011
In his annual report, Mike Hendrickson analyzes tech book sales and industry data: Part 1, Overall Market; Part 2, The Categories; Part 3, The Publishers; Part 4, The Languages. (Part 5 is coming next week.)

The do's and don'ts of geo marketing
During his session at this week's Where Conference, Placecast CEO Alistair Goodman examined the layers of context that make for rich, geo-targeted messages.



Fluent Conference: JavaScript & Beyond — Explore the changing worlds of JavaScript & HTML5 at the O'Reilly Fluent Conference, May 29 - 31 in San Francisco. Save 20% on registration with the code RADAR20.

Fence photo: Fence Friday by DayTripper (Tom), on Flickr

April 05 2012

Data as seeds of content

Despite the attention big data has received in the media and among the technology community, it is surprising that we are still shortchanging the full capabilities of what data can do for us. At times, we get caught up in the excitement of the technical challenge of processing big data and lose sight of the ultimate goal: to derive meaningful insights that can help us make informed decisions and take action to improve our businesses and our lives.

I recently spoke on the topic of automating content at the O'Reilly Strata Conference. It was interesting to see the various ways companies are attempting to make sense out of big data. Currently, the lion's share of the attention is focused on ways to analyze and crunch data, but very little has been done to help communicate results of big data analysis. Data can be a very valuable asset if properly exploited. As I'll describe, there are many interesting applications one can create with big data that can describe insights or even become monetizable products.

To date, the de facto format for representing big data has been visualizations. While visualizations are great for compacting a large amount of data into something that can be interpreted and understood, the problem is just that — visualizations still require interpretation. There were many sessions at Strata about how to create effective visualizations, but the reality is the quality of visualizations in the real world varies dramatically. Even for the visualizations that do make intuitive sense, they often require some expertise and knowledge of the underlying data. That means a large number of people who would be interested in the analysis won't be able to gain anything useful from it because they don't know how to interpret the information.

Fluent Conference: JavaScript & Beyond — Explore the changing worlds of JavaScript & HTML5 at the O'Reilly Fluent Conference (May 29 - 31 in San Francisco, Calif.).

Save 20% on registration with the code RADAR20


To be clear, I'm a big fan of visualizations, but they are not the end-all in data analysis. They should be considered just one tool in the big data toolbox. I think of data as the seeds for content, whereby data can ultimately be represented in a number of different formats depending on your requirements and target audiences. In essence, data are the seeds that can spout as large a content tree as your imagination will allow.

Below, I describe each limb of the content tree. The examples I cite are sports related because that's what we've primarily focused on at my company, Automated Insights. But we've done very similar things in other content areas rich in big data, such as finance, real estate, traffic and several others. In each case, once we completed our analysis and targeted the type of content we wanted to create, we completely automated the future creation of the content.

Long-form content

By long-form, I mean three or more paragraphs — although it could be several pages or even book length — that use human-readable language to reveal key trends, records and deltas in data. This is the hardest form of content to automate, but technology in this space is rapidly improving. For example, here is a recap of an NFL game generated out of box score and play-by-play data.

A long-form sports recap driven by data
A long-form sports recap driven by data. See the full story.

Short-form content

These are bullets, headlines, and tweets of insights that can boil a huge dataset into very actionable bits of language. For example, here is a game notes article that was created automatically out of an NCAA basketball box score and historical stats.

Mobile and social content

We've done a lot of work creating content for mobile applications and various social networks. Last year, we auto-generated more than a half-million tweets. For example, here is the automated Twitter stream we maintain that covers UNC Basketball.

Metrics

By metrics, I'm referring to the process of creating a single number that's representative of a larger dataset. Metrics are shortcuts to boil data into something easier to understand. For instance, we've created metrics for various sports, such as a quarterback ranking system that's based on player performance.

Real-time updates

Instead of thinking of data as something you crunch and analyze days or weeks after it was created, there are opportunities to turn big data into real-time information that provides interested users with updates as soon as they occur. We have a real-time NCAA basketball scoreboard that updates with new scores.

Content applications

This is one few people consider, but creating content-based applications is a great way to make use of and monetize data. For example, we created StatSmack, which is an app that allows sports fans to discover 10-20+ statistically based "slams" that enable them to talk trash about any team.

A variation on visualizations

Used in the right context, visualizations can be an invaluable tool for understanding a large dataset. The secret is combining bulleted text-based insights with the graphical visualization to allow them to work together to truly inform the user. For example, this page has a chart of win probability over the course of game seven of the 2011 World Series game. It shows the ebb and flow of the game.

Win probability from World Series 2011 game 7
Play-by-play win probability from game seven of the 2011 World Series.

What now?

As more people get their heads around how to crunch and analyze data, the issue of how to effectively communicate insights from that data will be a bigger concern. We are still in the very early stages of this capability, so expect a lot of innovation over the next few years related to automating the conversion of data to content.

Related:


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