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The trove of data kept under wraps is the stuff of dreams for a transit planner.

A passenger disembarks from an Uber. Photo by Emma Neiman.

In a 2017 comment filed with the CPUC, Lyft detailed the type of information it collects: trips completed, time and location of pick-ups and drop-offs, miles and hours traveled by drivers, all of which would shed light on the exact number of ride-hailing drivers entering and exiting the city and their patterns of travel within city limits.

This data, Henderson says, would allow city transportation planners to do their jobs. “Uber and Lyft are using the public right-of-way. They need to be telling us where they’re parking, where they’re picking people up and dropping them off, where they live, where they idle, and what their [vehicle miles traveled] is.”

Thornley agrees. “We have no way of knowing how many of them are out there, how often they’re running fares, or where the fares are going,” he said. “We don’t know their business. And therefore, we have a big blind spot about what’s happening on our streets.”

The city knows how many taxis are on the streets, however. The medallion system the city uses to cap the number of taxis is currently at 1,705, according to the most recent SFMTA report .

In contrast, there are no caps on the number of ride-hailing vehicles in San Francisco, a perennial sore point with San Francisco taxi drivers — and a problem for planners, who need to meet lowered emissions goals by 2030. (It also warrants mentioning that the estimated number of Ubers and Lyfts on San Francisco streets exceeds the city’s agreed-upon maximum for taxis by a factor of 20. That’s a lot of cars. That’s a lot of data.).

The city’s goals are clear: By 2030, San Francisco transit agencies want 80 percent of all trips within city limits to be taken on foot, bike or using high-occupancy mass transit, such as BART or Muni. This will allow the city to lower emissions below 1990 levels by 2050. California’s official goal, as expressed in AB 32 , is even tougher. The state wants a return to 1990 levels by 2020.

Henderson says we’re not going to succeed. “We’re not on track to make that goal. Lowering emissions will be impossible without purging the car out of the city.”

This is clearly at odds with Uber and Lyft’s business model, which both companies claim reduces car ownership. But both Henderson and Thornely say that car ownership is a red herring: Rather, it’s all about car usage .

As planners wait for data from Lyft and Uber, permits are being issued for e-bikes and stand-up scooters . Thornely noted that the pilot programs will capture user data, including location, charge level for e-bikes and stand-up scooters, collisions, and total trip distance.

While there is clearly value in evaluating long-term trends, experimenters running long-term experiments must be cautious, as results may be due to the above pitfalls more than the true delta between the Treatment and Control. We hope our real examples and analyses will sensitize readers to the issues and encourage the development of new methodologies for this important problem.

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by Pavel Dmitriev and Xian Wu. CIKM 2016

You get what you measure, and you can’t manage what you don’t measure. Metrics are a powerful tool used in organizations to set goals, decide which new products and features should be released to customers, which new tests and experiments should be conducted, and how resources should be allocated. To a large extent, metrics drive the direction of an organization, and getting metrics “right” is one of the most important and difficult problems an organization needs to solve. However, creating good metrics that capture long-term company goals is difficult. They try to capture abstract concepts such as success , delight , loyalty , engagement , life-time value , etc. How can one determine that a metric is a good one? Or, that one metric is better than another? In other words, how do we measure the quality of metrics? Can the evaluation process be automated so that anyone with an idea of a new metric can quickly evaluate it? In this paper we describe the metric evaluation system deployed at Bing, where we have been working on designing and improving metrics for over five years. We believe that by applying a data driven approach to metric evaluation we have been able to substantially improve our metrics and, as a result, ship better features and improve search experience for Bing’s users.

Online Controlled Experiments and A/B Tests by Ron Kohavi and Roger Longbotham Appears in the Encyclopedia of Machine Learning and Data Mining , edited by Claude Sammut and Geoff Webb 2017

The internet connectivity of client software (e.g., apps running on phones and PCs), web sites, and online services provide an unprecedented opportunity to evaluate ideas quickly using controlled experiments, also called A/B tests, split tests, randomized experiments, control/treatment tests, and online field experiments. Unlike most data mining techniques for finding correlational patterns, controlled experiments allow establishing a causal relationship with high probability. Experimenters can utilize the Scientific Method to form a hypothesis of the form "If a specific change is introduced, will it improve key metrics?" and evaluate it with real users.

The theory of a controlled experiment dates back to Sir Ronald A. Fisher’s experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, and the topic of offline experiments is well developed in Statistics (Box 2005). Online Controlled Experiments started to be used in the late 1990s with the growth of the Internet. Today, many large sites, including Amazon, Bing, Facebook, Google, LinkedIn, and Yahoo! run thousands to tens of thousands of experiments each year testing user interface (UI) changes, enhancements to algorithms (search, ads, personalization, recommendation, etc.), changes to apps, content management system, etc. Online controlled experiments are now considered an indispensable tool, and their use is growing for startups and smaller websites. Controlled experiments are especially useful in combination with Agile software development (Martin 2008, Rubin 2012), Steve Blank’s Customer Development process (Blank 2005), and MVPs (Minimum Viable Products) popularized by Eric Ries’s Lean Startup (Ries 2011).

Many authors have proposed some useful methods to solve some kinds of general split feasibility problems and general split equality problems in real Hilbert spaces, and under suitable conditions some strong convergence theorems have been proved; see, for instance, [ 17 , 18 , 19 ] and the references therein.

F\bigl(x^{\ast},y\bigr)\ge0, \quad \forall y \in C.
(1.6)
F\bigl(x^{\ast},y\bigr)+\phi(y)-\phi\bigl(x^{*}\bigr)\geq0, \quad\forall y \in C.
(1.7)

If \phi=0 , then the mixed equilibrium problem ( 1.7 ) reduces to ( 1.6 ).

\mbox{find } x^{\ast}\in C \mbox{ such that } \phi(y)\geq\phi\bigl(x^{*} \bigr),\quad \forall y \in C.
(1.8)

The mixed equilibrium problem (MEP) includes serval important problems arising in physics, engineering, science optimization, economics, transportation, network and structural analysis, Nash equilibrium problems in noncooperative games and others. It has been shown that variational inequalities and mathematical programming problems can be viewed as a special realization of the abstract equilibrium problems ( e.g. , [ 20 , 21 , 22 , 23 ]).

Recently, Bnouhachem [ Odema Mens Driving Casual Slip On Loafers Canvas Shoes Fashion Sneakers 1black 6o2iAjE
] introduced the following split equilibrium problems:

F\bigl(x^{\ast},x\bigr)\ge0, \quad \forall x \in C,
(1.9)
y^{*}=Ax^{*}\in Q \mbox{ solves } G\bigl(y^{\ast},y\bigr)\ge0, \quad \forall y \in Q.
(1.10)

In this paper, we consider the following pair of equilibrium problems called split equality equilibrium problems (SEEP).

F\bigl(x^{\ast},x\bigr)\ge0, \quad\forall x \in C, \qquad G \bigl(y^{\ast},y\bigr)\ge0, \quad \forall y \in Q \quad\mbox{and}\quad Ax^{*}=By^{*}.
(1.11)

The split equality mixed equilibrium problem (SEMEP) is defined as follows.

\begin{aligned} F\bigl(x^{\ast},x\bigr)+\phi(x)-\phi\bigl(x^{*}\bigr)\ge0,\quad \forall x \in C,\qquad G\bigl(y^{\ast},y\bigr)+\varphi(y)-\varphi\bigl(y^{*}\bigr) \ge0, \quad \forall y \in Q \\ \quad\mbox{and}\quad Ax^{*}=By^{*}. \end{aligned}
(1.12)

(1) In ( 1.12 ), if \phi=0 , then the split equality mixed equilibrium problem ( 1.12 ) reduces to ( 1.11 ).

\phi(x)\geq\phi\bigl(x^{*}\bigr), \quad\forall x \in C, \qquad \varphi(y)\geq \varphi \bigl(y^{*}\bigr), \quad\forall y \in Q \quad\mbox{and}\quad Ax^{*}=By^{*}.
(1.13)
\phi(x)\geq\phi\bigl(x^{*}\bigr), \quad\forall x \in C, \quad \mbox{and}\quad y^{*}=Ax^{*}\in Q,\quad \varphi(y)\geq\varphi\bigl(y^{*}\bigr), \quad \forall y \in Q.
(1.14)
(SIM-FPP)\left \{ \begin{array}{@{}l} x_{k+1}= U(x_{k}-\gamma_{k} A^{*}(Ax_{k}-By_{k}));\\ y_{k+1}= T(y_{k}+\gamma_{k} B^{*}(Ax_{k}-By_{k})), \end{array} \right .
(1.15)

In this paper, motivated by the above works and related literature, we introduce a new algorithm for solving split equality mixed equilibrium problems in the framework of infinite-dimensional real Hilbert spaces. Under suitable conditions some strong and weak convergence theorems are obtained. As application, we shall utilize our results to study the split equality mixed variational inequality problem and the split equality convex minimization problem. Our results presented in this paper improve and extend some recent corresponding results.

A portion of the proceeds to benefit the American Cancer Society.

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Mardi Gras Festival 2016
Saturday, February 6, 2016 - 11:00am to 10:00pm
Le Chance Vineyards

What does a French themed winery do on Mardi Gras?

Have a party of course, paw-paw French style!! We would like to invite everyone to join us on February 6th to enjoy some great wine, amazing New Orleans cuisine and outstanding music. Grab your friends, family and beads. This will be a party you won’t forget.

Featured menu items Provided By www.sistercitiescajun.com

Red Beans and Rice Cajun Smoked Wings Jambalaya King Cake, etc.

Scottie Kemp will be playing from 1pm – 4pm Vince Martin will be playing from 6pm – 9pm

Event URL: https://www.facebook.com/LaChance-Vineyards-133355700142569/

4th Annual Wine, Beer, Cheese,
Saturday, February 6, 2016 - 12:00pm to 4:00pm
Ozark Empire Fairgrounds

The 4th annual Midwest Regional Beer, Wine, Cheese, and Chocolate Festival will be held on February 6, 2016 in the heated EPlex East Hall. This is a unique tasting and shopping experience for guests 21 and older. There will be live music, educational seminars (including a Missouri Wine 101 Class) in the four categories, and live cooking demonstrations from top local chefs. For more information and to order tickets, click .

Tipple Hill Winery Vineyard- Quarter
Saturday, February 6, 2016 - 2:00pm to 5:00pm
Tipple Hill Winery Vineyard

Quarter Auction Fundraiser

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Mark Moebeck Live at Blumenhof Winery
Saturday, February 6, 2016 - 2:00pm to 5:00pm
Blumenhof Winery

Mark Moebeck (Classic Rock) will perform at Blumenhof Winery in Dutzow, MO on Saturday, February 6 from 2-5pm.

Tipple Hill Winery- Quarter Auction Fundraiser
Saturday, February 6, 2016 - 2:00pm to 5:00pm
Tipple Hill Winery Vineyard

What is a Quarter Auction, you ask? You purchase a bidding paddle for $3 and use it to bid on donated items from home based businesses and other items such as gift cards, wine baskets, handmade craft items, and so much more.

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