like Ola, Uber etc. how they calculate fare
Example when I start from one point to another (like 1 to 2km) shows amount that is 40 rs
how they calculate use map distance in code or something else in code can u please explain how it work? Simply how it works fare system works?
This is lifted directly from the Uber website:
Many data points go into calculating an upfront price, including the
estimated trip time, distance from origin to destination, time of day,
route, and demand patterns. It also includes tolls, taxes, surcharges,
and fees (with the exception of wait time fees).
Some cities do not provide upfront prices. Instead, you’re charged
either a minimum price or a price based on the time and distance for
your trip’s route, which may include a base fare, a Booking Fee,
surcharges, tolls, and dynamic pricing. Prices may vary by location,
the vehicle option you select, and other factors.
So it sounds like a combination of any relevant data points your app is able to collect and that you feel should be considered billable are applied to the calculation. I imagine you would want to use the map distance rather than a predefined route in case of detours, traffic, etc.
Related
I am struggling to come up with a formula that fits certain criteria and was hoping someone with a better math brain than me might be able to help. What I have is a Google Sheets based tool that determines how much a someone has purchased of a product and then calculates the amount of times a special additional offer will be redeemed based on the amount spent.
As an example, the offer has three tiers to it. Though the actual costs will be variable for different offers let's say the first tier is gained with a $10 purchase, the second with a $20 purchase and the third with a $35 purchase (the only real relationship between the prices is that they get higher for each tier but there is no specific pattern to the costing of different offers). So if the customer bought $35 worth of goods they would get three free gifts, if they bought $45 worth they would get 4 and then an additional spend of $5 (totaling $50) would then allow them to redeem 5 gifts in total. It can be considered like filling a bucket, each time you hit the red line you get a new gift, when the bucket is full it's emptied and the process begins again.
If each tier of the offer was the same cost (e.g. $5, $10 and $15) this would be a simple case of division by the total purchase amount but as there is no specific relationship between the cost of the tiers (they are based on the value of the contents) I am having trouble coming up with a simple 'bucket filling' formula or calculation method that will work for any price ranges given to it. My current solution involved taking the modulus, subtracting offer amounts from the purchase amount etc. but provides plenty of cases where it breaks . If anyone could give me a start or provide some information that might help in my quest I would be highly appreciative and let me know if my explanation is unclear.! Thanks in advance and all the best
EDIT:
The user has three tiers and then the offer wraps around to the start after the initial three are unlocked once, looping until the offer has been maxed out. Avoiding a long sheet with a dynamic column of prices would be preferable and a small, multicell formula would be ideal
What you need is a lookup table. Create a table with the tier value in the left column, and the corresponding number of gifts for that tier value in the right column. Then you can use Vlookup to match the amount spent to correct tier.
I am not quite sure about, everything into one entire formula(is there a formula for loop and building arrays?)
from my understanding the tier amounts are viable, so every time you add a new tier with a new price limit then it must be calculated with a new limit price number...wouldn't it be much easier to write such module in javascript than in a google sheet? :o
anyways here is my workaround, that may could help you to find an idea
Example Doc
https://docs.google.com/spreadsheets/d/1z6mwkxqc2NyLJsH16NFWyL01y0jGcKrNNtuYcJS5dNw/edit#gid=0
my approach :
- enter purchases value
-> filter all items based by smaller than or equal "<=" (save all item somewhere as placeholder)
-> then decrease the purchases value by amount of existing number(max value) based on filtered items
-> save the new purchases value somewhere and begin from filtering again and decreasing the purchases value
(this needs to be done as many times again, till the purchases is empty)
after that, sums up all placeholder
I'm having a tough time with Google Analytics, trying to understand why the value of metrics changes when segments are applied.
There is a standard audience overview report, which is based on 100% of sessions (no sampling) and the view is not filtered. The period is March of 2017.
Standard "All visitors" segment looks like this:
Then, there is another built-in segment called "Bounced Sessions". When I apply this segment, the "All visitors" values changes:
Amount of users increases, but the count of pageviews decreases.
Any ideas how to explain this?.. Thank you in advance!
Oki, there can be, multiple reasons. Let me explain first how these numbers are calculated, then we move on to your query.
There two types of data gathering and manipulation from google.
Pre-calculated data -- pre-aggregated tables
These are the precalculated data that Google uses to speed up the UI. Google does not specify when this is done but it can be at any point of the time. These are known as pre-aggregated tables
Data calculated on the fly
Some that you do which result in computation or manipulation falls under this category. Like using segments, creating custom reports etc.
Coming to your problem. When you apply segment, every metric that it effects will be calculated again. Thus it may result in numbers greater than you see in normal view.
Standard audience overview report is pre-aggregated at some point of the day. When you apply segment, the results will be calculated with the fresh data. Since latter is the latest, it will automatically give you increased number of the metrics. Even you can see a decrease as well, all depends on your data and user behavior.
Resolution: If you are a premium user, use Big Query. You must rely on big query for every metric as they are fresh and calculated on the fly
My company is producing a racing game where the best score is the fastest time. Facebook publishes the time as a regular point score, where a higher score is better. This of course is turning it all upside down.
Is there a way to control how a game's score shown in a story? Ideally we would like to show "seconds" instead of points as well.
No, the Scores API currently only supports 'higher is better' for scores.
If you can't rework your scoring scheme to take this into account, consider using Open Graph actions instead - you can have the aggregations which appear on a user's Timeline ordered by whichever field of the object and action you need them to be ordered by,
I'm looking for a rating system that does not only weight the rating on number of votes, but also time and "activity"
To clarify a bit:
Consider a site where users produce something, like a picture.
There is another type of user that can vote on other peoples pictures (on a scale 1-5), but one picture will only recieve one vote.
The rating a productive user gets is derived from the rating his/hers pictures have recieved, but should be affected by:
How long ago the picture was made
How productive the user has been
A user who's getting 3's and 4's and still making 10 pictures per week should get higher rating than a person that have gotten 5's but only made 1 pic per week and stopped a few month ago.
I've been looking at Bayesian estimate, but that only considers the total amount of votes independent of time or productivity.
My math fu is pretty strong, so all I need is a nudge in right direction and I can probably modify something to fit my needs.
There are many things you could do here.
The obvious approach is to have your measure of the scores decay with time in your internal calculations, for example using an exponential decay with a time constant T. For example, use value = initial_score*exp(-t/T) where t is the time that's passed since picture was submitted. So if T is one month, after one month this score will contribute 1/e, or about 0.37 that it originally did. (You can also do this differentially, btw, with value -= (dt/T)*value, if that's more convenient.)
There's probably a way to work this with a Bayesian approach, but it seems forced to me. Bayesian approaches are generally about predicting something new based on a (usually large) set of prior data, which doesn't directly match your model.
How are services like Alexa and Google Analytics capable of tracking visitors' age, gender, college education, and so forth?
http://www.alexa.com/siteinfo/stackoverflow.com
Alexa definitely gets its traffic info from its toolbar users. Since that is a relatively small and self-selecting group of people, this inevitably leads to a biased sample (which is why Alexa traffic doesn't match measured traffic on the sites I run). Even with the best statistical techniques for reducing bias, you can never get rid of it entirely when the sampling distribution is not uniform.
Unclear how Google does it, although it might involve tracking cookies.
A project I have been working on recently has bearing on this question.
Another way to do this (that also has biases, but different ones) would be to use an IP to location service to find the approximate latitude and longitude of each visitor to your site. Then use my project (full disclosure: I run that site and it is commercial):
http://askgeo.com
To get demographic information for that location. AskGeo actually provides demographic information on several geographic levels (state, county, county subdivision, city, ZIP code, census tract (a few thousand people), and census block group (about a thousand people). You'd presumably want to use the lowest level (i.e., census block group) for a given latitude and longitude.
The site returns a huge number of demographic variables. The idea would be to use soft counts from the demographic variables provided on the block group level. To take an example, if you are trying to track the age distribution of your users, then you'd use the age ranges provided in the AskGeo response and for a given sample, you'd add a fractional soft count to each range that corresponds to the percentage of the population in that block group from the corresponding age range. For example, take my neighborhood in San Francisco. It has the following age distribution:
CensusAgePercent0To4: 7.3%
CensusAgePercent5To9: 3.5%
CensusAgePercent10To: 3.2%
... (skipping a bit, as you probably get the idea) ...
CensusAgePercentOver85: 1.5%
If you got an IP address that you tracked to that census block group, you'd add each of those percentages (as a fraction from 0 to 1) to your (soft) counters for those age ranges. (A soft counter is just a counter that allows for non-integer counts.)
You could do the same with race, gender, income level, house values, etc.
This method also has biases, for sure, since it assumes that all the people in a given block group are equally likely to visit your site. But it is something that you can do on your own site, not just Google and Alexa, and it would still give you a relative sense of who is visiting your site if your soft counts in a given category are higher than the national average in that category.
It is also possible that a more sophisticated technique than simple direct counts could lead to a much richer result.
I did some research, and apparently these demographics are tracked the same way TV audience demographics are tracked. There are people who browse with their (Alexa's) toolbars, which keeps track of the sites visited. These people willingly (?) supply information like age, gender, etc. and Alexa extrapolates the general demographics from this sample. This of course leaves room for bias, but that's a problem with statistics.
Alexa gets its information from browser toolbars that you install on purpose or as part of a bundle with some software.
It asks questions to understand demographic params and also tracks sites that you visit. If you know that 80% of site visitors are women and you have new visitor who visits this site that you can think that there is high probability that this person is a woman. If you know a lot of sites this person visits you can guess a lot.
But as http://netberry.co.uk/alexa-rank-explained.htm says you can rely only on information from Alexa TOP100,000 because then Alexa has enough information from small amount of users visiting these sites. They say "millions" but it's small share of total