 Hello, everyone. Hope everyone had a good break, right? So today we'll be talking about a very different kind of business. Till now you have been hearing about e-commerce, about food deliveries, right? About different share mobility. But today we will be talking about bike rental. So how many of you have seen these green bubbly bikes around you? Right? Okay. So a lot of people already know about this. Great. So just to reiterate, we are pedal and pedal is a bike share. It's a shared mobility where you can rent a bike, go from point A to point B, right? And pay per use. Now how it works. So let me just give you a brief background about it. So you can go to a station, you can see a bike. It's QR enabled. So you scan a QR code, link your Paytm wallet first, scan the QR code and that's all, right? So you can go from one place to another. When you want to end a trip, you just lock it and on your app push the enter button and it ends. So as simple as that. So the purpose of starting pedal was to make shorter commutes convenient. You have cabs from going from one place to another that are far off. But imagine that you're going to a place where you end up in a metro and now you have to go to your home. How do you go to your home? Right? Do you want to take a cab again? That'll cost you around 100 bucks or 50 bucks. Or you can take a cycle from there and go to your home. So that is the whole point of starting this business. Now, just to explain a little further, if you have seen these bikes, there are two main components to it. One is this basket and trust me, this basket is a solar panel, right? What it does it, it gets charged by the sun and it powers this IoT lock. So this lock at the back is where all the magic happens, right? So there's a small box which have all the GPS tracking. It captures the battery status. It gives signals to lock or unlock a cycle, right? It tells you the strength of the signals and everything else. So these are the two features that are put on the bike because of this whole ecosystem works, right? And this is also the way we capture data. So let me take you through how we expanded because it's a very new business. So I thought that it'll be good to share with all of you. So it started somewhere in January 2017, where we built our minimum viable product and we set it out in HSI layout. So we had only 15 stations at that point of time and it used to be, there was no IoT behind it and it was completely manual. They were fleet at all these 15 stations and people used to find these good looking bikes and they would come and ask, you know, what are these bikes for? And they will say, I want to go, you know, you can take this bike and go to BDA complex. You can take this bike and go to 27 Main Road. And we had stations there and people will tell, okay, I want to go from this place to another. So we would communicate to the fleet at that station that, okay, this bike will be coming there and that is how it used to work, right? But what are the challenges in it? The challenge was obviously all the data was manually collected. There was no IoT, there was no tracking, right? So those are the major challenges. But what we learned from this was that people really used to like it. There was a void in the market that there was no way to go shorter commutes. It was either through walk or either through caps. And even the buses had very fixed stop and, you know, the routes were not convenient for residential areas. So we did this, we did this expansion for around six months. And then in November 2017, we expanded to 1000 cycles. Now this is when we actually introduced the lock. This is then when we actually introduced the solar panel and we started collecting all the data. Now what happened was the reason behind it was that we want to make it madness. There was no one present at the station. Now people used to discover themselves. They used to unlock themselves. They used to go from one place to another and they used to park it back themselves. Now initially we started in Bangalore, especially HSA layout, but then we expanded to Pune and Kolkata. The reason being these are smart cities. We had a lot of government support. They wanted to go eco-friendly, right? And they wanted such initiatives to come to the city. Then we expanded in Agra, Jaipur, Gurugram and all these cities. But we had a little setback there because there were high cases of vandalism. Now the obvious question would come, you know, why would I not see the cycle? How do you protect all those things? So though we had a lot of things by which we mitigated theft, so inside a lock, we have a buzzer. So if you try to move a cycle when it is locked, it will buzz off, right? If you try to shake a lock, it'll buzz off. So and it'll send an alert to the fleet or the city operations team that someone is trying to fill with the cycle. So this is how we tried to protect vandalism. Then we thought that okay, maybe it's a good idea to launch in closed circuit areas like college campuses. So we launched in ID Bombay, ID Chennai and IASC. And then when we had success in that, in around April 2018, now we have a fleet of 12,000 plus bicycles. We are in 15 cities across India, New York cities like Ranchi, Raipur, Varanasi and the maximum density we have in Bangalore, Kolkata and Poore. Now what is the problem here? So we had problems of vandalism. We have problem of the GPS location not coming in the correct way. We had problem that the battery is drying off, but what is the major problem? The major problem is when you expand to so many cities, right? How many cycles do you put at a station? How do you maximize your revenue? It's a very low ticket item, right? If you are taking a ride for 15 minutes, you hardly pay like three rupees. So how do you sustain this business? So for sustaining this business, this is the problem that we are trying to solve. And this is my main focus for the stock for today. How to increase the number of trips per cycle and hence maximizing utility and revenue. Now, why do you want to do that? So these are the challenges right now allocation of cycles at the station for heuristic base. So when you're launching in say HSI layout, so I know the places I would know that these BDA complex 20 cent main road, these are areas where you have a lot of crowd, where you have a lot of people shopping around. So I should put more cycles here. But when you go to newer areas, like for example, Kudluget, Victoria layout, right? How do you recite number of cycles you put there? It's very difficult. It cannot be heuristic base and you would know good about a city. But what about you go to Kolkata and they want to expand there? How do you recite the places? Then the trips for cycle right now was very low before we launched the experiment. The third was re-balancing of the cycles was totally fleet based and it was done only once a week. So on Saturdays or I think Sunday morning, they used to go and they used to just rebalance all the cycles there. If we have hoarding of cycles at one place because everyone took from point A to point B. No one took from point B to point A. How do you solve those problems? And last was identification of new sites. So I launched an SSL layout. I launched in Kormangala, but I launched in Indira Nagar. But what next? How do you expand? So to solve all this problem, we thought that it's not as easy as taking out a report or generating some statistics. We have to really visualize it. So to do that, we followed this approach. So there are two legs to it. One is how do I optimize the current network of stations? So if I have SSL layout and I have around 15 cycles and SSL layout, how do I make sure that I have the right number of cycles at each of these stations to maximize the number of trips from each station and overall network? For example, if I have, as I said, if a lot of cycles go from point A to point B and not coming back, then how many cycles should I put at point A at the start of the day? Because I know they will get depleted. So cycle rebalancing to optimize for number of trips per cycle. Second, identifying the dead zones. So you decided to start a station somewhere, put cycles there, but then you realize no one is taking cycles from there. So can I relocate those cycles to somewhere else? Third, identifying frequented routes. Now, for example, I did a station A. Now I take this cycle to a station B, but in between I stop at a hypermarket, right? Because I want to buy groceries. So can I open a station at the hypermarket because these areas are demand aggregators, right? They will build a network. So how do I find these things? Identifying areas where people abandon cycles is a very important point. The reason being, you can only end a trip at a station. You cannot take a cycle and leave it anywhere, right? Your money will keep deducting. So for example, we don't have station every 200 meters. That is what we want to achieve in future. But right now what do we do? So we find out which are the areas where people took cycle from and then abandoned it here. So maybe if a lot of people are banding there, if you open a station there, that will help people. The fifth point I'll just touch upon a little later in the presentation. The second leg to it is expanding into new areas. Now, for example, when Uber Eats started, right? So it started with Kormangala. But how did it identify? So if I was sitting in Whitefield and I wanted to avail that service, so they would tell you which area you're coming from and then we will shortly expand to that area. So these are called empty searches. This is a concept of empty searches that you get a search but you don't have a product there. So we used to capture that when we started this and identifying connecting neighborhoods. So for example, you have HSA layout, you have Kormangala, but these are pretty apart. They are at least five kilometers distance from each other, right? So can we open station that is in between these two? The demand does not originate from that, but people, you know, it's like a connecting station. So it helps bridge the demand between these two big stations so that the overall network density increases. So what we'll do is I'll first take you through what a pedal networks look like. So this is a short video. Okay. So what we have here is these yellow dots are the stations all around Baglow. So for example, this is JNagar. Then we have around residency road. We have around ISE. We have Indranagar, Kormangala and HSA layout. Now you will see blue dots coming, right? Now what are these blue dots? So blue dots are the places where the people are starting trips from. And the red dots are places where people are ending the trips. So now there are some obvious observations here. So you'll see a lot of people are starting and ending here, but these are kind of empty, right? JNagar, residency road, Kaban Park, ISE is pretty good, right? So there are some obvious insights that you get if you plot it well, right? What next? Let's go dig down a little deeper. So let's go to ISE, right? So this is ISE. Now you will see all these stations are doing well. A lot of tips are starting and ending at these stations, but these three stations, no one is either taking cycles, no one is either ending at these stations. So basically all the supply at these stations is kind of dead, right? So this frees up my supply from here. Similarly, let's go to the area near Kaban Park. Okay, you see here. So there's a whole set of stations, stations on the right-hand side. These have a lot of trips starting and ending, but station on the left-hand side, they're almost dead. Now there's so much of inventory lying idle. There are so many areas where I have a good network and areas where I have a bad network. So if I were to show you how the trips are panning out, so it'll just come in a second. So this is how the trips are happening, right? See here. All the people from this area, from Residency Road, are not really going within. They're transversing to either Indranagar or Kormangala. ISE is a very close circuit, right? And there's a huge network density in these areas. So this is the power of visualization. Now, how do you utilize this? Now we know this helped us in defining the problem that we really need to solve it now. How do you do it? So next is what we'll, the major talk is decoding a network. How do you decode it? So our objective is to identify the right number of cycles that we should deploy at the start of the day at each station so that the number of trips per cycle gets maximized and that's the revenue. So to understand this, there'll be two concepts that I'll be introducing. First is the rate of outgoing trips from a station. Second is the transition probability of going from station A to station B. Now, what is the rate of going, rate of this from a station? It means that it is the expected number of trips per day from a station given X number of cycles at the start of the day. So if I have said 10 cycles at the start of the day, how many outgoing trips can I expect? If I increase the cycles to 15, can I expect more trips? Right? So what we did is we had data. So because we had launched it for around more than one year. For every station, we had data when they were different availability of cycles at the start of the day. So we figured out when the availability differed over time, what was the increase or decrease in the odd trips per day? Now, initially we thought that we'll build a linear algorithm to it and if you keep increasing the supply, the demand will keep increasing because understand this is an impulsive business. You see a cycle and you write it. It's not that you know and then you come. But what happened was it's like a projectile. It has diminishing utility over time. It demand is capped after a certain point of time. So that is why what we did is we fitted in a polynomial equation. So this becomes our rate of trips. So what it tells us is that if I deploy C cycles at the start of the day, at what rate my trips outgoing trips will increase. So we defined this function using past data for each and every station we had across Bangalore. And this is the objective function of number of trips. So the trips at the start of the day into the rate of trips expected at the station. So if you do it for every station and sum it up, it will give you the total number of trips for the whole network. And this is what you want to maximize. Let's go to the next thing. So this is building a network chain. So say there's a station A and this station B. At the start of the day, they are 10 cycles and the rate of trips of the station is 0.5. It means that if you have 10 cycles, probably you will have five trips from that station. Now those five trips can either be a round trip. I can start from the station and that station or it can be a trip to a station B or it can be a trip to a station C. So the probability of round trip is 60% for the station A to B is 20% and A to C is 20%. So we calculated something called as cycles we are expecting at the end of the day. And I'll tell you why it is important because it helps us defining constraints. So cycle at the end of the day, cycle at the start of the day plus incoming minus outgoing, simple. What is incoming? Incoming is the cycles I expect to come from different station to station A. So incoming at station A is cycles at the start of station B into the rate of outgoing trips I'm expecting at station B into the transition probability from B to A that is 20%. Similarly from station C. So these, this will be the total incoming cycles I'll expect at station at the end of the day for outgoing. Again, cycles at the start 10 into rate of trips into one minus probability of a round trip. Because if I take a round trip, it will end up here. It will not be outgoing. So cycles at the end of the day is like this equation and it is 14. So see you started at 10, you ended up at 14, right? And this is the most important thing to analyze the network. How much should I keep at the start of the day? So now to optimize, we define three constraints. Constraints are important obviously. So the constraints are cycles at the start of the day at any station should be greater than equal to zero, right? Logical. Why greater than equal to zero? Because we want the algorithm to tell us, right, that you don't keep any cycles at all also at a station. That is why we said greater than equal to zero. We want to remove those death stations. Second is the cycles at the end of the day at any station should be greater than equal to zero. It cannot be negative, right? It has to be positive. And the sum of cycles at all stations is equal to the total cycles. So this is a macro constraint. So this is what it looked like before. This is just our layout. This is how the trips look like. And after we run the optimizer, this is how it looked like. You can clearly see the density of trips have increased. How much they have increased. Let's see. So we had around 150 total cycles. The uplift in trips for cycle was 20%. The uplift in revenue was 15%. Why the revenue uplift is less than the overall uplift in trips for cycle? Because there's a rebalancing cost also. So you have to pay the fleet to move a cycle from one place to another. But even after factoring in the rebalancing cost, the uplift in revenue was significantly higher. So that is the use of such kind of doing such kind of things. Now this solves for the first case that how do you optimize within a network? But how do you identify new network? That is the second problem. Again, three legs to it. Identifying the frequented routes as I talked about the hypermarket. Identifying areas with no station and high abandonment. And identifying areas with high empty searches. This is a route map. So what we did is we collected all the GPS traces when you're going on a cycle and we plotted it as a heat map. So this is HSL layout. You can see these places marked in red. This is the HSL flyover. A lot of people are going and stopping, but there's no station. No station. No station. So these give us probable areas where we can open new station because they already demand aggregators. We call it at the fountain graph. So this is Jaksandra. A lot of strips are starting from nearby areas in HSR and Kormangala ending up at Jaksandra. But there's no station. So these people are manning here. So this makes a use case for creating a station here. And then we plotted the empty searches you will see in Martha Lee Bridge, Outer Ring Road, Coven Park metro station where we don't have any network at all. Right. People are searching, but they do not find the product. So we by this we expand to newer ideas. So these are three use cases where you how you can expand. These are tools and techniques you can use. We use Kepler and deck dot gl we use folium map box techniques are heat mapping network analysis and operational research. Right. Then other initiatives that we took after we did this is that we launched subscription. So I don't think you have heard about it or not, but we lost subscription at 49 and 199 where you can take unlimited trips in a month. Right. And why we did this is so that we increase the frequency of trips from a particular user. So how we utilize it in mapping is the areas from where we get more subscribers. We make sure we have more number of cycles in those areas. So we put a layer on top of a maps and we also plan to reduce a rebalancing cost. We also plan to incentivize users will give them the credits to take a cycle from one area to another. Right. So that ways we don't take the burden and the user also benefits and makes money out of it. So implementation was not a cakewalk. Right. It was really difficult. It took took us several months. We started this initiative somewhere around six months back where we went and talked to the operations team and they said, you know, we don't want to do it. We don't see value into it. And then we attended, you know, various conferences, read, talk to various people and got to know about these tools and techniques. So these are learnings. Start with small experiments. We started with just a layout. We proved it to them and then pushed it to them that, you know, why aren't you using it when you are seeing the value? Keep measuring it and keep flashing the results. Right. Send them a report every day that if you do this, this is the result. If you do this is the result. There's no way they can deny that. Build map to highlight actions and not just describe data. So tell them that these are number of cycles, you know, required at the station at the start of the day. And don't just tell them how the tips are moving around and don't underestimate the power of making it look good. So you might see that it looks a little fancy, but that is one of the reasons people are using it. Right. Initially we use folium, but then we switched to get the dot GN and really looks fancy to us too. So we also like working on it. So how can you guys benefit from this? So I've listed on some of the use cases that you can use identifying areas to expand your operations using app search data in terms of food deliveries, groceries, medicines, e-commerce area where they don't have right now. Second is recruitment or allocation of free persons by areas to optimize delivery time. So if you know already know you have delivery orders from these areas, how many persons should be present at the start of the day? Second is decentralizing warehouses and distribution centers across the city to minimize the time of delivery. If you know that you are getting a lot of orders from this area, why don't not have a warehouse or distribution center near it and tracking of fraud during delivery. They talked about FE scores, right? Fleet executive scores where they're making fake attempts. So we can know that what is the distance of the GPS location the fleet executive went to and what was the geocode of the actual house and was it close enough? If it is not close enough, it's a fake attempt. So all these other things that we can use it. So that summarizes my talk and we are open to Q&A. Very interesting talk. That curve that you showed, right? The rate of outgoing as a function of the number of cycles. Now to the extent that it fits well, even if assuming it fits well, it's dependent on the properties of the other nodes. It's not something that's just independent of that station, right? The thing is that so you're sort of doing something local which is applicable only if you hold other things fixed. But then you're doing everything local there as well in each of the nodes. So there's no reason to assume that the same coefficients to the extent that, as I said, they fit well. They don't fit particularly well, but let's ignore that. That no longer is going to stay the same. So I'll tell you what we do is that that equation keep changes every day. So what we do is we base that equation basis on the last month data. And we filter out to make that equation that, you know, we have, for example, holidays, we would filter out. We would filter out when we had very less cycle availability and all cycles were damaged. So we would know that in recent time, the equation is as true as possible. And in future, when we'll have more data, we'll actually build a prediction algorithm to predict the number of trips for the cycle availability for each station. So right now we start with very less data in HSA layout. And that is why we, when we plotted the data, it looked like a polynomial equation. That is why he fitted a polynomial equation to start with. I think we'll start off with the buff. But meanwhile, while getting the chairs on the dice, I think maybe we can take a couple more questions. Hi. Sure. Why do you take a start of the day and the end of the end of the day? They are the only two relevant milestones. Okay, it's operation challenges. So why is the start of the day? I could do it at every time of the day that in the evening you should have these many cycles and starting these many cycles. But we have limited resources. We live in a practical world. So when we talked to the business team, they said, right now I'm doing rebalancing once a week. So for us to convince them to do once a day was a big job. No, no, no, it's not about rebalancing. Say, what if in the middle of the day, the number of cycles at a station goes to zero? You'll not be able to collect data on what would have happened if the cycles were there. Yeah. So if it goes to zero, we cannot do much. See, but what we are trying to do is we try to build a optimizer in such a way that it should not go to zero. So we over index the cycles that we keep as a start of the day, right? And we try to, for example, one of the constraint you said, we said that the cycle at the end of this should be greater than equal to zero. Right. So what we can do is we can do greater than equal to five so that it predicts to keep more cycles so that it there's a minimum viability at any point of time. So these are things that we can tweak. We started with this. We can always improve.