International Journal of Engineering Applied Sciences and Technology, 2020 Vol. 5, Issue 8, ISSN No. 2455-2143, Pages 180-185 Published Online December 2020 in IJEAST (http://www.ijeast.com) 180 SURVEY ON PEER-TO-PEER RIDE SHARING FOR “POOL” A RIDE SHARING APP Piyush Agrawal Dept. of Computer Engineering, NBN Sinhgad School of Engineering, Ambegaon Pune 411041, India Apurva Joshi Dept. of Computer Engineering, NBN Sinhgad School of Engineering, Ambegaon Pune 411041, India Harsh Agrawal Dept. of Computer Engineering, NBN Sinhgad School of Engineering, Ambegaon Pune 411041, India Ajinkya Ghorpade Dept. of Computer Engineering, NBN Sinhgad School of Engineering, Ambegaon Pune 411041, India Avinash Bagul Dept. of Computer Engineering, NBN Sinhgad School of Engineering, Ambegaon Pune 411041, India Abstract-- Many college students travel in public transports or walk a long distance to reach college. This is problematic because public transports can be slow and not available everywhere as they have a specific time of arrival in their stops and they have to halt at multiple places in the city which can make it quite time consuming for passengers to reach their destinations. The goal of our project is to reduce this problem by providing a ride sharing application for institutes. This will be mutually beneficial for the students providing a ride and the students wanting to reach their destination quickly and cheaply as those who bring their own vehicles anyhow have to go to their homes without anyone sharing the ride with them. This will help them to earn money to at least cover their transportation or fuel cost and in-turn help provide a cheap ride to the ones in need. In this paper, we survey the work that deals with various paradigms of ride sharing and coincides with our idea for the application. Keywords: Peer-to-Peer, Pooling, Ridesharing, Routing, Shortest path I. INTRODUCTION Peer pooling is a way to share transportation services. This concept has been around for quite some time now and it is used by many multinational companies like Uber, lyft etc. It is widely adopted because it not only saves fuel costs but provides a cheap alternative to getting personal rides. Consider a scenario where one person has to go from a source to a destination. Many times another person has to travel to the same location or a second set of source and destination which is on the way to the source and destination of the first person. Likewise, many subsets of source and destination can be present. Pooling allows the driver to efficiently pick-up the riders and drop them according to the route set by the routing algorithm. There are many challenges and techniques to create the routing algorithms. According to our goal of providing ride sharing options for university students many problems are to be tackled starting from pinpointing the source and destination, peer to peer communication, predicting fare for the ride by calculating total distance and fuel consumption and the most important optimal routing. The literature survey below addresses issues which are related to our project. II. BUS POOLING: A LARGE-SCALE BUS RIDESHARING SERVICE (Liu, K. et al, 2019). This paper deals with the problems which arise due to car sharing such as the low capacity of transport, higher costs and not being able to satisfy the demand for getting a ride. The authors have developed a bus sharing service where users can create their accounts and list demands to get a ride. They have proposed algorithms to maximize the success rate of getting rides and increase ride-matching optimization. The direct effect of this paper is reduction in use of vehicles and hence, fossil fuels. They have addressed constraints such as Departure Constraint, Time-Window constraint, Capacity constraint and Cost constraint. Their solution approach for ride-sharing issues are: Phase 1:  Matching key: Where an agency reviews the requests from both drivers and riders to provide a suitable match.  Primary search criteria: where the origin and destination is treated as two points and a linear path is drafted as a line segment.  Routing and Time: Finding similar routes which can provide options as well as the shortest time.  Dynamic Time Warping: This algorithm can calculate the divergence between two sequences with different phases and length.  Keyword/List: Can search for specific keywords on a predefined list. This predefined list consists of landmarks and their distance from each other.  Capacitated clustering: Some capacity constraints are employed according to which the demands are