(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 12, 2019 251 | Page www.ijacsa.thesai.org Real - Time Carpooling Application based on k - NN Algorithm: A Case Study in Hashemite University Subhieh El Salhi 1 , Fairouz Farouq 2 , Randa Obeidallah 3 , Yousef Kilani 4 , Esra’a Al Shdaifat 5 Department of CIS Hashemite University Jordan Abstract—The current revolution of mobile technology in different aspects of community directs the researchers and scientists to employ this technology to identify practical solutions for daily life problems using mobiles. One of the major challenges in our developing countries is the public transportation system. Public transportation system is an essential requirement for the welfare of modern society and has a critical impact on the people productivities and thus on the entire economic development process. Therefore, different solutions had been investigated to find applicable solutions. “Carpooling” is one of the initiative solutions that based on the usage of a single shared car by a group of people heading to the same location on a daily basis. In addition, carpooling can be considered as an efficient alternative to overcome the limitations of the conventional transportation system with an easier, quicker and more environmentally friendly car journeys. This paper presents an intelligent carpooling mobile app to commute students of the Hashemite University. The proposed solution is founded on using data mining technique, and more specifically the k-Nearest- Neighbour (k-NN) technique. Keywords—Mobile Application; Carpooling; Data mining; Classification; k-NN algorithms I. INTRODUCTION Jordan as many countries is effected by the increasing number of vehicles on streets. The enormous number of vehicles causes environmental pollution, social problems, traffic congestion and not enough parking spaces. The extensive number of vehicles on roads is a natural result for the inefficient public transportation system that leads to vehicle dependency and ownership. Transportation Demand Management is a collection of strategies and administrative regulations to encourage efficient traffic patterns to reduce travel demand and to reorganize mobility in space and time. Transportation Demand Management provides several solutions for traffic congestion. One solution to reduce the increasing number of vehicles on streets is car sharing. Car sharing is also known as carpooling. Carpooling is involved with a group of people who have similar time slots and going to the same office or school such that they share a vehicle owned by the driver [1]. Information and Communication Technology (ICT) play a major role for bringing potential carpoolers together and encourage carpooling. ICT contribute to establish communication with potential passengers, and make it available to a large number of potential users with enough variation in demographic and trip characteristics (e.g. timing, geography, etc.). Many algorithms were used in Carpooling systems to calculate the best route, shortest path and find potential passengers. Carpooling systems use algorithms and data mining techniques to allow both passengers and drivers to find a convenient trip route and to support a billing system. Examples of these mining algorithms are Dijkstra's algorithm, Bellman Ford's algorithm, Floyd–Warshall's Algorithm and k- Nearest Neighbor algorithm [2]. However, one of the most popular machine learning algorithms is the k-Nearest Neighbor algorithm (k-NN). Generally speaking, k-NN is an efficient, simple and easy-to-implement supervised machine learning algorithm that can be used to solve different classification and regression problems [3]. The University is located in city of Zarqa in Jordan. Students and employees of the Hashemite University (HU) have critical difficulties during their daily journey to the campus because of the poor public transportation. The lack and delay in public transportation system while travelling from different locations such as Amman, Zarqa and Irbid to campus motivate us to develop a carpooling application that directed mainly to students. Obviously, the proposed carpooling app is directed to a particular class of users --the students of the Hashemite University where the idea is to provide them with a reliable, safe and rapid ride to the campus with minimum fees. The proposed carpooling app based on the well-known k-NN algorithm and the novelty of our application comes from its simplicity where the app use the same recognized road to the university with predetermining stopping points to pick the student from or drop them in. Therefore, the k-NN is employed to identify the closest drivers at certain time. Taking into consideration that eligible drivers only are sent to passengers to select the most suitable one. The Deanship of Student Affairs at the Hashemite University determines the eligibility with accordance to its policy to assure the safety of the students. Our main goals of developing carpooling app are: (i) to minimize the overall travel cost and distance from students’ houses to HU campus that is located in ZARQA city, (ii) to reduce pollution and traffic jam and (iii) to help students to be comfortable and satisfied with their ride to the university and this would absolutely has a positive impact on the students productivity. Furthermore, an ethical value added since this app highlights the initiatives and high responsibilities towards community problems. Being initiatives and part of the suggested solution would raise the value of the team work as well to face different types of life problems.