THE OPTIMAL CARPOOL PLANNING, BASED ON IOS PLATFORM

2019

Abstract— Traffic congestion is a serious problem in many urban areas around the world, which generates endless negative impacts on society and the economy, among which are: air pollution due to carbon dioxide emissions, waste of fuel and increase travel time. In this sense, the Carpooling initiative, which is the dynamic in which a driver shares his car with one or more additional passengers who have a similar destination, emerges as one of the most effective solutions to deal with the problems generated by the vehicular congestion. This paper shows the development of an intelligent system for the use of Carpooling which allows users to have a tool to carry out this practice, which, unlike existing ones, allows access at any time and in any place to the user's data such as their current location, their destination, their tastes and personal preferences. To select the most optimal route, the ideal driver and the companions, the system uses one of the most used models in supervis...

Procedia - Social and Behavioral Sciences, 2015

Traffic jam is a growing problem especially in big cities like Riyadh. Wherever, streets are congested with cars and the need of cost-effective system is increasing in order to reduce the traffic jam impact. At the same time, each car's capacity can take up to four persons but this capacity is generally underexploited. If we can take advantage of the free space in each car, this will lead us to its best use with an optimal equation for achieving: fewer cars in the streets with more number of passengers. In this context, our mobile application "Where are you ?" is proposed as a solution that can help solving traffic jam problem. "Where are you ? " is a real-time application, which connects for free people living in the same city and having the same travel needs. Users rideshare their cars, based on the GPS position of the requester, the system searches the nearest and available car on the way of the requester. The system provides feedback and favorite driver's features, so that will help to recommend the best available driver. Our system is developed for android phones. It is considered as a social media since it provides communications between individuals or groups. Our application is based on trusted users. Users are authorized to register as part of a university or a company group or validated by their administrator. A "female only option" is also provided by the system. Our application is environmentally friendly because it provides carpooling service, which reduces carbon emissions, traffic jam, and the need for parking spaces.

Taxi sharing system receives passenger's real time request sent from smartphones and schedules proper taxis to pick up by taxi sharing with respect to passenger capacity, time and monetary constraints. The monetary constraint provides benefit for both passenger and taxi drivers: passenger will not pay more compared with no ridesharing and drivers get more profit compared with no ridesharing. While this system is of significant social and environmental benefits e.g., reducing traffic, saving fuel consumption and satisfying people's commute, real time taxi pooling is not well studied yet. To this end, we improved the taxi pooling system by using mobile-cloud architecture. Taxi drivers and taxi riders use the taxi pooling service by smartphones. Initially, cloud gets passenger's ride request and finds appropriate taxi for the customer by using taxi searching algorithm supported by spatio-temporal index. A scheduling process is then performed in the cloud to select a taxi that satisfies the request with minimum increase in travel distance. When the ratio of the number of ride requests to the number of taxis is 6, our proposed system serves three times as many taxi riders as that when no ridesharing is performed while saving 11 percent in total travel distance and 7 percent taxi fare per rider.

International Journal of Innovative Research in Computer and Communication Engineering, 2015

Carpooling (also known as car-sharing, ride-sharing and lift sharing), is the sharing of car journeys so that more than one person travels in a car. By having more one vehicle, carpooling reduces each person’s travels costs such as fuel costs, tolls, and the stress of driving. Carpooling is also seen as a more environmentally friendly and sustainable way to travel as sharing journeys reduces carbon emissions, traffic congestion on the roads, and the need for parking spaces. Authorities often encourage carpooling, especially during high pollution periods and high fuel prices. We intent on making an ANDROID based application that will enable to let people know if vehicles are available for carpool in their desired path they can sign in for it. This will enable people using this application to share expense, not worry about hiring a cab and making new connections. People having this application on their cell phone can easily carpool with unacquainted people without worrying about secur...

International Journal of Knowledge Engineering and Data Mining, 2019

Getting into a public transportation is now very difficult in the city of Dhaka. Moreover, they are overcrowded and getting public bus on time is also very difficult. The problem of other ride sharing services currently available in Dhaka is that if a person reserves a car, then other passengers cannot avail the car. Our main aim is to develop a match making algorithm by which a host (who offers a ride) can take multiple clients (passengers) from multiple routes efficiently without having to compromise fare, distance and other basic preferences. As in our proposed method, most of the cars offering a ride will carry passenger(s) from the host's route or multiple routes, the road utilisation will be much more effective. Reference to this paper should be made as follows: Sonet, K.M.M.H., Rahman, Md.M., Mehedy, S.R. and Rahman, R.M. (2019) 'SharY: a dynamic ridesharing and carpooling solution using advanced optimised algorithm', Int. in 2007 and 2003, respectively. He has authored more than 120 peer-reviewed journal articles and conference proceedings in the area of parallel and distributed computing, knowledge, and data engineering. His current research interest is in data mining focusing particularly on financial, medical, and educational data, cloud load characterisation, optimisation of cloud resource placements.

International Journal of Science and Applied Information Technology, 2024

The Para a Ride-pooling Application using a Geographical Information System (GIS) is a mobile application that enables the sharing of tricycle journeys, allowing multiple passengers to travel together for Pagadian City as our basis of study. This system utilizes location-tracking technology to determine passenger whereabouts. When matching passengers to drivers, the system provides comprehensive trip details and proposed payment to the driver. Adhering to ISO 25010 standards, the app assures top-tier performance, reliability, and security while introducing innovative features. The proponents are optimistic about the application's future-paving the way to better transport within the city. Through in-app chat functionality, drivers and passengers can communicate for ride confirmation. This system aims to enhance the tricycle transportation experience in Pagadian City by optimizing fuel efficiency for drivers and alleviating passenger transportation challenges.

International Journal of Innovative Research in Technology, 2021

We define a method of Ride Sharing System consisting of a web portal from which one easily book and share the rides or vehicle in an efficient manner. To reduce the ill effect of the private vehicle this technology is very necessary now a days. In this there are new services and facilities by which the effect on the environment like pollution, congestion etc. can be reduced and to provide support to the needy at the earliest. Mass transit system is the best solution if provided efficiently, but many persons do not prefer it because of its lack of door to door service, longer and fixed route and less reliable schedule Ride sharing is one of the emerging technologies adopted all over the world, in which users with same origin destination and time of travel and they share the ride. So in this project there is a responsive website which uses the technology of front end and back end along with database management system. And to provide convenience to the users. Machine learning algorithm are also implied by which one can share or book ride in an efficient manner. Concept of data base management system like query optimization, joins are been used to save the data efficiently in the data base. The relational data base MYSQL is been used to maintain server side.

2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013

Taxi ridesharing can be of significant social and environmental benefit, e.g. by saving energy consumption and satisfying people's commute needs. Despite the great potential, taxi ridesharing, especially with dynamic queries, is not well studied. In this paper, we formally define the dynamic ridesharing problem and propose a large-scale taxi ridesharing service. It efficiently serves real-time requests sent by taxi users and generates ridesharing schedules that reduce the total travel distance significantly. In our method, we first propose a taxi searching algorithm using a spatio-temporal index to quickly retrieve candidate taxis that are likely to satisfy a user query. A scheduling algorithm is then proposed. It checks each candidate taxi and inserts the query's trip into the schedule of the taxi which satisfies the query with minimum additional incurred travel distance. To tackle the heavy computational load, a lazy shortest path calculation strategy is devised to speed up the scheduling algorithm. We evaluated our service using a GPS trajectory dataset generated by over 33,000 taxis during a period of 3 months. By learning the spatio-temporal distributions of real user queries from this dataset, we built an experimental platform that simulates user real behaviours in taking a taxi. Tested on this platform with extensive experiments, our approach demonstrated its efficiency, effectiveness, and scalability. For example, our proposed service serves 25% additional taxi users while saving 13% travel distance compared with no-ridesharing (when the ratio of the number of queries to that of taxis is 6).

No, 2005

Abstract—This paper proposes a new Intelligent Transportation Systems (ITS) service, which solves a daily problem encountered by most people living in a metropolis: how to catch a taxi in the most time efficiently manner. The architecture that supports this new Personalized Public Transit (PPT) service takes advantage of the heterogeneity of the network environment and utilizes cellular and short-range communications in order to solve the problem locally and generate value for the user, the service provider and the taxi driver. ...

IEEE Transactions on Emerging Topics in Computing, 2014

Carpooling taxicab services hold the promise of providing additional transportation supply, especially in the extreme weather or the rush hour when regular taxicab services are insufficient. Although many recommendation systems about regular taxicab services have been proposed recently, little research, if any, has been done to assist passengers to find a successful taxicab ride with carpooling. In this paper, we present the first systematic work to design a unified recommendation system for both the regular and carpooling services, called CallCab, based on a data driven approach. In response to a passenger's real-time request, CallCab aims to recommend either (i) a vacant taxicab for a regular service with no detour, or (ii) an occupied taxicab heading to the similar direction for a carpooling service with the minimum detour, yet without assuming any knowledge of destinations of passengers already in taxicabs. To analyze these unknown destinations of occupied taxicabs, CallCab generates and refines taxicab trip distributions based on GPS datasets and context information collected in the existing taxicab infrastructure. To improve CallCab's efficiency to process such a big dataset, we augment the efficient M apReduce model with a M easure phase tailored for our CallCab. Finally, we design a reciprocal price mechanism to facilitate the taxicab carpooling implementation in the real world. We evaluate CallCab with a real-world dataset of 14, 000 taxicabs, and results show that compared to the ground truth, CallCab reduces 60% of the total mileage to deliver all passengers and 41% of passenger's waiting time. Our price mechanism reduces 23% of passengers' fares and increases 28% of drivers' profits simultaneously.