An Intelligent Carpooling App for a Green Social Solution to Traffic and Parking Congestions

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International Journal of Electrical, Electronics and Computer Systems (IJEECS)

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ISSN (Online): 2347-2820, Volume -4, Issue-10_11, 2016 18

An Intelligent Carpooling App for a Green Social Solution to Traffic and Parking Congestions

1Animesh Bhandra, ²Shilpa Chaudhari

1,2REVA UNIVERSITY, Bangalore

Abstract: Existing public transportation system is not the solution for daunting traffic and parking congestion problems seen in urban places. After investigating researchers have adapted carpooling method in which a person owns a vehicle share that with other one or more members having the same paths. This carpooling method involves Genetic Algorithms with the way out for minimal travel distance, timely arrival, efficient ride matching, and maximum fairness while taking into account the riding preferences of the car-poolers.

I. INTRODUCTION

As the population is growing economically, the number of private vehicles is also increasing in the cities worldwide which in turn resulting parking problems, traffic congestions, inordinate fuel consumption, and excessive pollution. To achieve the required resources of transportation carpooling proves to be an efficient and effective approach in which empty seats of private vehicles will be filled by the passengers having alike departure and destination locations.

Implementation of carpooling has many other advantages like congestion in the roads will be significantly reduced; availability of the parking area will be increased and in addition carpooling is eco- friendly.If a ride will be shared by four people then the required amount of fuel for transportation will be decreased by the factor of 4. The social benefits of carpooling are riding stress will be less, before reaching riders can snooze their destinations, during ride time new friendships will be made and it will increase the sense of responsibility of those who tend to be late as they would become more dependable and accountable to the other commuters and finally other family members can use the car for their need if a car is left at home.

II. LITERATURE SURVEY

E.Ferrari, R.Manzini published a paper in Journal of Advanced Transportation on “ The carpooling problem:

heuristic algorithms based on savings fuctions” in 2003 which included a solution for the problems faced in LTCPP. Here automatic and heuristic data processing routines will be developed to allow the proficient mapping of the rides for the commuters and the drivers.

A paper published by Baldacci R., Maniezzo V, and Mingozzi A named “An exact method for the carpooling problem based on Lagrangean column generation in 2004 includes two algorithms among them the first one is DCA which solves the carpooling problem and the second algorithm is DCA-Branch and Bound globalizes the acquired results.

According to Sghair, Manel, Hayfa, Zgaya, Slim Hammandi and Christian Tahon and their publication named “ A distributed Dijkstra’s algorithm for the implementation of a real time carpooling service with an optimized aspect on siblings” at IEEE annual conference on intelligent transportation systems, Madeira island, Portugal in 2010, the cloud of users is categorized into small areas centred on a driver. A check will be made on each commuter to see if a vehicle having empty seats passes nearby, and is found then assignment will be made incrementally. This clarification has a fast runtime compared to other carpooling methods but it can’t be implemented globally because considering incremental driven distances will not give a fair result.

“A model with heuristic algorithm for solving the long- term many-to-many carpooling problem” published by Yan, Shagyao, Chun-Ying Chen and Yu-Fang Lin in IEEE transactions on intelligent transportation systems, 2011, includes a Lagrangian relaxation method based network flow technique that facsimiles drivers and passengers’ routes and schedules.

III. SYSTEM DESIGN

The carpooling method requires three modules for its working,Server Rider and Users and Scheduler

Server:

The server side of the system has two main functionalities: it runs the carpooling scheduler and stores all users and rides information. After collecting all the needed information from the different subscribers, the scheduler is daily allocated a six hours’

time window to run starting 10PM. We assume that no change in schedules occurs after this time. As discussed earlier, matching passengers and drivers is done and all the rides are determined for the upcoming day. The server then communicates all the rides to the respective drivers and passengers.

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Rider and Users:

A principle screen shows up permitting a client to include rides or view her/his status. To include a ride, a client indicates the cause and goal by writing the name of the area or utilizing the guide to pinpoint it. Flight and landing times are checked for attainability against Google Maps before client's solicitations are sent to the server. Seeing the status of existing ride shows data about the ride timing and insights about the driver and the course to be taken. The cell phone needs a 3G availability to trade information with the server, and in addition a GPS capacity to encourage its directions into the framework.

Scheduler:

We expect that the quantity of autos on a specific day is sufficient to oblige every one of the clients who wish to carpool on that day. The model additionally requires that all members give their asked for rides in advance. This information incorporates the auto limit, the starting point and goal of the client for every outing, the wanted take- off and landing times, and individual inclinations.

These inclinations incorporate craved number of clients to ride with, smoking authorization, and a boycott.

Notwithstanding the previously mentioned client information, different measurements are expected to run the model. These are: the topographical directions of the considerable number of clients and goals, and the separation and travel time between each two hubs of the system.

GAs are versatile inquiry calculations in view of common choice and survival of the fittest idea. A GA uses a populace of people that experiences arrangement choice affected by change as well as hybrid administrators, A wellness capacity is then used to assess people, and the survivability of every individual relies on upon its wellness, which fits well the auto pooling issue.

The calculation that is utilized as a part of the framework keeps up the general structure of a GA yet has been adjusted to improveconvergence - which is in some cases an issue with standard GAs, GA union time is diminished because of an underlying arrangement that

generally chooses the early populace rather than a arbitrarily instated one, Since one of the goals is to minimize the quantity of drivers, and a populace has a settled number of drivers, the GA will be keeprunning in parallel over diverse populace sizes, and every string will be checked for survivability.

IV. IMPLEMENTATION

For Carpooling mode of transportation, the communication between the rider and the commuters is very important. So both the rider and the commuter need to install application for their communication. Once the rider and the commuter start installing the application, the server asks for the access permission to the user. If the user allows the application to access the data then the application will be installed successfully and if denied then the application installation will be stopped.

Once the application is installed then the user has to register himself to the server for the further use of the application. The person who owns the vehicle will register himself as a rider to the server and the person who needs drop from the other person will register himself as passenger to the server. After submitting the user details, check for the offline availability of the application. Then the user dashboard will be created for both rider and commuters. Both the rider and the commuter have to enter their source location and the destination location in their respective dashboards.

Update this information to the server.

When a rider logs-in to the application and enters his source and destination location information then the server searches for the commuters with the similar or the semi-common routes to the rider and then allows them to communicate with each other. And if a commuter enters his source and destination location information to the server then the server checks for the availability of the vehicle in that route and then acknowledges the commuter regarding that and allows the commuter to communicate to the rider.

Fig: Sequence Diagram

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V. CONTRIBUTION

The implantation of the current project includes a genetic algorithm (GA) which makes the system more accurate, faster and hop-to-hop connected which in turn makes the user and commuters’ communication convenient. Also, the system exploits all existing resources to ensure an easy and low-cost implementation.Carpooling saves the money of a rider on gas, wear and tear of the vehicle and parking fees.

VI. RESULTS and OBSERVATIONS

The application is deployed under the cloud environment for car-pooling mechanism. The system has successfully achieved the routing approach for synchronizing the overall incoming traffic on road. The overall application is programmed for limited cities.

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