International Journal of Emerging Technologies and Engineering (IJETE)

Volume 2 Issue 3, March 2015, ISSN 2348 – 8050

66 www.ijete.org

TAXI-FINDER: ONLINE RECOMMENDER SYSTEM

Reshma Supugade,Priyanka Thakur,Namrata Jagtap Students of BE(COMPUTER),

Under the guidance of Prof. Patil S.S., Prof. Sayyad S.N.

Al-Ameen College of engineering, Koregaon Bhima, Pune

Abstract A recommender system for both taxi drivers and

people expecting to take a taxi, using the knowledge of

passengers’ mobility patterns and taxi drivers’ picking-

up/dropping-off behaviors learned from the GPS

trajectories of taxicabs. First, this recommender system

provides taxi drivers with some locations and the

routes to these locations, towards which they are more

likely to pick up passengers quickly (during the routes

or in these locations) and maximize the profit of the

next trip. Second, it recommends people with some

locations (within a walking distance) where they can

easily find vacant taxis.

In our method, we learn the above-mentioned

knowledge (represented by probabilities) from GPS

trajectories of taxis. Then, we feed the knowledge into

a probabilistic model which estimates the profit of the

candidate locations for a particular driver based on

where and when the driver requests the

recommendation. We build our system using historical

trajectories generated by over 12,000 taxis during 110

days and validate the system with extensive

evaluations including in-the-field user studies.

Keywords— Location-based services, urban

computing, recommender system, trajectories,

taxicabs, parking place detection

I. INTRODUCTION The taxi fleet management system based on GPS has

become an important tool for efficient taxi business. It

can be used not only for the sake of fleet management,

but also to provide useful information for taxi drivers

to earn more profit by mining the historical GPS

trajectories. In this paper, we propose a taxi

recommender system for next cruising location which

could be a value added module of the fleet

management system. In the literature, three factors

have been considered in different works to provide the

similar objective, which are distance between the

current location and the recommended location,

waiting time for next passengers, and expected fare for

the trip. In this paper, in addition to these factors, we

consider one more factor based on drivers experience

which is the most likely location to pick up passengers

given the current passenger drop off location.

A location-to-location graph model, referred to as

OFF-ON model, is adopted to capture the relation

between the passenger get-off location and the next

passenger get-on location.

We also adopted an ON-OFF model to estimate the

expected fare for a trip started at a recommended

location. A real world dataset from CRAWDAD is

used to evaluate the proposed system. A simulator is

developed which simulates cruising behavior of taxies

in the dataset and one virtual taxi which cruises based

on our recommender system. Our simulation results

indicate that although the statistics of historical data

may be different from real-time passenger requests,

our proposed recommender system is still effective on

recommending better profitable cruising location.

Android is a software stack for mobile devices that

includes an operating system, middleware and key

applications.

The android SDK provides the tools and APIs

necessary to begin developing applications on the

Android platform using the Java programming

language.

II. OBJECTIVES

Different from other public transports like

buses or subways, which follow the fixed

routes everyday taxi drivers plan their own

routes once they drop off a passenger.

We develop an approach to detect the parking

places from GPS trajectories and segment the

GPS trajectories

A parking candidate could sometimes be

generated by taxis stuck in a traffic jam, or

waiting for signals at a traffic light, instead of

a real parking. To reduce such false selections,

we design a supervised model for picking out

the true parked status from the candidate.

Users are generally moving, and typical

context data includes elements such as current

locality, time, and user’s status.

International Journal of Emerging Technologies and Engineering (IJETE)

Volume 2 Issue 3, March 2015, ISSN 2348 – 8050

67 www.ijete.org

III. PROPOSED SYSTEM We use historical GPS data to analyze and retrieve the

habitation of residents in an area. Temporal and spatial

issues are important factors which the taxi drivers may

consider in determining passenger distribution. We

emphasize on impacts on the movement of the taxi

drivers after the passengers are dropped off by

analyzing the spatial and temporal factors.

This proposed system makes use of the cloud

technology to store and retrieve various telephony

information using SOAP protocol. Global Positioning

System, shortly known as GPS System, is the system

that enables you to know the location of a person or a

thing. It consists of minuscule chip which is attached

to the object to be tracked. This chip will give out

signals which are tracked by the satellite which sends

data to the earth giving the exact location of the object.

GPS tracking has come to be accepted on a global

scale. It has a number of users worldwide.

Three V’s in Big Data:

• Volume: there is more data than ever before,

its size continues increasing, but not the percent of

data that our tools can process.

• Variety: there are many different types of data,

as text, sensor data, audio, video, graph, and more.

• Velocity: data is arriving continuously as

streams of data, and we are interested in obtaining

useful information from it in real time.

Nowadays, there are two more V's:

• Variability: there are changes in the structure

of the data and how users want to interpret that data

• Value: business value that gives organization a

compelling advantage, due to the ability of making

decisions based in answering questions that were

previously.

considered beyond reach summarizes this in their

definition of Big Data in 2012 as high volume,

velocity and variety information assets that demand

cost-effective, innovative forms of information

processing for enhanced insight and decision making.

There are many applications of Big Data, for example

the following-

• Business: costumer personalization, churn

detection

• Technology: reducing process time from hours

to seconds

• Health: mining DNA of each person, to

discover, monitor and improve health aspects of every

one.

• Smart cities: cities focused on sustainable

economic development and high quality of life, with

wise management of natural resources.

These applications will allow people to have better

services, better costumer experiences, and also be

healthier, as personal data will permit to prevent and

detect illness much.

Fig.1 Block Diagram Representing the system

architecture

Taxi recommender system provide the best user

interface between the taxi driver and the passengers.

This system is more useful for increasing the revenue

of taxi drives. This gives the analysis report to taxi

driver which helps him to know how he gets more

profit on the particular route and at particular time.

This System contain two types of actors: 1) Taxi driver

, 2) Passenger. Their interaction is taken place through

the Central Server. At the time of registration of this

application it is divided into two part i.e. 1) registration

of Taxi Driver, 2) registration of Passenger. To use the

System both have to compulsory fill registration

details. And the Central Server keeps their details

safely into its Database.

Details about the actors in system :-

Taxi Driver: - The Taxi Driver has to keep it record

details updated daily to increase its revenue. At the

time of registration he has to fill some of important

details like Name, Mob. No., Taxi no., License no. and

some more details about the taxi.

Passenger:- The passenger has to fill its some details to

confirm its presence. The passenger has to insert their

details like Name, Mob. No., Address and working

status which show necessity of using Taxi.

3) Central Server:- The main part of the system which

manages the Taxi driver and passenger communication

along with providing the analysis part to the taxi

International Journal of Emerging Technologies and Engineering (IJETE)

Volume 2 Issue 3, March 2015, ISSN 2348 – 8050

68 www.ijete.org

driver. The central Server has its own Database which

has every detail about Taxi driver and passengers.

When customer send request for taxi firstly goes to

central server and central server broadcast passenger.

The central server has to keep updating it details alone

with every operation performed by taxi drivers and

passengers. The Central Server has important tasks

like registration, communication between taxi driver

and the passenger, and to give the analysis details

when required to taxi driver.

Task of Central Server:-

Registration: The Central Server has main part of

registration of both taxi driver and the passenger which

can keep it details in his Database which can be

updated according to his and actors need. At the time

of the registration of it divides the registration into two

parts i.e. 1) registration as a taxi driver, 2) registration

as a passenger. This initial Registration confirms the

operations of the users to be used in the application.

Communication: The communication takes place

between the Taxi Driver and the Passenger through the

Central Server. The initialization of this

communication takes place from the passenger side

always who has to confirm the taxi. The

communication has the following messages:

Request msg: When the passenger sends the Request

msg from a source location then it first goes to Central

Server and this request msg is broadcasted into a

specified source area.

Broadcast msg: This Broadcasted msg is send to all the

taxi drivers which are registered on the System and are

located in the specified area of the passenger.

Response msg: The Taxi drivers send the response

msg to the requested taxi passenger which has the

details about his waiting time, fare of its trip. Due to

this the passenger receives the list of repose taxi

drivers and its details like waiting time and fare of trip

along with it. Which helps the passenger to select taxi

according to this fare.

Confirmation msg: After selection of the particular taxi

the confirmation msg is send to the Central server.

And then Central Server send the confirmation msg to

selected taxi driver.

ACK / NACK msg: This msg is send by Central

Server to Taxi Drivers. In this ACK msg is send to the

specified i.e selected taxi driver by the passenger and

the NACK msg are broadcasted to the rest all taxi

drivers to information about the requested passenger

has got a Taxi.

Analysis: The central server has main part of analysis

of taxi drivers trips. On the basis of GPS trajectories

the Central server analysis the Spatiotemporal part and

give details to taxi driver to increase its revenue.

Spatio: In this the working day of taxi driver is divided

into 24 parts. Which helps the taxi driver to specifies

the profitable working time to get more income from

the Smart Traveller system.

Temporal: In this analysis part the central server

suggest the profitable path from their pervious GPS

historical trajectory. Taxi driver can increase its

revenue by visiting the suggested path by analysis

factor of taxi recommender system.

IV. METHOD OF IMPLEMENTATION 1. Hardware and Software:

The Android mobile terminal is Google Dev Phone

1and 2. The operating system for the terminal is

Android2.1 (Eclipse). We develop mobile ad hoc

network software using Java programming language

and SDK for Android 2.1.

2. Functions:

So far, we have implemented communication software

to construct a 3G network by GPS for the employee

tracking system. We took care of security in

communication between each pair of mobile terminals

using WS-Security. When a mobile terminal

communicates with another mobile terminal, it is

necessary to establishpairingof such two mobile

terminals before their communication occurs. When

the employee mobile terminal crosses a particular

boundary region an immediate alert message send to

the manager mobile phone using 3G network and

simultaneously messages stored in the centralized

server. Data stored are secured using encryption

algorithm.

V. ALGORITHM

Algorithm 1: Parking Candidate Detection

Input: A road network G, a trajectory J, distance

threshold _, time threshold _

Output: A set of parking candidates P = fPg

1 i = 0;M kJk; P ?; P ?;

2 while i < (M � 1) do

3 j = i + 1; flag false;

4 while j < M do

5 dist Distance (pi; pj );

6 if dist < _ then j = j + 1;flag =true;

7 else break;

8 if pj�1:t � pi:t > _ and flag =true then

9 for each point p 2 J[i; j) and p =2 P do

10 P.Add(p);/* build a candidate */

11 if i = j � 1 then

12 P.Add(MB (P)); P ?;

International Journal of Emerging Technologies and Engineering (IJETE)

Volume 2 Issue 3, March 2015, ISSN 2348 – 8050

69 www.ijete.org

/* add the minimum bounding box of P

into P */

13 i = i + 1;

14return

VI. CONCLUSION to save the time for finding a taxicab and reduce

unnecessary traffic flows as well as energy

consumptions caused by cruising taxicabs, we

proposed a taxi-passenger recommender system based

on the pick-up behaviors of high-profit taxi drivers and

the mobility patterns of passengers learned from a

large number of taxi trajectories. We built the

recommender system with a dataset generated by

12,000 taxicabs in a period of 110 days, and evaluated

the system by extensive experiments including a series

of in-the-field studies. as a result, the taxi

recommender accurately predicts the time varying

queue length at parking places and effectively provides

the high-profit parking places; the passenger

recommender successfully suggests the road

1segments where users can easily find vacant taxis,

e.g., the top-1 road segment recommended by our

system considering day of the week and weather

conditions matches the ground truth for all of the

tested areas. in the future, we plan to deploy our

recommender in the real world so as to further validate

and improve the effectiveness and robustness of this

system.

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