ARTIFICIAL INTELLIGENCE AIDED RECOMMENDATION BASED MOBILE TRIP PLANNER FOR ESKISEHIR CITY

Guide: Prof Farhana Kausar

Presented By:Amani Sharieff (1at12cs009)

05-04-2016 Atria Institute Of Technology

AGENDA

Proposed System and Advantages

References

Motivation

Introduction

Proposed Methodology

Problem Statement

Existing System and Disadvantages

Conclusion

INTRODUCTION

Intelligence exhibited by machines or software . Deals with studies as to how to create computers and computer software that are capable of intelligent behaviour.

Such as reasoning, planning, learning, natural language processing, perception, decision making , recommendingetc..

Cont..

These days in order to plan a vacation , business trip or to even simply just travel to a city people use planning trip applications.

The smart phones have an inseparable association in our lives and these offer convenience for various mobile applications.

There are several trip planning applications which have been developed to plan for the trip and these have rich content for popular cities eg : London, New York etc.

PROBLEM STATEMENT

Although there are some mobile trip planning applications available for a big country like Istanbul they lack important feature that would be necessary to enhance the overall

experience and provide the best trip quality .For other cities which are less popular, most applications have poor or user-based, unreliable

content

OBJECTIVES

Database with reliable and rich content.

An application that is dynamic, capable of responding quickly , well

designed etc.

Provides recommendations to user.

Route must be calculated quickly and must be changeable at any time.

It should track user’s trip , also it should have detailed information to make accurate plans.

EXISTING SYSTEMThere are several mobile trip planning applications available in

the market. Let us consider top 3 such applications:

1.

DO’s: Offers a complete hassle free way to combine all your

travel confirmations, itineraries, tickets, hotel bookings, rental car reservations, and the rest in one simple view.

That view then becomes the central hub for all of your travel needs.

DON’T’s: It won't suggest destinations for you

it won’t help you plan the best possible way to spend your time in town wherever you go,

2.DO’s: Helps user build a wishlist of destinations he

wishes to travel. This is shared with their family and friends and

thus they collaborate

DON’Ts: Does not help you organise the trip Does not help in finding the best prices

DO’s: Plan the trip of the user from the beginning like

tourist eye

DON’Ts Doesn’t collect information and help the user

plan each leg of the trip Doesn’t plan the time limits or suggest

places of interest.

3.

DISADVANTAGES OF THE EXISTING SYSTEM

These applications have reliable database only for famous cities, they lack authenticity when it comes to smaller, lesser known places.

None of them recommend suggestions to the user at real time.

They do not facilitate sub-planning for a larger plan ie: within a large park.

They don’t do anything to optimize the visiting time of each point of interest.

PROPOSED SYSTEM

This study proposes a new mobile trip planner for real time navigation developed for Eskisehir city, Turkey.

Given the current GPS location of travellers and their preferences, the mobile trip planner allows finding a route which minimizes the total travelling time while optimizing the visiting time for each point of interest (POI).

The proposed application also gives some recommendations to the travellers which helps them to re-plan their route respectively

ADVANTAGES OF PROPOSED SYSTEM Two types of data are collected

Artificial aided recommendations are provided.

Facilitates sub-planning for a larger plan.

Optimizes the visiting time for each point of interest

Data from usersReliable data provided by the local Authorities such as Tourism BoardAnd Municipality

PROPOSED METHODOLOGY

A.Designing the database.

B. Collecting and Categorizing data. Here we consider Eskisehir city, Turkey.

Tables

Keys

Relations

Data is collected from local authorities ie; ‘Eskisehir Provincial Directorate of Culture and Tourism ‘ and ‘EskisehirMetropolitan Municipality’.

Data

Preliminary design work

30 points of interest.

(POI)

150+ SUB POIS

ARCHITECTURE MUSEUM ENTERTAINMENT NATURE ART POPULAR

LIFECYCLE OF APPLICATION

Make a plan considering ideal hours to visit , transportation time etc

Separates places to days and returns routes to the user

The application runs algorithm to sort places.

Tracks user and makes recommendation depending on schedule

Application encourages to make a sub route when user visits a complex place

After user is done ,the application takes the rating from the user

A* ALGORITHM

It chooses the next point by current cost from starting point and heuristic distance to ending point.

f(n) = g(n) + h(n)

Basically, it calculates the heuristic function f(n) (1) for each point.

According to calculated f(n) values, points are stored in an ordered stack. The algorithm calculates the route by selecting points from stack by order and checks the route if it is optimum.

It stops when the next point in the stack is end point.

GC

B

A

S

1

4

2

5

2

12

3

NODE Heuristic Distance S 7

A 6

B 2

C 1

G 0

Table of heuristic distace

Ant Colony Optimization It is based on ants’ path finding to their

nest.

While ants carrying food from food source to their nest, they choose random paths and leaves pheromones on the road.

These pheromone are volatile.Thus, it is more intense on shorter paths.

Other ants prefers the road with more

pheromones but not always. Some ants prefers new roads to seek shorter paths.

By time the shortest paths will be preferred by ants and total shortest path reveals.

ARTIFICIAL INTELLIGENCE AIDED RECOMMENDATIONS OF THE APPLICATION

START UP SCENARIO

SCENARIO AFTER CHOSEN RECOMMENDATION

RECALCULATED ROUTE AFTER DELAYING

SUB ROUTE IN A COMPLEX PLACE

Sazova park is also known as the Turkish Disneyland. It is a complex place with sub-placesSuch a Castle, Artificial pond, Café, Aquariums etc. Therefore the app facilitates sub-Planning to make sure the user has the best possible experience, without missing any of theAttractions.

RECOMMENDATION COSTPOIs GPS Coordinate  POI1 39.765505, 30.471746

POI2 39.781275, 30.513441

POI3 39.771555, 30.516892

POI4 39.765234, 30.521856

POI5 39.774921, 30.549236

POI6 39.776077, 30.515681

POI7 39.765720, 30.513063

POI8 39.781222, 30.526839

POI9 39.757967, 30.531173

POI10 39.772172, 30.520269

POIREC1 39.775023, 30.518881

POIREC2 39.771243, 30.529220

Scenario 1 User plans a trip with first 5 places in Table I. Between POI2 and POI3 , the application offers a recommended place POIREC1. If user adds this place to trip, the application produces two alternative plans Alternative 1 is POI1 , POI2 , POIREC1 , POI5 Alternative 2 is POI1 ,POI2 , POIREC1, POI3, POI4.

Scenario 2User plans a trip with 10 places POI1 , POI2 , POI6 , POI3,

POI10 , POI7 , POI4 , POI8 , POI5 , POI9 in order. Between POI3 and POI10 , the application offers a recommended place POIREC1 and between POI5 and POI9 , another recommended place POIREC2. Alternative 3 is POI1 , POI2 , POI6 , POI3, POIREC1, POI10, POI8,

POI5, POI9 Alternative 4 is POI1, POI2 , POI6, POI3, POI10, POI7 , POI4,

POI8, POI5, POIREC2, POI9.

PSTARTRECOMM

.

PREC.TOTAL TRAVEL TIME (MIN)

TOTAL DIST.(KM)

TOTAL VISIT DURATION

ALG. RUNNING TIME

A* ANT

5 N 5 26 12.4 6h 30m 1 2

5 Y(ALT.1) 4 31 14.0 5h 50m 1 2

5 Y(ALT.2) 5 23 10.3 6h 1 2

10 N 10 47 19.7 11h 20m 2 16

10 Y(ALT.3) 9 48 19.1 10h 10m 1 15

10 Y(ALT.4) 10 50 20.0 10h 30m 2 17TABLE II. RESULTS FOR ALTERNATIVE PLANS

Σ 1 ≤ i ≤ P-1, ttimeVij is represented by

Total Travel Time.

For alternative plans, new routes are calculated

and Total Travel Time, Total Distance

and Total Visit Duration are affected.

Total Visit Duration is calculated by sum of

the visit duration of POIs that is stored in

database.

PSEDOCODE

calculate(G(P,A));while(POI in P) visit(POI); if(POI has sub-POIs) P=sub-POIs; calculate(G(P,A)); endif scanArea(radius); if(recommendation) produceAlternatives; recalculate(G(P,A)); endifEndwhile

SUMMARY AND CONCLUSIONS

Using the user-friendly Google Maps interface, the mobile application allows users to choose categorized places. After that preference application calculates the best route in specified time interval.

At present, A* and Ant Colony Optimization algorithms are chosen for computation of the best routes. These algorithms are examined for different scenarios.

Additionally artificial intelligence aided application presents the user with alternative

suggestions during the trip, which help increase the total quality of the trip experience. Experimental results show that A* algorithm calculates the complete route %50-90 faster

than Ant Colony Organization under the same scenarios. Overall, the mobile trip planner can assist travelers to optimally design their travel routes online before the trip begins.

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