SEARCHING TRAJECTORIES BY REGION OF INTEREST (TSR QUERY)

ABSTRACT

With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.

EXISTING SYSTEM:

In existing system the user doesn’t know the famous places in the cities which we want to see, and how far it is located from the user current location. The user can’t find the exact route for the destination of our travels, and it leads more time consumption, the user faces many problems while travelling one place to another places, in-between many problem were occurred because of road conditions and weather condition. In existing system the user can face many problems while they travelling, the user can’t know that what is happening in those travelling places, and user doesn’t know that how famous is the place in the city is the difficult job.

Disadvantage:

  • User doesn’t know how famous is the places
  • Waste of time
  • Bad weather and road condition

PROPOSED SYSTEM:

But in proposed system “TSR Query” Application is used to search the famous places in the cities which we want to visit.in this application having all cities in India and famous place in the relevant cities. The user can choose the cities and view the famous places in the cities and view the reviews and ratings to plan the tour, the user view the current places in the map and view the trajectories in the map for riding by the road. The existing user information is useful for the places to visit and know the weather and road condition of the places.

Advantages:

  • Less time consumes
  • Easy to know the routes
  • Easy to know the weather conditions

ARCHITECTURE

MODULE DESCRIPTION

MODULES:

Planning tour module

Existing Visitors Reviews Module

Current Visitor Upload Information Module

Routing Module

Planning tour module:

In planning module the user should plan before they ready to move from home, in this module the user search the destination, In this searching place having so many important tourism places were there But the user does not know the exact location and important place of the searching destination.

So the user can search the important places and decides to go for the tour. Once the place were search then the user can view the exact place and view the reviews and ratings by decide that how famous is the place.

If the place having good reviews and rating, the user want to visit the places, But the user does not know the exact location, the map is used to search the exact location of the places.

Existing visitor review module:

The Existing Visitor Module is used to get the details and review for the places which we want know about.

The existing visitor were gathering the details and share the experience of the tour and rating and also reviews are uploading in the admin side. For other visitors.

In this previous visitors also share the images and videos of the place. And view the place by using the map

Current visitor upload information module:

In this Current visitor module the Visitor were upload the current scenario of the tour place and images and videos can be shared upload in the admin page for other users.

The Current visitor also knows the current location by using the map and shares the location in public.

Routing module:

In routing module the user search the current location to the destination location and view the distance and travelling timing.

In this routing the visitor can know the important places which are placed near by the user.

{\displaystyle \ell _{2}={\frac {x-x_{0}}{x_{2}-x_{0}}}\cdot {\frac {x-x_{1}}{x_{2}-x_{1}}}={\frac {x-2}{5-2}}\cdot {\frac {x-4}{5-4}}={\frac {1}{3}}x^{2}-2x+{\frac {8}{3}}\,\!}SYSTEM SPECIFICATION

HARDWARE REQUIREMENTS:

System : Pentium IV 2.4 GHz.

Hard Disk : 40 GB.

Monitor : 14’ Colour Monitor.

Mouse : Optical Mouse.

Ram : 4 GB.

SOFTWARE REQUIREMENTS:

Operating system : Windows 7 Ultimate.

Coding Language: Java.

Front-End: android studio.

Back-End: php

Data Base: MySQL.

CONCLUSION

With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources.