A Landmark Based Shortest Path Detection by Using A* and Haversine Formula

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 6 Issue: 7 98 - 101
______________________________________________________________________________________
98
IJRITCC | July 2018, Available @ http://www.ijritcc.org
_______________________________________________________________________________________
A Landmark Based Shortest Path Detection by Using A* and Haversine Formula
Ms. Megha G. Mathpal
Department of Information Technology
Government Engineering College
Modasa, Gujarat, India
mathpalmegha@gmail.com
Abstract—In 1900, less than 20 percent of the world populace lived in cities, in 2007, fair more than 50 percent of the world populace lived in
cities. In 2050, it has been anticipated that more than 70 percent of the worldwide population (about 6.4 billion individuals) will be
city tenants. There's more weight being set on cities through this increment in population [1]. With approach of keen cities, data and
communication technology is progressively transforming the way city regions and city inhabitants organize and work in reaction to
urban development. In this paper, we create a nonspecific plot for navigating a route throughout city
A asked route is given by utilizing combination of A* Algorithm and Haversine equation. Haversine Equation gives least distance between any
two focuses on spherical body by utilizing latitude and longitude. This least distance is at that point given to A* calculation to calculate
minimum distance. The method for identifying the shortest path is specify in this paper.
Keywords- Haversine Formula, Dijkstra Algorithm, Google Map, XML.
________________________________________________*****_________________________________________________
I. INTRODUCTION
The downsides happened in past paper of shortest path
discovery by utilizing Dijkstra calculation is recuperated in
this paper by employing A* calculation. A* calculation is
heuristic in nature. The point of Paper is to discover the route
between two places inside a city entered by client utilizing
the Intersections between Source and Goal intersections. The
witticism behind it is to progress navigation of client inside a
city; particularly in India where Town Arranging approach
doesn’t take after a standard rule for naming the diverse
places. Most of the times an unknown person can’t discover
indeed the foremost popular places inside the city due to
nonappearance of noteworthy identities. Hence the paper is
planning to allow a suitable route to client by coordinating it
through different intersections and streets which will be
effortlessly distinguished by the related landmarks and a
Google map. The route is given in two parts as: 1) Content
route containing route giving a intersection to intersection
movement to client in conjunction with the appropriate
directions and turnings directing the client to induce the
precise halfway intersections or landmarks 2) Google Map
for correct requested route. Paper uses client-server
engineering. Communication between them is entirely in
XML for adaptability. The client has client interface from
where an input is taken in XML for processing. The server
comprises of a Java Processing Application and Database for
it. The Database utilized by handling application could be a
Social database containing whole information around city.
The processing application after parsing request computes
route between them with all necessary subtle elements with
Latitude/Longitude for Google map and sends it as XML
response. Client once more parsing response gets it on Client
Interface with Google map handling done in JavaScript.
II. PROBLEM DEFINATION
The Point of Paper is to discover out the route in between two
spots/junctions inside a city entered by client by making use of
the Intersections in between the Source and Goal spots/junctions.
The main motto behind it is to progress the navigation of client
inside a city; particularly in Indian cities where Town Arranging
approach doesn’t take after a standard rule for numbering or
naming the distinctive spots or places. Most of the times an
obscure individual can’t discover indeed the most famous places
inside the city due to nonappearance of naming sheets or other
noteworthy identities. Subsequently the project is intended to
allow a suitable route to client by coordinating it through various
junctions and streets which is able be easily identified by the
related landmarks given with the route. The requested route is
given to client in terms of the intersections present in between the
source and goal route along with landmarks and streets
interfacing them. The Landmarks utilized within the route may be
noteworthy Buildings, Statues, Streets, Complexes, Landmarks,
Sanctuaries, etc. The use of Landmarks includes an advantage of
getting to the exact place having no critical personality whereas
voyaging through route provided to client making the application
friendly to the client obscure of the city to discover out the route
in between any two spots or intersections within the city. The
application is bound to deliver the shortest route providing a
junction to junction movement to client in conjunction with the
appropriate directions and turnings directing the client to induce
the precise intermediate intersections (with their significant
landmarks) or landmarks in specific zones in between two
junctions/spots provided by client.

______________________________________________________________________________________
99
_______________________________________________________________________________________
III. LITERATURE SURVEY
This paper contains “great circle distance” which speaks to
the shortest path for distance modeling and optimal facility
area on spherical surface. Great circle distances take into
consideration the geometrical reality of the spherical Earth
and offers an elective to broadly held idea that travel over
water can be precisely displayed by Euclidean distances. The
need for geometrical presentation of the spherical earth gets
to be exceptionally important when we take into thought an
ever-expanding junction inside a city. The utilize of “Great
circle distances” opens another avenue for merging of
Navigation and Spherical Trigonometry into progression of
logistics and facility location. In this paper an assessment of
distance area utilizing great circle distances is utilized to
illustrate the application of the concept [4][3]. This paper
proposes and executes a strategy for performing shortest
path calculations taking crowdsourced information, in the
form of imperatives and impediments, into consideration.
The strategy is built on top of Google Maps (GM) and
employments its directing benefit to calculate the most
limited distance between two areas. Clients give the
limitations and obstacles in the form of polygons which
identify closed areas in the real world [5].
A.Haversine Formula
The Haversine formula is an equation important in
navigation, giving great-circle distances between two points
on a sphere from their longitudes and latitudes [4]. These
names follow from the fact that they are customarily written
in terms of the haversine function, given by haversin (θ) =
sin2 (θ/2). The haversine formula is used to calculate the
distance between two points on the Earth’s surface specified
in longitude and latitude. d is the distance between two points
with longitude and latitude (ψ,φ) and r is the radius of the
Earth. Translation to SQL statement[1] 3956 * 2 * ASIN (
SQRT (POWER(SIN((orig.lat - dest.lat)*pi()/180 / 2), 2)
+COS(orig.lat *pi()/180) *COS(dest.lat * pi()/180)
*POWER(SIN((orig.lon - dest.lon) * pi()/180 / 2), 2)) ) AS
distance [2].
B.A* Algorithm
A* uses a best-first search and finds a least-cost path from a
given beginning node to one objective node (out of one or
more possible objectives). As A* navigates the chart, it takes
after a path of the least anticipated total cost or distance,
keeping a sorted priority queue of alternate path segments
along the way. It uses a knowledge-plus-heuristic cost
function of node (usually indicated) to decide the arrange in
which the search visits nodes in the tree. The cost function is
a whole of two functions: • the past path-cost function, which
is the known distance from the beginning node to the current
node • a future path-cost function, which is an allowable
"heuristic estimate" of the distance from to the goal
Pseudo Code:
function A*(start,goal)
closedset := the empty set // The set of nodes already evaluated.
openset := {start} // The set of tentative nodes to be evaluated,
initially containing the start node
came_from := the empty map // The map of navigated nodes.
g_score[start] := 0 // Cost from start along best known path.
// Estimated total cost from start to goal through y.
f_score[start] := g_score[start] + heuristic_cost_estimate(start,
goal) while openset is not empty
current: = the node in openset having the lowest f_score[] value
if current = goal
return reconstruct_path(came_from, goal)
remove current from openset
add current to closedset
for each neighbour in neighbor_nodes(current)
tentative_g_score:=g_score[current]+
dist_between(current,neighbor)
if neighbor in closedset
if tentative_g_score>= g_score[neighbor]
continue
if neighbor not in openset or tentative_g_score<g_score[neighbor]
came_from[neighbor] := current
g_score[neighbor] := tentative_g_score
f_score[neighbor]:=g_score[neighbor]+heuristic_cost_estimate(ne
ighbor, goal)
if neighbor not in openset
add neighbor to openset
return failure;
function reconstruct_path(came_from, current_node)
if current_node in came_from
p:=reconstruct_path(came_fromcame_from[current_node])
return (p + current_node)
else
return current_node.
The above pseudo code assumes that the heuristic function is
monotonic, which is a frequent case in many practical problems,
such as the Shortest Distance Path in road networks. However, if
the assumption is not true, nodes in the closed set may be
rediscovered and their cost improved.
IV. SYSTEM DESIGN
The Point of the paper is to discover out the route in between any
two spots inside a city entered by the client. This may be
implemented using a client-server design where a request having
two intersections as Source and Goal is sent from client to server
and requested route is returned to client as a response from server.
The client-server execution assumes that the client gets to the
functional application remotely from client end to server one. This
makes a clear thought of having client at one machine remotely
accessing the application and server at the other. Hence the plan
incorporates noteworthy components shown in functional project
plan below: The client end consists of client interface from where
an input is taken for processing. The server end consists of a Java
Processing Application and Database for it.

______________________________________________________________________________________
100
_______________________________________________________________________________________
The preparing application essentially takes as it were begin
and end junctions and computes the route in between them
with all fundamental subtle elements having middle
intersections with landmarks and streets in a specific region.
The Database utilized by preparing application may be a
Social database containing entirety data almost city in terms
of intersections, landmarks, streets and areas. The input
containing source and goal intersections for the asked route is
sent to the server conclusion as a request from client end.
This request is inserted in a XML record can be called as
XML request to be sent to server. At server on accepting a
XML request; it is provided to a XML parser for extricating
fundamental information i.e. source and destination junctions
which are in turn provided to Java Processing application as
an input. This application computes a requested route (a most
limited one) by connection with the database utilizing SQL
inquiries to get vital data for computation. For a computed
route to be sent to client, it is once more implanted into a
XML forming a XML response. This reaction on accepting at
client end is once more sent to a parser to extricate a route to
be shown to the client at to user interface.
A. Shortest route in the form of text route:
A client has arrangement to know the shortest way from
source to goal in two ways content-based route and graphical
route by utilizing Google outline. A content-based route
gives correct way from source to goal in the shape of
directions, turns, middle spots and distance between that
spots. A path is given to client by utilizing SQL query. At
last it gives the overall shortest distance from source to
destination.
B. Shortest route graphical representation:
Graphical representation of shortest route is appeared in figure. It
highlighted the shortest course from source to destination. Client
can utilize both the procedures to effortlessly know the route
between source to goal. GPI provides different strategies to get to
the highlighted route.

______________________________________________________________________________________
101
_______________________________________________________________________________________
V. FUTURESCOPE
The downsides happened in past paper of shortest way
discovery by utilizing Dijkstra calculation is recovered in
this paper by employing A* algorithm. In this paper, we
utilize A* calculation for deciding shortest way between two
junctions. But A* is heuristic in nature it implies that A*
algorithm does not grant any guaranty for great solution.
This is property of any heuristic algorithm A* algorithm. So,
able to utilize any algorithm related with A* algorithm
which can donate best arrangement to calculate shortest path
between two cities. A* algorithm is combination of Dijkstra
algorithm and Breadth First Search algorithm. In expansion
to that, A* is heuristic in nature. This System can be applied
as a navigation system which can navigate through out city.
Together with intracity shortest path detection, we will
implement same concept for intercity.
VI. CONCLUSION
“Landmark Based Routing in Indian Cities” is bound to
allow the briefest route giving a junction to junction
movement to client together with the appropriate bearings
and turnings directing the client to urge the precise
intermediate junctions (with their noteworthy points of
interest) or points of interest in specific zones in between
two junctions/spots provided by user. The client moreover
gets correct route with direction of inserted Google Map.
VII. ACKNOWLEDGEMENT
We would like to thank Government engineering college,
Modasa for providing all the required amenities. I am also
grateful to Prof. J.S. Dhobi, Head of Information Technology
Department, GECM, Gujarat for their indispensable support,
suggestions and motivation.
REFERENCES
[1] By Mr. Reid “Shortest distance between two points on earth”
http://wordpress.mrreid.org/haversine-formula/ This is an
electronic document. Date of publishing 20/12/2011.
[2] Samuel Idowu, Nadeem Bari, “A Development Framework for
Smart City,” Luleå University of Technology International
journal of Computer Application, vol 6, 9 Nov. 2012
[3] Javin J. Mwemzi, YoufangHuang,” Optimal Facility location
on spherical surfaces”, New york science Journal, April 2011.
[4] Ben Gardiner, Waseem Ahmad, Travis Cooper,”Collision
Avoidance Techniques for unmanned Aerial Vehicles”, Auburn
University, National Science Foundation, 08/07/2011.
[5] Simeon Nedkov, SisiZlatanova, “Enabling Obstacle Avoidance
for Google maps”,June 2011.
[6] Bing Pan,john C. Crotts and Brian Muller,”Developing Web
Based Tourism Information using Google Map” Departemnt of
Huminity and Tourism Mangement,Charston ,USA.
[7] ElinaAgapie.jason Ryder, Jeff Burke, Deborth Estrin,”
Probable Path Interference for GPS traces in cities”, university
of California,2009.
[8] K.M.Chandy,J. Misra, “Distributed Computation on Graphs:
Shortest Path Algorithm”, University of Texas, March 1982.
[9] Siemens AG. Munchen, Ulrich Lauther “An Extremely Fast
Exact Algorithm for Finding Shortest Path in static network
with geographic backgroung”, International journal on
Computer science and Engineering, June2006.
[10] Philip Klein,Satish Rao ,Monika Rauch, Sairam Subramnyam,
“Faster Shortest Path Algorithm for Planner Graphs”, March
1994.
[11] Andrew v. Goldberg, Haim Kaplan, Renato F. Werneck,
“Efficient Point to point shortest Path Algorithm”, Internation
Research on Advance Research in Computer Science and
Software Engineering, Oct 2005.

A Landmark Based Shortest Path Detection by Using A* and Haversine Formula

More Related Content

What's hot (11)

Similar to A Landmark Based Shortest Path Detection by Using A* and Haversine Formula (20)

More from rahulmonikasharma (20)

Recently uploaded (20)

A Landmark Based Shortest Path Detection by Using A* and Haversine Formula