=Paper=
{{Paper
|id=Vol-2144/paper2
|storemode=property
|title=GIS-Based Network Analysis for the Roads Network of the Greater Cairo Area
|pdfUrl=https://ceur-ws.org/Vol-2144/paper2.pdf
|volume=Vol-2144
|authors=Sayed Ahmed,Romani Ibrahim,Hesham Hefny
}}
==GIS-Based Network Analysis for the Roads Network of the Greater Cairo Area==
https://ceur-ws.org/Vol-2144/paper2.pdf
GIS-Based Network Analysis for the Roads Network of the
Greater Cairo Area
Sayed Ahmed Romani Farid Ibrahim Hesham A. Hefny
Computer Sciences Dept, High Institute of Computer Computer Sciences Dept,
Institute of Statistical Studies Science and Information - City Institute of Statistical Studies
and Research, Cairo of Culture and Science and Research, Cairo
University, Giza, Egypt 6 October City, Egypt University, Giza, Egypt
se.sayedahmed@gmail.com romanifarid@gmail.com Hehefny@ieee.org
Abstract It has been proven to be valid and efficient to solve
real-life problems, such as responding and resolving
In a crowded city like Grater Cairo Region emergency situations [1]. A geographic information
(GCR), Egypt, finding a desired location system is a computerized system that is designed to
becomes a difficult task, especially in emergency capture, store, manipulate, analyze, manage,
situations. The main criteria of any emergency visualize, and present all types of geographical data
response system (ERS) are its readiness to solve associated with geographical locations [2]. GIS can
the immediate emergency situation such as fire bring all that data together quickly and enable users
emergency response, police station emergency to analyze and visualize information in an efficient
response, healthcare emergency response system, way. It has been used in several fields such as
etc. The main purpose of this paper is to provide transportation management, emergency services, gas
an enhanced network analysis that uses the station mapping, and healthcare planning [2]. The
capabilities of Geographic Information System shortest path between two vertices “s” and “t” in a
(GIS) to identify the best route from the location network is defined as the directed simple path from
of an incident for any healthcare service “s” to “t” with the property that no other such path
providers in the Greater Cairo metropolitan area. has a lower weight [8]. Most applications solve the
The results obtained in this paper showed that shortest path problem based on the distance as a
the best route travel time is much better than the weight. In this paper, we used the time parameter
shortest route travel time by 22%. In emergency instead of the distance which calculated the path
situations, it is essential to reach the location of between two points that takes minimum time based
an incident as fast as possible to rescue people on one or more parameters other than the distance.
life. So, based on the obtained results, this paper Examples of these parameters are road width,
recommended that the GIS best route algorithm average speed, waiting time, etc. In emergency
is better than the shortest route algorithm in situations, the best path is preferred, that it takes the
emergency situations especially in a crowded minimum time to reach a destination which helps to
city like GCR. save people life. The main objective of this research
is to find the best path and representing this valuable
1 Introduction spatial information to end-users in an efficient way
using GIS software. Most of shortest path algorithms
Geographic Information System (GIS) technology is (Dijkstra’s shortest path algorithm, Euler’s algorithm,
one of the hottest research tools in the world recently etc.) are finding the shortest path that has only the
and one of the fastest growing high technology of least distance between a source node and a
monitoring. destination node. Applying one of these algorithms
on GIS software that resolve emergency situations is
not suitable for real road network because it
Copyright © by the paper’s authors. Copying permitted for
private and academic purposes. considers only the length of the path to find the
shortest one and does not consider other real-time
In: Proceedings of the International Conference on Applied traffic information (i.e. Road width, speed limit,
Research in Computer Science and Engineering ICAR'17,
surface condition, turn restrictions, etc.) which should
Lebanon, 22-06-2017, published at http://ceur-ws.org
be defined to identify more realistic routes.
The road network of the Greater Cairo region was (OD) cost matrix, network analysis, proximity
taken as a case study to apply the proposed enhanced analysis, and buffer analysis.
method. In [6], the authors established a GIS based fire
emergency response service in Kumasi Metropolis,
2 Related Works Ghana where the Ghana National Fire Service
In [2], the authors tried to solve the problem of (GNFS) can identify the optimal route from its
finding a specialized hospital and its shortest path to location of any fire incident. The optimal route was
reach in Aurangabad city, Maharashtra State, India. modeled based on the travel distance, travel time, the
They used the ArcGIS software and Dijkstra’s slope of the roads and the delays in travel times.
algorithm that provide the shortest path from one In [7], the authors provided a study that depicted the
location to another for finding the nearest location of preliminary results for a decision support tool to
the hospitals from user’s location. The calculations of model network congestion routing and provide an
the shortest path were based on road distances; traffic alternative route during rush hours in emergency
congestion and state of the roads were not cases. The system predicts traffic flow and barriers
considered. during rush hours and suggests the alternative route
In [3], the authors developed a GIS based application to reach hospitals at the time of emergency. The
for healthcare emergency response system services to authors used ArcGIS 9.3 network analyst tool and
manage healthcare in the ALMOKATAM Zone in Dijkstra’s algorithm for performing shortest path
the south of Cairo, Egypt. The optimal route was analysis. The main objective of this study was to find
modeled based on the distance to the closest the best route from the nearest hospital ambulance to
healthcare service providers. The system integrated the incident location and from the incident location to
data acquisition from databases and plotted the the nearest hospital with alternate routes.
location-based features of satellite image through a In [8], the authors proposed an optimized version of
web base interface which gives access to all different the shortest path based on the Dijkstra’s Algorithm.
tasks by different end users to be a decision maker or In the optimized version, the starting node is changed
policy makers in system management. They didn't with the searching process, and uses the stack
consider any factor other than the distance. structure to maintain it, in order not to revisit the
nodes. This improved the searching efficiency to the
In [4], the authors discussed the shortest path analysis
shortest path practically. But this system was also
based on Dijkstra’s algorithm and implemented an considering only the length of the path and did not
emergency response system based on GIS. They also consider other real-time traffic information.
integrated GIS, web services, and Asynchronous
JavaScript and XML (Ajax) technologies and 3 Methodology
provided a web-based application for finding the best
routes from specialized response team stations In this research, the flowchart of the proposed
locations and incident locations. Their proposed enhanced roads network analysis methodology using
GIS software is shown in Figure 1. Six stages of the
system provided the optimal route depending on the
process have been applied, beginning with collecting
distance of route without considering road conditions
and preparing the data that will be used in the
and traffic congestion. analysis (the study area base map, road network data,
In [5], the authors developed a desktop-based healthcare service provider data, and historical traffic
emergency response system for emergency readiness data), then Geo-referencing the base map of the study
and management through GIS in Delhi, India. The area. Following this is the creation of a Geo-database
main objective of this application was to provide that will store the prepared data. Then building both
immediate response to any incident or accident. A the network topology and the network dataset.
detailed transportation network was maintained and Finally, the network analysis process has been
integrated with real-time traffic data provided by applied to the road network of the Greater Cairo
Region (GCR).
NAVTEQ in India. The near real-time traffic
information was used to analyze suitable routes to the 3.1 Data Preparation
incident location by avoiding highly congested routes
and therefore reducing the response time. Using GIS This phase includes downloading the study area base
capabilities, various analyses were performed such as map, preparing the road network data, downloading
finding the shortest route using Origin–Destination
the healthcare service provider’s data, and preparing The Greater Cairo road network data were
the historical traffic data. downloaded using the ArcGIS Online Service as
The study area is the Greater Cairo metropolitan area. shown in Figure 3. The data contain an attribute
It is extended from 30° 11′ 10″ N and 31° 27′ 50″ E. (Meters) to store the length of each road segment in
Greater Cairo is the largest metropolitan area the roads network, an attribute (Direction) to store the
in Egypt, and the largest urban area in Africa and the direction of each segment, and two fields
world's 16th largest metropolitan area. It consists (TF_Minutes and FT_Minutes) to store the time
of Cairo Governorate, parts of Giza Governorate, and required to travel over each road segment in minutes
parts of Qaliobia Governorate, with a total population in both directions, and an attribute (Name) to store
estimated at 20,500,000; and its area is about the name of each road segment. The healthcare
1,709 km2; as well as its density is 10,400/km2 [9]. service providers’ data were downloaded from the
Cairo is the capital of Egypt and it is a vibrant city. It OpenStreetMap. The data contain an attribute (Name)
is associated with Ancient Egypt, as the famous Giza to store the name of each healthcare service provider,
pyramid complex and the ancient city of and another attribute (Type) to store the type of this
Memphis are located in its geographical area. It is healthcare service provider.
located near the Nile Delta [10].
The base map of Greater Cairo was downloaded from
OpenStreetMap (OSM). OSM can be accessed as an
ArcGIS Online Service that provides free read-only
access to OpenStreetMap as a base map for GIS work
in ESRI products such as ArcGIS Desktop It is
shown in Figure 2
Figure 2: Base Map of Greater Cairo (from OSM).
Figure 1: Enhanced Network Analysis Process Flow
Figure 3: The Greater Cairo Roads Network
Diagram
The last step in the data preparation phase is the Table 1: Daily Profiles Table Structure
preparation of the road network traffic data. Traffic
data are given information about how travel speeds
on specific road segments change over time. In Field Data Type Notes
network analysis, traffic is important because it
affects travel times, which in turn affect results. If we
Unique identifier
don’t account for traffic routing from one location to
Object ID Long for each record in
another, the expected travel and arrival times could the table.
be far from accurate. Another reason to account for
traffic is that it gives the routing opportunities that Represent free-
SpeedFactor_0000
avoiding the slower, more congested roads, which flow scale factor at
to Double
saves time. Traffic data can be stored using two different times of
SpeedFactor_2300
different models: historical and live traffic. In this the day.
paper, traffic data were stored as historical traffic
data.
The Streets_DailyProfiles join table identifies road
The historical traffic data were modeled based on the features, their free-flow travel speeds, and their
idea that travel speeds follow a weeklong pattern. related traffic profiles for each day of the week
Thus, the travel speeds of a given road segment at a (Table2).
certain time of a day of a week are expected to be
similar to those of the same road segment at the same Table 2: Streets_DailyProfiles Table Structure
time of the same day in another week. The expected
speeds are usually determined by averaging multiple
observations over some time span, such as a year. Field Data Type Notes
Also, the historical traffic data were created
according to the ArcGIS Network Analyst Unique identifier for
specifications. Object ID Long each record in the
3.2 Geo-processing of Toposheet table.
Identifies the feature
In this phase, a Geo-referencing process for the
EdgeFCID Long class that the street
downloaded roads network data is being performed.
feature is stored in.
The Geo-referencing process allows the registration
Identifies the road
of the digitized top sheet on the earth’s surface [2]. It EdgeFID Long
feature.
is considered a very critical stage as it affects the
Work together to
accuracy of the road network data.
identify the direction
3.3 Creation of Geo-database EdgeFrmPos
of travel (0 since the
And Double
The Geo-database is the native data structure used in beginning of the
EdgeToPos
ArcGIS and is the fundamental data format used for road, 1 for the
both editing and management of the data. A Geo- opposite end)
database can be personal, file, or enterprise. In this Represents the free-
proposed method, a personal Geo-database has been BaseSpeedKPH Double
flow speed
created using ARCGIS. A personal Geo-database is a
Represents the
database that can store, query, and manage both Profile_1 Short
traffic for Sunday
spatial and non-spatial data. It will contain the data of
healthcare service providers, road network, and Represents the
Profile_2 Short
traffic tables. The road network data and the traffic for Monday
healthcare service providers’ data were discussed Represents the
earlier in the methodology. DailyProfiles and Profile_3 Short
traffic for Tuesday
Streets_DailyProfiles tables were used to store traffic
information. The “DailyProfiles” table is used to Represents the
store the speed profiles for each day of the week Profile_4 Short traffic for a
(Table1). The times of the day are split into time Wednesday
intervals, or time slices (one hour) of equal duration. Represents the
Profile_5 Short
traffic for Thursday
Represents the powerful extension of ArcGIS that provides network-
Profile_6 Short based spatial analysis, including route analysis, travel
traffic for Friday
directions, closest facility analysis, and service area
Represents the analysis [2]. It enables users to dynamically model
Profile_7 Short
traffic for a Saturday realistic roads network factors, such as turn
restrictions, speed limits, and traffic conditions at
3.4 Building Network Topology different times of the day. The ArcGIS Network
Analyst Extension uses the standard Dijkstra’s
To get good analysis and results, it is necessary to
algorithm to calculate the least accumulated cost
build a topology of the road network to discover
between the destination node and every other node in
whatever errors in the data and correcting them. This
the network. Two types of network analyses were
was performed by applying some topology rules such
applied; the best route analysis, and the closet
as ensuring that there are no dangles in the road
facilities analysis.
network and the roads do not intersect or overlap
with themselves. 3.6.1 Best Route Analysis
3.5 Building Network Dataset The best route analysis generates the best route
between two locations based on travel time which
After correcting the road network errors, it is ready
depends on the traffic conditions available on the
for being used in building the network dataset that
network at a particular time of a day. The network
will be used in the network analysis. To create a
analyst extension makes it is easy to set the best rote
network dataset that renders traffic data, we need a
analysis parameters, such as the travel time that will
Geo-database that contains a line feature class, and
be used as an impedance factor, the start time of
the two traffic data table created earlier. The line
traveling which produce different results based on the
feature class will represent the road network and
day profile selected, the restrictions on the analysis,
must be stored in a feature dataset. The traffic tables
such as the road directions (unidirectional or
will represent the traffic data and its relationship with
bidirectional), and the ability to ignore invalid
the road network. The network dataset is well suited
network locations that may cause the analysis to fail.
to model the transportation network. It consists of a
set of edges that represent the links over which agents After adjusting the best route analysis settings, we
will travel, and a set of junctions that connect edges chose the start location and the end location, and then
and facilitate navigation from one edge to another. using the best route solver tool to generate the best
The Network analyst extension was used in ArcGIS route between these two locations. Figure 5 shows
for Desktop to create the network dataset shown in the best route between a start location (Location 1)
Figure 4. and end location (Location 2).
Figure 4: Network Dataset Renders Traffic Results Figure 5: The Best Route Result
3.6 Performing Network Analysis The directions window of the previous analysis is
shown in Figure 6.
The road network analysis has been implemented
using ArcGIS Network Analyst Extension. It is a
3.6.2 Closet Facilities Analysis 4 Results and Discussion
The closet facilities analysis finds the closest In this paper, we provide analysis and comparison of
facilities that can be reached in a specific period from the results of the network analysis using two different
an incident location based on travel time and traffic methods. To navigate from one location to another,
information available. This helps in emergency either the route with the least length (shortest route)
situations to know the closest facilities that can be will be selected, or the route with the least travel time
reached from the incident location, which in turns (best route) will be selected depending on the
reduces time, effort, resources and saving people life impedance factor you choose to solve for. Figure 9
[3]. The network analysis extension makes it is easy shows the shortest route between a source location
to set the analysis parameters for the closet facilities (The Autostrad Road, El-Maadi, Cairo, Egypt) and a
analysis, such as the impedance factor in the analysis, destination location (The Ring Road, New El-Marg,
the start time, the period to reach the closet facilities, Cairo, Egypt). In this analysis, the road length has
the number of facilities to find, and the directions of been chosen as the impedance factor, the start time of
travel (from incident to the facility or from the travelling to be 3:00 PM which is the evening rush
facility to the incident). Then, by using the network hour traffic on the road network in the Greater Cairo
analyst extension solver, the closest facilities to the area.
location of an incident can be found as shown in
Figure 7. The directions window of the previous The distance of the route obtained from the shortest
analysis is shown in Figure 8. route analysis represents the accumulated lengths of
the road segments over which agents will travel. In a
similar manner, the total time of the route obtained
from the shortest route analysis represents the
accumulated time in minutes for each route segment
over which agents will travel. The shortest route
results can be represented graphically as shown in
Figure 10.
Figure 6: The Best Route Directions Result
Figure 8: The Closet Facilities Directions Result
Figure 7: The Closet Facilities Analysis Result
Figure 9: The Shortest Route Analysis Results Figure 11: The Best Route Analysis Results
Figure 11 displays the best route between the same
two locations. In this analysis, we have chosen the
road’s travel time as the impedance factor, and this
analysis was performed at the same time and over the
7 days of the week as the shortest route analysis. The
best route results can be represented graphically as
shown in Figure 12. We have repeated the two
analyses for the same two locations in 7 days from
Saturday to Friday, at the same time and calculated
the total distance (in KM) and the average travel time
(in Minutes) for the two obtained roads as shown in
Table 3.
Figure 12: The Best Route Analysis at 3:00 PM
within the Week Days.
Table 3: Times and Lengths of the Best and the
Shortest Route Analyses
Shortest Route Best Route
Day
Length Time Length Time
Saturday 38.6 82 42.9 80
Sunday 38.6 240 42.9 204
Monday 38.6 330 42.9 206
Tuesday 38.6 164 42.9 144
Wednesday 38.6 197 42.9 156
Figure 10: The Shortest Route Analysis at 3:00 PM Thursday 38.6 315 42.9 300
within the Week Days. Friday 38.6 109 42.5 82
Avg Time 205.29 167.43
To give an evidence for the superiority of the best
route on the shortest route, we have performed the
previous analysis at different periods for each day in
the week on a time slice of 120 minutes starting at
8:00 AM and ending at 12:00 AM for both the
shortest route and the best route. We excluded the
periods from 1:00 AM to 7:00 AM from the
calculations as it will not give us any differences
between the shortest route and the best route because
in these periods there are no traffic jam on the road
network. The average travel time in minutes for each
analysis was calculated and recorded as shown in
Table 4.
Table 4: The Average Travel Times (in minutes) for
both the Shortest and the Best Route Analysis
Methods for each Time Slice.
Shortest Difference
Time Best Route
Route (%)
8:00 AM 133 120 11 %
10:00 18 %
98 83
AM
12:00 PM 108 81 33 %
Figure 13: Average Travel Time Comparison
2:00 PM 193 170 14 % between the Shortest and the Best Route Methods
4:00 PM 184 164 12 %
5 Conclusion and Future Work
6:00 PM 143 126 14 %
In this paper, an enhanced GIS-based network
8:00 PM 105 93 13 %
analysis was implemented and applied to the Greater
10:00 PM 92 81 14 % Cairo road network. It focuses on finding the best
12:00AM 92 54 70 % route between two locations on the road network and
finding the nearest healthcare service providers to an
incident location based on the travel time. Also, the
The obtained results can be represented graphically
proposed method integrates historical traffic data to
as shown in Figure 13. The superiority percentage
be used in the analysis, which in turn produces more
(SP) is a measure for the preference of one alternative
accurate results that are suitable for realistic road
over another alternative. In our case, it gives an
networks. The Dijkstra best routing algorithm built
indication for the preference of the best route over the
into the ARCGIS software is the best method for the
shortest route. The superiority percentage for the best
network analysis, especially in the crowded city such
route travel time was calculated according to the
as Cairo city. This algorithm can preserve the travel
following formula:
time with 20% to 22%, depending on the travel
𝟗 distances. In the future work, we suggest to use live
𝑺𝑹𝑻𝒊 traffic data when it is available instead of historical
𝑺𝑷 = [(( ) − 𝟏)/𝟗] ∗ 𝟏𝟎𝟎
𝑩𝑹𝑻𝒊 traffic data and consider other factors such as road
𝒊=𝟏 width, road state, road type, and time delay on the
Such that: road to get realistic results. Also, we plan to enhance
SP = Superiority Percentage the Dijkstra routing algorithm used by the ArcGIS
network analysis extension to improve its
SRT = Shortest Route Time performance.
BRT = Best Route Time References
Substituting the values of SRT and BRT from Table
[1] Abdel Rahim Elhag Abdel Aziz Elhag,
4 in the previous formula, we get:
RanyaFadlallaAbdalla ,Nagla Ali Gism ,Aisha Elhadi
SP = 22 % Mohammed ,Salah EddeenKhidirSideeg, “Route
Network Analysis in Khartoum City”, Journal of
Which means that the best route travel time is much
Engineering and Computer Science (JECS), Vol. 17,
better than the shortest route travel time by 22%.
No. 1, 2016, pp. 50-57.
In emergency cases, it is essential to reach the
[2] AmrapaliDabhade, Dr. K. V. Kale, Yogesh
location of an incident as fast as possible to rescue
Gedam, “Network Analysis for Finding Shortest Path
people life. So, according to the superiority
in Hospital Information System”, IJARCSSE, Vol.5,
percentage, the best route is more suitable for being
No.7, July 2015, pp. 618-623.
used in emergency situations than the shortest route.
[3] A. Gubara, A. Amasha, Z. Ahmed and S. El
Ghazali, “Decision Support System Network
Analysis for Emergency Applications”, Informatics
and Systems (INFOS), 2014 9th International
Conference on, Cairo, 2014, pp. ORDS-40-ORDS-
44.
[4] Ni Kai, Zhang Yao-ting, Ma Yue-peng, “Shortest
Path Analysis Based on Dijkstra's Algorithm in
Emergency Response System”, TELKOMNIKA
Indonesian Journal of Electrical Engineering, Vol.12,
No.5, May 2014, pp. 3476-3482.
[5] Anshul Bhagat, Nikhil Sharma, “GIS-Based
Application for Emergency Preparedness and
Management Accelerating Response System through
GIS”, 14th Esri India User Conference 2013,
December 2013, pp. 1-7.
[6] Eric K. Forkuo and Jonathan A. Quaye-Ballard,
“GIS Based Fire Emergency Response System”,
International Journal of Remote Sensing and GIS,
Vol.2, No.1, January 2013, pp. 32-40.
[7] SuneetNaithani, Abhishek Choudhry, Sandeep
Chauhan, “Decision Support System for Emergency
Response”, European Scientific Journal, vol.2, No.3,
NOV-DEC, 2013, pp. 680-687.
[8] Dechuan Kong, Yunjuan Liang, Xiaoqin Ma,
Lijun Zhang, “Improvement and Realization of
Dijkstra Algorithm in GIS of Depot”, IEEE, 2011.
[9]WikiVisually,Cairo”,
http://wikivisually.com/wiki/Greater_Cairo
/wiki_ph_id_16.
[10]WorldAtlas. " Cairo Photos, Luxor
PhotosEgypt",
http://www.worldatlas.com/webimage/countrys/afric
a/cairoegyptphotos.htm.