PEDESTRIAN VULNERABILITY ANALYSIS USING QUANTUM GIS€¦ · PEDESTRIAN VULNERABILITY ANALYSIS USING QUANTUM GIS M. Kamalanandhini Assistant Professor, Department of Civil Engineering,

http://www.iaeme.com/IJCIET/index.asp 1242 [email protected]

International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 4, April 2017, pp. 1242–1249 Article ID: IJCIET_08_04_140

Available online at http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=4

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

PEDESTRIAN VULNERABILITY ANALYSIS

USING QUANTUM GIS

M. Kamalanandhini

Assistant Professor, Department of Civil Engineering,

SRM University, Chennai, India

ABSTRACT

Traffic and transportation problems in large and medium sized cities of many

developing countries have become grave matters of concern to the planning and

administration. Growing population with rapid increase of heterogeneous traffic

constrained by limited transport infrastructure facilities and by the encroachment of

footpath results in increase of pedestrian vulnerability. Geographic Information System

has proved to be a powerful tool for urban transport planning. It helps to improve the

decision making process and necessitates an efficient, feasible and faster means of

transportation and traffic planning. A study on the vulnerability of road users can be

made use of in obtaining at a conclusion that a certain amount of area is at high risk

or at low risk depending upon the obstructions in the footpath which makes the

pedestrians to walk on the road. In this study, the pedestrian volume count, traffic

volume count and accident data for certain road networks have been analyzed and the

vulnerability of the pedestrians has been obtained. Quantum GIS software package is

user friendly and easy for the analysis operation. Vulnerable zones are identified with

the help of field data incorporated in the software which shows the areas prone to high

and low risk of accidents. With the help of the results obtained, one could easily identify

the areas of vulnerability and act accordingly.

Key words: Geographic Information System, Transportation, Pedestrian Volume Count,

Traffic Volume Count, Accident Data, Vulnerability Analysis

Cite this Article: M. Kamalanandhini, Pedestrian Vulnerability Analysis Using

Quantum GIS. International Journal of Civil Engineering and Technology, 8(4), 2017,

pp. 1242–1249.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=4

1. INTRODUCTION

Transportation is a measure of relation between areas and it is an essential part of geography

[1]. Road traffic crash hotspots are associated with certain risk facts that are modifiable such

as improper footpath, encroachments, distracted driving, drugs, alcohol and speeding [2].

Typical Indian cities have arrangements that are highly unstructured, and one can find large

M. Kamalanandhini


diversity within short metric distances [3]. Information system plays a vital role in planning

and development of rural areas. The most advanced computer based information technology

tool for spatial planning is a Geographic Information System, which would become

indispensable in planning and management of database. Geographic Information System (GIS)

is a system used for visualizing, questioning, analysing, and interpreting data to understand

relationships, patterns, and trends. It allows us to produce maps, graphical displays of

geographic information for analysis and presentation. Hence Geographic Information System

(GIS) helps in building decision support system in various organisations. It enables the safety

experts to compare accidents along a road way length utilizing various data such as land use

data, population data and other demographic data to gain a better perceptive of the relationship

of crash incidents with other data to deduce a true picture [4]. The development of Geographical

Information Systems (GIS) began in the early 1960s and rapidly advanced since the late 1980s.

Over the past 50 years, GIS technology has been increasingly introduced to a wide range of

sectors. In addition to planning agencies and local governments, many other sectors have been

involved, such as social science, transportation, earth science, military, agriculture,

environmental protection, etc. [5]. Road accidents are mostly caused due human error. Different

factors attributing to road accidents are rash driving, violation of rules, alcohol, carelessness,

crossing at wrong places moving on carriageway, pavement characteristics, traffic, lack of

enforcement, geometric, design of the pavement damaged road, eroded road merging of rural

roads with highways, diversions, illegal speed breakers and finally weather conditions.

Pedestrians are exposed to traffic accident when they cross the street, mainly in urban areas. A

pedestrian, in a trip spent time during crossing in a micro-environment is characterised by the

volume and the speed of the vehicles plying [6]. GIS is used to develop an effective road

information system which will help the administrators to collect data about the road networks,

to identify the problems associated with road development activities, location and provision of

appropriate facilities, monitoring and maintenance management of the assets created in rural

areas. In these cases the information generated as well as the decision taken at the official level

will be faster that boosts the developmental activities. Therefore, there is an urgent need to

develop a simple method for collection and collation of data of roads which will help in

planning and provision of various facilities [7]. With the help of geospatial analysis, specific

hotspots which might be amenable to intervention can be identified [8]. GIS offers a

comprehensive platform to conduct spatial data analysis, graphic display, visual interface, data

editing and query. These features provide a useful environment for comprehensive analysis of

traffic safety problems [9]. Alternate optimum route can also be located for road users by

allowing only light weighted vehicles to pass such routes which in turn help in reducing the

accidents caused by them [10].

The main objectives of the study were to generate a road layer of Chennai region from the

Google Earth by digitizing the layer, to represent the peak value of pedestrian using the

particular road by creating shape files for each data and to represent the data collected for

pedestrian and traffic volume count and the accident data in the attribute table.

2. MATERIALS AND METHODS

2.1. Study area

The area selected for the study was road layer of Chennai city. Chennai was the capital city of

Tamil Nadu, located at 13° N latitude and 80° E longitude. The area around Chennai has been

a part of successive South Indian kingdoms through centuries. Chennai was one of the cities in

India that was connected by the Golden Quadrilateral system of National Highways. Chennai’s

economic development has been closely tied to its port and Transport infrastructure and it was

considered as the best infrastructure in India. The city and metropolitan area were served by

Pedestrian Vulnerability Analysis Using Quantum GIS


major roads from all directions. The roads selected for the study were NSC Bose road,

Muthuswamy road and Rajaji Salai. NSC Bose road runs from Parry’s Corner on the east to

Wall Tax road on the west for a length of 1.5km. The width of the road ranges from 50m near

Parry’s Corner to 10m near Wall Tax road junction. Rajaji Salai, also known as North Beach

road or First Line Beach, is one of the main thoroughfares of the commercial centre of George

Town in Chennai. Muthuswamy road consists of Chennai Fort station on one end near the

junction of Frazer bridge road. Figure 1 indicates the road layers of Chennai with the roads

selected which were used for the study analysis.

Figure 1 Study area with the selected roads for study

2.2. Data Used

The different data which was required to carry out the study were Pedestrian Volume count,

Traffic Volume count, Accident data, and Footpath data and digitized road network of Chennai

from Google Earth. The pedestrian volume count data for the study area was obtained by

conducting surveys on the roads. The collected data was used to fine the peak value of the

pedestrians crossing the road at a particular hour and the pedestrian walking on the footpath.

The pedestrian vulnerability can be found using the pedestrian volume count data and the risk

could be managed accordingly. The traffic volume count data for the study area was also

obtained by conducting survey. The collected data was used to find the peak hour traffic

volume. It also provides the number of vehicles using the road and also the type of vehicles

such as two wheeler, car, bus, auto/share auto, heavy loaded vehicles, light loaded vehicles,

hand cart, cycle etc. The accident data was calculated from the data collected such as traffic

volume and pedestrian volume using certain formulas as specified by the Indian Road Congress

(IRC) code book. The data provides the details about the high risk locations of accidents.

Footpath was one of the major divisions of the road. Most of the road-users use the footpath

also to reach their destination by walk. The footpath encroachment was one of the major

concerns in the analysis. The length, width and the encroachment of the footpath was surveyed

and those data were used for the analysis of the pedestrian vulnerability. The Chennai road

layer was digitized from the Google Earth and was used for performing the analysis. The roads

such as NSC Bose road, Rajaji Salai and Muthuswamy road were separated from the whole

Chennai road layer to provide necessary details for those roads. Details such as pedestrian

volume count, traffic volume count and the accident data were provided for those roads in each

of its attribute table in Quantum GIS software. Using the pedestrian volume count data the

vulnerability analysis was done and the risk of pedestrian was identified and the cause of

accident at that particular road was found out.

M. Kamalanandhini


2.3. Methodology

The data such as pedestrian volume count, traffic volume count and accident data were

collected on site and with the help of these data the road layers were prepared for the study

analysis. The collected data were marked for the selected roads. The roads and the foot

pathwere buffered for showing the width which was measured in the field. This helps to identify

the encroachments on the foot path by various factors. By clustering the Pedestrian volume

count with the geospatial map along with the buffered road and foot path, the peak value of

pedestrian vulnerability has been identified. The methodology used for the assessment of

pedestrian vulnerability was shown in figure 2. The foot path details for the selected roads were

available which was surveyed in field. The foot path details such as the length, width and

encroachment of the foot path was used for the analysis. The width of the road as well as the

width of the foot path was also specified in QGIS software.

The other details of foot path such as the pedestrian volume count and the obstructions on

the foot path were also mentioned using QGIS software. The details of the obstructions on the

foot path of Rajaji Salai, NSC Bose Road and Muthuswamy Road were also collected using

field survey. The pedestrian volume count and the obstructions were presented together for all

the three roads.

Figure 2 Methodology adopted for the study

Some area of the footpath of NSC Bose road have been encroached by the people for their

usage such as car and two-wheeler parking. This encroachment was also represented using

QGIS software. Figure 3 represents the area of obstruction and area excluding obstruction.



Figure 3 Area with and excluding obstruction of footpath of NSC Bose road

3. RESULTS AND DISCUSSION

The details such as the pedestrian volume count (PVC), traffic volume count (TVC) and

accident data for the roads surveyed were specified in Quantum GIS software. Roads taken for

the analysis were NSC Bose road, Rajaji Salai and Muthuswamy road which was digitized from

the Google earth and used in the software. The PVC, TVC and the accident data for these roads

were already surveyed and the data has been specified in the excel sheet. The foot path details

such as the length, width and encroachment for all these roads were also surveyed for the study

and was used for the analysis. The details such as width of the road, width of the foot path and

the obstructions on the foot path were also found out and digitized as a layer. Some area of foot

path of NSC Bose Road have been encroached by the people for their usage such as car and

two-wheeler parking. Buffering option was used to represent the width of the road and the foot

path of the three roads. Some area of foot path has been occupied by obstructions such as

parking, encroachment by people, tree, lamp post, EB junction box etc. PVC data was used to

identify whether the available foot path satisfies the requirements of pedestrians. The values of

peak hour were entered in the attribute table of the respective layer created for each road.

Remote sensing and GIS were very useful for the traffic study and vulnerability zonation. With

the help of zonation maps, the vulnerable areas in a region for the pedestrian can be identified.

This information changes from time to time and can be updated easily. Figure 4 represents the

output of the Pedestrian vulnerability analysis. Queries were framed considering the range of

PVC for identifying the vulnerable zones for the pedestrians. The footpath width for a particular

range of PVC was also given accordingly and the output was represented using the software.

Table 1 shows the queries given for identifying the zones of vulnerability as specified by the

Indian Road Congress code specification. The results of the query were denoted in figures 5,

6, 7, 8 and 9 respectively.

M. Kamalanandhini


Figure 4 Map of study area with PVC and obstructions

Table 1 Queries for identifying vulnerable zones

Query No. For Foot path width (in m)

1 PVC < 800 1.5

2 801 < PVC < 1600 2

3 1601 < PVC < 2400 2.5

4 2401 < PVC < 3600 3

5 PVC > 3600 4 - 5

Figure 5 PVC < 800 (Query 1) Figure 6 801 < PVC < 1600 (Query 2)

Figure 7 1601 < PVC < 2400 (Query 3) Figure 8 2401 < PVC < 3600 (Query 4)



Figure 9 PVC > 3600(Query 5)

4. CONCLUSION

The road has been digitized as line feature and the details such as PVC, TVC and accident data

were given as attributes for the locations along the road where the survey has been conducted.

The accident analysis with respect to the PVC, the Pedestrian and road condition reveals the

vulnerability to accidents. The vulnerability was categorized into four zones such as extreme

danger zone, moderate danger zone, medium danger zone and low danger zones according to

the peak hour value of pedestrian volume at a particular region. Thus the study concludes that

pedestrian were more vulnerable to accidents in the selected study area and preventive

measures such as clearing of encroachments and demolishing structures such lamp post, EB

junction boxes, trees, encroachment by people and even parking in foot path must be avoidedin

order to safeguard the pedestrians using the foot path. The study can be further improved by

preparing zonation maps which will be user friendly. Preventive measures such as clearing the

encroachment and making the pathway free to walk can also be adopted to reduce the event of

accidents.

REFERENCES

[1] Ilayaraja K, Road network analysis in Neyveli Township, Cuddalore District by using

Quantum GIS, International Journal of Computer Science and Engineering, 2013, Vol 4

[2] Zachary Dezman, Luciano de Andrade, Joan Ricardo Vissoci, Deena El-Gabri, Abree

Johnson, Jon Mark Hirshon CAS, Hotspots and causes of motor vehicle crashes in

Baltimore, Maryland: A geospatial analysis of five years of police crash and census data,

Injury, 2016

[3] Chaturvedi R, Rajan KS, Exploring the impact of road traffic impedance and built

environment for vulnerability mapping of evacuation areas – Case study of Hyderabad city,

Proceedings of International Space Syntax Symposium, 2015

[4] Choudary J, Ohri A, Kumar B, Identification of Road Accidents Hot Spots in Varanasi

using QGIS, Proceedings of National Conference on Open Source GIS: Opportunities and

challenges, 2015

[5] Ye H, Brown M, Harding J, GIS for All: Exploring the Barriers and Opportunities for

Underexploited GIS applications, OSGEO Journal, 2014, Vol 13

[6] Lassarre S, Bonnet E, Papadimitriou E, Risk exposure assessment of pedestrians in urban

area using a GIS, Proceedings of ICTCT Extra Workshop, Beijing, Session 3, pp 124-137

[7] Prasada Rao I, Kangadurai B, Jain PK, Jain N, “Information system for rural road network

planning – A case study”, Map India Conference, 2003

[8] Patel A, Krebs E, Andrade L, Rulisa S, Vissoci RN, Staton CA, “The epidemiology of road

traffic injury hotspots in Kigali, Rwanda from police data”, BMC Public Health, 2016

M. Kamalanandhini


[9] Pavan R Vyas, Madan L.H, Balakrishna H.B, “Identification of black spots for safe

commuting using weighted severity index and GIS”, 3rd World Conference on Applied

Sciences, Engineering and Technology, 2014

[10] Ayman A. Abdul-Mawjoud, Mohammed G. Jamel, “Using the Analytic Hierarchy process

and GIS for decision making in rural highway route location”, International Journal of Civil

Engineering and Technology, 2017, Vol 7, issue 2, pp 359-375

Documents

PEDESTRIAN VULNERABILITY ANALYSIS USING QUANTUM GIS€¦ · PEDESTRIAN VULNERABILITY ANALYSIS USING QUANTUM GIS M. Kamalanandhini Assistant Professor, Department of Civil Engineering,