PEDESTRIAN VULNERABILITY ANALYSIS USING QUANTUM GIS

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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?

1 International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 4, April 2017, pp Article ID: IJCIET_08_04_140 Available online at ISSN Print: and ISSN Online: 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 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 editor@iaeme.com

2 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 editor@iaeme.com

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.

3 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 editor@iaeme.com

4 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 editor@iaeme.com

5 Pedestrian Vulnerability Analysis Using Quantum GIS 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 editor@iaeme.com

M. Kamalanandhini Figure 4 Map of study area

5 2 801 < PVC < 1600 2 3 1601 < PVC < 2400 2.

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

2400 (Query 3) Figure 8 2401 < PVC < 3600

6 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 < < PVC < < PVC < < PVC < PVC > Figure 5 PVC < 800 (Query 1) Figure < PVC < 1600 (Query 2) Figure < PVC < 2400 (Query 3) Figure < PVC < 3600 (Query 4) editor@iaeme.com

7 Pedestrian Vulnerability Analysis Using Quantum GIS 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 [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, editor@iaeme.com

8 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 editor@iaeme.com

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