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Least Cost Highway Alignment Using GIS Technique
B Srirama Project Scientist, TRIPP, IIT Delhi, India M R Bhatt Professor, M E ( Transportation ) -- L D College of Engineering ,Gujarat University. Ahmedabad India S K Pathan Head , Landuse Planning & Photogrammetry Division, SAC (ISRO), Ahmedabad, India ABSTRACT Highways are part of the infrastructure that makes up the spinal cord of modern society. GIS provides a valuable tool in the process of planning and design of highways. To obtain an optimum highway route alignment which is economical, suitable and compatible with the environment, various types of data have to considered simultaneously. Handling and managing this large amount of data manually, is not easy. It is here GIS comes to help, because of its inherent property of handling large bulk of spatial data, non spatial data and its analysis. Remote sensing images of the study area were used as the source (spatial data). Various collateral data from various offices was collected to be used as non spatial data. These images were used to prepare the digitized formats required for the GIS techniques. Using the Resistance concept ( such as areas suitable for the new alignment were assigned a low resistance value, whereas the areas not suitable for the new alignment were assigned a high resistance value) the data was prepared for analysis. Spatial Analyst tool of ArcGIS version 8.1 was used for performing the analysis. INTRODUCTION For decades transportation planners, managers, and other decision makers have relied on surveying and engineering information derived from manual methods for making decisions on transportation issues. These methods usually have been field and ground surveys that produced location information, some of which was represented on maps produced manually by cartographers. For the surveyor, the main tools include theodolites, tripods , transits and levels while the map maker has drafting equipments and ink pens. Now use of aerial photography, photogrammetry and remote sensing technologies has enhanced the information content required by the surveyors. However the integration is done manually , that is to say that automation and use of computers were not employed until Geographic Information System ( GIS ) technology was introduced in 1980's. These technologies introduced digital data, new ways of acquiring , developing , analyzing and interpreting information for crucial transportation decisions. GIS technology not only saves time but also enables us to work out many alternate options within less time and with less investment. For all transportation problems, it is essential to carry out many studies for connectivity, accessibility , location an allocation , and preparation of master plans etc. All the studies require generation of databases at regular intervals and on different scales and levels. It is here that remotely sensed data and GIS systems offer the optimal data sources at different spectral and spatial resolutions and at different intervals of time that could help to carry different studies on transportation. PREVIOUS RELEVANT WORK The paper on 'Highway Route Alignment using GIS and Groutes Algorithm' (P L N Raju et al,1997 ) uses the GROUTES Algorithm to get the shortest path come out with the result of Highway Alignment between Roorkee and Haridwar. Author has used GIS layered concept for realigning the final alignment based on various obligatory points that are to be avoided. The paper on 'GIS Approach for Economic Evaluation of Highway Alignment' by ( G Chandra Sekhar and K M Lakshmana Rao,1997 ) also shows the use of GIS approach. The study area considered for the economic evaluation is Medak District of Andhra Pradesh. Here first the optimum route is found considering the demographic characteristics as nodal potentials and the Euclidian distance is taken as the impedance factor. The landuse map was prepared using imagery as layer one, road network from SOI topo sheets as layer two, village administrative boundaries stored as layer three. The CBR values of the soils were stored as attributes. The optimum route obtained was then put on each of the layers and necessary deviations were made to avoid any obstruction and the cost was calculated from the length of the route obtained OBJECTIVE In order to construct any new road, the first pre-requisite is to select an alignment. It is always advisable to select an optimum low cost alignment. The objectives of this research work are: (1) To determine least cost path on grid based modeling. (2) To finalize the Least-Cost Path based upon weighted criterion. STUDY AREA The study area considered for this research are the districts of Gujarat (India), namely Panchmahals and Dahod (erstwhile one district ). The Study area is designated as 46E16 as in regards to the terminology prescribed by Survey of India ( SOI ). DATA USED Spatial and non-spatial data both were used for this research work. Spatial data used is given in table 1.1 and table 1.2 while the non spatial data is given in table 1.3. 
RESEARCH METHODOLOGY The methodology adopted for this research work is shown in the flowchart at the end of the paper. DATA MODELS Data for any GIS analysis is mainly in two data formats, namely Vector Data Model and Raster Data Model. ArcGIS uses the data in either vector or raster or in combination. In a vector data model, each location is recorded as a single x,y coordinate. Points are recorded as a single coordinate In a raster data model, each location is recorded as a single x,y coordinate and a third reference called its value. Both types of data models have been used in this research. All the available data mentioned are in vector data model. The remote sensing images were interpreted and they were digitized to the format , acceptable for further study and analysis using computers. These data in digitized format (vector data model), were then converted to raster data model using inbuilt conversion tools in the software. Selecting the Grid Size The first step in preparing the rasters is to decide the raster cell size or 'grid size'. Selection of cell size is based on minimum mapping unit while visual interpretation of remote sensing imagery is carried out. Thus the minimum mapping units used for interpretation was 3 mm X 3 mm ( 150 m X 150 m ) in case of polygon and 1 mm X 1 mm ( 50 m X 50 m ) in case of line. Hence the cell size selected is 50 m X 50 m. All the rasters ( grids ) prepared are of size 50 m and all the analysis done in 50 m X 50 m grid size environment . Assigning weights based on resistance concept Resistance defines the resistance offered to move through the cell , ie., while finding the shortest path the algorithm will be selecting the cells with lower value , so based on the various categories in a layer, resistances are assigned to each category. It can be understood as - it will be cheaper to move through a waste land than move through a built-up, or it will be easier to move through plain area than hard rock. Similarly it easier to move around a hill than to cut across it. It is easier to acquire government land than to pass through a private land, hence governments lands area given low resistance than private land. So , for every layer, the categories are identified and assigned a weighted value. The resistance range is from 1-100, the category identified as favourable or easier to align a road is given low resistance value ( say 1, for the most easiest ) and the category identified as not favourable or difficult to align a road is given a higher resistance ( say 100 , for the worst case ). Generation of DEM And Slope Slope map was generated using the elevation information derived from ancillary topographical and GIS techniques. ARCGIS's TOPOGRID functions were used to generate DEM and slope maps. A sampling method was used to extract representative points to build a surface model that approximates the actual surface. The contour map, was prepared from the SOI topographic maps. About 169 points were selected representing various distinct features like depressions, undulating terrain , hilly terrain etc. Later using GIS environment a point coverage was built , from which the percent slope and surface views were prepared. Source and destination raster A source and a destination raster of the required settlement are prepared by pointgrid function of the software. In this case three rasters namely of Sanjeli, Narsingpur and Suliyat were prepared. MODEL DEVELOPMENT Four different models were prepared. The two steps involved in generating the four Cost Raster Models ( CRM ) for analysis are Weighting datasets according to percent influence The next step in producing the cost raster is to integrate the reclassified datasets together. The simplest approach is to just add them together, while more complex methods can be employed However, based on certain factors some grids are more important than others. For instance, avoiding steep slopes may be twice as important as the landuse type, so , influence of this slope raster can be given a high weightage. While low weightage for others grids. But care is taken that the total influence is equal to 100%) as shown in table 1.4 Integrating the datasets The final cost raster is the result of adding together the weighted datasets After preparing the influence raster they are combined by the command at ArcGRID raster they are combined by the command at ArcGRID CRM1 = luse1 + slope1 + soil1 + drainp1 + roads1 + litho1 CRM2 = luse2 + slope1 + soil2 + drainp2 + roads2 + litho2 CRM3 = luse3 + slope3 + soil3 + drainp3 + roads3 + litho3 CRM4 = luse4 + slope4 + soil4 + drainp4 + roads4 + litho4 
Cost weighted distance raster Using the cost raster and the source, the Cost Weighted Distance function produces an output raster in which each cell is assigned a value that is the least accumulative cost of getting back to the source. The function takes the cost raster and calculates a value for each cell in the output cost weighted raster that is the accumulated least cost of getting from that cell to the nearest source. Every cell in the cost weighted raster is assigned a value representing the sum of the minimum travel costs that would be incurred by traveling back along the least-cost path to its nearest source. This function is available in Spatial Analyst Module Direction Raster The Cost Weighted Distance raster shows the least accumulated cost of getting from each cell to the nearest source, but it doesn't decide as which way to go to get there. The direction raster provides a road map, identifying the route to take from any cell, along the least-cost path, back to the nearest source.This function is available in Spatial Analyst Module SHORTEST PATH ANALYSIS The inputs required for shortest path analysis are a source and a destination raster, cost raster surface, cost weighted distance, direction raster. After preparing all the required inputs Spatial Analyst is used to generate the shortest path and the results for analysis. ANALYSIS & RESULTS For this research analysis, two case studies have been carried out considering three settlements within the Study Area. The first case study for "Determining the shortest path between Sanjeli to Suliyat and Sanjeli to Narsingpur" . The second case study for "Determining the shortest path between Suliyat to Sanjeli , Suliyat to Narsingpur". The results for both the case studies are shown in Table 1.7. Calculating the costs For calculating the construction costs the volumetric costs of a existing pavement were considered as shown in table 1.6. For calculating the acquisition costs the shortest path coverage obtained as output from the software was overlayed on the cost raster and the pixel values beneath the shortest path alignment were summed up. 
The table 1.5 shows the costs on sq.m basis which were used in addition to the costs on a cu.m basis for different table 1.6 shows the volumetric costs of pavement layers 
CONCLUSIONS For alignment form Sanjeli to Suliyat the shortest path comes out to be 11.76 Km, which is based on cost raster model 2 (more weightage to Slope Raster). Total Cost = Land acquisition cost + Construction cost of 7.5 m Bituminous Road = Rs 14,040,000.00 + Rs 512,199,715, which equals to Rs 526,239,715. For alignment from Sanjeli to Narsingpur the shortest path comes out to be 16.29 Km, which is based on cost raster model 3 ( more weightage to litho Raster). Total Cost = Land acquisition cost + Construction cost of 7.5 m Bituminous Road = Rs23,265,000+ Rs 709,501,135 which equals to Rs 732,766,135 For alignment from Suliyat to Narsingpur the shortest path comes out to be 16.28 Km, which is based on cost raster model 2 (more weightage to Slope Raster). Total Cost = Land acquisition cost + Construction cost of 7.5 m Bituminous Road = Rs 27,810,000 + Rs 709,065,591 , which equals to Rs 736,875,591 The various conclusions from the research are (1) Various physical characteristics of the terrain viz. landuse / landcover , soil, lithology, slope, land values etc ., have been considered in generating the road alignment. (2) Special attention has also been taken to check that the passage of road should not cross over the productive agricultural lands, forests, and it should not be affected by natural hazards such as floods, erosion etc., high gradient areas. It has also been checked that it should not go amidst high land values areas. (3) The satellite imagery has provided valuable information on the natural resources accurately, reliably, timely and with less cost. (4) The appeal of GIS in highway route alignment is that one can compare as many alternate routes as desired in a very short period of time consisting of large amount of highway route alignment parameters without any extra cost. 
APPENDIX : REFERENCES
 Flowchart showing the overall methodology
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