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The potential of space geomatics engineering applications in transportation analysis and planning
The Egyptian Journal of Remote Sensing and Space Sciences xxx (xxxx) xxx
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Research Paper
The potential of space geomatics engineering applications in transportation analysis and planning Mahmoud M.S. Albattah, Salah Edin Youssef Civil Engineering, Jordan University and Alhusein Bin Talal University, Jordan University of Jordan, Jordan
a r t i c l e
i n f o
Article history: Received 8 May 2019 Accepted 26 October 2019 Available online xxxx Keywords: Remote sensing Land-use classification DTM extraction Traffic modelling GIS Analytic hierarchy analysis Least cost route
a b s t r a c t Megapoleses in developing countries, are subject to persistent increase in road traffic due to immigration, increase in prosperity, fast development expansion of economy, travel and tourism. Road traffic congestion is increasing exponentially while road infrastructure are not and cannot be increased in the same ratio. The present study proposes the exploration of the potential of Satellite Imagery, GIS and traffic flow information in solving traffic congestion in one of these cities in developing countries. The Land-use has been generated from a combination of the imagery of the Landsat and SPOT. A Digital Terrain Model (DTM) was extracted from a large-scale Stereo-pair of aerial photographs with a geometric resolution of 60 cm. Up-to-date information such Land use, DTM, Access site information and Restricted Access Area information were combined in a GIS for analysis and visualization. Analytic Hierarchy Process is used for obtaining the optimal alternatives to the congested roads. Ó 2019 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B. V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
1. Introduction Amman the capital of Jordan is the subject of this study. Almadena Street is the most congested road in the city. It carries heavy traffic volume creating intense traffic congestion causing considerable rise in transportation cost and pollution. Fig. 1 shows the study area location. In this study the potential of High Resolution Remote Sensing Imagery is explored to provide precise up to date information of concern and helping in deriving optimal alternatives to the highly congested existing route. Remote Sensing is a technology for sampling electromagnetic radiation to acquire and interpret non-immediate geospatial data from which to extract information about features, objects, and classes on the Earth’s land surface, oceans, and atmosphere Barros and Sobreira, 2002. It has various applications in multiple scientific disciplines including Civil Engineering Geology, Geophysics, Archeology, Oceanology, Meteorology, Natural Resources Management, Agri-
Peer review under responsibility of National Authority for Remote Sensing and Space Sciences. E-mail addresses: [email protected], [email protected] (M.M.S. Albattah)
culture and Climatology. Transportation Engineering, Remote Sensing, is used for Road Route Design and Setting out, Infrastructure Management and Traffic Engineering Albattah, 2015. 2. Objectives This study aims to explore the potential of High Resolution Remote Sensing Techniques to get precise and up to date data and information on a large scale of the area of concern, and to find out the optimal alternatives to the highly congested existing Almadena Almunawera Street. 3. The importance of Remote Sensing for the present study Precise Remote Sensing helps in providing accurate and up to date information that contributes to the definition of a decision support system (DSS) for the achievement of the study objectives. The generated DSS helps in the selection of the optimal alternatives to the existing path while minimizing cost. High Resolution Remotely sensed imagery are processed using segmentation and classification techniques which provide landuse and landcover layers which are then combined with field data and used in a GIS system.
https://doi.org/10.1016/j.ejrs.2019.10.001 1110-9823/Ó 2019 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
(L) image coordinates to the x, y, and z coordinates on the ground. A stereo ground control point (GCP) is a cross between a regular GCP and a tie point. It is a feature with known ground coordinates that you can clearly identify in two or more images. Tie points identify how the images in the project relate to each other. The DTM is extracted from the stereoscopic pair of aerial photographs using PCI Geomatica Ortho Engine Barros and Sobreira, 2002.
4.3. Satellite image classification
Fig. 1. Study Area Amman (Capital of Jordan). The circle including the area of exploration is centered at Al-Waha roundabout. It is of 4 km of diameter.
Information extracted from satellite images include the location of Land cover types such as; bare land, buildings, roads, forest and vegetation. With the help of GIS system, the images can be exported as layers in the software, then used for analysis and GIS modeling. 4. Methodology 4.1. Data Collection: A Digital Terrain Model (DTM) has been extracted from the stereoscopic pair of aerial photographs with an overlap of 60%. Six ground control points (GCP) was collected from the field by a professional surveyor using GPS Real-Time Kinematic (RTK) technique to properly triangulate their positions. The ground control points are necessary for the DTM derivation from the aerial photos. Since the aerial photos are not georeferenced (without coordinates) they first need to be georeferenced using the GCP then a DTM is extracted. The Land-use and the Land-cover of the study area were extracted from a Landsat Image and a SPOT image using the usual classification and segmentation techniques. 4.2. DTM extraction The aerial photographs are in TIFF format and are raw data, meaning the interior and exterior orientation are not defined. Interior orientation of the photos is defined from the camera calibration file. The camera calibration file contains information such as focal length, radial lens distortion, decentering distortion and the principal point offset. The principal point is a point at the center of the image from which the fiducial marks and focal length is measured. Radial lens distortion is the symmetric distortion caused by the lens due to imperfections in curvature Albattah, 2015, 2016. Decentering distortion is the non-symmetric distortion due to the misalignment of the lens elements when the camera is assembled. The exterior orientation in this study is defined from collected GCPs and TPs. A GCP determines the relationship between the raw image and the ground by associating the pixel (P) and line
In this study a satellite Landsat image is used, the date the image was taken is August 13th, 2016. The image is a level one terrain corrected product meaning it underwent some processing like radiometric correction, geometric correction, atmospheric correction with Georeferencing and ready for analysis. Layers used for GIS Modelling: The following layers are used for the GIS modelling: DTM, Land-use and Land-cover, Access sites which represents candidate sites for creating a new route which have lower cost, and restricted areas where passing a road route are not suitable or simply defended. The Access sites and Restricted areas layer is created using the land-use map from Amman municipality. Deriving Models: The investigation area is divided to three zones as shown in Fig. 2: In a first stage, the investigation has been carried in a disk of 2 km radius and centered at Al Wahah roundabout. The second investigation has been carried out through a concentric disk of 1.5 km radius and the final stage has been carried out through a concentric disk of 1 km radius. Two models are created and implemented at each stage: The first model is to find the most suitable sites for the start and end of the road. The second model is to find the least cost path between the alternatives. The first model for finding the most suitable path through the first investigation zone is displayed in Fig. 2. The only difference in the first model used in stage two and three is the extent of the input layers. The DTM is restricted to 1.5-km radius disk in the second stage and to 1.0-km radius disk in stage three. In the layer ‘‘Access sites and restricted areas”, any point outside the investigation area is excluded. The second model for finding the least cost path for stage one is displayed in Fig. 3. A detailed discussion of the findings is shown later in ‘‘Suitability Modelling in GIS”. Using ArcGIS package GIS models are created. As shown in Fig. 4 in the first model, elliptical figures indicate input or output layers, while rectangular figures indicate a tool. Each tool requires an input to work and produces an output, that output can be used as input to another tool until a fully structured model is produced. The model is built from left to right and is executed in the same manner.
5. Results and discussion In this paper we investigate the use of satellite imagery to determine where we can develop a road to alleviate congestion in an areas of Amman. In this section we will list the various processing procedures we carried to derive suitable data to input into the developed model. Digital Terrain Model (DTM) Extraction: DTM extraction was done using PCI Geomatica OrthoEngine. The DTM extraction is a sequential step-by-step process. It is extracted from ‘‘Epipolar Images” which are a stereo pair of aerial photographs projected in the same geometry so that the left and right images have a common orientation and their homologues pixels are located on epipolar lines.
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
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Fig. 2. Investigation area is divided to three stages; In a first stage, the investigation has been carried in a disk of 2 km radius and centered at Al Wahah roundabout, the second investigation has been carried out through a concentric disk of 1.5 km radius, the final stage has been carried out through a concentric disk of 1 km radius.
Fig. 3. Model for finding the most suitable sites and is used for all different modelling sizes of the input layer.
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Fig. 4. showing unsupervised classes.
5.1. Satellite image classification The Landsat image was processed using PCI Geomatica Focus application. Two main classification processes are used; unsupervised classification and supervised classification. 5.2. Unsupervised classification The software differentiates between the spectral signatures in each cell and classify them according to a method and number of classes specified earlier in the software. The method used for the unsupervised classification is K-means algorithm, which helps in clustering the spectral signature (grouping same or similar spectral signatures together) in a number of desired classes. The software performs a series of iterations for the K-means algorithm method for better results, the number of iteration as specified for the software is 16 which is a default value. The total number of classes specified for the software is 30 (by default), the software created 29 classes at the end of the unsupervised classification process. After the unsupervised classification process ends, the user can aggregate any resulting classes to a new class as he/she sees fit. Fig. 4, shows the unsupervised classification result after the classification process Barros and Sobreira, 2002. 5.3. Unsupervised classification accuracy assessment Classification Accuracy Assessment is a method for evaluating the classification results. PCI Geomatica Focus support the calculation of an Accuracy Assessment on the results of both unsupervised and supervised classification, the accuracy assessment can be launched through the same drop-down menu used for executing Aggregation displayed in Fig. 5 result for Accuracy assessment on aggregated unsupervised classification. The software creates a number of random points, the point is present in a certain class, then comparing between the class specified by the user and the
ground data, the less discrepancies in the results the higher accuracy will be. The rule of thumb for the number of random points used is 50 points (Fig. 6 and Fig. 7). As shown in Fig. 4, the software created 50 random points, each where compared between the class the point is present in and the actual class. Table 1 displays the error (confusion) matrix created from the random points. Table 1, shows the Producer’s Accuracy, User’s Accuracy and Kappa Statistic for each class. The Producer’s Accuracy refers to errors of omission, where pixels of a known class are classified as something other than that class. The User’s Accuracy refers to errors of commission, where pixels are incorrectly classified as a known class when they should have been classified as something else. The Kappa Statistic is an index of agreement gives an overall assessment of the accuracy of the classification. It is a measure of how the classification results compare to values assigned by chance. It can take values from 0 to 1. If kappa coefficient equals to 0, there is no agreement between the classified image and the reference image. If kappa coefficient equals to 1, then the classified image and the ground truth image are totally identical. So, the higher the kappa coefficient, the better accuracy of the classification Albattah and Kharabsheh, 2000. The results of the Accuracy Statistics, are displayed in Table 2. The overall Accuracy is 94%, the result is accepted (generally results over 90% is considered accurate enough). 5.4. Supervised classification The supervised classification process starts by assigning training sites (training sites is training the software to a specific spectral signature as a certain class), training sites are designated by the user, after finishing the training stage the software compares the entire spectral signatures to the specified class by the user (training spectral signatures). The method used by the software for comparing between the spectral signatures and the specified classes is
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
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Fig. 5. showing aggregated unsupervised classification.
Fig. 6. result for unsupervised classification plus Accuracy assessment.
called maximum likelihood classification (this method calculates the maximum resemblance between spectral signature in a cell and its adjacent). Fig. 8 displays the result for supervised classifica-
tion and the classification accuracy assessment. While collecting training sites, in order to reach better results, classes from the unsupervised classification result guide the location to best
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Fig. 7. result for supervised classification plus Accuracy assessment.
Table 1 Error (Confusion) Matrix for the unsupervised classification. Error (Confusion) Matrix Classified Data
Road Forest Bare-land Buildings Buildings Low-Dens Planted area Urban with Road Unknown Total
Reference Data Road
Forest
Bare-land
Buildings
Buildings Low-Dens
Planted area
Urban with Road
Total
6 0 0 1 0 0 0 0 7
0 0 0 0 0 0 0 0 0
0 0 10 1 0 1 0 0 12
0 0 0 21 0 0 0 0 21
0 0 0 0 2 0 0 0 2
0 0 0 0 0 5 0 0 5
0 0 0 0 0 0 3 0 3
6 0 10 23 2 6 3 0 50
avoiding conflict in spectral signatures. Six supervised classes were created; Bare-land, Road, Forest, Buildings, Low Density Buildings and Planted area Gakenhiemer, 1999.
Table 2 Accuracy Statistics for the unsupervised classification. Accuracy statistics Overall accuracy
94.000%
Overall Kappa Statistic Confidence Interval Class Name
0.917
Road Forest Bare-land Buildings Buildings Low-Dens Planted area Urban with Road
95% Producer’s Accuracy 85.714% 0.000% 83.333% 100.000% 100.000% 100.000% 100.000%
5.5. Supervised classification accuracy assessment
User’s Accuracy 100.000% 0.000% 100.000% 91.304% 100.000% 83.333% 100.000%
Kappa Statistic 1.0000 0.0000 1.0000 0.8501 1.0000 0.8148 1.0000
The method for Accuracy Assessment in Supervised Classification is the same as discussed in Unsupervised Classification Accuracy Assessment. The software creates several random points, then compares between the class entered by the user and the ground data. The software created 50 random points, each where compared between the class the point is present in and the actual class. Table 3 displays the error (confusion) matrix created from the random points. Fig. 8 displays result for Accuracy assessment for the Supervised Classification. From Table 3, the software calculates Producer’s Accuracy, User’s Accuracy and Kappa Statistic.
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Fig. 8. the product of supervised classification on Landsat 8 image used as an input to the Arc Map model.
Table 3 Error (Confusion) matrix for the supervised classification. Error (Confusion) Matrix Classified Data
Bare-land Road Forest Buildings Buildings Low-Dens Planted area Unknown Total
Reference Data Bare-land
Road
Forest
Buildings
Buildings Low-Dens
Planted area
Total
6 0 0 2 0 0 0 8
0 5 0 0 0 0 0 5
0 0 0 0 0 0 0 0
0 0 0 27 0 0 0 27
0 0 0 0 4 0 0 4
0 0 0 0 0 6 0 6
6 5 0 29 4 6 0 50
5.6. Suitability modelling in GIS
Table 4 Accuracy statistics for the supervised classification. Accuracy Statistics Overall Accuracy Overall Kappa Statistic Confidence Interval Class Name Bare-land Road Forest Buildings Buildings Low-Dens Planted area
96.000% 0.937 95% Producer’s Accuracy 75.000% 100.000% 0.000% 100.000% 100.000% 100.000%
User’s Accuracy 100.000% 100.000% 0.000% 93.103% 100.000% 100.000%
Kappa Statistic 1.0000 1.0000 0.0000 0.8501 1.0000 1.0000
Table 4 displays Accuracy Statistics for the supervised classification. As shown in Table 4, the overall Accuracy is 96%, the result is accepted (generally results over 90% is considered accurate enough).
5.6.1. Layers used in suitability modelling Four layers has been used in the ArcGIS/ArcMap software package for performing suitability modelling. The layers are: The Supervised Classification results obtained from LandSat imagery, the DTM extracted from the stereo pair of aerial photographs, the Access sites and theRestricted areas Fig. 8 displays the results of supervised classification extracted from LandSat imagery, and used as an input to the ArcMap model. The layer is labeled in the table of contents as ‘‘Land_use_projec t2Cass”. The classes represent the land-use/land cover for the study area. Landsat images comes with a global projection on Datum 1984 -WGS 84- and needed to be projected to Cassini projection to match other layers Christina Albert Rayed, 2012. Classes extracted are Bare-land, Road, Forest, Buildings, Low Density Buildings and Planted area. Fig. 9 displays the extracted DTM from aerial photographs as a layer in ArcMap labeled in the table of contents as ‘‘elevation4study Area”. The extracted DTM represent one third, the rest of the DTM was obtained from Royal Jordanian Geographic Centre (RJGC).
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Fig. 9. DTM extracted from aerial photographs as a layer entered to ArcMap; DTM extracted represent one third, the rest two third were obtained from RJGC, a minor gap of 80 m between both DTMs was observed and where filled by DTM obtained from a specialized engineer.
Fig. 10. Access site layer and restricted area with land-use layer as a background layer with 30% transparency.
Fig. 10 displays Access site and restricted area layer with landuse layer as a background layer with 30% transparency. Access sites and restricted areas are point feature classes created in ArcMap.
Access sites and restricted areas are chosen based on the criteria discussed in sections Methodology for choosing Access Sites and Methodology for choosing restricted Areas.
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
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Fig. 11. The model on the left is used to find the most suitable sites for the start and end of the road, the model on the right is used to calculate the least cost path between the selected sites.
As shown in Fig. 10, the Attribute table for restricted areas displays the name and facility land-use type. Exhibits and offices shown in the attribute table are defined by the Greater Amman Municipality (GAM) land-use map. They are usually located along the major road networks, which makes the acquisition cost extremely high since the land is licensed as commercial. Hospitals and hotels are present in the study area, hotels and hospitals represent a strong restricted area since the moving process would be extremely expensive which makes the process highly unlikely.
The slope is calculated from the DTM layer. Slope layer is the mean criteria used in the models since steep slopes are unsuitable. Both Access sites and restricted areas represents some implicitly defined criteria. Access sites are areas with least cost and spatially accessible, while restricted areas have high acquisition cost and creating a route through these areas are extremely difficult due to the presence of facilities of special interest (hospital, hotel etc. . .). 6. Findings
5.7. Analysis and modelling Two models are created in ArcMap. As discussed earlier, the study area is divided into three stages. The two models are applied to each stage PCI Geomatics,‘‘Geomatica II, 2015. The first model is used for finding individual optimal suitable sites out of which two locations are to be chosen. The second model calculates the optimal route between the two chosen locations. The layers used in creating the models are the layers described above in the suitability modelling section.
We conducted a three stages of analysis as described in Fig. 2 earlier, where we applied two models on each stage to find the optimal alternative route for each stage. Each stage yielded an alternative solution for a best route. We describe the outputs in the following. Two models are implemented on each stage: The first model is to find the most suitable sites for the start and end of the road (Fig. 11 left), and the second model is to find the least cost path between the selected sites (figure on the right).
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Fig. 12. The most suitable sites for the start and end of the road.
Fig. 13. The alternative in the first investigation zone.
By applying the first model the result is the most suitable sites for the start and end of the road as displayed in the Fig. 12 below. By applying the second model the result is the optimal path between the selected sites. It is shown by Fig. 13 below. This is considered to be the first alternative in the first zone of investigation.
Identical processing steps are repeated for the second zone of investigation. Fig. 14 below displays the outcome of the two models. The outcome of the first model is the scattered blue points. The outcome of the second model is represented by the green route. Alternative 2 is represented by the green line (Fig. 14).
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
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Fig. 14. Alternative 2 is represented by the green line. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 15. Alternative 3 is represented by the red line. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 15 shows alternative 3 suitable for the 3rd zone of investigation. The outcome of the two models: the first model is the scat-
tered brown points. The second model is represented by the red route is alternative 3.
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
7. Discussion of solution found Suitability Modelling is done in a GIS system. Layers used in the modelling process are Land use, DTM plus two additional layers which is Access sites and restricted areas. Access sites and Restricted areas are point feature class layers created in ArcMap. Access sites are candidate sites for creating a route, while restricted areas are to be avoided. Two models are created. The first model is used for locating the optimum suitable sites in the 2-km radius circle. Two locations are chosen from the output of the first model, one for the start of the route and the other for the end. The second model is used for calculating the least cost path between the locations chosen earlier. AHP is used to find the optimum alternative from the 3 obtained alternatives. The set of criteria chosen to best satisfy the goal are Length, Intersection with major highways, Acquisition cost and Mobility. Alternative 1 is the optimal alternative since its higher mobility compensate for its higher construction and acquisition cost. 8. Conclusion It is well known that Remote Sensing techniques have high potential in providing geospatial precise data on large scale with short time and a minimum of effort. Information extracted covers a large extent which saves time, money and effort and reduces the number of field trips if not making them expendable. GIS, MultiCriteria Decision Analysis, photogrammetry and remote sensing techniques can be used together to solve transportation problems such as alternative routes for highly congested urban road segments. Two models are created. The first model is used for locating the optimum suitable sites in the 2-km radius circle. Two locations are chosen from the output of the first model, one for the start of the route and the other for the end. The second model is used for calculating the least cost path between the locations chosen ear-
lier. The two models are applied to the 1.5 and 1.0 km radius circles. Three alternatives are created. AHP is used to find the optimum alternative from the 3 obtained alternatives. The set of criteria chosen to best satisfy the goal are Length, Intersection with major highways, Acquisition cost and Mobility. Alternative 1 is the optimal alternative since its higher mobility compensate for its higher construction and acquisition cost.
References Albattah, M.M., 2015. Remote sensing and topographic information in a GIS environment for urban growth and change: case study amman the capital of Jordan. Int. J. Innovat. Educat. Res. 3–2. Albattah, M.M.S., 2016. Optimum highway design and site location using spatial geoinformatics engineering. J. Remote Sens. GIS 2016. 5:1. Barros, J., Sobreira, F., 2002, ‘‘City of slums: Self-organization across scales”. Centre for Advanced Spatial Analysis Working Paper Series 55.202. Albattah, M., Kharabsheh, H., 2000. Topographic and satellite information for preliminary route location. Photogramm. Rec. 16 (96), 987–996. Gakenhiemer, R., 1999. Urban mobility in the developing world. Transport. Res. part A 33, 671–689. Christina Albert Rayed, 2012. Using GIS for Modeling a Spatial DSS for Industrial Pollution in Egypt. Am. J. Geograph. Inform. Syst. 1 (4), 100–104. PCI Geomatics,” Geomatica II Course Guide‘‘ PCI geomatics, Version 2.0 (2015).
Further reading Jensen, J.R., 2006. Remote Sensing of the Environment: An Earth Resource Perspective. Upper Saddle River, NJ Prentice Hall. Meyer, M.D., 1999. Demand management as an element of transportation policy: using carrots and sticks to influence travel behavior. Transport. Res. part A 33, 575–599. Palubinskas, Gintautas, Kurz, Franz, Reinartz, Peter, 2008. Detection of traffic congestion in optical remote sensing imagery.‘‘ Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE Int. 2. Tawil, M., Reicher, C., Ramadan, K.Z., Jafari, M., 2014. Towards more pedestrian friendly streets in jordan: the case of Al medina street in amman. J. Sustain. Dev. 7 (2).
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
Contents lists available at ScienceDirect
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Research Paper
The potential of space geomatics engineering applications in transportation analysis and planning Mahmoud M.S. Albattah, Salah Edin Youssef Civil Engineering, Jordan University and Alhusein Bin Talal University, Jordan University of Jordan, Jordan
a r t i c l e
i n f o
Article history: Received 8 May 2019 Accepted 26 October 2019 Available online xxxx Keywords: Remote sensing Land-use classification DTM extraction Traffic modelling GIS Analytic hierarchy analysis Least cost route
a b s t r a c t Megapoleses in developing countries, are subject to persistent increase in road traffic due to immigration, increase in prosperity, fast development expansion of economy, travel and tourism. Road traffic congestion is increasing exponentially while road infrastructure are not and cannot be increased in the same ratio. The present study proposes the exploration of the potential of Satellite Imagery, GIS and traffic flow information in solving traffic congestion in one of these cities in developing countries. The Land-use has been generated from a combination of the imagery of the Landsat and SPOT. A Digital Terrain Model (DTM) was extracted from a large-scale Stereo-pair of aerial photographs with a geometric resolution of 60 cm. Up-to-date information such Land use, DTM, Access site information and Restricted Access Area information were combined in a GIS for analysis and visualization. Analytic Hierarchy Process is used for obtaining the optimal alternatives to the congested roads. Ó 2019 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B. V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
1. Introduction Amman the capital of Jordan is the subject of this study. Almadena Street is the most congested road in the city. It carries heavy traffic volume creating intense traffic congestion causing considerable rise in transportation cost and pollution. Fig. 1 shows the study area location. In this study the potential of High Resolution Remote Sensing Imagery is explored to provide precise up to date information of concern and helping in deriving optimal alternatives to the highly congested existing route. Remote Sensing is a technology for sampling electromagnetic radiation to acquire and interpret non-immediate geospatial data from which to extract information about features, objects, and classes on the Earth’s land surface, oceans, and atmosphere Barros and Sobreira, 2002. It has various applications in multiple scientific disciplines including Civil Engineering Geology, Geophysics, Archeology, Oceanology, Meteorology, Natural Resources Management, Agri-
Peer review under responsibility of National Authority for Remote Sensing and Space Sciences. E-mail addresses: [email protected], [email protected] (M.M.S. Albattah)
culture and Climatology. Transportation Engineering, Remote Sensing, is used for Road Route Design and Setting out, Infrastructure Management and Traffic Engineering Albattah, 2015. 2. Objectives This study aims to explore the potential of High Resolution Remote Sensing Techniques to get precise and up to date data and information on a large scale of the area of concern, and to find out the optimal alternatives to the highly congested existing Almadena Almunawera Street. 3. The importance of Remote Sensing for the present study Precise Remote Sensing helps in providing accurate and up to date information that contributes to the definition of a decision support system (DSS) for the achievement of the study objectives. The generated DSS helps in the selection of the optimal alternatives to the existing path while minimizing cost. High Resolution Remotely sensed imagery are processed using segmentation and classification techniques which provide landuse and landcover layers which are then combined with field data and used in a GIS system.
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Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
(L) image coordinates to the x, y, and z coordinates on the ground. A stereo ground control point (GCP) is a cross between a regular GCP and a tie point. It is a feature with known ground coordinates that you can clearly identify in two or more images. Tie points identify how the images in the project relate to each other. The DTM is extracted from the stereoscopic pair of aerial photographs using PCI Geomatica Ortho Engine Barros and Sobreira, 2002.
4.3. Satellite image classification
Fig. 1. Study Area Amman (Capital of Jordan). The circle including the area of exploration is centered at Al-Waha roundabout. It is of 4 km of diameter.
Information extracted from satellite images include the location of Land cover types such as; bare land, buildings, roads, forest and vegetation. With the help of GIS system, the images can be exported as layers in the software, then used for analysis and GIS modeling. 4. Methodology 4.1. Data Collection: A Digital Terrain Model (DTM) has been extracted from the stereoscopic pair of aerial photographs with an overlap of 60%. Six ground control points (GCP) was collected from the field by a professional surveyor using GPS Real-Time Kinematic (RTK) technique to properly triangulate their positions. The ground control points are necessary for the DTM derivation from the aerial photos. Since the aerial photos are not georeferenced (without coordinates) they first need to be georeferenced using the GCP then a DTM is extracted. The Land-use and the Land-cover of the study area were extracted from a Landsat Image and a SPOT image using the usual classification and segmentation techniques. 4.2. DTM extraction The aerial photographs are in TIFF format and are raw data, meaning the interior and exterior orientation are not defined. Interior orientation of the photos is defined from the camera calibration file. The camera calibration file contains information such as focal length, radial lens distortion, decentering distortion and the principal point offset. The principal point is a point at the center of the image from which the fiducial marks and focal length is measured. Radial lens distortion is the symmetric distortion caused by the lens due to imperfections in curvature Albattah, 2015, 2016. Decentering distortion is the non-symmetric distortion due to the misalignment of the lens elements when the camera is assembled. The exterior orientation in this study is defined from collected GCPs and TPs. A GCP determines the relationship between the raw image and the ground by associating the pixel (P) and line
In this study a satellite Landsat image is used, the date the image was taken is August 13th, 2016. The image is a level one terrain corrected product meaning it underwent some processing like radiometric correction, geometric correction, atmospheric correction with Georeferencing and ready for analysis. Layers used for GIS Modelling: The following layers are used for the GIS modelling: DTM, Land-use and Land-cover, Access sites which represents candidate sites for creating a new route which have lower cost, and restricted areas where passing a road route are not suitable or simply defended. The Access sites and Restricted areas layer is created using the land-use map from Amman municipality. Deriving Models: The investigation area is divided to three zones as shown in Fig. 2: In a first stage, the investigation has been carried in a disk of 2 km radius and centered at Al Wahah roundabout. The second investigation has been carried out through a concentric disk of 1.5 km radius and the final stage has been carried out through a concentric disk of 1 km radius. Two models are created and implemented at each stage: The first model is to find the most suitable sites for the start and end of the road. The second model is to find the least cost path between the alternatives. The first model for finding the most suitable path through the first investigation zone is displayed in Fig. 2. The only difference in the first model used in stage two and three is the extent of the input layers. The DTM is restricted to 1.5-km radius disk in the second stage and to 1.0-km radius disk in stage three. In the layer ‘‘Access sites and restricted areas”, any point outside the investigation area is excluded. The second model for finding the least cost path for stage one is displayed in Fig. 3. A detailed discussion of the findings is shown later in ‘‘Suitability Modelling in GIS”. Using ArcGIS package GIS models are created. As shown in Fig. 4 in the first model, elliptical figures indicate input or output layers, while rectangular figures indicate a tool. Each tool requires an input to work and produces an output, that output can be used as input to another tool until a fully structured model is produced. The model is built from left to right and is executed in the same manner.
5. Results and discussion In this paper we investigate the use of satellite imagery to determine where we can develop a road to alleviate congestion in an areas of Amman. In this section we will list the various processing procedures we carried to derive suitable data to input into the developed model. Digital Terrain Model (DTM) Extraction: DTM extraction was done using PCI Geomatica OrthoEngine. The DTM extraction is a sequential step-by-step process. It is extracted from ‘‘Epipolar Images” which are a stereo pair of aerial photographs projected in the same geometry so that the left and right images have a common orientation and their homologues pixels are located on epipolar lines.
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
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Fig. 2. Investigation area is divided to three stages; In a first stage, the investigation has been carried in a disk of 2 km radius and centered at Al Wahah roundabout, the second investigation has been carried out through a concentric disk of 1.5 km radius, the final stage has been carried out through a concentric disk of 1 km radius.
Fig. 3. Model for finding the most suitable sites and is used for all different modelling sizes of the input layer.
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Fig. 4. showing unsupervised classes.
5.1. Satellite image classification The Landsat image was processed using PCI Geomatica Focus application. Two main classification processes are used; unsupervised classification and supervised classification. 5.2. Unsupervised classification The software differentiates between the spectral signatures in each cell and classify them according to a method and number of classes specified earlier in the software. The method used for the unsupervised classification is K-means algorithm, which helps in clustering the spectral signature (grouping same or similar spectral signatures together) in a number of desired classes. The software performs a series of iterations for the K-means algorithm method for better results, the number of iteration as specified for the software is 16 which is a default value. The total number of classes specified for the software is 30 (by default), the software created 29 classes at the end of the unsupervised classification process. After the unsupervised classification process ends, the user can aggregate any resulting classes to a new class as he/she sees fit. Fig. 4, shows the unsupervised classification result after the classification process Barros and Sobreira, 2002. 5.3. Unsupervised classification accuracy assessment Classification Accuracy Assessment is a method for evaluating the classification results. PCI Geomatica Focus support the calculation of an Accuracy Assessment on the results of both unsupervised and supervised classification, the accuracy assessment can be launched through the same drop-down menu used for executing Aggregation displayed in Fig. 5 result for Accuracy assessment on aggregated unsupervised classification. The software creates a number of random points, the point is present in a certain class, then comparing between the class specified by the user and the
ground data, the less discrepancies in the results the higher accuracy will be. The rule of thumb for the number of random points used is 50 points (Fig. 6 and Fig. 7). As shown in Fig. 4, the software created 50 random points, each where compared between the class the point is present in and the actual class. Table 1 displays the error (confusion) matrix created from the random points. Table 1, shows the Producer’s Accuracy, User’s Accuracy and Kappa Statistic for each class. The Producer’s Accuracy refers to errors of omission, where pixels of a known class are classified as something other than that class. The User’s Accuracy refers to errors of commission, where pixels are incorrectly classified as a known class when they should have been classified as something else. The Kappa Statistic is an index of agreement gives an overall assessment of the accuracy of the classification. It is a measure of how the classification results compare to values assigned by chance. It can take values from 0 to 1. If kappa coefficient equals to 0, there is no agreement between the classified image and the reference image. If kappa coefficient equals to 1, then the classified image and the ground truth image are totally identical. So, the higher the kappa coefficient, the better accuracy of the classification Albattah and Kharabsheh, 2000. The results of the Accuracy Statistics, are displayed in Table 2. The overall Accuracy is 94%, the result is accepted (generally results over 90% is considered accurate enough). 5.4. Supervised classification The supervised classification process starts by assigning training sites (training sites is training the software to a specific spectral signature as a certain class), training sites are designated by the user, after finishing the training stage the software compares the entire spectral signatures to the specified class by the user (training spectral signatures). The method used by the software for comparing between the spectral signatures and the specified classes is
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
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Fig. 5. showing aggregated unsupervised classification.
Fig. 6. result for unsupervised classification plus Accuracy assessment.
called maximum likelihood classification (this method calculates the maximum resemblance between spectral signature in a cell and its adjacent). Fig. 8 displays the result for supervised classifica-
tion and the classification accuracy assessment. While collecting training sites, in order to reach better results, classes from the unsupervised classification result guide the location to best
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Fig. 7. result for supervised classification plus Accuracy assessment.
Table 1 Error (Confusion) Matrix for the unsupervised classification. Error (Confusion) Matrix Classified Data
Road Forest Bare-land Buildings Buildings Low-Dens Planted area Urban with Road Unknown Total
Reference Data Road
Forest
Bare-land
Buildings
Buildings Low-Dens
Planted area
Urban with Road
Total
6 0 0 1 0 0 0 0 7
0 0 0 0 0 0 0 0 0
0 0 10 1 0 1 0 0 12
0 0 0 21 0 0 0 0 21
0 0 0 0 2 0 0 0 2
0 0 0 0 0 5 0 0 5
0 0 0 0 0 0 3 0 3
6 0 10 23 2 6 3 0 50
avoiding conflict in spectral signatures. Six supervised classes were created; Bare-land, Road, Forest, Buildings, Low Density Buildings and Planted area Gakenhiemer, 1999.
Table 2 Accuracy Statistics for the unsupervised classification. Accuracy statistics Overall accuracy
94.000%
Overall Kappa Statistic Confidence Interval Class Name
0.917
Road Forest Bare-land Buildings Buildings Low-Dens Planted area Urban with Road
95% Producer’s Accuracy 85.714% 0.000% 83.333% 100.000% 100.000% 100.000% 100.000%
5.5. Supervised classification accuracy assessment
User’s Accuracy 100.000% 0.000% 100.000% 91.304% 100.000% 83.333% 100.000%
Kappa Statistic 1.0000 0.0000 1.0000 0.8501 1.0000 0.8148 1.0000
The method for Accuracy Assessment in Supervised Classification is the same as discussed in Unsupervised Classification Accuracy Assessment. The software creates several random points, then compares between the class entered by the user and the ground data. The software created 50 random points, each where compared between the class the point is present in and the actual class. Table 3 displays the error (confusion) matrix created from the random points. Fig. 8 displays result for Accuracy assessment for the Supervised Classification. From Table 3, the software calculates Producer’s Accuracy, User’s Accuracy and Kappa Statistic.
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Fig. 8. the product of supervised classification on Landsat 8 image used as an input to the Arc Map model.
Table 3 Error (Confusion) matrix for the supervised classification. Error (Confusion) Matrix Classified Data
Bare-land Road Forest Buildings Buildings Low-Dens Planted area Unknown Total
Reference Data Bare-land
Road
Forest
Buildings
Buildings Low-Dens
Planted area
Total
6 0 0 2 0 0 0 8
0 5 0 0 0 0 0 5
0 0 0 0 0 0 0 0
0 0 0 27 0 0 0 27
0 0 0 0 4 0 0 4
0 0 0 0 0 6 0 6
6 5 0 29 4 6 0 50
5.6. Suitability modelling in GIS
Table 4 Accuracy statistics for the supervised classification. Accuracy Statistics Overall Accuracy Overall Kappa Statistic Confidence Interval Class Name Bare-land Road Forest Buildings Buildings Low-Dens Planted area
96.000% 0.937 95% Producer’s Accuracy 75.000% 100.000% 0.000% 100.000% 100.000% 100.000%
User’s Accuracy 100.000% 100.000% 0.000% 93.103% 100.000% 100.000%
Kappa Statistic 1.0000 1.0000 0.0000 0.8501 1.0000 1.0000
Table 4 displays Accuracy Statistics for the supervised classification. As shown in Table 4, the overall Accuracy is 96%, the result is accepted (generally results over 90% is considered accurate enough).
5.6.1. Layers used in suitability modelling Four layers has been used in the ArcGIS/ArcMap software package for performing suitability modelling. The layers are: The Supervised Classification results obtained from LandSat imagery, the DTM extracted from the stereo pair of aerial photographs, the Access sites and theRestricted areas Fig. 8 displays the results of supervised classification extracted from LandSat imagery, and used as an input to the ArcMap model. The layer is labeled in the table of contents as ‘‘Land_use_projec t2Cass”. The classes represent the land-use/land cover for the study area. Landsat images comes with a global projection on Datum 1984 -WGS 84- and needed to be projected to Cassini projection to match other layers Christina Albert Rayed, 2012. Classes extracted are Bare-land, Road, Forest, Buildings, Low Density Buildings and Planted area. Fig. 9 displays the extracted DTM from aerial photographs as a layer in ArcMap labeled in the table of contents as ‘‘elevation4study Area”. The extracted DTM represent one third, the rest of the DTM was obtained from Royal Jordanian Geographic Centre (RJGC).
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Fig. 9. DTM extracted from aerial photographs as a layer entered to ArcMap; DTM extracted represent one third, the rest two third were obtained from RJGC, a minor gap of 80 m between both DTMs was observed and where filled by DTM obtained from a specialized engineer.
Fig. 10. Access site layer and restricted area with land-use layer as a background layer with 30% transparency.
Fig. 10 displays Access site and restricted area layer with landuse layer as a background layer with 30% transparency. Access sites and restricted areas are point feature classes created in ArcMap.
Access sites and restricted areas are chosen based on the criteria discussed in sections Methodology for choosing Access Sites and Methodology for choosing restricted Areas.
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
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Fig. 11. The model on the left is used to find the most suitable sites for the start and end of the road, the model on the right is used to calculate the least cost path between the selected sites.
As shown in Fig. 10, the Attribute table for restricted areas displays the name and facility land-use type. Exhibits and offices shown in the attribute table are defined by the Greater Amman Municipality (GAM) land-use map. They are usually located along the major road networks, which makes the acquisition cost extremely high since the land is licensed as commercial. Hospitals and hotels are present in the study area, hotels and hospitals represent a strong restricted area since the moving process would be extremely expensive which makes the process highly unlikely.
The slope is calculated from the DTM layer. Slope layer is the mean criteria used in the models since steep slopes are unsuitable. Both Access sites and restricted areas represents some implicitly defined criteria. Access sites are areas with least cost and spatially accessible, while restricted areas have high acquisition cost and creating a route through these areas are extremely difficult due to the presence of facilities of special interest (hospital, hotel etc. . .). 6. Findings
5.7. Analysis and modelling Two models are created in ArcMap. As discussed earlier, the study area is divided into three stages. The two models are applied to each stage PCI Geomatics,‘‘Geomatica II, 2015. The first model is used for finding individual optimal suitable sites out of which two locations are to be chosen. The second model calculates the optimal route between the two chosen locations. The layers used in creating the models are the layers described above in the suitability modelling section.
We conducted a three stages of analysis as described in Fig. 2 earlier, where we applied two models on each stage to find the optimal alternative route for each stage. Each stage yielded an alternative solution for a best route. We describe the outputs in the following. Two models are implemented on each stage: The first model is to find the most suitable sites for the start and end of the road (Fig. 11 left), and the second model is to find the least cost path between the selected sites (figure on the right).
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
Fig. 12. The most suitable sites for the start and end of the road.
Fig. 13. The alternative in the first investigation zone.
By applying the first model the result is the most suitable sites for the start and end of the road as displayed in the Fig. 12 below. By applying the second model the result is the optimal path between the selected sites. It is shown by Fig. 13 below. This is considered to be the first alternative in the first zone of investigation.
Identical processing steps are repeated for the second zone of investigation. Fig. 14 below displays the outcome of the two models. The outcome of the first model is the scattered blue points. The outcome of the second model is represented by the green route. Alternative 2 is represented by the green line (Fig. 14).
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
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Fig. 14. Alternative 2 is represented by the green line. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 15. Alternative 3 is represented by the red line. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 15 shows alternative 3 suitable for the 3rd zone of investigation. The outcome of the two models: the first model is the scat-
tered brown points. The second model is represented by the red route is alternative 3.
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001
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M.M.S. Albattah, S.E. Youssef / Egypt. J. Remote Sensing Space Sci. xxx (xxxx) xxx
7. Discussion of solution found Suitability Modelling is done in a GIS system. Layers used in the modelling process are Land use, DTM plus two additional layers which is Access sites and restricted areas. Access sites and Restricted areas are point feature class layers created in ArcMap. Access sites are candidate sites for creating a route, while restricted areas are to be avoided. Two models are created. The first model is used for locating the optimum suitable sites in the 2-km radius circle. Two locations are chosen from the output of the first model, one for the start of the route and the other for the end. The second model is used for calculating the least cost path between the locations chosen earlier. AHP is used to find the optimum alternative from the 3 obtained alternatives. The set of criteria chosen to best satisfy the goal are Length, Intersection with major highways, Acquisition cost and Mobility. Alternative 1 is the optimal alternative since its higher mobility compensate for its higher construction and acquisition cost. 8. Conclusion It is well known that Remote Sensing techniques have high potential in providing geospatial precise data on large scale with short time and a minimum of effort. Information extracted covers a large extent which saves time, money and effort and reduces the number of field trips if not making them expendable. GIS, MultiCriteria Decision Analysis, photogrammetry and remote sensing techniques can be used together to solve transportation problems such as alternative routes for highly congested urban road segments. Two models are created. The first model is used for locating the optimum suitable sites in the 2-km radius circle. Two locations are chosen from the output of the first model, one for the start of the route and the other for the end. The second model is used for calculating the least cost path between the locations chosen ear-
lier. The two models are applied to the 1.5 and 1.0 km radius circles. Three alternatives are created. AHP is used to find the optimum alternative from the 3 obtained alternatives. The set of criteria chosen to best satisfy the goal are Length, Intersection with major highways, Acquisition cost and Mobility. Alternative 1 is the optimal alternative since its higher mobility compensate for its higher construction and acquisition cost.
References Albattah, M.M., 2015. Remote sensing and topographic information in a GIS environment for urban growth and change: case study amman the capital of Jordan. Int. J. Innovat. Educat. Res. 3–2. Albattah, M.M.S., 2016. Optimum highway design and site location using spatial geoinformatics engineering. J. Remote Sens. GIS 2016. 5:1. Barros, J., Sobreira, F., 2002, ‘‘City of slums: Self-organization across scales”. Centre for Advanced Spatial Analysis Working Paper Series 55.202. Albattah, M., Kharabsheh, H., 2000. Topographic and satellite information for preliminary route location. Photogramm. Rec. 16 (96), 987–996. Gakenhiemer, R., 1999. Urban mobility in the developing world. Transport. Res. part A 33, 671–689. Christina Albert Rayed, 2012. Using GIS for Modeling a Spatial DSS for Industrial Pollution in Egypt. Am. J. Geograph. Inform. Syst. 1 (4), 100–104. PCI Geomatics,” Geomatica II Course Guide‘‘ PCI geomatics, Version 2.0 (2015).
Further reading Jensen, J.R., 2006. Remote Sensing of the Environment: An Earth Resource Perspective. Upper Saddle River, NJ Prentice Hall. Meyer, M.D., 1999. Demand management as an element of transportation policy: using carrots and sticks to influence travel behavior. Transport. Res. part A 33, 575–599. Palubinskas, Gintautas, Kurz, Franz, Reinartz, Peter, 2008. Detection of traffic congestion in optical remote sensing imagery.‘‘ Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE Int. 2. Tawil, M., Reicher, C., Ramadan, K.Z., Jafari, M., 2014. Towards more pedestrian friendly streets in jordan: the case of Al medina street in amman. J. Sustain. Dev. 7 (2).
Please cite this article as: M. M. S. Albattah and S. E. Youssef, The potential of space geomatics engineering applications in transportation analysis and planning, The Egyptian Journal of Remote Sensing and Space Sciences, https://doi.org/10.1016/j.ejrs.2019.10.001