Najmeh Neisany Samany; Ali Asghar Alesheikh; Zahra Abedi
Abstract
Extended Abstract
Introduction
Since urban bus networkis considered to be the most important part of transportation system in developing countries, optimal design of this networkis crucial for improving the status of public transportation. To reach this aim, it is necessary to locate these stations ...
Read More
Extended Abstract
Introduction
Since urban bus networkis considered to be the most important part of transportation system in developing countries, optimal design of this networkis crucial for improving the status of public transportation. To reach this aim, it is necessary to locate these stations in areas which increase users of this system in different parts of the city. The present study seeks to identify suitable places for the construction ofproposedbus stations in the 6th district of Tehran municipality using GIS functions, Analytic Network Process and Allen’s temporal model.Proposedstationswere then optimized.
Materials & Methods:
Based on necessary investigations about the 6th district of Tehran, 17 indicators were identified: access criterion (sub criteria: business, administrative, medical, religious, educational and sports centers, and urban facilities, subway, roads), demographic criterion (sub criteria:population and employeesdensity) and traffic status (sub criteria: BRT lines, one way and two way streets, street width, traffic load, slop of the area and kind of road).
At the first phase, questionnaires were distributed among 35 experts of transportation and traffic. Based on the results of DEMATEL questionnaires and their analysis in MATLAB, the severity of relationship between the criteria were calculated and pairwise comparison questionnaires were designed.
Using DEMATEL technique, the presence or absence of a relationship between the aforementioned criteria and sub criteria was investigated. As a decision makingtechnique based on pairwise comparison, DEMATEL uses experts’ judgments to extractelements of a system and find a systematic structure for them using the principles of graph theory. This technique provides a hierarchical structure of the factors of the system along with their corresponding relationship, and determines the effect of these relations in the format of numerical scores. DEMATEL technique is used to identify and investigate the mutual relationships between criteria and to produce a map of network relations.
The ANP model not only calculates the relationship between the criteria, but also the relative weight of each criterion. The result of these calculations make a supermatrix, from which it is possible to derive dependency between each criterion and selection and their weights. An increase in this weight shows higher priority, so it is possible to choose the best option. (Saa’ti, 2003)
It is possible to calculate ANP process in both Super Decision and and ANP-solver software. After calculating weight of the criteria, spatial layers are created in GIS software and finally suitable digital layer is created through integration of the criteria. The obtained digital layer shows the best spatial zones for the construction of bus stations in the study area.
Results & Discussion:
Time and place are inseparable parts of each phenomenon in our world. Since, the first step of processing and analyzing a phenomenon in spatial information systemsismodeling, creating a model with necessary capabilities to include temporal dimension is inevitable. One of the main requirements of spatio-temporal modelling is the ability to investigate the topological temporal -spatial relations betweendifferent phenomena. The present study used Allen’s Interval Algebra to extract all relations between different dimensions of time. These include 3 relations between two temporal events, 6 relations between one event and a time mode, and 13 relations between two time modes.
Based on Allen’s model, the rush hours were investigated and common temporal – spatial features of each station were obtained. New stations were proposed based on existing stations and the desirable layer, and a desirable time was determined for the buses to pass stations based on land uses around the stations, the rush hours of each land useand common temporal – spatial features of each station (based on Allen’s model).
Conclusion
Results indicate that the ANP and Allen model can only search a very small number of possible answers and reach the required answer. 6thdistrict of Tehran municipality covers an area of 1557.65 hectares, from which 18.10% are in a suitable condition, 21.41% are relatively suitable, 30.45% are moderate, 23.88% are relatively improper and 6.17% are completely improper.
281.923 hectares of the district has no problem regarding the access criterion and donot need a station. This district has 185 bus stations and 61 new stations are proposed (a total number of 246).
From the aforementioned 246 stations, 17 stations do not have a common schedule, 87 stations have a common point in their schedule, 89 stations have 2, 42 have 3, 10 stations have 4 and one station have 5 common points in their schedule.
In terms of time,42.28% stations are in a suitable condition, 36.18% are relatively suitable, 17.07% are moderate, 4.07% are relatively improper and 0.41% are completely improper.
Accordingly it is recommended that a bus should pass every 5 minutesfrom stations with 5 and 4 common points in their schedule.For stations with 4 common points in their schedule, this time reaches 10 minutes.Stations with two common points in their schedule need a bus every 15 minutes and stations with 1 common point in their schedule need a bus every 20 minutes.
Mohammad Karimi Firozjaei; Amir Sedighi; Najmeh Neisany Samany
Abstract
Introduction Remote sensing data provide valuable information for the agricultural section and natural resources managers. Nowadays, performance management and estimation via using various methods such as classification and mapping have gained great significance. An example of such data is the mapping ...
Read More
Introduction Remote sensing data provide valuable information for the agricultural section and natural resources managers. Nowadays, performance management and estimation via using various methods such as classification and mapping have gained great significance. An example of such data is the mapping of crops cultivation and orchards at national and regional levels, which is one of the key tools in sustainable agricultural planning and management. These studies appear necessary especially in the field of strategic commodities such as rice and citrus which are among the most important food items for the Iranian people. The spatial information on agricultural lands in the field of agricultural planning and management can help the prevention of the spread of pests, management of the environmental stresses, crop performance estimation and vulnerability assessment in crop production. Field surveys and observations for crops mapping in the growing season in different years are very time-consuming, costly, and only suitable for small-scale studies. In contrast, over the past decades, remote sensing has been recognized as a suitable method for crops mapping for large areas in the shortest time and at low cost. Due to the climatic conditions of the areas in North of Iran, green spaces including vegetation and orchards, and rice fields are located near each other. At the time of the maximum growth of rice products, the spectral characteristics of these land covers are very similar. Therefore, the separation of these two land covers using satellite image classification process faces serious challenges. The aim of this study is to investigate the efficiency of the satellite images and the optimization algorithms for separating green spaces and rice fields from each other at the time of maximum growth. The present study differs from others in this field from two aspects; first, the study compares the capabilities of multispectral and hyperspectral satellite images with each other; additionally, it aims at comparing and evaluating the efficiency of the Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) so as to determine the optimal features for increasing the separation accuracy of green spaces and rice fields. Materials and methodology This research was carried out based on the two objectives of studying the capabilities of the Hyperion and Landsat images and comparing the efficiency of the PSO and GSA to determine optimal features for the separation of green spaces and rice fields. For this purpose, the two Landsat and Hyperion satellite images as well as ground data sets of the case study in North of Iran were employed. In the first step, preprocessing of the Hyperion and Landsat images was performed. In the second step, various features were extracted from the Hyperion and Landsat images using different spectral indices and transformations. In the third step, the Support Vector Machine (SVM) classifier was applied with two strategies, i.e. the usage of spectral bands and the usage of spectral bands as well as indices as the features in the classification process to extract green spaces and rice fields. In the fourth step, PSO and GSA were employed to extract optimal features from the Hyperion image to distinguish between green spaces and rice fields; then, classification was done with the extracted optimal features; and finally, the efficiency of PSO and GSA were compared to determine the optimal features for the separation of green spaces and rice fields using ground data sets. Results and discussion The results indicate that the use of Landsat image is not effective for the separation of rice fields and green spaces. In other words, due to the high spectral similarity of these land covers, a large percentage of pixels related to the two classes are mistakenly classified in another class. However, the accuracy of the producer and user relating to each class has increased by an average of 10 percent with the addition of spectral indices to the classification process. Using Hyperion image is more effective than Landsat image for the separation of rice fields and green spaces. Moreover, the accuracy for the separation of rice fields and green spaces has increased with the simultaneous consideration of the bands and spectral indices in the classification process. It should be noted that one of the key factors in the efficiency evaluation process of the classification methods is the processing time. The results of using optimization algorithms for determining the optimal features indicate that out of the 150 spectral features (including 140 Hyperion image bands and 10 spectral indices and transformations), using PSO and GSA, only 25 and 31 optimal features were selected for the separation of green spaces and rice fields, respectively.The use of optimal features in the classification increases the accuracy for the separation of green spaces and rice fields more, compared to the use of all features in the classification. Additionally, GSA is superior to PSO when used for extracting optimal features for the separation of green spaces and rice fields. Conclusion The results of this research indicate that the separation accuracy of green spaces and rice fields using Landsat image,is less than that of Hyperion image. With the addition of spectral indices to the classification process, the separation accuracy in both Landsat and Hyperion data increases. Moreover, using an optimization algorithm to determine the optimal features in the classification process will increase the separation accuracy of green spaces and rice fields. Given the overall accuracy values, the efficiency of GSA for separating green spaces and rice fields is higher than PSO.
Saman Nadizadeh Shorabeh; Najmeh Neisany Samany; Yaghob Abdali
Abstract
Extended Abstract Introduction There is a huge potential in the usage of renewable energy sources because these natural resources are inexpensive and harmless to the environment. Solar, wind, and geothermal energies are among the renewable energies. Solar photovoltaic (PV) technology is one of the fastest ...
Read More
Extended Abstract Introduction There is a huge potential in the usage of renewable energy sources because these natural resources are inexpensive and harmless to the environment. Solar, wind, and geothermal energies are among the renewable energies. Solar photovoltaic (PV) technology is one of the fastest growing renewable energy technologies across the world. Solar energy is a practical and suitable technology, especially in arid areas with high solar energy potential. The first step in using renewable energy in Iran was in 1994, and since then, much attention has been paid to this type of energy in the society and the government. In Iran, 850 million tons of greenhouse gases are produced annually. Consequently, renewable energy sources such as solar energy can have a significant impact on reducing the greenhouse gas emissions. The integration of GIS and MCDA helps the decision maker to perform decision analysis functions such as ranking the options to select a suitable location so that the GIS is used as a powerful and integrated tool for storing, manipulating and analyzing the solar energy criteria. The use of the MCDA method can facilitate the evaluation and selection of the most appropriate location (s), taking into account the key criteria in the decision-making process. In this study, the optimal areas for the construction of the solar power plants have been identified in five highly optimistic, optimistic, moderate, pessimistic, and highly pessimistic levels using the spatial criteria and the OWA model. One of the most prominent features of this research in relation to the other articles is the inclusion of the concept of risk into the solar power plant site selection process to determine the optimum areas for the construction of solar power plants using the OWA model. Materials and methods The primary data used in this study include the Digital Elevation Model (DEM) derived from the Aster satellite data for the extraction of solar radiation and the region slope, the extraction of the mean land surface temperature for 2017 using the Terra Sensor MOD11A1, the preparation of the average map of the vegetation for 2017 using MODRA13A2 Terra sensor, the 1.250000 fault map prepared by the geological organization, the statistics and data of the rainfall prepared by the Meteorological Organization of Chahar mahal-o-Bakhtiari province, shapefile of road network prepared by the Organization of Roads and Urban Development, the climaticshapefile of the country prepared by the Iran Meteorological Organization, the shapefile of urban areas generated by the National Cartographic Center (NCC).The proposed methodology works by employing AHP to obtain the appropriate weights for each criterion, and utilizing OWA to extract suitable locations to varying degrees of risk. Sensitivity analysis for the criteria weights were conducted by virtue of the OAT method. Results and discussion The northern sectors of Razavi Khorasan province are endowed with cold temperatures and cold mountainous climate, which has had a substantial contribution to the increased cloudy and rainy days as well as the relatively extensive vegetation cover in this area. In this light, with respect to all ‘ORness’s, the target areas fall within the ‘very unsuitable’ and ‘unsuitable’ classes for construction of solar power plants. Moreover, the high slope factor in these areas has contributed to high levels of surface radiation, albeit, as the slope criterion is considered a constraint, the target areas are, in fact, not suitable for the construction of solar power plants. Moving southwards, the suitability of the regions, in terms of construction of solar power plants, tends to shift in the positive direction (very suitable class), which is most likely the result of the low rainfall and vegetation cover in conjunction with high surface temperatures in these areas, as opposed to their counterparts in the north. Areas falling within the very suitable class for construction of solar power plants in Razavi Khorasan can be realized by dint of calculating the percentage of area attributed to each class at ORness = 0.5 per city. The findings show that cities located towards the south and southwest of the province contribute to the highest area in the suitable class, while counties in the northern regions have the lowest share of area in the very suitable class. The highest sensitivity in locating suitable areas in Razavi Khorasan province were observed among the factors of slope, road, and urban criteria. Alterations in the weights assigned to these criteria would entail a significantly strong impact on the extent of the very suitable class. This highlights the significance of accurately determining the weights for these three criteria in Razavi Khorasan Province. Based on the findings, the rate of change in weight assigned to the of fault criteria ranges from 0 to 0.2, which in turn causes substantial change in the area of regions in the very suitable class extent. However, setting the criteria weight at between 0.2 and 1 appears to have no significant effects in the area of this class. Conclusion The results of this research indicate that the northern parts of Razavi Khorasan province are highly unsuitable and unsuitable for all of ‘ORness’ values, while a significant extent of the highly suitable class for the construction of solar power plants is comprised of sectors of the southern regions. Areas within the very suitable class corresponding to an ORness=1 comprise 5% of the class, whereas those with an ORness=0 have a 74% share. The three cities of Ferdows, Bardaskan, and Gonabad, had the highest share of the area attributed to the very suitable class (0.8-1), as maintained by a per city analysis of the area for each class. However, the cities of Dergas, Quchan, Mashhad, and Kalat had no share of the areas within the very suitable class. This most probably stems from the high geographic latitudes of said regions, which has engendered unsuitable climatic conditions in these areas. Finally, results from sensitivity analysis of the criteria showed that increases in the weights assigned to the factors of slope, road, and urban criteria, would cause a further increase in the area of the very suitable class. Stated differently, the selection of suitable locations for the establishment of solar power plants is highly sensitive to these criteria. Changes in the weight of the surface temperature criterion had no considerable effect on the area of the very suitable class. Moreover, shifts in the weight allotted to solar radiation and precipitation in the province, ranging from 0 and 0.6, brought about substantial changes in the area of the very suitable class. Whereas, shifts within the 0.6–1 range had no significant effects on the area of the very suitable class.
Ali Kazemzadeh; Najmeh Neisany Samany; Ali Darvishi Boloorani; Ara Toomanian; Ahmad Pourahmad
Abstract
Extended abstract
Introduction
Life in the modern cities takes shape through interaction with various environmental, socio-economic, infrastructural, health, security, political and cultural conditions. The result of this interaction shapes the quality of urban life (QOUL). Quality of lifeis a complex ...
Read More
Extended abstract
Introduction
Life in the modern cities takes shape through interaction with various environmental, socio-economic, infrastructural, health, security, political and cultural conditions. The result of this interaction shapes the quality of urban life (QOUL). Quality of lifeis a complex concept involving social, economic, environmental, physical, psychological and political aspects (El Din et al, 2013). In general, Quality of life (QOL) has been evaluatedbytwo objective and subjective points of view. Researches in this field, have mainly been conducted in the form of social studies and in the macro geographical scales of countries or cities,and less attention has been paid to the spatial differences of the life quality in the complex urban environments. In these studies, the principal components analysis (PCA) method has been the most common method used for combining and overlaying of the life quality indicators (Lo, 1998; Jun, 2006; Li and Weng, 2007; Motakan et al, 2010; HatamiNejad et al, 2014; Messer et al, 2014). But,Oneof the disadvantages of PCA is the possibility of deleting some of the useful information.Using Multi-Criteria Decision-Making (MCDM) and Fuzzy Logic methods can also be useful in spatial modeling of life quality. Moreover, QOL as one of the features of geographical environment is a dynamic concept. This means that this feature changesover time and location. The spatiotemporal modeling of this concept can help monitoringthe quality of urban life and planning for its improvement.
Data and Methods
This study offers a framework and process for spatiotemporal modeling of QOUL. For spatial modeling of QOUL, effective indiceswere taken into consideration at first. In this study,the indicators related to the urban quality of life were extracted in 3 three environmental, infrastructural/physical, and socio-economic dimensions.The Analytical Hierarchy Process (AHP) method was used for weighing the parameters(Uyan, 2013). Then, the indicators were combined with each other using the GammaFuzzyModel(Vafai, 2013) and Vikor-Fuzzy overlay technique(Huang et al, 2009). Furthermore, QOUL was modeled temporally due to the variability of environmental indicators and some of infrastructural / physical indicators during the seasons of the year. For this purpose,the cyclic model (developed based on the snapshot approach (Worboys and Duckham, 2004)) was used. In order to assess the developed framework, the quality of lifewasmodeled at urban blocks level in regions 3,6,11 of the city of Tehran.
Conclusion
The obtained results showed that applying multi-criteria decision-making and Fuzzy logicmodels in modeling of life quality is capable of showing the spatial difference oflife quality in urban environments. Based on the results of spatial modeling, the quality of life is more desirable in northern parts of the area (region 3) while the desirability decreases towards the southern areas (region 11). The study of Moran’s spatial autocorrelation index (greater than 0.35 for the results of both models and all seasons) emphasize on the non-randomness of the distribution method of the QOL feature in urban blocks and shows the existence of cluster pattern in the study area.The results of temporal modeling indicated that most of the blocks are more favorable in the spring and autumn seasons than in the winter and summer in terms of environmental conditions.
Mostafa Kheyrollahi; saeed nadi; Najmeh Neisany Samany
Abstract
Abstract
Due to the sensitivity oftheir missions, urban emergency vehicles are alwayslooking forthe shortest timeto reach the destination. In big cities, in addition todistance, several factors and parameters with respect to the complexityand extent of thetransport and traffic, are influencing time ...
Read More
Abstract
Due to the sensitivity oftheir missions, urban emergency vehicles are alwayslooking forthe shortest timeto reach the destination. In big cities, in addition todistance, several factors and parameters with respect to the complexityand extent of thetransport and traffic, are influencing time of arrival of an emergency vehicle, some of which are qualitative or quantitative, dynamic or static. In this paper, the modern approach used, is based on composing conflation models, Gamma quantification methods, travel time prediction formulas and meta-heuristic algorithms in order to find most optimal route. In this paper, first we have tried to introduce all the calculated, available, qualitative and quantitative, affecting factors related to emergency routing, thenwith converting qualitative parameters to quantitative ones, we normalize each parameter by the maximum approach and conflate them in such a way that thepriority and impact of each parameteris determined to find the optimal route. In order to calculate the priority and impact of factors, the Gamma test method, as a data derived method is selected. The procedure is implemented by the use of road network and traffic volume data from two regions of Tehran. Based on this approach, the considered weights for each following criterion of degree of difficulty including quality, width, slope, category, and route directness are 0.331, 0.286, 0.188, 0.172 and 0.020, respectively. Finally, genetic meta-heuristic algorithm is used to select the optimal route and the results compared with common Dijkstra routing algorithm. The length of the selected route by GA is about 130 meters in one time and about 300 meter in the other time more than the selected one by Dijkstra algorithm. Based on the implemented comparison, the represented approach in this paper had a considerable superiority over the simple current methods.
Nagmeh Neisany Samany; Mahmoud Reza Delavar; Mohammad Reza Malek
Abstract
Navigation is one of the most important daily activities of individuals in an urban environment. The spatial information systems of the user guidance are the most common services which guide people to navigate the various routes with different goals.The main challenge of such systems is providing context-aware ...
Read More
Navigation is one of the most important daily activities of individuals in an urban environment. The spatial information systems of the user guidance are the most common services which guide people to navigate the various routes with different goals.The main challenge of such systems is providing context-aware navigation information. The location, time,and the identifier of the individual person are among the primary contexts and the other related contexts are modeled based on these contexts. The present paper attempts to model the spatial-temporal communication of the moving user based on the identifier of the individuals. So that it covers all spatial communications (topologic, metric and directional) in time dimension andtakes the characteristics of the user and the related contexts into consideration. In this regard, the proposed model utilizedthe advantages of Allen’s Multi-Interval Algebra (MIA) and dynamic Voronoi-based Continuous Range Query (VCRQ), and introduceda new method by following the large calculus principles and the existing customization methods. The designed model (MIA25) was implemented in a software applicable in mobile systems. The evaluation of the implemented model in the tourist’s navigation scenariointhe regions 3, 6, and 11 of Tehran municipality was carried out based on 3 parameters of accuracy, runtimeand users’ satisfaction. These three regions were selected as the case study area. In order to test the accuracy of the model, the designed software was iterated 100 times in three different routes in the study area by three different tourists at three time intervals with two different average speed. Then, the recognition of each one of the textures existing on the way, was examined by the one-way binomial approximationwith 95% of confidence level over 100 iterations. Also, two indices of correctness and recall were used to evaluate the recognition of the textures through the entire route. The results of implementation and evaluation of the model based on three parameters of accuracy, runtime and users’ satisfaction demonstrate the efficiency of the proposed model in an urban context-aware navigation system.
