فرشاد حکیم پور

One of the most well-known methods used by the public to share spatial knowledge is the sketch maps. These types of maps can be considered as an effective way to collect and share spatial data by depicting some of the spatial cognition of individuals from the real world. Because of the difference in the cognition of individuals from the environment, each of the sketch maps drawn from a specific region can include different information. An important question to be asked here is how the potential of these maps can be benefited more and better in order to collect spatial data in spite of these differences. One of the proposed methods for this purpose is to combine and integrate information extracted from different sketch maps of a region with the aim of obtaining a map with more complete information than any of the original sketches. Although this integration can provide a better use of the potential of the sketch maps for collecting spatial data, prior to that, it is necessary to consider the matching process between the features in the sketch maps. However, there are several types of features in sketch maps, but all sketches contain pathways and these pathways can be identified by name, or based on specific places or Points of Interest (POI) located in the proximity of them. Accordingly, in this paper, a new solution is proposed for matching between the existing pathways in the sketch maps. In order to describe each pathway in the sketch maps, in addition to the name of the pathway, we can use information that can be extracted indirectly from these sketch maps. To describe a specific address, even if we do not know the correct names of the pathways, we can use the names of near POIs or other related pathways. The same point is used in this paper to describe the pathways in the sketch Maps. Accordingly, the matching parameters are considered as two main parts. These parts are the proximity of POIs and intersections of pathways with matched well-known pathways. Therefore, in the first step of implementation, based on the descriptive information as well as the relative distance between the POIs, the clustering process is performed on POIs location. Subsequently, based on the qualitative spatial relations between clusters and pathways, descriptive information of the pathways, the arrangement of the pathways around the intersection points, pathway intersection with roundabouts and matched well-known pathways, the process of matching of the four sketch maps is done. In order to evaluate the accuracy of this method, the outputs of the matching process are compared with the manually matching, and then, the precision and recall values ​​for each matching are calculated. The average values ​​of these two parameters (89.63% for precision and 87.1% for recall) indicate that this solution has an acceptable accuracy compared to other studies in this field and therefore, the proposed solution can be exploited in future works as the basis for the integration to enrich the existing  metric maps with less details.

These days Floating Car Data (FCD) is one of the major data sources in Intelligent Transportation System (ITS) applications like route suggestion, traffic monitoring, traffic flow analysis and etc. Due to GPS limited accuracy and noises and road network errors, utilizing of FCD in ITS applications needs an efficient and accurate map matching framework. Map matching is a well-established problem which deals with mapping raw time stamped location traces to edge of road network graph. Along with high success rate, novel map matching applications faces several challenges including variable sampling frequency and processing speed of FCD big data. In this paper we have proposed a general, efficient, accurate and distributed map matching framework. The proposed framework can handle variable sampling frequency data accurately. Although this framework does not depend on additional data other than road network and GPS, achieved high success rate shows effectiveness of our system. We have used spatial proximity, heading difference, bearing difference and shortest path as our matching criteria. We also employed dynamic weights for each criteria to make our framework independent from local parameters. We have also employed confidence levels to improve our matching success rate. To answer low frequency data challenges, we have present an extra criteria based on A* shortest path method with dynamic weighting method. We have used HDOP for weighting shortest path criteria. When we are not confident enough about a point matching, we use shortest path criteria to improve success rate and by this method we keep our overhead low. For the evaluation we have studied New York City (NYC) OSM trajectories as the case study. We also used OSM NYC road network as the base map. The evaluation results indicate 95.2% MM success rate in high sampling mode (10s) along with 89.5% success rate in low sampling frequency (120s). We have compared our method with a known map matching method that In the case of low sampling frequency, our method has improved matching accuracy up to 9.7%. We have evaluated the effect of utilizing shortest path criteria in low frequency scenario. Our results show that using shortest path have improved our result up to 3.5%. One of the major challenges in using FCD is storage, managing, analysis and batch processing of this big data. To face this challenge in this framework we have used cloud computing technologies along with MapReduce paradigm based on Hadoop framework. The proposed cloud computing based framework can answer technical challenges for efficient and real-time storage, management, process and analyze of traffic big data. Our evaluation results indicate we have matched 7000 points/second on a cluster with 5 processing nodes. We have also processed 5 million records in 530 seconds using a cluster with 5 processing nodes. The main contributions are as follows: 1) we have proposed a general, distributed map matching framework using cloud computing technologies to answer to upstream ITS applications, 2) We have improved an efficient and accurate map matching algorithm which can handle different sampling frequencies using shortest path method and confidence level, 3) we have used dynamic method for weighting geometric, directional and shortest path constrains.

Urban land-use allocation is a complicated problem due to diversity in land-uses, a large number of parcels and different stakeholders with various and conflicting interests. A variety of studies presents many criteria in this regard. Different methods have been proposed for the optimal allocation of urban land-uses. The outputs of these methods are near optimum layouts that offer a suitable land-use for every land unit. However, because of some limitations such as disagreement of stockholders by a specific land-use or the high cost of land-use conversion to a certain land-use, it is not possible for planners to propose desirable land-uses for all parcels and as a result, have to use next priorities of the land-uses. Thus, prioritizing land-uses for parcels along with optimal land-use allocation could be essential in urban land-uses planning. Furthermore, due to the approximate nature of land-use evaluation criteria, using fuzzy calculations can be more compatible with the urban land-uses allocation models. Therefore, in this study, a parcel-level urban land-use prioritization model based on fuzzy calculation is presented. In the proposed model, at first evaluation criteria are estimated by fuzzy calculations for each parcel. Urban land-use evaluation criteria include neighborhood effects (i.e. compatibility, dependency, and proximity), physical suitability, and per capita. Compatibility and dependency factors depend on the different service level of each land-use. Each land-use is defined in three service level of local, district and regional and different radius of effect is considered according to these service levels. Furthermore, suitability criterion is calculated according to the characteristics and physical properties of land units for each land-use as a fuzzy number. Per-capita criterion is calculated as per capita violation in a fuzzy manner and is considered in land-use prioritization. After fuzzy calculations of criteria, the importance of each criterion must be determined. To determine the importance of each criterion in proposed model, the weight of criteria is estimated by subjective and objective weighting approaches. Expert knowledge is used for estimating subjective weights, and Shannon's entropy method applied for determining objective weights. The Fuzzy TOPSIS[1] technique is used to prioritize land-uses for each parcel. In fuzzy TOPSIS, after normalization and applying weights to each criterion, positive and negative ideal points are calculated based on the best and worst values of criteria. Finally, with calculating the distance of each land-use as alternatives from worst and best ideal points, land-uses will be ranked for each parcel. This procedure is repeated for all parcels in the study area, and therefore, all land-uses are ranked for all parcels. The proposed model was implemented on spatial data of region 7, district 1 of Tehran. Ranking urban land-uses for parcels in the study area showed that 77.2 percent of current land-uses have the first priority for their own parcels. It came that 22.8 percent of parcels in the study area were not allocated the first priority of land-use, and the land-use of these parcels can be susceptible to change. In the land-uses of the study area, in terms of susceptibility to changes, residential units have the best situation, and industrial units have worst situation. As a future research, based on proposed model, different scenarios can be proposed for optimal allocation of urban land-uses by taking into account stockholder's preferences. For modeling the stakeholder's preferences, approaches such as multi-agent systems and game theory can be used.

The era of big data is approaching with the rapid growth in development of complex information and communication technologies like internet and 3rd and 4th generation of Mobile Networks (3G/4G). These days more than ever, real-time and concurrent access to heterogeneous data from different sources and with different formats is made possible. Meanwhile, with recent advances in sensor technology and in order to monitor, explore and visualize complex spatial systems, a large amount of data in different Spatio-temporal scales is being generated every day. For example, according to the estimation by United Nations Initiative on Global Geospatial Information Management (UN-GGIM), 2.5 quintillion bytes of data are generated every day which large portion of the data is location-aware. This unprecedented trend of spatial data generation provides new opportunities for information and knowledge extraction, industrial development and business decision making. Although the big data brings new chances for scientific, business and engineering fields, it presents some challenges. To be more specific, storage, management, process and analysis of the spatial big data on traditional spatial information platforms is difficult and expensive. Such challenges affect modeling, analysis, simulation and concurrent access to spatial data. The need of real-time analysis in some applications like dynamic routing, fleet management or emergency management is also influenced by such limitations. In order to face spatial big data challenges, cloud computing technology to support spatial information applications appears to be very promising. Emergence of cloud computing technology provides an effective, scalable and affordable solution to big data challenges in spatial information application. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software. In this paper, we discussed fundamental theories and key technologies of cloud computing in storage, process and analysis of spatial big data. We have surveyed storage and management of big data using distributed file systems and NoSQL databases and made a comparison between different types of this databases. We also discussed recent trends and methods in parallel processing and big data analysis. MapReduce as a prominent parallel programming method and Hadoop as the most popular implementation of MapReduce are surveyed. We reviewed and made a comparison of spatial tools which is developed on cloud platforms. One of the most important challenges in spatial cloud computing that geospatial scientists is facing is spatial indexing and query processing. Due to distributed systems limits, developing dedicated spatial indexing and query processing techniques is needed. So we focused on novel spatial indexing methods and query processing technologies. As the case study, this paper surveys usage of cloud computing technologies in transportation, traffic and Intelligent Transportation Systems (ITS) and remote sensing and earth observations. The aim of this paper is reviewing and introducing fundamental theories, new technologies and recent trends of spatial big data to researchers of geospatial sciences.

Nowadays the selective access to information on the Web is provided by search engines, but in the cases which the system includes spatial information the search task becomes more complex and requires special capabilities in the search engine system. The purpose of this study is to extract the information which lies in the GML documents also implementation and evaluation of this extracted information retrieval method in an integrated approach. Our proposed system consists of three components: crawler, database and user interface.
1- Crawler: The main innovation of this study is this component. Crawler is a piece of software that after receiving the initial feed enters into Web pages and open links on each page and enters into the pages of these links. The crawler repeats this for new pages until all pages are reviewed and there are no new pages. The typical spatial search engines crawlers analyze and process the HTML documents and extract spatial information contained in these documents. In our proposed system, the crawler processes GML documents text instead of HTML documents, and extracts the spatial information from these documents. Crawler in this system has two main tasks:- Detection of GML documents among the documents with different formats.
- Parsing of GML documents and extracting the spatial information
2-Database: database has two major tasks in this system:- Storing data which collected by crawlers
- Information indexing
3-User Interface: this section provides interaction between user and system and users send their queries to the system through this interface
In general, this system's search process is done in two phases: online and offline. Offline phase includes the crawler's searching and storing the information into the database. And the online phase includes user interface and ranking operation.
All in all, in this study the following objectives discussed:1- Extraction of spatial information which is embedded in Web documents: Spatial documents include spatially explicit information such as the coordinates of the feature or the type of feature that extracting this information improves the response rate of spatial queries in search engines.
2- Implementation and evaluation of an integrated spatial information retrieval approach.
We have implemented this system as a pilot system on an Application Server as a simulation of Web. Our system as a spatial search engine provided searching capability throughout the GML documents and thus an important step to improve the efficiency of search engines has been taken.
Despite the fact that today's engineers and specialists in many fields need raw spatial data and looking for it on the World Wide Web, most of spatial search engines are based on map representation and less attention is paid to spatial data. There is a substantial volume of spatial documents and information on the Web, however, the extent of the Web has caused this huge volume of documents and information hard to find among other information.Our proposed system as a spatial search engine provides the possibility of searching throughout the GML documents and thus it improves the efficiency of spatial search engines. Since GML documents include explicit spatial information along with non-spatial information, the main advantage of this system compared to other spatial search engines is an integrated approach to spatial and non-spatial data.

Bank branches location-allocation problem belongs to NP-Hard problems which can be possibly solved only in exponential time by the increase in the number of banks and the large number of customers; especially when the location model includes various datasets, several objectives and constraints. As a consequent, we need to use heuristic methods to solve this type of problems. Also, since majority of data and analyses applied in the location-allocation problems are spatial; GIScience’s abilities should be employed beside optimization methods.
Nowadays, to perform particular financial tasks bank customers often need to be present at their bank. For the sake of its customers, a bank should increase its branches in the city to attract more customers in the race with competing banks. However, establishing new branches is too expensive and banks prefer to carry out an optimal location finding procedure. Such procedures should consider many criteria and objectives including spatial data of customers, new and existing bank branches as well as level of attraction of banks –in the real-life. Customers often select a bank that is closer to them, has better services or financial records and also consider other human or physical factors. Hence, planning to increase the number of customers for a new branch of a bank considering spatial criteria and various other objectives appear necessary.

This paper determines the location of bank branches. Finding an optimum location of branches depends on many factors and these problems are known as NP-hard problems. Despite being approximate methods, meta-heuristic algorithms seem suitable tools for solving NP-hard problems. In this paper, Grey Wolf Optimizer (GWO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Cultural Algorithms (CA) and Invasive Weed Optimization (IWO) are applied for finding the best location of bank branches. From marketing point of view, the aim is to attract more customers while the number of attracted persons to a new branch should be acceptable. The new methods have capability to find the optimum location of new branches. The location of a new branch should be as far away as possible from branches of the same bank. The other condition is that the total number of customers for the new branch should not be less than a specified number, while the new branch should not attract customers of old branches of the same bank more than a threshold. To fulfill this propose a part of the Tabriz city was selected for implementation.
The assumptions for the defined problem can be expressed as the following statements: a)We consider four different banks (Melli, Mellat, Sepah and Meher) in our study area.
b)Population density (of people over 15 years of age) is available at the building block level.
c)Banks have infinite capacity for accepting customers.
d)Each customer refers to only one bank.
e)New bank branches should have maximum distance from branches of the same bank, so that; it attracts minimum number of customers from branches of the same bank.
According to the above-mentioned assumptions, mathematical model of the function for optimization is as follows: Maximizes the distance between newly established branch and other existing branches of the same bank.
Constraint1: Not less likely to attract new customers to the bank established a certain extent.
Constraint2: Other branches of the same bank customers not less so after the creation of a new bank branch.
To assess the accuracy of the algorithms in the problem, suggests, in this study, repeatability and convergence of the algorithm is used. The results from the convergence of the algorithms used in this study, 100 iteration, is provided. For comparison, the cost for the logarithmic axis is provided. The axis can be said that IWA algorithm has better convergence than the other four algorithms. The convergence of the algorithm optimization methods, PSO and GW are next in priority. The answer and the cost of repeated 5 times 50 algorithm implementation of this algorithm is compared. It also answers the PSO algorithm and GW are next in priority. It should be noted that the number of adjustable parameters optimization algorithm optimization method IWA far more than the PSO and GW.
Finally, to evaluate quality and accuracy of the algorithms, several iterations are performed. The results of statistical and final tests indicate that the accuracy and convergence speed of Invasive Weed Optimization are more than other Algorithms in finding optimal location of bank branches.

This paper determines the location of bank branches under competitive conditions with different attractive conditions. Finding an optimum location of branches depends on many factors and these problems are known as NP-hard problems. Despite being approximate methods, meta-heuristic algorithms seem suitable tools for solving NP-hard problems. In this paper, Genetic Algorithm (GA), Simulated Annealing (SA) and Cuckoo Optimization Algorithm (COA) are applied for finding the best location of bank branches. From marketing point of view, the aim is to attract more customers while the number of attracted persons to a new branch should be acceptable. The new methods have capability to find the optimum location of new branches under competitive conditions. The location of a new branch should be as far away as possible from branches of the same bank. The other condition is that the total number of customers for the new branch should not be less than a specified number, while the new branch should not attract customers of old branches of the same bank more than a threshold. To fulfill this propose a part of the Tabriz city was selected for implementation. Finally, to evaluate quality and accuracy of the algorithms, several iterations with different seeds are performed. The results of statistical and final tests indicate that the accuracy and convergence speed of Cuckoo Optimization Algorithm are more than the Simulated Annealing and Genetic Algorithms in finding optimal location of bank branches under competitive conditions.

Nowadays, geocoding is a crucial part of the different science process, So that in most of the geospatial and spatial-related applications; geocoding services play an important role as a data-provider. Modern geocoders are fundamentally different from their original versions, and all aspects of these software services (coverage area, response time, spatial and non-spatial data completeness) have improved dramatically. Despite the fact that these developments have truly extent the area of geocoding applications, there are several issues with these improvements which are important as user point of view. These issues contain the hypotheses in the interpolation process, precision of the reference data, the uncertainty in matching algorithms and spatial unit of the geocoding service. Literally, Geocoding means assigning a geographic code to a set of non-spatial (attribute) data. This definition comes from two root words: "geo" means earth and "coding" is defined in Latin as applying a rule for converting a piece of information to another part. "Input", "processing algorithm", "reference data" and "output" are the fundamental parts of an abstract geocoder. Due to the importance of geocoders in different applications, in this article we decide to evaluate several open-source and commercial geocoding services from different perspectives. Hence, different measures (response time, data completeness, ease of use and etc.) are used in this article in order to address this issue in a fair and precise manner.

Using spatial data mining and weighted comparison methods can have an efficient role in determining the value of local utilites for efficient indices in real estate pricing. In this study, the factors affecting the price of real estate are divided into four general categories: economic and market factors, physical and welfare factors, neighborhood and access characteristics and organizational characteristics. Organizational characteristics considered as a group of influencing indices in determining property prices that its information is collected from relevant agencies and organizations can contribute significantly in pricing, marketing and management to the different users involved in the real estate sector including buyers, sellers and real estate agencies. The classification proposed assuming stable economic and market factors at the time of user requests and the influence of each index on the quantity and quality is finally divided into three groups that are: Boolean, Multi-Value and Fuzzy/Logarithmic. Where Boolean indices denote those that exist or does not exist on a property that value in order 1 and 0. Multi value indices are those that get more than one value and their influence coefficients are calculated like Boolean indices. Finally, Fuzzy/Logarithmic indices are those that their influence functions are continuous and like fuzzy or logarithmic functions and should be determined by available information and related techniques. Influence coefficient is the influence that the presence or absence of an index affects the property price. For example how presence of elevator affects the property price. Influence function for Fuzzy/Logarithmic indices is the influence that the amount of increasing or decreasing of an index affects the property price. For example how far to main road affects the property price. In this study spatial data mining and compared weighted coefficients are used interactively for gathered information in a crowd-sourced environment. With these methods, indices and impact factors and impact functions are obtained from using different information classifications that have considerable influences in determining local utilities in property pricing. The study area of this research is part of metropolitan area of Kerman (Iran) that is located within 57˚01 ́and 57˚04 ́of longitude and 30˚16 and 30˚19 of latitude from UTM coordinate system(zone 40). Required information includes both spatial and descriptive information. Spatial information includes the location of properties, public places (such as educational, religious, shopping and industrial centers, parks, offices and official buildings) and main and secondary streets. Hence, by using available maps such as maps provided by the National Cartographic Centre (NCC) in 2003 and cadastre maps from registration organizations under the preprocessing GIS ready, the shape files from properties, public places and streets are prepared. All these files are prepared in UTM coordinate system from reference elliptical WGS84. Thus 18053 numbers of properties, 260 numbers of public places and 1009 numbers of streets include main and secondary streets are prepared after preprocessing GIS ready in polygon format to enter to database. Also the description information of 150 properties were gathered from the agencies in the study area.
The results obtained for 16 Boolean indices show that the maximum influence and utility is belong to indices parking, store, elevator and pool and indices the basement and furnished have the lowest utility. Substructure area index is highly dependent on the number of bedrooms. This means that an increase in the number of bedrooms will not necessarily led to an increase in the property price. Therefore the best number of bedrooms up to substructure area 90 squared meter is 2, up to area 120 squared meter is 3, up to area 150 squared meter is 4 and up to area 200 squared meter is 5.
For Multi-Value indices the 15 indices are studied. For the index the number of stories with an increase in class between 2 and 3 percent is added to the price of each property. The index the number of units in a class is inversely related to the property price. This means that if the number of units in each class is higher in the same conditions like area the property price will be lower. For the index the registration document of property the highest utility belongs to full document and the lowest belongs to full devoted document (The full document means the document that completely belongs to its owner with the entire legal rights of a property. When some restrictions apply to the property by the previous owner for example in application style the degree of its ownership will be decreased. The level of restriction in any stage is one sixth of ownership. The restrictions start from full to full devoted document that has the highest restriction.) The registration range of property is a proper index for the regional utilities that depends on different factors. The results show that the highest utility belongs to the registration tag 5 and the lowest belongs to 2773. The organizational restrictions index such as being located in the heritage policy and being located in the urban future plan have a negative influence in local utilities and property price. The results show that being located in the heritage policy has the lowest utility and highest negative influence in property price. For the index view of property there is no utility in the study area and therefore this index is considered as ineffective index.
Finally, for the Fuzzy/Logarithmic indices, calculating the influence function or obtaining a clear process for the changes aren’t easy as two previous groups and the influence rate of these indices is different in various distances. The influences of some indices are estimated by one influence function that are: land area, construction time, land price, reconstruction time, substructure area, distance from shopping centers and distance from main streets. For the indices distance from health centers, distance from educational centers, distance from administrative centers and distance from city center, the influence functions take various modes in different distances. The highest utility for these indices is situating in the distances that are not too far away or to close so that not to take negative effects of too close nor to disturb the appropriate access. There is neutral utility in some distances. This means that situating properties in these distances hasn’t any significant effect on property price. The influence functions for the indices distance from industrial centers, distance from historical and social centers and distance from religious centers are almost the same. According to the results obtained, Boolean and Multi-Value indices obtained with disciplined and more predictable process than the Fuzzy/Logarithmic indices. Finally for evaluation, the prices that were calculated by the system for 10 numbers of properties compared with their actual prices and their accuracy was calculated.

The World Wide Web Consortium (W3C) defines the semantic web as a framework to share and reuse of data between groups, communities and applications. In this relation Tim Berners-Lee proposed the idea of “web of data” that can be processed by machines. In order to reach the aims of semantic web, there are needs to data representation languages, knowledge bases and rule bases that are known as semantic web technologies. In the world of spatial data, the semantic information has important roles in different activities of spatial analysis and data management. In this research the spatial semantics are dealt with a difference in two levels. The first is all the semantics about the location and geometry of an individual object and the second is about the arrangement and mixture of objects in an extent. The latter is more related to spatial relations that cause complexities to cope with. The semantic explicating of such concepts in relational spatial databases has some limitations such as non-flexibility of database schema, non-flexibility of data types, the restricted defining of sub-classes, implicit semantics of relations and finite number of joins between tables. The exclusive characteristics of semantic web technologies in storing, managing and retrieving of data and knowledge lead to a wide range of applications; also the applications independent of web environment. The databases defined in semantic web technologies are based on graph structures and present the advantages of logical databases, object-oriented data bases and network databases. The introduced capabilities are applied to explicate the concept of "deteriorated structures" in urban environments. This concept is inherently non-spatial but has two complementary spatial interpretations; permeability and fine granularity; which make it possible that the concept to be explicit in spatial data of urban layouts. The scenario which based on, the semantic of " deteriorated area" is made explicit starts from a point, that by acquiring its coordinates it is determined whether this point is located within the such area or not? Handling of the spatial data of the case study in district 12 of Tehran and the procedure to semantic explicating of “deteriorated area” are done in the spatial and semantic extension of Oracle DBMS. The spatial data of urban parcels and streets accompanied with area of parcels and width of streets are used. In order to model the spatial relations, all the neighborhood relations between parcels and intersection relations between streets are also entered as linked data to triple store. In addition to RDF data, a rule base to exploit the concepts of “connected streets” and “co-blocked parcels” and also to define the conditions of narrow streets and small parcels is needed. Using some SPARQL queries the percentages of parcels with less than 200 m2 area and the streets with less than 6 m width are presented. The infinite number of inter tabular joints which have many applications in the spatial relations, is the main shortcoming of conventional databases and means we cannot express an infinite, recursively defined sequence of similar expressions. The procedure shows the deficiency of relational spatial database about recursive relations and implicit relations especially the spatial ones can be satisfied by semantic web data models and knowledge management techniques.

Link travel time is the most important variable for determining travel route and start time of journeys. Link travel time is the basis of navigation and routing systems. Time dependent algorithms in Geographic Information Systems (GISs) calculate fastest routs based on the travel duration of links of a network depending on different times or dates. In fact, travel time estimation for future is the basis of time dependent routing. Currently different means of monitoring traffic flow such as traffic cameras or electromagnetic sensors are present [1]. However, these methods cannot estimate link travel time efficiently; high cost, low accuracy and dependency on human agents are major problems of using these methods. By emerging usage of portable receivers of positioning systems, researchers are more interested in exploiting the data produced by such equipments for monitoring traffic speed flow. Nowadays most public transit buses are equipped by AVL systems for monitoring purposes. In this research, travel durations of arterial links are estimated in real time by obtaining data from transit buses. Essential corrections for complications caused by buses slower speed and their leaving traffic flow at bus stops are modeled and applied towards a better assessment [5, 6].
Determining link travel durations in time intervals needs an analysis on spatio-temporal data. We estimate travel duration for time intervals of 15 minutes length (7am to 9pm) assuming invariant parameters in calculation of travel duration for each interval. In our approach, first we calculate the travel duration for buses and compensate for the delays caused by bus stops, which include the acceleration and deceleration time at each stop. Simultaneously, timing data of traffic lights control signals are also incorporated in computations to improve accuracy of bus travel duration estimation. We use historical data to find required parameters for calculating bus travel duration. In addition to historical data, we also integrate real-time data and time series analysis to improve our travel duration estimation. In this research we use Holt-winters analysis [14] for a short term prediction of travel time. Finally, we obtain a set of observation equation that is solved by an optimization method.
Buses movement data of two different bus routes in five days (6th to 10th December 2014) are used to estimate travel duration of three links in Motahari Street. Position information of each bus is provided every two minutes plus the time and position of every time the bus doors opens and closes. On the fifth day (10th December) three test vehicles equipped with GPS recievers are employed to collect validation movement data every one second. Drivers of the test vehicles are instructed to avoid extreme low or hight speed and drive with the flow of traffic in the middle lanes insofar as possible. Finally, calculated travel times are compared with results of two well-known methods namely baseline estimation algorithm [8] and Helinga method [4]. RMSE of the proposed approach indicates 22 percent improvement compared to Helinga approach and 30 percent improvement in comparison to baseline algorithm. This improvement shows that information obtained from a public bus monitoring system can be used efficiently for arterial link travel time estimation.

Many transportation planning systems are based on travel time. People usually use location based services to find fastest rout in cities. Such services need accurate information about travel times of network links. With the advent of modern positioning approaches and AVL equipped vehicles, estimating travel times is more possible than ever. Vehicles that collect information about route condition are called Probe Vehicles. Currently in Tehran, city buses are equipped with GPS enabled AVL systems. Determining travel time of links using spatial and temporal data requires spatio-temporal analysis. In this study a new method is proposed for determining the travel time of vehicles using bus probe and signal timing data. The method is based on historical and real-time data. Techniques for considering bus stop dwell time and signal delay is introduced. In addition travel time is divided to link’s speed and signal’s delay. Results of model shows that bus probes are promising to determine travel time in signalized arterial links.

Motor vehicle crashes is one of the main problems of road transportation network in Iran. Exploring the significant variables related to concentration of motor vehicle crashes is vitally important in reducing crashes on a highway corridor. This study integrates spatial analysis with a K-Mean-based Hierarchical Agglomerative Clustering method to identify the correlation between major crash types at the concentration points of crashes and explore the most spatial factors that may lead to crashes. An experiment is designed and conducted on Qazvin-Rasht highway corridor using real crash records and spatial factors related to roadway geometry and its proximity features. Results showed that clustering crash records using the proposed method is 6.7 percent better than hierarchical agglomerative clustering and approximately 10 percent better than k-mean clustering method. Moreover, analyzing the concentration points of crashes using spatial functions and discovery of patterns and rules data mining approach explored type and specification of each cluster's crashes and revealed the impact of road geometry, traffic, urban development, activities and land uses in the proximity of highway corridors on increasing the rate and severity of motor vehicle crashes.

Semantic Web utilizes different data model rather than relational data model. Tables are well-known for relational data bases while RDF represents data in a model based on directed graph and formed of the triple composition. Although some RDBMS offer storage of graph data but with respect to time and consumed resources، their query efficiency is not appropriate enough. On the other hand، another encountering challenge can be addressed as the interoperable and flexible representation of geospatial data in the Semantic Web standards. A variety of standards from different companies has been designed as a solution for handling the interoperability challenges. One should determine which spatial semantic standard is most appropriate for data of interest، each of which has advantages and disadvantages. This paper reviews standards for spatial semantic data storage and retrieval، moreover by considering the extent of usage، available technologies will be surveyed. Every technology follow specific standard and rather than complete conformance، the technology may support some sections of the standard. In the following، this paper reviews the procedure of establishment and implementation of a semantic Web system that benefit from the approach of spatial semantic data known as “linked geospatial data”. Moreover in the last section of the paper، important points and necessities of each implementation step will be covered.

Cellular Automata are common approach to simulate urban expansion and different implementations based on them have done for modeling urban growth in recent years. Scale of model is one of the important properties that are effective on the results of these models in urban expansion area. In this study، the effect of changing scale was considered based on results of modified continuous cellular automata. At the first step، fundamentals and basic components of proposed model were represented and uncertain effects of changing scale in cells were demonstrated. Furthermore، the proposed model was implemented on a real city. The study area is the Isfahan city and land cover data for modeling of this area were derived from satellite images using supervised classification methods. Scale change experiments were done based on two different strategies in this study. In the first strategy the growing factor of cells was constant in experiment but the value of this factor was changed according to cell size during the next experiment. The estimations of error were done by the Root Mean Square error. In the first strategy، the best simulation results were given at phase with the cell size of 68. 5 meters and the maximum deviation between the upper and lower error based on this index was 0. 0035 meters. In the later strategy، the best simulation results were given at phase with the cell size of 63. 5 meters and the maximum deviation between the upper and lower error was 0. 1055 meters.

Ever growth of population and following of that, increasing of landfills have caused that environmental experts pay attention to it. Understanding of environmental processes require the knowing of ecological relations between life and non-life creatures and nutrient cycle and important cycles such as water cycle and nitrogen cycle. Latexes in produced landfills sometimes persist with heavy metals in many years. Usually heavy metals containing with organic compounds in molecular structure show no severance. Landfill excretion management in suburban areas of large cities such as Tabriz is inevitable. To observe multiplicity layers in landfill excretion we used geographic information system for site selection. Results showed that landfill excretion in Tabriz is none desirable due to the status of groundwater and geological formations and the sense that compost industry to be established in this city.

Rapid expansion of web data containing spatial information requires new approaches which are capable of huge data handling. Semantic web paradigm offers appealing solutions for heterogeneous and sporadic data sets by setting new and different definitions of Data Base Management Systems (DBMS). On the other hand, due to various understanding of place among different individuals, there are many criteria for cognition of place and place categories, so these different criteria lead to more distinctive annotations (so-called affordance) for Points Of Interest (POI) modeling. The design of specific schemas for these types of POI’s affordances is heinous in relational DBMS while spatial semantic modeling through graph DBMS expedites deployment of affordance-based storage and retrieval frameworks. In this study, available specifications and technologies related to spatial semantic functionalities are addressed and the current state of spatial semantic data formatting and interpretation is presented. The designed information system utilizes semantic web specifications to store and retrieve affordance-based POI’s information and a sample data set is used for applicability of the system. In order to evaluate achieved results, retrieved data by spatial semantic queries are compared to similar information in relational data bases, therefore spatial data loading in GDBMSs and RDBMSs are compared and results are analyzed.

This paper evaluates moving objects database (MODs) data models and data structures by focusing on history management approach for MODs. MODs algorithms and their complexity are being discussed. By introducing benchmarks as conventional tools for comparison of MODs, we use queries and data utilized within BerlinMod benchmark for evaluation and analysis. Finally, we propose methods to enhance queries performance.

Web-based Social Networks are increasingly becoming one of the key media of communication over the Internet. The potential of these services as the basisto gather statistics and exploit information about user behavior is exciting. With theadvances in the location-aware hardware and software technologies, location-basedsocial networking applications have been proposed to provide services, taking intoaccount both the spatial and social aspects. In this paper, the concept of location basedsocial networks including architecture and functions are provided and a prototype todemonstrate the capabilities and features of LBSNs is presented.

One of the most important issues in GIS is Data interoperability. Emerging of web service technologies and SOA paradigm encouraged the evolution from Classical and data-centric GIS to distributed and loosely coupled architecture. With the increasing amount of users requesting spatial data, organizations published their spatial web services according to Existing standards(OGC,ISO). At the other hand, many organizations are providing similarservices with overlapping functionalities. So there would be more web services toresponse for each request in UDDI. Selection of the best web service to response is a challenge. Web services have functionality and non-functionality aspect that they’re may be similar in functionality aspects but they’re different in nonfunctionality aspects (quality of service). Web service selection by QoS can be a good solution for this challenge that it can introduce the suitable web service to users. In this paper, we will describe QoS, Webservice Level Agreement (WSLA) Standards for representing quality of web services and represent a model for spatial web services quality. Finally we will investigate QoS-Driven service selection methods for web service composition.

Location-based Services become part of our everyday life, ranging from traffic prediction systems to services over mobile phones. Services, users and the concepts of time, space and movement are fundamental sub domains of LBS applications and have commoncharacteristics. As these sub domains are complicated and heterogeneous, theontology is used as the core of fundamental structure in these services. In this paper after defining the existing methods of ontology in Location-based Services, in order to share and clarify the common concepts of space, time and movement that are basics of Domain Ontology and all the LBS applications, a systematic research about meaning ofthese concepts has been done. Finally, in order to help understanding of Domain Ontology, its implementation in an open source ontology editor, Protégé, is analyzed.

By rapid development of Web envirement, using of Web has changed. Presenting the data and process using Web services technology is one of the most significant changes. Web services are Independet of operating system, Web browser and programming languages; also these services can present data in a standard format such as XML. One of the most important types of information is spatial data. Therefore, Web services are used for search, access, integrate, process and visualize spatial data from different data sources. Spatial Web services by supporting 3D spatial data are known as new generation of Web services. This research investigates capabilities of current 3D Spatial Web Services: Web Feature Service (WFS), Web View Service (WVS), Web Terrain Service (WTS) and Web 3D Service (W3DS). Next, these Web services are compared based on three criterias: overall architecture, presentation capabilities and level of user interaction. Finally, based on these comparisons and review, we propose several applicatons for each 3D spatial Web services.

The planning and decision making processes to reduce the effects of natural disasters are depends on the spatial data and making efficient decisions without these information is impossible. On the other hand by increasing the sources of spatial data production and different attributes of the datasets, standardization in the field of production, sharing and processing of spatial data has been taken into consideration. Nowadays GIS is the representation of GIS functionalities through the web as autonomous and interoperable services. Systems that implement OGC standards reduce the time required to find, analyze and update crucial information, as a result of open standards is that Emergency Response and Disaster Management stakeholders have more information available to them before, during and after an emergency or disaster occurs. In this paper, some of the OGC's common web service standards, that used in the modeling of fire detection use case, are presented. The used services are data access service, WFS and WCS, portrayal service, WMS, and processing service, WPS.

Transportation vehicles, especially taxis, which are equipped with GPS, are moving sensors, mining roads traffic. The transportation vehicle (taxi) drivers are skilled drivers who are usually experienced in finding the fastest route from a place to a destination. In this paper by mining the movement history of transportation vehicles, a user with the predefined source and destination points and the time of departure, is provided by practically the fastest route. The utilized approach is the use of a landmark graph that its nodes are road segments that are traversed by vehicles frequently and in that, the intelligence of drivers and dynamic properties of streets network are modeled. Based on the landmark graph, we take advantage of a two levels routing approach to compute the efficient path. The data being used are the trajectory of 140 vehicles moving in the city of Tehran in the period of 30 days. In comparison to other approaches such as, speed constraint, the employed method has higher performance in terms of processing time and the proposed route derived from the system has less travel time.