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Introducing a novel model of belief–desire–intention agentfor urban land use planning
Saeed Behzadi , Ali A. AlesheikhDepartment of Geographic Information System, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, No 1346,Mirdamad Cross, Valiasr Street, Tehran 19967-15433, Iran
a r t i c l e i n f o
Article history:Received 6 June 2012Received in revised form23 June 2013Accepted 28 June 2013Available online 22 July 2013
Keywords:Geospatial Information System (GIS)AgentBDI architectureCommitmentInteractionUrban land use planning
a b s t r a c t
Land use planning is a potentially demanding search and optimization task that has been challenged bynumerous researchers in the field of spatial planning. Agent and multi-agent systems are examples of themodern concepts, which have been gaining more attention in challenging spatial issues recently.Although the efficiency of belief, desire, and intention (BDI) architecture of agents is validated in varietiesof sciences, its uses in Geospatial Information Systems (GIS) and specifically among spatial planners isstill burgeoning. In this paper, we attempted to integrate the concepts of BDI agent architecture intospatial issues; as a result, a novel spatial agent model is designed and implemented to analyze the urbanland use planning. The proposed approach was checked in urban land use planning problems using acase study in a municipal area. The result of implementation showed the effects of spatial agents'behaviors such as intention, commitment, and interaction on their decision.
& 2013 Elsevier Ltd. All rights reserved.
1. Introduction
Spatial planning is aimed at changing the organization of aspatial environment to meet the demands of a society (Ligtenberget al., 2004). As space becomes a limited resource, the spatialenvironment is expected to fulfill multiple functions. This causesactors to conflict more often in their desires and expectationsabout the spatial environment (Van Der Valk, 2002). Land useplanning is considered as a subset of spatial planning. Land useplanning is a potentially challenging search and optimizationtask, as the planner must frequently take into account complexnon-linear interactions among parcels of land allocated to parti-cular uses (Matthews, 2001). The planning not only influencesthe environmental and political dimensions (Eldrandaly, 2010),but also affects social and economic development in that region(Qi and Altinakar, 2011).
In these circumstances, land use allocation must try to recon-cile multiple conflicting interests as rationally and transparently aspossible (Carsjens and Van der Knaap, 2002). That involvesevaluating land units not only with regard to their suitability forcompeting uses but also with regard to such factors as contiguityamong units assigned to the same use, and the compactnessof the single-use land masses so created (Eldrandaly, 2010;Sante´-Riveiraa et al., 2008; Aerts et al., 2003). Controlling land
use planning is often hampered by a complex procedure andunexpected behavior of the process (Coulelis, 1987), and as such itpresents itself as a complicated and ill-defined problem(Ligtenberg et al., 2004, 2010; Richardson, 2005). Therefore, it isimpossible to propose universal rules for land use allocation tocontrol the process in different places (Wu, 2002).
Scientists associated with the land use planning communityhave backgrounds in different disciplines ranging from anthro-pology to mathematical programming (Verburg and Veldkamp,2005). So, the variety in disciplinary origin of the researcherscontributing to this problem has head to a wide range of differentmodeling approaches and techniques (Verburg and Veldkamp,2005). These solutions involve highly complex spatial data analysisprocesses that frequently require advanced means to addressphysical suitability conditions (Eldrandaly, 2010). Geographicinformation techniques are amongst the earlier methods whichhave increasingly been used for solving spatial decision problemssuch as land use planning. Geospatial Information Science (GISc) isthe study that addresses the concepts, which concern the hand-ling, and analysis of spatial data. Geospatial Information Systems(GIS) are a key technology for GISc. During the last decade, GIS hasbeen developed from relative straightforward systems for storage,retrieval, and presentation of spatial information towards systemsthat support complex spatial analysis (Ligtenberg et al., 2004).To increase the capability of GIS, it is often used in conjunctionwith other technologies to form geo-computational environments.So during the last decade, the use of numerous methods such asGenetic Algorithms (GA) (Behzadi et al., 2008), Cellular Automata
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/engappai
Engineering Applications of Artificial Intelligence
0952-1976/$ - see front matter & 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.engappai.2013.06.015
Corresponding author. Tel.: +98 21 8878 6212; fax: +98 21 8878 6213.E-mail address: [email protected] (S. Behzadi).
Engineering Applications of Artificial Intelligence 26 (2013) 2028–2044
INTRODUCING AN AGENT-BASED OBJECT RECOGNITION OPERATOR FOR PROXIMITY ANALYSIS
S. Behzadi a, *, A. Ali. Alesheikh b
a PhD student in GIS Dept., Faculty of Geodesy and Geomatics Eng., K.N. Toosi University of Technology, Tehran, Iran, [email protected]
b Associate Professor of the GIS Dept., Faculty of Geodesy and Geomatics Eng., K.N. Toosi University of Technology, Tehran, Iran, [email protected]
KEY WORDS: Geographic Information System (GIS), Agent based model, Geometric buffer, Proximity analysis
ABSTRACT:
Object selection is a basic procedure in a Geographic Information System (GIS). Most current methods for doing so, select objects in two phases: create a simple distance-bounded geometric buffer; and intersect it with available features. This paper introduces a novel and intelligent selection operator based on the autonomy of the agent-based approach. The proposed operator recognizes objects around one object only in one step. In the proposed approach, each point object acts as an agent-automata object. It then senses its vicinity and identifies the surrounding objects. To assess the proposed model, the operator is designed, implemented, and evaluated in a case study. Finally, the results are evaluated and presented in details in the paper.
* Corresponding author
1. INTRODUCTION
The analytical capabilities of GIS made the system more practical. Proximity analysis is one of the most basic GIS capabilities that is used to discover proximity relation (deBy et al., 2001). The analysis recognizes objects with buffer features (Basnyat et al., 2000). The buffer operation generates polygon feature regardless of geographic features and delineates spatial proximity (Shekar and Xiong, 2008). Although this method works well for many applications, it has limitations when dealing with complex queries such as finding the nearest object in the buffered area, counting the number of objects in the buffered one, and so on. Producing a buffer or service area around a feature is a basic command of a GIS (Basnyat et al., 2000, Sieber et al., 2009),which has been exercised by numerous researchers. Rebolj and Sturm (1999) generated a buffer to identify the population residing in a vicinity of a road that is affected by noise pollution above the recommended levels. Žalik et al. (2003) presented a four steps algorithm for constructing the buffer of a given line segments, using a sweep-line approach. The buffer introduced by Žalik is called geometric outlines. It presented a specific area around line segments. The area was asymmetric; therefore, it deviated from the other practiced buffer operators. The buffer proposed by Basnyat (Basnyat et al., 2000) was called forested buffer to determine the affect of various attributes in water quality. In this paper, the buffer is generated based on a mathematical model. The governing factors of the model were land use/land cover, slope, and soil types. The buffer was asymmetric; it expanded differently in various directions.Although buffer generations are mostly based on the combination of Thiessen polygons and maximum-distance Euclidean (Haggett et al., 1977), Upchurch (Upchurch et al., 2004), proposed a new type of buffer for defining service areas that supplement the previous studies by introducing network-distance buffer.
The majority of researchers determined the objects of interest in two phases: generate a polygon around the specific object; intersecting the buffer area and specific object to select the interested objects in that area. Therefore, object selection was practiced in two steps. This paper introduces a new agent-based operator that selects objects only in one step. Moreover, the proposed operator is object-based and works for vector data.This paper is constructed in 4 sections. The first section introduces the concept of agents. The second part illustrates the method used to implement the operator. The third section briefly evaluates the efficiency of the operator. The paper ends up with some conclusions and recommendations in section 4.
2. AGENT
An agent is a computer system that is situated in environment. Agent is capable of autonomous action in this environment to meet its design objectives (Wooldridge, 2002, Weiss, 1999, Schumacher, 2001, Dunin-Keplicz and Verbrugge, 2010, Vidal, 2010). Agent-based solution is a new view for solving problems. If the conditions of the problem are structured based on the above definition, the solution follows an agent-based approach. In general, four basic types of agent programs are available: simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. These four types of agents are shown in Figure 1 (Russell and Norvig, 2003).In the simple reflex agent, the agent selects actions on the basis of the current percept, ignoring the rest of the percept history. In the Model-based reflex agent, the agent maintains some sort of internal state that depends on the percept history; therefore, the agent simulate at least some of the unobserved aspects of the current state (Uhrmacher and Weyns, 2009). In the Goal-based agent, beside the current state description, the agent needs some sort of goal information that describes desirable situations (Vidal, 2010). In the utility-based agent, a utility function is
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013SMPR 2013, 5 – 8 October 2013, Tehran, Iran
This contribution has been peer-reviewed. The peer-review was conducted on the basis of the abstract. 91
Caspian Journal of Applied Sciences Research3(9), pp. 1 15, 2014Journal Homepage: www.cjasr.comISSN: 2251 9114
*
1 Department of Geospatial Information System (GIS), K. N. Toosi University of Technology
Keywords:
*
1. Introduction
Developing a Genetic Algorithm for Solving Shortest Path Problem
SAEED BEHZADI, and ALI A. ALESHEIKH Department of GIS, Faculty of Geodesy and Geomatics Engineering,
K.N. Toosi University of TechnologyValiasr Street, Mirdamad Cross, Tehran, P.C. 19967-15433
IRAN
Abstract: Routing is gaining prime importance in our increasingly mobile and highly Information Technology enabled world. The shortest path problem is widely applied in transportation, communication and computer networks. It addresses the challenges of determining a path with minimum distance, time or cost from a source to the destination. This paper introduces a novel Genetic Algorithm (GA) approach to solve the shortest path problem. A connected graph is assumed in which all the weights of the paths are positive. New mutations as well as crossover parameters are defined. The use of mutation parameters depends on the extent of the area under consideration, and the distance between the start and end points. In order to evaluate the proposed algorithm, an urban road map is selected. In all case studies, the algorithm was successful in determining the shortest path. The details of the experimental results are discussed and presented in the paper.
Key-Words: Genetic Algorithm, Shortest path problem, Mutation, Crossover, Graph theory.
1 Introduction The problem of estimating a shortest path between two nodes is a well-known problem in network analysis. Shortest path algorithms are subject of extensive research, resulting in a number of approaches for various conditions and constraints [2, 4, 5]. The shortest path problem; finding the path with minimum distance, time or cost from a source to a destination; is one of the most fundamental problem in transportation networks. It arises in a wide variety of scientific and engineering problem settings, both as stand-alone models and as subproblems in more complex problem settings [1]. This paper presents a new Genetic Algorithm to solve the shortest path problem. Practical examples can be found in finding the shortest path (i.e. the shortest possible distance) between two nodes in a graph or two cities in a map with potential connections (assuming that the path distances are always positive). Typically, a transportation network is represented by a graph with each node representing a city and each edge being a path between two cities. Genetic algorithm is appealing as a solution, since it deviates from traditional algorithms that try to compare every possibility to find the best solution that might be a time consuming algorithm for a graph containing a large number of nodes and edges.
2 Genetic Algorithm
Genetic algorithms are inspired by Darwin's theory about evolution. The genetic algorithm is an optimization solution that is based on natural selection. The genetic algorithm repeatedly changes a population of individual solutions [13]. At each step, the genetic algorithm chooses individuals randomly from the current population to be parents and uses them to reproduce the children for the next generation. Over successive generations, the population "evolves" to an optimal solution [7, 11]. The algorithm is started with a set of solutions (represented by chromosomes) called population. Solutions from one population are taken and used to form a new population. This is motivated by a wish, that the new population will be better than the old one. Solutions which are selected to form new solutions (offspring) are selected according to their fitness - the more suitable they are the more chances they have to reproduce. Basically, several random sets of parameters are considered for an algorithm, and a fitness value (optimization value) is calculated for each. Based on the fitness values, the best sets are mixed (Selection, Crossover and Mutation are combined) together and new sets are again applied to the algorithm until an optimal parameter(s) is obtained. This effect is usually obtained by breaking the genetic algorithm into a few small parts [10]. The algorithm stops when predefined conditions (for example the number of populations or improvement of the best solution) are met.
WSEAS International Conference on URBAN PLANNING and TRANSPORTATION (UPT'07), Heraklion, Crete Island, Greece, July 22-24, 2008
ISBN: 978-960-6766-87-9 28 ISSN 1790-2769
A PSEUDO GENETIC ALGORITHM FOR SOLVING BEST PATH PROBLEM
S. Behzadi *, Ali A. Alesheikh
Dept. of GIS, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology Valiasr Street, Mirdamad Cross, Tehran, P.C. 19967-15433 -
[email protected] , [email protected]
Commission II, WG II/2
KEY WORDS: Genetic Algorithm, Shortest path problem, Mutation, Crossover, Pseudo GA.
ABSTRACT:
Within the last few decades, there has been exponential growth in the research, development, and utilization of Geospatial Information Systems (GIS). While GISs have been developed to challenge most types of spatial analysis problems, many of the morecomplex spatial problems are still beyond their current capabilities to solve. These types of problems often encounter large searchspaces with large numbers of potential solutions. In such cases, standard analytical techniques typically cannot find optimal solutionsto the problem within practical temporal and/or computational limits. One such problem within the field of spatial analysis is that of the routing problem. This paper focuses on the development of algorithmic solutions for the best path problem. Finding optimum path has many practical applications within the fields of operations research, logistics, distribution, supply chain management and transportation. In general, best path routing involves finding efficient routes for travellers along transportation networks, in order to minimize route length, service cost, travel time, number of vehicles, etc. This is a combinatorial optimization problem for which no simple solutions exist. As an alternative, solution techniques from the field of evolutionary computation is implemented and tested for solving instances of the best path. The field of evolutionary computation (EC) has developed to integrate several previouslyresearched fields of related study into one. The sub-fields of EC include genetic algorithms, evolutionary programming, evolutionary strategies, and genetic programming. EC uses computational techniques that are analogous to the evolutionary mechanisms that workwithin natural biological systems, such as natural selection (i.e., survival of the fittest), crossover, mutation, etc. Within EC, these operators are used as a means of quickly evolving optimal or near-optimal solutions to a problem within a computational frameworkdesigned to represent a relevant search space.This paper scientifically reviews evolutionary algorithms in solving GIS problems.Based on their advantages and drawbacks of the methods a new algorithm called pseudo GA is developed. The algorithm is tested for various case studies. The results are presented and discussed. The result of performance analysis of the new algorithm is encouraging.
* Corresponding author. Tel: (+98 21)8887 7072, ext. 303 Fax: (+98 21) 8878 6213
1. INTRODUCTION
Genetic algorithms are inspired by Darwin's theory about evolution[3]. Solution to a problem solved by genetic algorithms is evolved. The genetic algorithm is a method for solving optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. Algorithm is started with a set of solutions (represented by chromosomes) called population. Solutions from one population are taken and used to form a new population. This is motivated by a hope, that the new population will be better than the old one. Solutions which are selected to form new solutions (offspring) are selected according to their fitness - the more suitable they are the more chances they have to reproduce[4].
This is repeated until some condition (for example number of populations or improvement of the best solution) is satisfied.
The following summarizes how the genetic algorithm works[2]:
1. The algorithm begins by creating a random initial population.
2. The algorithm then creates a sequence of new populations, or generations. At each step, the algorithm uses the individuals in the current generation to create the next generation. To create the new generation, the algorithm performs the following steps:
a. Scores each member of the current population by computing its fitness value.
b. Scales the raw fitness scores to convert them into a more usable range of values.
c. Selects parents based on their fitness.d. Produces children from the parents. Children are
produced either by making random changes to a single parent -- mutation -- or by combining the vector entries of a pair of parents -- crossover.
e. Replaces the current population with the children to form the next generation.
3. The algorithm holds when one of the stopping criteria is met.
253
International Journal of Geography and Geology, 2013, 2(4):36-51
36
HOSPITAL SITE SELECTION USING A BDI AGENT MODEL
Saeed Behzadi Geospatial Information System, K.N. Toosi University of Technology, Tehran, Iran
Ali A. Alesheikh Department of Geospatial Information System, K.N. Toosi University of Technology Tehran, Iran
ABSTRACT This paper presents a newly developed Belief-Desire-Intention (BDI) Agent-based model for estimating suitable hospital sites. Our model makes use of existing geospatial functions and a novel BDI architecture of agent techniques. More specifically, the fundamental concepts of practical reasoning architecture such as belief, desire, intention, along with commitment, and interaction have been combined with analyses and applications of Geographical Information System (GIS). The proposed model can be customized for a wide range of decision making problems in GIS, one of which is site selection. In this model, minimal travel time, air pollution and land cost are considered as the goals of agents, and then the agents observe, and believe in the environment. The agent then determines the intention to implement on the environment for achieving their desires. The desires are generated from agents’ goals. The interactions among agents are considered as a part of process for achieving contemporarily goals. In this paper, the fundamental components of agent such as observation, belief, desire, intention, commitment, and interaction are introduced spatially, and a BDI-GIS model is defined based on these components. The Desktop GIAgent software, introduced in this paper, has the advantage of using agents for spatial analysis. The interface helps guiding decision makers through the sequential steps for site selection, namely; importing data, defining goals, determining actions and identifying the agent’s characteristics. For demonstrating the robustness of our new model, a case study was planned and executed in Tehran, Iran. The efficiency of the BDI-GIS model in the decision making process for selecting suitable hospital sites was also demonstrated based on the characteristics of the agents and the types of their interactions.
Keywords: Geographical Information System (GIS), Agent, Belief, Desire, Intention, Interaction, Hospital Site Selection
International Journal of Geography and Geology
journal homepage: http://aessweb.com/journal-detail.php?id=5011
Modeling the spread of spatio-temporal phenomena through theincorporation of ANFIS and genetically controlled cellular automata:
a case study on forest fire
Mohammad H. Vahidniaa*, Ali A. Alesheikha, Saeed Behzadia and Sara Salehib
aGeodesy and Geomatics Engineering, Department of Geospatial Information System, K.N.Toosi University of Technology, Tehran, Iran; bFaculty of Geodesy and Geomatics Engineering,
Department of Photogrammetry, K.N. Toosi University of Technology, Tehran, Iran
(Received 26 February 2011; final version received 30 June 2011)
Virtual representation and simulation of spatio-temporal phenomena is apromising goal for the production of an advanced digital earth. Spread modeling,which is one of the most helpful analyses in the geographic information system(GIS), plays a prominent role in meeting this objective. This study proposes a newmodel that considers both aspects of static and dynamic behaviors of spreadablespatio-temporal in cellular automata (CA) modeling. Therefore, artificialintelligence tools such as adaptive neuro-fuzzy inference system (ANFIS) andgenetic algorithm (GA) were used in accordance with the objectives of knowledgediscovery and optimization. Significant conditions in updating states areconsidered so traditional CA transition rules can be accompanied with theimpact of fuzzy discovered knowledge and the solution of spread optimization.We focused on the estimation of forest fire growth as an important case study fordecision makers. A two-dimensional cellular representation of the combustion ofheterogeneous fuel types and density on non-flat terrain were successfully linkedwith dynamic wind and slope impact. The validation of the simulation onexperimental data indicated a relatively realistic head-fire shape. Furtherinvestigations showed that the results obtained using the dynamic controllingwith GA in the absence of static modeling with ANFIS were unacceptable.
Keywords: geographic information system; adaptive neuro-fuzzy inferencesystem; cellular automata; genetic algorithm; spatio-temporal spread; forest fire;digital earth
1. Introduction
Advancing approaches for the improvement of the virtual representation and
simulation of the planet and its complex systems is a promising goal for digital
earth. Geographic information system (GIS) is one of the most important paradigms
to reach this aim. An important function of GIS is the modeling of dynamic physical
phenomena. It is very important for such a system to incorporate more complicated
models than conventional static and spatial models using the temporal and dynamic
aspects of phenomena (Wagner 1997). One group of these dynamic models is a group
in which a given spatial entity spreads or its characteristics change over time. Models
of complex systems and phenomena, such as cities, ecologies, forest fires and
*Corresponding author. Email: [email protected]
International Journal of Digital Earth,
2011, 1�25, iFirst article
ISSN 1753-8947 print/ISSN 1753-8955 online
# 2011 Taylor & Francis
DOI: 10.1080/17538947.2011.603366
http://www.informaworld.com
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MapASIA 2008, Paper Number 227
Solving Shortest Path Problem in a Local Network Using Genetic Algorithm
1S.Behzadi (Presenting Author) M.Sc. Student, Dept. of GIS Eng.Email: [email protected]
2Ali A. Alesheikh Associate Professor, Dept. of GIS Eng.
Email: [email protected]
1,2Faculty of Geodesy and Geomatics Eng. K.N.Toosi University of Technology No 1346, Mirdamad cross, Valiasr st., Tehran, Iran 19967-15433
Tel: (+98 21)8888 8445, Fax: (+98 21) 8878 6213
Abstract:Within the last few decades, there has been exponential growth in algorithms for solving network problem. Finding shortest path has many practical applications within the fields of operational research, logistics, distribution, supply chain management, and transportation. Numerous algorithms are proposed to finding the shortest path in a network. One of these algorithms is Genetic Algorithm (GA). This paper introduces a novel Genetic Algorithm (GA) approach to solve the shortest path problem. A connected graph is assumed in which all the weights of the paths are positive and the lengths of the arcs are considered as weights of the arcs. New mutations as well as crossover parameters are defined. In order to evaluate the proposed algorithm, a local network is selected. In all case studies, the algorithm was successful in determining the shortest path.
Keywords: Shortest path problem, Genetic Algorithm, Mutation, Crossover.
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مسئله کی یابیریمسشده است. یابیارزن گوناگو يکاربردهامتفاوت و در يها روشمختلف، با استفاده از يها دگاهیدمسئله تا کنون از مسئله ارائه شده است نیاحل يبرا یمختلف يها کیتکن. باشد یم کستراید تمیالگورحل آن يبراروش نیتر متداولکه است يساز نهیبه
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يبرامدل میمفاهمدل، نیا متنوع يهاتیقابل رغمیعل. دکن یمباور استفاده و ، خواستن، لیم لیقباز یاطالعاتاز فیتوص نیاو در دینماحل مسئله يبرامدل کیمقاله نیا. در شود یاحساس م شتریب يها یبررسو لیتحلبه ازینو دهینرسبه بلوغ کامل یمکان مسائل مختلف
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که در مدل دهد یمنشان جینتا یابیارز. شود یم انیبمدل نیا ییکارا انیب يبرا ییها مثالمقاله نیا يانتها. در دینماارائه يبردارشبکه یقبولقابل جینتاعامل انتخاب شود يبرامناسب يرفتارهاکه یصورتاست و در ها عاملوابسته به رفتار ریمسارائه شده نحوه انتخاب
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www.ijrsa.org International Journal of Remote Sensing Applications Volume 4 Issue 1, March 2014
60
Cellular Automata vs. Object-Automata in Traffic Simulation Saeed Behzadi *1, Ali.A. Alesheikh 2
Department of Geographic Information System, Faculty of Geodesy and Geomatics Eng., K.N. Toosi University of Technology ValiAsr Street, Mirdamad cross Tehran, Iran 19967-15433 *[email protected]; [email protected] Abstract
Cellular Automata (CA) recently has been used in variety of fields related to continuous areas such as Geospatial Information System (GIS), traffic, air pollution, and so on. Having been used only in continuous raster-based area is the most weakness of CA, so defining the shape of the cellular and their adjacency for the study area is always a big challenge for researchers. Despite CA, agent-based modeling is complex and progressive type of intelligent object. Agent-based model is used in variety fields. Having efficiency such as moving makes it extremely distinctive from the concept of static intelligent entity. In this paper, at first, an intermediate type of object named Object-Automata (OA) was defined based on the simplicity of CA and complexity of agent which is suitable for both continues and discontinues area. Secondly, to asses and investigate the proposed OA, the simulation of traffic was implemented by OA and the model was compared with CA. As roads are defined as discrete entities, OA is much more suitable for such problem. At the end of this paper, the rules about the problem were described.
Keywords
Cellular Automata; Object Automata; Traffic Simulation; Geospatial Information System
Introduction
The investigation of traffic systems has attracted much attention recently as its results are useful in scientific scheduling transportation construction and efficient utilization of traffic resources. Many kinds of traffic models have been proposed (Shi et al., 2007), one of which is agent-based modeling and simulation. Most of these agent-based modeling and simulation are used as moving objects (cars), traffic signals and so on. Cellular Automata (Wolfram, 1986) (CA) provides a simple, flexible way for modeling. It is also suitable for computer simulations. Many interesting phenomena can be observed in such simulation, so CA leaves challenges to mathematicians. In most of the researches, CA was used as a microscopic model in
which space, time and state variables were considered as discrete (Wolfram, 1986); moreover, the simple regulation reflects all kinds of factors in traffic process. The discreteness of space is suitable for high-performance computer simulations for every cell site on the road occupied by one car at most (Shi et al., 2007). CA was originally introduced by von Neumann and Ulam (under the name of ‘‘cellular spaces’’) as a possible idealization of biological systems (Von Neumann, 1951, Von Neumann and Burks, 1966). Although the concept of CA was first proposed long ago, the CA began to receive wide attention from the traffic and transportation community only after the simple formulation by Creamer and Ludwig in 1986 (Cremer and Ludwig, 1986). After that, Cellular automata models were employed to represent several traffic scenarios from rather simple ones to more complex ones. In 1992, Nagel and Schreckenberg (Nagel and Schreckenberg, 1992) proposed a one-dimensional cellular automata model to simulate traffic flow on a freeway, providing the basic principles for more complex surroundings such as city traffic flow. In 1997, they (HU, 1999) presented a more realistic one-dimensional cellular automaton model for the project TRANSIMS (Nagel et al., 1997). In traffic cellular automata model, the roadway is represented by a uniform cell lattice in which each cell belongs to a discrete set of states. The state of the cells is updated at discrete time steps with a set of update rules that combine a few vehicle motion models. The models are governed by a small set of parameters (Sun and Wang, 2007). Improvements have been made to the N-S (Nagel-Schreckenberg) model by adapting to more realistic circumstances, such as the Fukui–Ishibashi (FI) model, TT model, VDR model, VE model, and so on (Li et al., 2006, In-nami and Toyoki, 2007, Maerivoet and De Moor, 2005). Those models are single-lane ones. In the model proposed by In-nami and Toyoki (In-nami and Toyoki, 2007) a traffic flow model of cars on a two-dimensional lattice was investigated in which a
به با بهره گيري از الگوريتم ژنتيكمسير يابي بهينهالگوريتم ارتقاءمسئله تخصيص و مكان يابيمنظور استفاده در
دانشگاه خواجه نصيرالدين طوسي–) GIS(محمدرضا قانعي، دانشجوي كارشناسي ارشد سيستم اطالعات مكانيEmail:[email protected]
دانشگاه خواجه نصيرالدين طوسي–) GIS(اسي ارشد سيستم اطالعات مكانيسعيد بهزادي، دانشجوي كارشنEmail:[email protected]
علي اصغر آل شيخ،دانشيار گروه سيستم هاي اطالعات مكاني دانشگاه خواجه نصيرالدين طوسيEmail:[email protected]
دانشگاه خواجه نصيرالدين انشكده نقشه برداري د-باالتر از تقاطع ميرداماد-خيابان وليعصر-تهران: آدرسطوسي
88779473: تلفن
:چكيده-1آتش نشاني و ,از قبيل پليس مي دانيم كه يكي از مهمترين پارامترها در زمينه ارائه خدمات شهري
ت از طرف ديگر ،با توجه به اين كه اكثر حجم اين خدما. اورژانس ،زمان پاسخگويي به درخواست ها مي باشداز طريق شبكه حمل و نقل زميني ارائه مي شود بنابر اين ايجاد يك الگوريتم جهت تخصيص منابع به محل
.درخواست ها بر اساس شبكه حمل و نقل زميني و معيار ها، امري ضروري به حساب مي آيدكلكل بهدر اين پروژه سعي بر آن گرديده تا با توجه به الگوريتم كوتاهترين مسير در حالت
)All to All( و با ايجاد تغييرات در اين الگوريتم بر اساس نياز مسئله يك الگوريتم جديدي ارائه گردد كه بهرا براساس شبكه حمل و نقل، ) Location/Allocation(تخصيص و مكان يابي كمك آن بتوان عمليات
.در داخل يك محدوده مشخص انجام دادمفاهيم رياضي مربوط به اين اول در قسمت واند به دو بخش تقسيم شود ،به طور كلي مي تمقالهاين
سازيالگوريتم بيان مي گردد و در قسمت دوم به كمك نرم افزار ويژوال بيسيك اين مفاهيم رياضي پيادهامكان اين محيط اين . يده استگرديده است و يك محيط نرم افزاري بصري براي ارائه خدمات مشخص گرد
مشخص كردن مراكز خدمات در داخل شبكه، محدوده هايي كه هر مركز به آن دهد تا بااربر مي را به ك.اختصاص داده شده است را مشاهده نمايد
مسيريابي بهينه كل به كل، الگوريتم) Location/Allocation(تخصيص و مكان يابي: واژگان كليدي(All to All shortest path)قل، خدمات شهري، شبكه حمل و ن
O. Gervasi et al. (Eds.): ICCSA 2009, Part II, LNCS 5593, pp. 581–588, 2009. © Springer-Verlag Berlin Heidelberg 2009
A Novel Tree Graph Data Structure for Point Datasets
Saeed Behzadi, Ali A. Alesheikh, and Mohammad R. Malek
Department of GIS, Faculty of Geodesy and Geomatics Engineering K.N. Toosi University of Technology
Valiasr Street, Mirdamad Cross, Tehran, Iran [email protected], {alesheikh, mrmalek}@kntu.ac.ir
Abstract. Numerous data structures are developed to organize data and their re-lations. Point set data in GIS are managed mostly through TIN (Triangulated Ir-regular Network) or grid structure. Both methods have some disadvantages which will be discussed in this paper. In order to remove these weaknesses, a novel method will be introduced which is based on tree graph data structure. Tree graph data structure is a kind of data structure which shows the relation-ship between points by using some tree graphs. This paper assesses the com-monly used point structures. It then introduces a new algorithm to address the issues of previous structures. The new data structure is inspired by snow falling process in natural environment. In order to evaluate the proposed data structure, a Digital Train Model (DTM) of sample points is constructed and compared with the generated DTM of TIN model. The RMSE of proposed method is 0.585933 while the one which is obtained by TIN method is 0.748113. The details of which are presented in the paper.
Keywords: Data Structure, TIN, Grid, DTM, GIS.
1 Introduction
There exists lots of data structure to explain the relationship between points, all of which have some advantages and drawbacks. The drawbacks are mostly attributed to the unreal assumption of component behaviors [3]. In this paper, grid and TIN data structure are scientifically evaluated. Then, the new data structure called snowing layer data structure is introduced. Finally, the advantages of using this new data structure are highlighted.
2 Grid Data Structure
In grid data structure, the point is collected in a regular way, which means that the position of points and the distance between points are known [14]. This method has some advantages. For example, the spatially systematic positioning of the points ensures evenness of coverage across the study area. Moreover, the configuration re-sembles to a matrix structure of any high level programming languages. This charac-teristic facilitates computer manipulation. There are some weaknesses in this method
Using Geospatial Information System (GIS) For Locating the best Water Quality Monitoring Stations
Ali Babaei*, Ali.A Alesheikh**, Saeed Behzadi*** * B.Sc. Student, faculty of Geodesy & Geomatics Eng. K.N.Toosi University
Email:[email protected] **Associate Professor, Dept. of GIS Eng. K.N.Toosi University
Email: [email protected] *** M.Sc. Student, Dept. of GIS Eng. K.N.Toosi University
Email: [email protected]
Abstract Crisis of drought threats the earth. Shortage of fresh water makes the supervision of water quality more important than before. One of the most important resources of fresh water is flowing waters. Today water pollution assessment has been noticed from different governmental and non-governmental organizations. A set of water quality monitoring stations established on the flowing water network for the quality control. In this paper, the objectives of water quality monitoring network are studied. The criteria include the representativeness of flowing water system (River), compliance with water quality standards (that in this paper will be considered as a necessity to be observed), supervision of water use, surveillance of pollution sources and examination of water quality changes. Then ,with respect to the effect of each factor, appropriate places for founding water quality monitoring stations has been presented. For this purpose, three layers of data that contain position of influent pollution resources and wastewaters, position of municipal and industrial facilities water intake and the place of factories and towns around flowing water system are gathered. Through such processes, the most efficient places for establishment of water quality monitoring network is determined. If two stations get similar scores, the station that has easier access is chosen. The proposed method was applied to design a monitoring network in a river in Tehran, the Capital of Iran. The results showed that a few current stations coincide with new designed stations. From this study, it was concluded that Geographic Information System is a powerful tool for optimizing the design of water quality monitoring network. Keywords: Geospatial information system (GIS), water quality monitoring stations, optimization 1. Introduction Crisis of drought threats the Earth. Shortage of fresh water makes the supervision of water quality more important than before. Water is required to sustain human life. Rapid growth of population and industrialization has resulted in scarcity and pollution. Recently, as urbanization and industrialization have increased and water pollution has become a threat for more areas, both the general public and policy makers have called for improvements in the design and operation of monitoring networks in river systems. Modernized management of water resources requires a large amount of temporal and spatial information on variations in water quality and quantity, in order to protect communities from floods or drought, to support various types of water use and to
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