agent-based modeling

Among the various air pollutants in Tehran, particulate matter with a diameter of less than 2.5 micron is considered one of the most important air pollutants that is created through fuel combustion and can cause various diseases and increase the mortality of people. Due to the importance of the subject, so far, various researches have been conducted on the effects of PM2.5 pollutants that most of them have only investigated statistical and epidemiological effects. In this study, the aim is to investigate and show the effect of PM2.5 pollutant concentration with the help of ABM, where the effect of pollution on a car as a moving agent and showing the effects of pollution in the form of color change on it was considered. For this purpose, PM2.5 pollutant distribution map for tow days with different pollution intensity was prepared in ArcGIS software environment. For doing this job, the information related to the concentration of pollution on the December 24, 2022 and January 1, 2023 was used and the IDW interpolation method was used to interpolate and prepare the dispersion map. After that, from the obtained data, the concentration of pollution and its integration with the location of the streets and squares of the studied area, the agent's movement environment was created in Netlogo software and the relevant environment was called. By determining the origin and destination, the route and the speed of the car, the model was implemented for two days, December 24, 2022 and January 1, 2023. The results of the model implementation showed that due to the higher intensity of pollution on the Januay 1, the effect of pollution on the agent was much higher than on the December 24. With the implementation of the model on the January 1, the amount of pollution in a short period of time reached above 100, which is an unhealthy range for sensitive groups. According to the AQI index, this level of concentration causes serious breathing problems for the elderly and children.

The increasing importance of transportation in urban spaces, as well as the numerous problems it has created, have prompted decision-makers to encourage citizens to use public transportation as much as possible. Therefore, scientists and urban decision-makers are looking for a comprehensive system for more connections between transportation systems. On the other hand, the use of public transportation is very important and will create spatial justice, will bring proper access for all people. In this paper, a factor-based multiprocessor routing including taxis, buses and agencies was designed. Agents appear in the role of travelers who have the opportunity to choose any possible means of transportation to cross from origin to destination and also fill the gaps in their path by walking. The main criteria are choosing the path of time and cost along the way. Field data were collected to evaluate the results of the model. The results of analyzing the field data by multicriteria methods revealed that the priorities of people’s travelling mode are internet taxi, agency (called by telephone), bus and taxi (with predetermined rout and taking more than one passenger) with values of 0.4, 0.27, 0.19 and 0.14 respectively. The observations differed somewhat from the model output, which in fact represents optimal choices. This seems to be due to complex human decision-making in which many factors, including risk-taking and personality traits and habits and other factors, play a role.

One of the advantages of the remote sensing method is that it minimizes surface surveys, especially in inaccessible areas based on spectral information obtained from satellite images. The presence of minerals can be explored by their spectral signatures recorded in satellite images. The main hypothesis of this research is that the combination of such processes with agent-based modeling (ABM) can lead to better planning of the mining exploration and reduce cost and time. In this study, ASTER and OLI sensors were used to identify areas containing minerals in the Yanesar section of Behshahr city. Due to the combination of mineralogy and lithology, the study area is mainly made of lime, shale, clay, and marl. The result of the processing is the exposure of lime units. After pre-processing on the information and satellite images, band ratio methods have been used to identify areas with lime mineral potential. Then, Spectral Angle Mapper (SAM) method was used to more accurately separate these areas using the USGS laboratory spectral library. In order to optimize time and cost in order to identify areas containing lime minerals, agent-based modeling has been used as a new approach. By considering several strategies based on random movement and movement in mineral potential areas, time tables and cost were obtained and compared with each other, and finally, the best results in terms of time, cost, and a number of explorer agents were obtained.

Nowadays, cities’ population has been encountered with rapid growth. However, the urban transport infrastructures are not developed with this rate. This issue yield to the common problem which is the traffic jam in streets. The practical approach to solve this problem is the efficient use of the infrastructure and proper traffic control which plays an important role in this performance. Based on the aforementioned issues, the aim of this study is an urban traffic control system using the agent-based simulation approach. In this study, the microscopic model for efficient network traffic junctions in the context of the flow of traffic at intersections has been used. The proposed approach, by defining the main parameters of traffic as a collection of micro-scale smart agents, is expected to address the issue mentioned more effectively. These agents consist of three categories (vehicles, traffic control centers and traffic lights) with different functions. In our approach in addition to the defined scenarios for intersection with a focus on optimizing time, total optimization of traffic flow will also be followed. Also, path finding is used to test the performance of the model. Having the traffic lights with three phase and two methods of path finding, six scenarios are defined and are implemented in a simulated environment. The results of the comparison parameter stop time and the average speed of vehicles reveals the fact that using the intelligent path finding and smart traffic light (sixth scenario) would lead to the downturn about 3.12 seconds per kilometer for stop time and upturn near 1.83 km/h for average speed of cars using the proposed method. We used the controlled data for the evaluation process and the relative accuracy was 83% for stop time and 94% for average speed of vehicles. The results of this study revealed the efficiency as well as reliability of the developed agent-based model in traffic smoothing.

On the one hand, the amount of productivity in construction projects is influenced by different factors with continuous behavior over time, and on the other hand, by the interaction and interaction between the human resources present in the project. In this research a new tool for modeling and predicting productivity in construction projects is presented by integrating the system dynamics simulation method and agent-based modeling method. Using a combination of system dynamics simulation and agent-based modeling enables us to consider a variety of factors affecting productivity simultaneously. In this research, all the effective factors and factors that have continuous behavior over time will be modeled using system dynamics simulation. In the following, factor-based modeling approach is used to consider the effect of the interactions between the manpower present in the project on productivity. Finally, by combining and integrating the two systems of system dynamics simulation and agent-based modeling, a hybrid model for predicting the productivity with all the effective factors is presented.

Cutaneous Leishmaniasis (CL) is an endemic, parasitic and a vector-borne human disease that is transmitted by the bites of female sand fly. The little attention has been paid to control it, which has imposed an economic burden on societies and has created a public health issue in the Middle East and worldwide. An approximated 12 million humans are infected, and about 1.5 to 2 million new cases of CL happen each year worldwide. More than two-thirds of new CL cases currently happen in six countries: Iran, Colombia, Algeria, Syria, Brazil and Afghanistan. The spread of the CL is highly associated with the spatial parameters, spatial and socio-ecological interactions of vectors (sand fly), hosts (human), reservoirs (rodents) and the environment. The heterogeneity of these interactions has hindered the accurate modelling of the CL for healthcare preventive measures in endemic areas. Agent-Based Modeling (ABM) can be used as a new technology to study these behaviors and to simulate the spread of the disease and complex interactions. The capabilities of geospatial information systems (GIS) can also be used to facilitate the collection, analysis and display of disease information. Therefore, the contribution of this paper is the integration of GIS, ABM and an improved Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model for exploring the CL spread. To evaluate the proposed model, data from the Maraveh-Tappeh County in Golestan Province, an endemic area in Iran, including CL notification, landscape and census data, were used. The physical and social environment of northeastern Golestan has been mainly characterized by the presence of different factors that have been recognized as strongly associated with CL, large rural populations, suitable climatic conditions, riverbeds, desert and lowland area. Using various simulations and scenarios in this study, indicated how the CL spreads in different parts of the study area with various socio-ecological conditions. The results of this model indicated that the spread of the CL occurs principally in arid and low altitude areas, riverside and population centers. The results also showed that the restriction on human activities in rural areas leads to reduce the severity of the epidemic. The results also showed that the spread of the CL has a particular pattern, and the prevalent cases of the CL occur in the second half of the year. This finding could be due to the latent period of this disease and inactivity of sand flies in the cold seasons of the year. As a result, preventive behaviors can be implemented in order to control this disease by the obtained patterns and achieve the effects of these behaviors. Moreover, the impact of preventive measures on reducing and controlling of the CL was shown by the visual presentation of the propagation of this disease through the different scenarios, including population size, latent period, infection period, sensory range of sand fly's bite, insecticides, bed nets, health services and rodents control. Finally, the outbreak of the CL has a spatiotemporal pattern in the case study which is related to a complex cycle where the sand fly, rodent, human and the environment have a significant influence. This modeling effort can lead to a better understanding of disease spread dynamics and eventually to take better steps towards the prevention and control of CL spread.

The physical conditions in informal settlements are not appropriate due to uncontrolled and unplanned growth in these areas. Simulation of informal settlements growth leading to better understanding of the complexity of its spatial process and how its formation. The phenomenon of informal settlements growth as a complex system has a macro pattern that results from the interaction of downstream agents at the micro level, and accordingly the feedback of the macro patterns affects the interactions at the micro level. In this study, it is attempted to consider this complex process of informal settlements growth. Agent-based models have the innate ability to model the behavior and interaction of agents and provide an ideal framework to study the informal settlements growth.
Since the informal settlements are often formed organically the geometric shape of the land parcels is highly irregular, therefore choose a suitable spatial unit is among the challenges that local scale of simulation is confronted. In this study, it is attempted to address this challenge. Decision maker agents were combined with land parcel agents and simulation was performed on a local scale. In this model, the suitability of the land parcel agents was calculated based on factors of neighborhood, accessibility, physical suitability and spatial constraints using the spatial analyses in the GIS environment. Each parcel calculates its own general suitability value using these factors. Decision making for selecting the parcels was carried out by household agents who worked in downtown and those who worked in industrial areas around the city. For urban worker agents, a land parcel is desirable which has high suitability, small area, close distance to the city center and same agent type in the majority of the inhabitants around the land parcel. Besides, for the industrial worker agents a land parcel is desirable that has all conditions mentioned above and is also close to the industrial areas. The sensitivity of developed model was investigated on the basis of shape and size of land parcels more specifically. In this study, the rectangle and square shapes were used as the common geometric shapes of the land parcels in 20 different scenarios. The area of the residential parcels is always considered among the most important physical indicators for investigation of informal settlements at a local scale. So, in this study, it is attempted to investigate the land parcels in various areas. Besides, in this study, a fishnet grid of cells was created in two cell sizes of 2 and 5 m and the study area was also investigated with this configuration. In other words, by creating a raster-like space and comparing its results with the vector status it is attempted that the role of selecting the spatial unit in simulation process of informal settlements local growth also be analyzed from the perspective of the data model. All of these 22 scenarios was implemented using the developed agent based model on one of the informal settlements neighborhoods in the Kashan city of Isfahan province of Iran. Evaluation the results by real data showed that the accuracy of the model developed by rectangular land parcels with an area of 50 to 150 m2 is better than other vector and raster-like spatial units and is equal to 64%. Besides, the results showed that the overall accuracy of the raster-like models is significantly decreased compared to the vector structures.

Investigate the phenomenon of land use change and land development in terms of object-oriented has led to the increasing popularity of the use of agent-based models (ABM) in land use planning studies. Agents can be considered as self-directed objects which sense their environment and react to it using certain functions and adjust their activities to reach their goals. Agent-based models considered as a method to determine the global outcome of a system which is derived from the behavior of a group of local factors. These models in a bottom-up approach and using local components in a heterogeneous environment are trying to retrieve the global patterns. Land use change and land development is a dynamic and complex process and agent-based models have inherent ability to modeling complex systems and provide an ideal framework for modeling of these process. In this paper¡ a comprehensive survey has been conducted on the articles published in the field of agent-based modeling of land use change and land development during the last few years. This paper aims to classify research issues to provide a better understanding of the prospects ahead for further studies on this type of modeling.