Nagmeh Neisany Samany; Mahmoud Reza Delavar; Mohammad Reza Malek
Abstract
Navigation is one of the most important daily activities of individuals in an urban environment. The spatial information systems of the user guidance are the most common services which guide people to navigate the various routes with different goals.The main challenge of such systems is providing context-aware ...
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Navigation is one of the most important daily activities of individuals in an urban environment. The spatial information systems of the user guidance are the most common services which guide people to navigate the various routes with different goals.The main challenge of such systems is providing context-aware navigation information. The location, time,and the identifier of the individual person are among the primary contexts and the other related contexts are modeled based on these contexts. The present paper attempts to model the spatial-temporal communication of the moving user based on the identifier of the individuals. So that it covers all spatial communications (topologic, metric and directional) in time dimension andtakes the characteristics of the user and the related contexts into consideration. In this regard, the proposed model utilizedthe advantages of Allen’s Multi-Interval Algebra (MIA) and dynamic Voronoi-based Continuous Range Query (VCRQ), and introduceda new method by following the large calculus principles and the existing customization methods. The designed model (MIA25) was implemented in a software applicable in mobile systems. The evaluation of the implemented model in the tourist’s navigation scenariointhe regions 3, 6, and 11 of Tehran municipality was carried out based on 3 parameters of accuracy, runtimeand users’ satisfaction. These three regions were selected as the case study area. In order to test the accuracy of the model, the designed software was iterated 100 times in three different routes in the study area by three different tourists at three time intervals with two different average speed. Then, the recognition of each one of the textures existing on the way, was examined by the one-way binomial approximationwith 95% of confidence level over 100 iterations. Also, two indices of correctness and recall were used to evaluate the recognition of the textures through the entire route. The results of implementation and evaluation of the model based on three parameters of accuracy, runtime and users’ satisfaction demonstrate the efficiency of the proposed model in an urban context-aware navigation system.
Abolghasem Sadeghi Niaraki; Mahmoud Reza Delavar; Somaiieh Rokhsari Talemi
Abstract
Nowadays Smart traffic sensor network is considered as one of the newest ways of data acquisition in traffic management which with the possibility of intelligent monitoring of urban roads, leads to road accident reduction. Despite the importance of installing and deployment of such equipment, the most ...
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Nowadays Smart traffic sensor network is considered as one of the newest ways of data acquisition in traffic management which with the possibility of intelligent monitoring of urban roads, leads to road accident reduction. Despite the importance of installing and deployment of such equipment, the most important concern is to determine the optimal location for their installations. Therefore, what we are aiming at in this research, is to provide a suitable method to optimize the location of traffic sensors. The proposed method is a combination of FUZZY AHP and TOPSIS method. It should be noted that, in order to test the proposed method in this study, part of the urban road network in North America was selected as sample data. In the next step, according to traffic experts, the criteria for determining the optimal location were selected which included average annual traffic, crash severity, average slope and the distance of each connection in the urban network to places requiring traffic control. The FUZZY hierarchy method was used to determine the significance of input criteria. This method using FUZZY numbers in a pairwise comparison of criteria to calculate their weights, leads to an increase in the accuracy of computations. In the next stage, the weights calculated using TOPSIS method were used to rank the urban connections in the study area.
Eventually, after applying the above analysis using the score obtained from TOPSIS method, urban connections in the study area were classified in 3 different categories. Urban connections in the 1st category were selected as the ones with the highest priority for the installation of sensors. Therefore, these connections will be the top priority for the installation of traffic sensors.
