Traffic stream determining is an essential part of the intelligent transportation management system. Precise prediction of traffic flow provides a basis for other tasks, like forecasting travel time. While traditional methods have some merits for improving traffic prediction precision in some ways, high precision, considering different circumstances, is still difficult to achieve. This paper presents a short-term traffic flow prediction model based on the Modified Elman Recurrent Neural Network model (GA-MENN) to deal with this practical problem. In GA-MENN, the algorithm of Elman Recurrent Neural Network is modified, optimized through the Genetic Algorithm (GA) and considered weather conditions, weekday, hour and day's classification to forecast the vehicle velocity in Tehran streets and highways. The traffic data were collected from the online Google Map API service for 139 routs in 7 districts in Tehran. The method improves prediction precision and also lowers the prediction error rate, according to experimental results. Exploratory outcomes verify the superior performance of the proposed traffic condition prediction model over Regression Multi-layer Perceptron, Linear Regression, Logistic Regression, Probabilistic Neural Network, Regression Generalized Feedforward, Time-lag Recurrent Network, Support Vector Machine model, Elman neural network, K-NN model, ARIMA, Kalman filter model, Convolutional Neural Networks (CNNs), SARIMA, and Long Short-Term Memory (LSTM) model. To the best of our knowledge, this is the first occasion when that traffic stream is gauged in urban roads and avenues in this specific way.

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

The purpose of this paper is to model one of the urban network problems, the issue of water leakage. In order to manage water leakage, the specific area should be partially isolated from the rest of the network. As Geospatial Information System (GIS) is a powerful technology in spatial modeling, analysis and visualization of the water network management, a web GIS system for finding optimal valves to close in the event of an incident was developed. The system consists of a new GIS based algorithm for identifying the ideal valves to isolate the desired pipeline. The algorithm is able to identify optimum valves in a water distribution network in the shortest time by using the traceability in GIS web services. The system uses the functions of storing and managing the spatial data by expert users based on web 2.0 technology. The system was implemented and evaluated for Tehran’s district 5 water distribution network using Silverlight, C# and ArcGIS SDK (Software Development Kit). The evaluations demonstrated the accuracy of the algorithm and the operational viability of the system developed.

Cultural heritage (CH) reflects on the history of a society and its traditions and it is treated as the nation’s memory and identity. Digitizing and web, beside its benefits, brought some challenges in disseminating and retrieving CH information, which has heterogeneous content varying widely in type and properties yet encompassing rich semantic links. Semantic web technologies, especially ontologies, provide a common understanding inside a domain that helps sharing knowledge and interoperability. They can be very helpful in data modeling for a better information retrieval compared to relational databases as they take into account the semantics of information, guarantee reusability, and make information machine-readable that can offer more flexibility to intelligent services and applications. CH community is one of the first domains to make use semantic web technologies to deal with this issue. CIDOC CRM is the most used and famous ontology in CH domain, which is an ISO standard since 2006. Heritage sites are composed of many points of interest that attract visitors to find out about them. However, information about a particular POI is complex and interconnected with other people, events, and objects. In this paper, we aim to develop a POI-based data model for heritage sites in Iran using concepts from CIDOC CRM integrated with GeoSPARQL, the standard ontology in geospatial field, to incorporate spatial semantics with heritage information. This way the user can freely explore their preferred information about the places they desire. This can make it possible to use the data model for location-based services and applications in heritage sites.

This study proposed a context-aware ontology-based route finding algorithm for self-driving tourists. In this algorithm, two ontologies—namely drivers’ experiences and required tourist services—were used according to tourist requirements. Trips were classified into business and touristic. The algorithm was then compared with Google Maps in terms of travel time and travel length for evaluation. The results showed that the proposed algorithm performed similarly to Google Maps in some cases of business trips and better in other cases, with a maximum 10-min travel time difference. In touristic trips, the capabilities of the proposed algorithm were far better than those of Google Maps.