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The location of highway signs has a significant impact on timely route-finding and also on avoiding potential driver confusion. This paper proposes an approach based on three-dimensional spatial analysis to determine the best attributes for signs and traffic flow concerning the visibility of signs. The position, height and perceived area of signs; maximum speed limit; and size of cars were utilized as indicators. Traffic flow was simulated using the Nagel-Schreckenberg model, while other vehicles were considered as dynamic obstacles. The performance of the proposed approach was evaluated in a case study by designing different scenarios. The results reveal that the proposed method can be used as an appropriate tool for evaluating the visibility of existing signs along highways and for determining the optimal place and height for new signs in three-dimensional, based on several parameters such as the maximum allowable speed, traffic flow, average density, and type of vehicles. [ABSTRACT FROM AUTHOR]

During the past years, air quality has become an important global issue, due to its impact on people's lives and the environment, and has caused severe problems for humans. As prevention to effectively control air pollution, forecasting models have been developed as a base for decision-makers and urban managers during the past decades. In general, these methods can be divided into three classes: statistical methods, machine learning methods and hybrid methods. This study's primary intent is to supply an overview of air pollution prediction techniques in urban areas and their advantages and disadvantages. A comparison has also been made between the methods in terms of error assessment and the use of geospatial information systems (GIS). In addition, several approaches were applied to actual data, and the findings were compared to those acquired from previous published literatures. The results showed that forecasting using machine learning and hybrid methods has provided better results. It has also been demonstrated that GIS can improve the results of the forecasting methods. [ABSTRACT FROM AUTHOR]

In point‐of‐interest (POI) group recommender systems, suggesting the most suitable visit places for a heterogeneous group in which members are far from each other would be difficult in terms of finding a consensus among group members. In this article, we argue that the geographical proximity of POIs to users' locations has a notable influence on group decisions to visit the POI and their check‐in behavior. We develop a new geographical model based on the check‐in behavior of the group in location‐based social networks to improve group recommendations by introducing a new concept called users' spatial ratio in a group vectors and using a 2D kernel density estimation method. To evaluate the performance of the approach, experimental studies were conducted on a real dataset of users and group check‐in activities collected from Foursquare Swarm. In our experiments, the proposed method makes better recommendations compared with other existing approaches. Besides, analyzing group check‐ins shows that in a group visit, the distance between the most visited place by a group member alone and the places visited by the group follows a generalized gamma distribution. Also, the ratio of this distance for the group member to the distance of another member depends on the group size. [ABSTRACT FROM AUTHOR]

Increasing population in urban areas and limitations of suitable lands for developing houses and urban infrastructure have led to the vertical development in cities. However, these developments are managed by a cadastral system which is mainly two-dimensional and cannot efficiently represent Rights, Restrictions, and Responsibilities (RRRs) in complex scenarios. In fact, a three-dimensional cadastre is required for efficiently registering and representing RRRs. In this paper, a 3D proximity analysis was proposed and implemented to determine RRRs and associated easement rights in non-topology-based data structures. This method can be used to investigate the surrounding spaces of a subject apartment unit or storage in a high-rise. The performance of the developed method was evaluated in a large complex high-rise in Tehran, Iran. The results confirmed that the proposed method can correctly identify the neighbor spaces in complex scenarios. [ABSTRACT FROM AUTHOR]

Time is a fundamental dimension in urban dynamics, but the effect of various definitions of time on urban growth models has rarely been evaluated. In urban growth models such as cellular automata (CA), time has typically been defined as a sequence of discrete time steps. However, most urban growth processes such as land‐use changes are asynchronous. The aim of this study is to examine the effect of various temporal dynamics scenarios on urban growth simulation, in terms of urban land‐use planning, and to introduce an asynchronous parcel‐based cellular automata (AParCA) model. In this study, eight different scenarios were generated to investigate the impact of temporal dynamics on CA‐based urban growth models, and their outputs were evaluated using various urban planning indicators. The obtained results show that different degrees of temporal dynamics lead to various patterns appearing in urban growth CA models, and the application of asynchronous (event‐driven) CA models achieves better simulation results than synchronous models. [ABSTRACT FROM AUTHOR]

Today, urban lifestyles lead to people suffering from a variety of illnesses, most of which are caused by a lack of physical activity. Walking is one of the best ways to prevent such illnesses from occurring. In this study, an integration method is proposed to help people choose the most appropriate and healthiest walking paths in the city of Shiraz in Iran. This method was developed based on data captured from remote sensing images and GIS-based analysis, the aim being to apply parameters related to people's health. The study directs its attention away from merely improving the path-finding method based on the shortest path, and instead analyzes parameters such as the existence of vegetation, reasonable temperatures and the presence of slopes, as well as the diversity of land uses found along road sections to find the optimum path. Paths with these characteristics should motivate people to walk further and for longer, and as a result improve their health. In this research Spatial Multi-criteria Evaluation (SMCE) process was used to determine the healthiest paths. Travel costs for each road section were calculated, to help develop a walkability index, and then by using path-finding analysis within GIS, the healthiest pathway was determined and compared with the shortest one. The proposed method was implemented in a case study. The results show that the simultaneous use of SMCE, remote sensing and GIS techniques along road sections, and the use of appropriate built environment indices, can help provide both urban planners and the public with the data and tools needed to take account of both the length and healthiness of a walking route, plus the services to be found along that route, when choosing to travel within a city. [ABSTRACT FROM AUTHOR]

This research presents an intelligent planning support system based on multi-agent systems for spatial urban land use planning. The proposed system consists of two main phases: a pre-negotiation phase and an automated negotiation phase. The pre-negotiation phase involves interaction between human actors and intelligent software agents in order to elicit the actors’ social preferences. The agents employ social value orientation theory, which is rooted in social psychology, in order to model actors’ social preferences. The automated negotiation phase involves negotiation among autonomous software agents, the aim being to achieve consensus about the spatial problem on behalf of the relevant actors and using the information obtained. This study employs a computationally effective Bayesian learning technique, along with social value orientation theory, to design socially rational intelligent agents who work on behalf of real actors. The proposed system is applied to a real world urban land use planning case study. Human actors participate in a pre-negotiation phase, and their social preferences are elicited by intelligent software agents through a number of interactions. Then, software agents come together to engage in an automated negotiation phase and eventually reach an agreement on the spatial configuration of urban land uses on behalf of the actors. The results of the study show that the proposed system is effective at performing an automated negotiation, plus that the final plan – which is the output of the automated negotiation – produces higher social utility and better spatial land use configurations for the agents. [ABSTRACT FROM PUBLISHER]