Development of its and application of 3S in ITS

This article introduces the background and development of ITS and the emphasis on developing ITS in China in the forthcoming years. Furthermore, it describes the application of the 3S (GIS, GPS, RS) technology in developing ITS.     

Integration of neural networks and cellular automata for urban planning

This paper presents a new type of cellular automata (CA) model for the simulation of alternative land development using neural networks for urban planning. CA models can be regarded as a planning tool because they can generate alternative urban growth. Alternative development patterns can be formed by using different sets of parameter values in CA simulation. A critical issue is how to define parameter values for realistic and idealized simulation. This paper demonstrates that neural netowrks can simplify CA models but generate more plausible results. The simulation is based on a simple three-layer network with an output neuron to generate conversion probability. No transition rules are required for the simulation. Parameter values are automatically obtained from the training of network by using satellite remote sensing data. Original training data can be assessed and modified according to planning objectives. Alternative urban patterns can be easily formulated by using the modified training data sets rather than changing the model.     

Automatic road change detection and GIS updating from high spatial remotely-sensed imagery

This paper presents a framework for road network change detection in order to update the Canadian National Topographic DataBase (NTDB). The methodology has been developed on the basis of road extraction from IRS-pan images (with a 5.8 m spatial resolution) by using a wavelet approach. The feature matching and conflation techniques are used to road change detection and updating. Elementary experiments have showed that the proposed framework could be used for developing an operational road database updating system.     

Road hierarchy with integration of attributes using fuzzy-AHP

Road objects in a network data model are categorised into a hierarchical structure in accordance with their functions and capacities. In this study; five road attributes derived from semantic, geometric and topological properties of network data set (i.e. road class, road length and centralities of degree, closeness and betweenness) are utilised for the creation of road network hierarchy. The relationships with each attributes except road class and their effects on the determination of road importance are analysed by using a distribution graph and the equation of Pearson correlation coefficient. For creating road network hierarchy, integration process is achieved through the application of fuzzy analytic hierarchy process assuming attributes as the fuzzy criteria. The integration process is followed by the calculation of new priority attribute that indicates the importance of road objects. At the end of the process, road class, which is the most important attribute, is also used for the validation of proposed methodology. The results show that the new priority value of a road is superior to its each attribute value in hierarchical organisation.     

Using Neural Nets to Model the Spatial Distribution of Seasonal Homes

Seasonal and second homes are important aspects of recreational tourism. Owning a summer cottage, time-share condominium, hunting cabin, or a part-time residence in a location away from home affects development patterns in significant ways. This paper presents the results of using an artificial neural network and a geographic information systems-based approach to identify and quantify the principal predictors of seasonal home distribution within the Lake States region of Minnesota, Wisconsin, and Michigan. Representative variables, such as proximity and spatial configuration of lakes, proximity to the Great Lakes shore, surrounding forest acreage, and public land access have been quantified using geographic information systems at the minor civil division (MCD) scale for the three states. The GIS data have then been fed into artificial neural networks to enable these pattern recognition tools to identify the principal predictors of seasonal home distribution in the Upper Great Lakes States.     

On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks

ABSTRACT

Artificial neural networks (ANNs) become widely used for runoff forecasting in numerous studies. Usually classical gradient-based methods are applied in ANN training and a single ANN model is used. To improve the modelling performance, in some papers ensemble aggregation approaches are used whilst in others, novel training methods are proposed. In this study, the usefulness of both concepts is analysed. First, the applicability of a large number of population-based metaheuristics to ANN training for runoff forecasting is tested on data collected from four catchments, namely upper Annapolis (Nova Scotia, Canada), Biala Tarnowska (Poland), upper Allier (France) and Axe Creek (Victoria, Australia). Then, the importance of the search for novel training methods is compared with the importance of the use of a very simple ANN ensemble aggregation approach. It is shown that although some metaheuristics may slightly outperform the classical gradient-based Levenberg-Marquardt algorithm for a specific catchment, none performs better for the majority of the tested ones. One may also point out a few metaheuristics that do not suit ANN training at all. On the other hand, application of even the simplest ensemble aggregation approach clearly improves the results when the ensemble members are trained by any suitable algorithms.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR E. Toth     

Revisiting gateway cities: connecting hubs in global networks to their hinterlands

Gateway cities have received much attention from urban geographers. In spite of outstanding contributions being made, we think that the concept needs to be revisited with regard to regional development implications. Bringing together research on global production networks (GPNs) and world cities, this article shows that gateway cities are critical for development in networks, generating impulses for peripheral locations by engaging them in processes of “strategic coupling.” Yet, gateway cities also concentrate segments of GPNs to the detriment of their hinterlands. We conceptualize gateway cities with the aid of five features: logistics and transport, industrial processing, corporate control, service provision and knowledge generation. Our concept allows for an understanding of cities in global and regional economic processes beyond corporate headquarters, corporate services and governance – that is, beyond the boundaries of existing research. It unsettles traditional understandings of strategic coupling and world cities, filling a lacuna on city–hinterland connections.     

Hierarchical polygonization for generating and updating lane-based road network information for navigation from road markings

Lane-based road network information, such as lane geometry, destination, lane changing, and turning information, is important in vehicle navigation, driving assistance system, and autonomous driving. Such information, when available, is mainly input manually. However, manual methods for creating and updating data are not only costly but also time-consuming, labor-intensive, and prone to long delays. This paper proposes a hierarchical polygonization method for automatic generation and updating of lane-level road network data for navigation from a road marking database that is managed by government transport department created by digitizing or extraction from aerial images. The proposed method extends the hierarchy of a road structure from ‘road–carriageway–lane’ to ‘road–carriageway–lane–basic lane’. Basic lane polygons are constructed from longitudinal road markings, and their associated navigational attributes, such as turning information and speed limit, are obtained from transverse road markings by a feature-in-polygon overlay approach. A hierarchical road network model and detailed algorithms are also illustrated in this paper. The proposed method can accelerate the process of generating and updating lane-level navigation information and can be an important component of a road marking information system for road management.     

Self-Organized Cognitive Maps

This paper argues cognitive mapping is a learning process that can be simulated by a self-organizing neural network. The learning of city locations was considered in two studies. One study focused on the learning of city locations on four continents. Results indicated the neural network aligned the cities producing systematic errors similar to those in human cognitive maps. A second study had a neural network learn a biased sample of city locations in the United States. Results indicated a non-linear relationship between cognitive and physical distances. Self-organized cognitive maps naturally produce this non-linear relationship when information from more than one scale is mapped into one space.