This paper will present a knowledge layer used by a hierarchical on-road driving planner that represents a road network as a discrete set of attributed road states. This knowledge layer facilitates the construction of a planning graph by providing simulation and prediction services to the planning system. These services allow the determination of possible spatial transitions along a road networ...

The popularity of location-based services and the need to perform real-time processing on them has led to an interest in queries on road networks, such as finding shortest paths and finding nearest neighbors. The challenge here is that the efficient execution of operations usually involves the computation of distance along a spatial network instead of “as the crow flies,” which is not simple. T...

 Abstract: The shipments of hazardous materials (HAZMATs) induce various risks to the road network. Today, one of the major considerations of transportation system managers is HAZMATs shipments, due to the increasing demand of these goods (because it is more used in industry, agriculture, medicine, etc.), and the rising number of incidents that are associated to hazardous materials. This paper ...

To many geographic systems (GIS) application scheme such as urban planning and navigation, updating road network database is critical problem. Rapidly changing urban environments accelerate the need for frequent updates or revisions of road network databases. With the advent of high-resolution satellite images, there has been a resurgence of research interest in road extraction techniques. Howe...

The uses of road map in daily activities are numerous but it is a hassle to construct and update a road map whenever there are changes. In Universiti Malaysia Sarawak, research on Automatic Road Extraction (ARE) was explored to solve the difficulties in updating road map. The research started with using Satellite Image (SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space Segmenta...

Road network is an important infrastructure in society. On average, every American drives 13,476 miles a year, spending over 200 hours on traveling. Out of the 200 hours, 38 hours are wasted during congestion, wasting $121 billion on fuel cost a year.1,2 Thus, a well-designed road network, which leads to less congestion and thus have significant economic ramifications. Unlike many other network...

Network-level multi-step road condition forecasting is an important step in accurate road maintenance planning, where correct maintenance activities are defined in place and time of road networks. Forecasting methods have developed from engineering models to non-linear machine learning methods that make use of the collected condition and traffic data of the road network. Least Squares Support V...

The long production cycle and huge cost of collecting road network data often leave the data lagging behind the latest real conditions. However, this situation is rapidly changing as the positioning techniques ubiquitously used in mobile devices are gradually being implemented in road network research and applications. Currently, the predominant approaches infer road networks from mobile locati...

The paper focuses on mapping the road network into 4-regular grid structures. A mapping algorithm is proposed. To model the road network GIS data have been used. The Geographic Information System (GIS) data for the road network are composed with different size of line segment lengths. The manipulation of the line segment length for mapping is analyzed. The algorithms and simulation results are ...

In this paper we report on research on road extraction at TUM (Technische Universität München). We propose a scheme for road extraction in rural areas that integrates three different modules with specific strengths. The first module employs local grouping and uses multiple scales and context to reliably extract most parts of the road network. In order to connect these parts, the second module e...