Applications of graph algorithms in GIS

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Studying cities to learn about minds: some possible implications of space syntax for spatial cognition

What can we learn of the human mind by examining its products? The city is a case in point. Since the beginning of cities human ideas about them have been dominated by geometric ideas, and the real history of cities has always oscillated between the geometric and the ‘organic’. Set in the context of the suggestion from cognitive neuroscience that we impose more geometric order on the world than it actually possesses, and intriguing question arises: what is the role of the geometric intuition in how we understand cities and how we create them? Here I argue, drawing on space syntax research which has sought to link the detailed spatial morphology of cities to observable functional regularities, that all cities, the organic as well as the geometric, are pervasively ordered by geometric intuition, so that neither the forms of the cities nor their functioning can be understood without insight into their distinctive and pervasive emergent geometrical forms. The city is often said to be the creation of economic and social processes, but here it is argued that these processes operate within an envelope of geometric possibility defined by the human mind in its interaction with spatial laws that govern the relations between objects and spaces in the ambient world

The genetic code for cities – is it simpler than we thought?

September 200

Vehicle Tracking Using the k-shortest Paths Algorithm and Dual Graphs

Vehicle trajectory descriptions are required for the development of driving behavior models and in the calibration of several traffic simulation applications. In recent years, the progress in aerial sensing technologies and image processing algorithms allowed for easier collection of such detailed traffic datasets and multiple-object tracking based on constrained flow optimization has been shown to produce very satisfactory results, even in high density traffic situations. This method uses individual image features collected for each candidate vehicle as criteria in the optimization process. When dealing with poor image quality or low ground sampling distances, feature-based optimization may produce unreal trajectories. In this paper we extend the application of the k-shortest paths algorithm for multiple-object tracking to the motion-based optimization. A graph of possible connections between successive candidate positions was built using a first level criteria based on speeds. Dual graphs were built to account for acceleration-based and acceleration variation-based criteria. With this framework both longitudinal and lateral motion-based criteria are contemplated in the optimization process. The k-shortest disjoints paths algorithm was then used to determine the optimal set of trajectories (paths) on the constructed graph. The proposed algorithm was successfully applied to a vehicle positions dataset, collected through aerial remote sensing on a Portuguese suburban motorway. Besides the importance of a new trajectory dataset that will allow for the estimation of new behavioral models and the validation of existing ones, the motion-based multiple-vehicle tracking algorithm allowed for a fast and effective processing using a simple optimization formulation. Keywords: vehicle trajectories; image processing; driver behaviour; remote sensing

Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing

Research in road users’ behaviour typically depends on detailed observational data availability, particularly if the interest is in driving behaviour modelling. Among this type of data, vehicle trajectories are an important source of information for traffic flow theory, driving behaviour modelling, innovation in traffic management and safety and environmental studies. Recent developments in sensing technologies and image processing algorithms reduced the resources (time and costs) required for detailed traffic data collection, promoting the feasibility of site-based and vehicle-based naturalistic driving observation. For testing the core models of a traffic microsimulation application for safety assessment, vehicle trajectories were collected by remote sensing on a typical Portuguese suburban motorway. Multiple short flights over a stretch of an urban motorway allowed for the collection of several partial vehicle trajectories. In this paper the technical details of each step of the methodology used is presented: image collection, image processing, vehicle identification and vehicle tracking. To collect the images, a high-resolution camera was mounted on an aircraft's gyroscopic platform. The camera was connected to a DGPS for extraction of the camera position and allowed the collection of high resolution images at a low frame rate of 2s. After generic image orthorrectification using the flight details and the terrain model, computer vision techniques were used for fine rectification: the scale-invariant feature transform algorithm was used for detection and description of image features, and the random sample consensus algorithm for feature matching. Vehicle detection was carried out by median-based background subtraction. After the computation of the detected foreground and the shadow detection using a spectral ratio technique, region segmentation was used to identify candidates for vehicle positions. Finally, vehicles were tracked using a k- shortest disjoints paths algorithm. This approach allows for the optimization of an entire set of trajectories against all possible position candidates using motion-based optimization. Besides the importance of a new trajectory dataset that allows the development of new behavioural models and the validation of existing ones, this paper also describes the application of state-of-the-art algorithms and methods that significantly minimize the resources needed for such data collection. Keywords: Vehicle trajectories extraction, Driver behaviour, Remote sensin

Computing the Fewest-turn Map Directions based on the Connectivity of Natural Roads

In this paper, we introduced a novel approach to computing the fewest-turn map directions or routes based on the concept of natural roads. Natural roads are joined road segments that perceptually constitute good continuity. This approach relies on the connectivity of natural roads rather than that of road segments for computing routes or map directions. Because of this, the derived routes posses the fewest turns. However, what we intend to achieve are the routes that not only possess the fewest turns, but are also as short as possible. This kind of map direction is more effective and favorable by people, because they bear less cognitive burden. Furthermore, the computation of the routes is more efficient, since it is based on the graph encoding the connectivity of roads, which is significantly smaller than the graph of road segments. We made experiments applied to eight urban street networks from North America and Europe in order to illustrate the above stated advantages. The experimental results indicate that the fewest-turn routes posses fewer turns and shorter distances than the simplest paths and the routes provided by Google Maps. For example, the fewest-turn-and-shortest routes are on average 15% shorter than the routes suggested by Google Maps, while the number of turns is just half as much. This approach is a key technology behind FromToMap.org - a web mapping service using openstreetmap data.Comment: 12 pages, 5 figures, and 4 tables, language editing, some significant revisions, missing references adde