The role of angularity in route choice: an analysis of motorcycle courier GPS traces

The paths of 2425 individual motorcycle trips made in London were analyzed in order to uncover the route choice decisions made by drivers. The paths were derived from global positioning system (GPS) data collected by a courier company for each of their drivers, using algorithms developed for the purpose of this paper. Motorcycle couriers were chosen due to the fact that they both know streets very well and that they do not rely on the GPS to guide their navigation. Each trace was mapped to the underlying road network, and two competing hypotheses for route choice decisions were compared: (a) that riders attempt to minimize the Manhattan distance between locations and (b) that they attempt to minimize the angular distance. In each case, the distance actually traveled was compared to the minimum possible either block or angular distance through the road network. It is usually believed that drivers who know streets well will navigate trips that reduce Manhattan distance; however, here it is shown that angularity appears to play an important role in route choice. 63% of trips made took the minimum possible angular distance between origin and destination, while 51% of trips followed the minimum possible block distance. This implies that impact of turns on cognitive distance plays an important role in decision making, even when a driver has good knowledge of the spatial network

A heuristic model of bounded route choice in urban areas

There is substantial evidence to indicate that route choice in urban areas is complex cognitive process, conducted under uncertainty and formed on partial perspectives. Yet, conventional route choice models continue make simplistic assumptions around the nature of human cognitive ability, memory and preference. In this paper, a novel framework for route choice in urban areas is introduced, aiming to more accurately reflect the uncertain, bounded nature of route choice decision making. Two main advances are introduced. The first involves the definition of a hierarchical model of space representing the relationship between urban features and human cognition, combining findings from both the extensive previous literature on spatial cognition and a large route choice dataset. The second advance involves the development of heuristic rules for route choice decisions, building upon the hierarchical model of urban space. The heuristics describe the process by which quick, 'good enough' decisions are made when individuals are faced with uncertainty. This element of the model is once more constructed and parameterised according to findings from prior research and the trends identified within a large routing dataset. The paper outlines the implementation of the framework within a real-world context, validating the results against observed behaviours. Conclusions are offered as to the extension and improvement of this approach, outlining its potential as an alternative to other route choice modelling frameworks

Many to many mobile maps

The rapid development of mobile computing devices along with a variety of Web 2.0 social networking tools has led to a dramatic change in the way maps and other spatial displays are utilized. The evolution from stand-alone desktop GIS to the interactive, mobile devices, in which information from one or more sources and is sent to one or more sinks, is discussed. The result is access to real-time information, which is generated from both traditional sources, social networks, and other specialized geowikis. Both the benefits of many to many mobile maps and the emergence of new problems, such as understanding the needs of the user and providing appropriate context, are discussed

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

User Preferences and the Shortest Path

Indoor navigation systems leverage shortest path algorithms to calculate routes. In order to define the "shortest path", a cost function has to be specified based on theories and heuristics in the application domain. For the domain of indoor routing, we survey theories and criteria identified in the literature as essential for human path planning. We drive quantitative definitions and integrate them into a cost function that weights each of the criteria separately. We then apply an exhaustive grid search to find weights that lead to an ideal cost function. "Ideal" here is defined as guiding the algorithm to plan routes that are most similar to those chosen by humans. To explore which criteria should be taken into account in an improved pathfinding algorithm, eleven different factors whose favorable impact on route selection has been established in past research were considered. Each factor was included separately in the Dijkstra algorithm and the similarity of thus calculated routes to the actual routes chosen by students at the University of Regensburg was determined. This allows for a quantitative assessment of the factors' impact and further constitutes a way to directly compare them. A reduction of the number of turns, streets, revolving doors, entryways, elevators as well as the combination of the aforementioned factors was found to have a positive effect and generate paths that were favored over the shortest path. Turns and the combination of criteria turned out to be most impactful

Exploring the Role of Spatial Cognition in Predicting Urban Traffic Flow through Agent-based Modelling

Urban systems are highly complex and non-linear in nature, defined by the behaviours and interactions of many individuals. Building on a wealth of new data and advanced simulation methods, conventional research into urban systems seeks to embrace this complexity, measuring and modelling cities with increasingly greater detail and reliability. The practice of transportation modelling, despite recent developments, lags behind these advances. This paper addresses the implications resulting from variations in model design, with a focus on the behaviour and cognition of drivers, demonstrating how different models of choice and experience significantly influence the distribution of traffic. It is demonstrated how conventional models of urban traffic have not fully incorporated many of the important findings from the cognitive science domain, instead often describing actions in terms of individual optimisation. We introduce exploratory agent-based modelling that incorporates representations of behaviour from a more cognitively rich perspective. Specifically, through these simulations, we identify how spatial cognition in respect to route selection and the inclusion of heterogeneity in spatial knowledge significantly impact the spatial extent and volume of traffic flow within a real-world setting. These initial results indicate that individual-level models of spatial cognition can potentially play an important role in predicting urban traffic flow, and that greater heed should be paid to these approaches going forward. The findings from this work hold important lessons in the development of models of transport systems and hold potential implications for policy

Navigating complex buildings: cognition, neuroscience and architectural design

This paper is in two sections, the first section presents a review of recent research in the areas of neuroscience, cognitive science and architecture with particular respect to what is currently understood about how buildingusers find their way around complex buildings. It goes on to define four areas of promising, potential future research located on the boundaries between these three disciplines, these being: spatial knowledge acquisition, orientation, multilevel environments and environment intelligibility. In the second half of the paper, it suggests how such current research and/or any future program of research could be used to aid architects in the design of new buildings. One such method suggested is the creation of designguidelines or heuristics based upon research into navigation and wayfinding. The paper concludes with an example list of eight sample guidelines

Exosomatic Route Choice in Navigation: Evidence from video game player data

We investigate the extent to which navigation may be performed using exosomatic cues directly viewed in the environment, as opposed to relying on memory of a map or mental representation. Using trajectory data from a virtual navigation game app, Sea Hero Quest, we analyse the moment to moment route choices of 200 participants, and compare these against the expected routes based on several spatial variables measured from current isovists. Observations suggest that there is substantial evidence that for most participants navigation in a novel environment is indeed largely based on direct exosomatic information, and is based specifically on the space actually viewed, as opposed to that inferred by the shape of occluding edges. We also find evidence that strategies differ between individuals, in that the better navigators will deviate more from the exosomatic method, and rely more on their own memory and internal knowledge of the environment

Route Choice from Local Information: Comparing Theories of Movement and Intelligibility

Intelligibility, the extent to which non-local structure can be inferred from local properties, is examined using new methods based on angular segment analysis, demonstrating that such a property is consistent with exosomatic navigation. In many urban networks, effective movement is possible without knowledge of the broader structure, or memory, using information conveyed to a navigator only by the angles of each intersection. Results suggest that this is not due to a particular optimisation of the grid unique to cities, as has been suggested, but can result in many possible networks, including random ones. A relationship between intelligibility and predictability of movement, implied in previous literature, is shown not always to hold. Additional methodological and theoretical contributions are made in proposing a novel measure of immediate angular intelligibility, and in demonstrating equivalences between this and traditional axial line intelligibility, and between a number of other methods proposed to predict movement in the literature