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

Challenges for identifying the neural mechanisms that support spatial navigation: the impact of spatial scale.

Spatial navigation is a fascinating behavior that is essential for our everyday lives. It involves nearly all sensory systems, it requires numerous parallel computations, and it engages multiple memory systems. One of the key problems in this field pertains to the question of reference frames: spatial information such as direction or distance can be coded egocentrically-relative to an observer-or allocentrically-in a reference frame independent of the observer. While many studies have associated striatal and parietal circuits with egocentric coding and entorhinal/hippocampal circuits with allocentric coding, this strict dissociation is not in line with a growing body of experimental data. In this review, we discuss some of the problems that can arise when studying the neural mechanisms that are presumed to support different spatial reference frames. We argue that the scale of space in which a navigation task takes place plays a crucial role in determining the processes that are being recruited. This has important implications, particularly for the inferences that can be made from animal studies in small scale space about the neural mechanisms supporting human spatial navigation in large (environmental) spaces. Furthermore, we argue that many of the commonly used tasks to study spatial navigation and the underlying neuronal mechanisms involve different types of reference frames, which can complicate the interpretation of neurophysiological data

The Floor Strategy: Wayfinding Cognition in a Multi-Level Building

This short paper is concerned with strategies and cognitive processes of wayfinding in public buildings. We conducted an empirical study in a complex multi-level building, comparing performance measures of experienced and inexperienced participants in different wayfinding tasks. Thinking aloud protocols provided insights into navigation strategies, planning phases, use of landmarks and signage. Three specific strategies for navigation in multi-level buildings were compared. The cognitively efficient floor strategy was preferred by experts over a central-point strategy or a direction strategy, and overall was associated to better wayfinding performance

Predefining regionalised environments for assisted navigation : does incorporating regions into navigation instructions assist a user’s spatial understanding of the environment they aretravelling through?

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThis thesis proposes introducing pre-defined regionalised areas into navigation instructions to allow drivers to learn more about the environment they’re travelling through. Following detailed navigation instructions, drivers are no longer required to learn about and understand their environment, which leaves drivers reliant on these navigation devices. An experiment using a virtual environment was conducted to evaluate if a group with additional regional instructions would complete tasks more effectively than a group with traditional instructions. While the regional group performed better on all accounts, statistically significant results were only found in three of ten variables. There were however, large differences in task completion rates, suggesting that incorporating pre-defined regions to navigation instructions does make a difference in drivers’ understanding of their environment

Acquisition and consolidation of hierarchical representations of space

Navigation – the ability to reach targets which are no visible from the current position - depends on the correct recall of the desired target and the environment between one's current position and this target. The content of these representations are subject to influences from different modalities, e.g. vision, and language. A place can be recognized through different cues, e.g. due to a salient object, but also because of the angles of the routes at an intersection, or a name. The location of places as well as the routes connecting them can be integrated and memorized in an allocentric, survey-like representation. Depending on the amount of detail, the granularity level of a representation can be coarser or finer; the different levels are organized hierarchically. Characteristics of a superordinate category, like a region, can affect the perception of its constituting elements, the places; an inheritance of qualities from region to place levels is possible. The formation of superordinate categories depends both on environmental factors as well as individual ones: what is recognized, what is remembered, and which predictions are drawn from this representation? In this dissertation I examine the acquisition of representations of space, in order to identify features that are well suited for being remembered and auxiliary for navigation. I have two research foci: First, I examine the impact of language by using different hierarchically structured naming schemes as place names. Wiener & Mallot (2003) found that characterizing places only with landmarks belonging to different semantic categories influenced route choice as well as the representations of space. I compare these findings to the impact of different naming schemes. I show that there are naming schemes that may influence behavior in a similar way as a landmark does, but that seeing something and reading its name is by far the same thing. The second part focuses on the content of representations established during navigation. With three different navigation experiments, I examine the content of the concepts of space that are acquired during navigation. What is remembered - the location of places, the routes, or the hierarchical structure of the experimental environment? Are there features that are more likely to be consolidated during sleep, e.g., the transfer of concrete knowledge about places and routes into an abstract, survey-like representation? I show that there are improvements in one wayfinding task correlated to sleep. In the other experiments, learning effects were found for both groups. I also address the question of suitable parameters for measuring survey knowledge

Shortest path or anchor-based route choice: a large-scale empirical analysis of minicab routing in London

Understanding and modelling route choice behaviour is central to predicting the formation and propagation of urban road congestion. Yet within conventional literature disagreements persist around the nature of route choice behaviour, and how it should be modelled. In this paper, both the shortest path and anchor-based perspectives on route choice behaviour are explored through an empirical analysis of nearly 700,000 minicab routes across London, United Kingdom. In the first set of analyses, the degree of similarity between observed routes and possible shortest paths is established. Shortest paths demonstrate poor performance in predicting both observed route choice and characteristics. The second stage of analysis explores the influence of specific urban features, named anchors, in route choice. These analyses show that certain features attract more route choices than would be expected were individuals choosing route based on cost minimisation alone. Instead, the results indicate that major urban features form the basis of route choice planning – being selected disproportionately more often, and causing asymmetry in route choice volumes by direction of travel. At a finer scale, decisions made at minor road features are furthermore demonstrated to influence routing patterns. The results indicate a need to revisit the basis of how routes are modelled, shifting from the shortest path perspective to a mechanism structured around urban features. In concluding, the main trends are synthesised within an initial framework for route choice modelling, and presents potential extensions of this research

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

From Isovists via Mental Representations to Behaviour: First Steps Toward Closing the Causal Chain

This study addresses the interrelations between human wayfinding performance, the mental representation of routes, and the geometrical layout of path intersections. The virtual reality based empirical experiment consisted of a route learning and reproduction task and two choice reaction tasks measuring the acquired knowledge of route decision points. In order to relate the recorded behavioural data to the geometry of the environment, a specific adaptation of isovist-based spatial analysis was developed that accounts for directional bias in human spatial perception and representation. Taken together, the applied analyses provided conclusive evidence for correspondences between geometrical properties of environments as captured by isovists and their mental representation