Vector-based pedestrian navigation in cities

CoolWalks for active mobility in urban street networks

Walking is the most sustainable form of urban mobility, but is compromised by uncomfortable or unhealthy sun exposure, which is an increasing problem due to global warming. Shade from buildings can provide cooling and protection for pedestrians, but the extent of this potential benefit is unknown. Here we explore the potential for shaded walking, using building footprints and street networks from both synthetic and real cities. We introduce a route choice model with a sun avoidance parameter α and define the CoolWalkability metric to measure opportunities for walking in shade. We derive analytically that on a regular grid with constant building heights, CoolWalkability is independent of α, and that the grid provides no CoolWalkability benefit for shade-seeking individuals compared to the shortest path. However, variations in street geometry and building heights create such benefits. We further uncover that the potential for shaded routing differs between grid-like and irregular street networks, forms local clusters, and is sensitive to the mapped network geometry. Our research identifies the limitations and potential of shade for cool, active travel, and is a first step towards a rigorous understanding of shade provision for sustainable mobility in cities.

Towards a characterization of human spatial exploration behavior

Spatial exploration is a complex behavior that can be used to gain information about developmental processes, personality traits, or mental disorders. Typically, this is done by analyzing movement throughout an unknown environment. However, in human research, until now there has been no overview on how to analyze movement trajectories with regard to exploration. In the current paper, we provide a discussion of the most common movement measures currently used in human research on spatial exploration, and suggest new indices to capture the efficiency of exploration. We additionally analyzed a large dataset (n = 409) of human participants exploring a novel virtual environment to investigate whether movement measures could be assigned to meaningful higher-order components. Hierarchical clustering of the different measures revealed three different components of exploration (exploratory behavior, spatial shape, and exploration efficiency) that in part replicate components of spatial exploratory behavior identified in animal studies. A validation of our analysis on a second dataset (n = 102) indicated that two of these clusters are stable across different contexts as well as participant samples. For the exploration efficiency cluster, our validation showed that it can be further differentiated into a goal-directed versus a general, area-directed component. By also sharing data and code for our analyses, our results provide much-needed tools for the systematic analysis of human spatial exploration behavior.

Approximate planning in spatial search

How people plan is an active area of research in cognitive science, neuroscience, and artificial intelligence. However, tasks traditionally used to study planning in the laboratory tend to be constrained to artificial environments, such as Chess and bandit problems. To date there is still no agreed-on model of how people plan in realistic contexts, such as navigation and search, where values intuitively derive from interactions between perception and cognition. To address this gap and move towards a more naturalistic study of planning, we present a novel spatial Maze Search Task (MST) where the costs and rewards are physically situated as distances and locations. We used this task in two behavioral experiments to evaluate and contrast multiple distinct computational models of planning, including optimal expected utility planning, several one-step heuristics inspired by studies of information search, and a family of planners that deviate from optimal planning, in which action values are estimated by the interactions between perception and cognition. We found that people's deviations from optimal expected utility are best explained by planners with a limited horizon, however our results do not exclude the possibility that in human planning action values may be also affected by cognitive mechanisms of numerosity and probability perception. This result makes a novel theoretical contribution in showing that limited planning horizon generalizes to spatial planning, and demonstrates the value of our multi-model approach for understanding cognition.

Using games to understand the mind

Board, card or video games have been played by virtually every individual in the world. Games are popular because they are intuitive and fun. These distinctive qualities of games also make them ideal for studying the mind. By being intuitive, games provide a unique vantage point for understanding the inductive biases that support behaviour in more complex, ecological settings than traditional laboratory experiments. By being fun, games allow researchers to study new questions in cognition such as the meaning of 'play' and intrinsic motivation, while also supporting more extensive and diverse data collection by attracting many more participants. We describe the advantages and drawbacks of using games relative to standard laboratory-based experiments and lay out a set of recommendations on how to gain the most from using games to study cognition. We hope this Perspective will lead to a wider use of games as experimental paradigms, elevating the ecological validity, scale and robustness of research on the mind.

Predicting network congestion by extending betweenness centrality to interacting agents

We present a simple model to predict network activity at the edge level by extending a known approximation method to compute betweenness centrality with a repulsive mechanism to prevent unphysical densities. By taking into account the strong interaction effects often observed in real phenomena, we aim to obtain an improved measure of edge usage during rush hours as traffic congestion patterns emerge in urban networks. In this approach, the network is iteratively populated by agents following dynamically evolving fastest paths who are progressively attracted towards uncongested parts of the network as the global traffic volume increases. Following the transition of the network state from empty to saturated, we study the emergence of congestion and the progressive disruption of global connectivity due to a relatively small fraction of crowded edges. We assess the predictive power of our model by comparing the speed distribution against a large experimental data set for the London area with remarkable results, which also translate into a qualitative similarity of the congestion maps. Also, percolation analysis confirms the quantitative agreement of the model with the real data for London. We perform simulations for seven other topologically different cities to obtain the Fisher critical exponent τ that shows no common functional dependence on the traffic level. The critical exponent γ, studied to assess the power-law decay of spatial correlation, is found to be inversely proportional to the number of vehicles for both real and simulated traffic. This simulation approach seems particularly fit to describe qualitative and quantitative properties of the network loading process, culminating in peak-hour congestion, by using only topological and geographical network features.

Wayfinding across ocean and tundra: what traditional cultures teach us about navigation

Research on human navigation by psychologists and neuroscientists has come mainly from a limited range of environments and participants inhabiting western countries. By contrast, numerous anthropological accounts illustrate the diverse ways in which cultures adapt to their surrounding environment to navigate. Here, we provide an overview of these studies and relate them to cognitive science research. The diversity of cues in traditional navigation is much higher and multimodal compared with navigation experiments in the laboratory. It typically involves an integrated system of methods, drawing on a detailed understanding of the environmental cues, specific tools, and forms part of a broader cultural system. We highlight recent methodological developments for measuring navigation skill and modelling behaviour that will aid future research into how culture and environment shape human navigation.

Characterizing network circuity among heterogeneous urban amenities

The spatial configuration of urban amenities and the streets connecting them collectively provide the structural backbone of a city, influencing its accessibility, vitality and ultimately the well-being of its residents. Most accessibility measures focus on the proximity of amenities in space or along transportation networks, resulting in metrics largely determined by urban density alone. These measures are unable to gauge how efficiently street networks can navigate between amenities, since they neglect the circuity component of accessibility. Existing measures also often require ad hoc modelling choices, making them less flexible for different applications and difficult to apply in cross-sectional analyses. Here, we develop a simple, principled and flexible measure to characterize the circuity of accessibility among heterogeneous amenities in a city, which we call the pairwise circuity (PC). The PC quantifies the excess travel distance incurred when using the street network to route between a pair of amenity types, summarizing both spatial and topological correlations among amenities. Measures developed using our framework exhibit significant statistical associations with a variety of urban prosperity and accessibility indicators when compared with an appropriate null model, and we find a clear separation in the PC values of cities according to development level and geographical region.

A map of spatial navigation for neuroscience

Spatial navigation has received much attention from neuroscientists, leading to the identification of key brain areas and the discovery of numerous spatially selective cells. Despite this progress, our understanding of how the pieces fit together to drive behavior is generally lacking. We argue that this is partly caused by insufficient communication between behavioral and neuroscientific researchers. This has led the latter to under-appreciate the relevance and complexity of spatial behavior, and to focus too narrowly on characterizing neural representations of space-disconnected from the computations these representations are meant to enable. We therefore propose a taxonomy of navigation processes in mammals that can serve as a common framework for structuring and facilitating interdisciplinary research in the field. Using the taxonomy as a guide, we review behavioral and neural studies of spatial navigation. In doing so, we validate the taxonomy and showcase its usefulness in identifying potential issues with common experimental approaches, designing experiments that adequately target particular behaviors, correctly interpreting neural activity, and pointing to new avenues of research.

Beyond simple laboratory studies: Developing sophisticated models to study rich behavior

Psychology and neuroscience are concerned with the study of behavior, of internal cognitive processes, and their neural foundations. However, most laboratory studies use constrained experimental settings that greatly limit the range of behaviors that can be expressed. While focusing on restricted settings ensures methodological control, it risks impoverishing the object of study: by restricting behavior, we might miss key aspects of cognitive and neural functions. In this article, we argue that psychology and neuroscience should increasingly adopt innovative experimental designs, measurement methods, analysis techniques and sophisticated computational models to probe rich, ecologically valid forms of behavior, including social behavior. We discuss the challenges of studying rich forms of behavior as well as the novel opportunities offered by state-of-the-art methodologies and new sensing technologies, and we highlight the importance of developing sophisticated formal models. We exemplify our arguments by reviewing some recent streams of research in psychology, neuroscience and other fields (e.g., sports analytics, ethology and robotics) that have addressed rich forms of behavior in a model-based manner. We hope that these "success cases" will encourage psychologists and neuroscientists to extend their toolbox of techniques with sophisticated behavioral models - and to use them to study rich forms of behavior as well as the cognitive and neural processes that they engage.

Home-to-school pedestrian mobility GPS data from a citizen science experiment in the Barcelona area

The analysis of pedestrian GPS datasets is fundamental to further advance on the study and the design of walkable cities. The highest resolution GPS data can characterize micro-mobility patterns and pedestrians' micro-motives in relation to a small-scale urban context. Purposed-based recurrent mobility data inside people's neighbourhoods is an important source in these sorts of studies. However, micro-mobility around people's homes is generally unavailable, and if data exists, it is generally not shareable often due to privacy issues. Citizen science and its public involvement practices in scientific research are valid options to circumvent these challenges and provide meaningful datasets for walkable cities. The study presents GPS records from single-day home-to-school pedestrian mobility of 10 schools in the Barcelona Metropolitan area (Spain). The research provides pedestrian mobility from an age-homogeneous group of people. The study shares processed records with specific filtering, cleaning, and interpolation procedures that can facilitate and accelerate data usage. Citizen science practices during the whole research process are reported to offer a complete perspective of the data collected.

Future directions in human mobility science

We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.

Entropy and a sub-group of geometric measures of paths predict the navigability of an environment

Despite extensive research on navigation, it remains unclear which features of an environment predict how difficult it will be to navigate. We analysed 478,170 trajectories from 10,626 participants who navigated 45 virtual environments in the research app-based game Sea Hero Quest. Virtual environments were designed to vary in a range of properties such as their layout, number of goals, visibility (varying fog) and map condition. We calculated 58 spatial measures grouped into four families: task-specific metrics, space syntax configurational metrics, space syntax geometric metrics, and general geometric metrics. We used Lasso, a variable selection method, to select the most predictive measures of navigation difficulty. Geometric features such as entropy, area of navigable space, number of rings and closeness centrality of path networks were among the most significant factors determining the navigational difficulty. By contrast a range of other measures did not predict difficulty, including measures of intelligibility. Unsurprisingly, other task-specific features (e.g. number of destinations) and fog also predicted navigation difficulty. These findings have implications for the study of spatial behaviour in ecological settings, as well as predicting human movements in different settings, such as complex buildings and transport networks and may aid the design of more navigable environments.

Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation

When faced with navigating back somewhere we have been before we might either retrace our steps or seek a shorter path. Both choices have costs. Here, we ask whether it is possible to characterize formally the choice of navigational plans as a bounded rational process that trades off the quality of the plan (e.g., its length) and the cognitive cost required to find and implement it. We analyze the navigation strategies of two groups of people that are firstly trained to follow a "default policy" taking a route in a virtual maze and then asked to navigate to various known goal destinations, either in the way they want ("Go To Goal") or by taking novel shortcuts ("Take Shortcut"). We address these wayfinding problems using InfoRL: an information-theoretic approach that formalizes the cognitive cost of devising a navigational plan, as the informational cost to deviate from a well-learned route (the "default policy"). In InfoRL, optimality refers to finding the best trade-off between route length and the amount of control information required to find it. We report five main findings. First, the navigational strategies automatically identified by InfoRL correspond closely to different routes (optimal or suboptimal) in the virtual reality map, which were annotated by hand in previous research. Second, people deliberate more in places where the value of investing cognitive resources (i.e., relevant goal information) is greater. Third, compared to the group of people who receive the "Go To Goal" instruction, those who receive the "Take Shortcut" instruction find shorter but less optimal solutions, reflecting the intrinsic difficulty of finding optimal shortcuts. Fourth, those who receive the "Go To Goal" instruction modulate flexibly their cognitive resources, depending on the benefits of finding the shortcut. Finally, we found a surprising amount of variability in the choice of navigational strategies and resource investment across participants. Taken together, these results illustrate the benefits of using InfoRL to address navigational planning problems from a bounded rational perspective.

Fluctuations in pedestrian dynamics routing choices

Routing choices of walking pedestrians in geometrically complex environments are regulated by the interplay of a multitude of factors such as local crowding, (estimated) time to destination, and (perceived) comfort. As individual choices combine, macroscopic traffic flow patterns emerge. Understanding the physical mechanisms yielding macroscopic traffic distributions in environments with complex geometries is an outstanding scientific challenge, with implications in the design and management of crowded pedestrian facilities. In this work, we analyze, by means of extensive real-life pedestrian tracking data, unidirectional flow dynamics in an asymmetric setting, as a prototype for many common complex geometries. Our environment is composed of a main walkway and a slightly longer detour. Our measurements have been collected during a dedicated high-accuracy pedestrian tracking campaign held in Eindhoven (The Netherlands). We show that the dynamics can be quantitatively modeled by introducing a collective discomfort function, and that fluctuations on the behavior of single individuals are crucial to correctly recover the global statistical behavior. Notably, the observed traffic split substantially departs from an optimal, transport-wise, partition, as the global pedestrian throughput is not maximized.

A virtual experiment on pedestrian destination choice: the role of schedules, the environment and behavioural categories

Which locations pedestrians decide to visit and in what order drives circulation patterns in pedestrian infrastructure. Destination choice is understood to arise from individuals trading off different factors, such as the proximity and busyness of destinations. Here, a virtual experiment is used to investigate whether this behaviour depends on the layout of buildings, whether planned or imposed destination schedules influence decisions and whether it is possible to distinguish different choice behaviour strategies in pedestrian populations. Findings suggest that virtual experiments can consistently elicit a range of destination choice behaviours indicating the flexibility of this experimental paradigm. The experimental approach facilitates changing the environment layout while controlling for other factors and illustrates this in itself can be important in determining destination choice. Destination schedules are found to be relevant both when imposed or generated by individuals, but adherence to them varies across individuals and depends on prevailing environmental conditions, such as destination busyness. Different destination choice behaviour strategies can be identified, but their properties are sensitive to the detection methods used, and it is suggested such behaviour classification should be informed by specific use-cases. It is suggested that these contributions present useful starting points for future research into pedestrian destination choice.

Quantifying navigation complexity in transportation networks

The complexity of navigation in cities has increased with the expansion of urban areas, creating challenging transportation problems that drive many studies on the navigability of networks. However, due to the lack of individual mobility data, large-scale empirical analysis of the wayfinder's real-world navigation is rare. Here, using 225 million subway trips from three major cities in China, we quantify navigation difficulty from an information perspective. Our results reveal that (1) people conserve a small number of repeatedly used routes and (2) the navigation information in the subnetworks formed by those routes is much smaller than the theoretical value in the global network, suggesting that the decision cost for actual trips is significantly smaller than the theoretical upper limit found in previous studies. By modeling routing behaviors in growing networks, we show that while the global network becomes difficult to navigate, navigability can be improved in subnetworks. We further present a universal linear relationship between the empirical and theoretical search information, which allows the two metrics to predict each other. Our findings demonstrate how large-scale observations can quantify real-world navigation behaviors and aid in evaluating transportation planning.

Entropy of city street networks linked to future spatial navigation ability

The cultural and geographical properties of the environment have been shown to deeply influence cognition and mental health^1-6. Living near green spaces has been found to be strongly beneficial^7-11, and urban residence has been associated with a higher risk of some psychiatric disorders^12-14-although some studies suggest that dense socioeconomic networks found in larger cities provide a buffer against depression¹⁵. However, how the environment in which one grew up affects later cognitive abilities remains poorly understood. Here we used a cognitive task embedded in a video game¹⁶ to measure non-verbal spatial navigation ability in 397,162 people from 38 countries across the world. Overall, we found that people who grew up outside cities were better at navigation. More specifically, people were better at navigating in environments that were topologically similar to where they grew up. Growing up in cities with a low street network entropy (for example, Chicago) led to better results at video game levels with a regular layout, whereas growing up outside cities or in cities with a higher street network entropy (for example, Prague) led to better results at more entropic video game levels. This provides evidence of the effect of the environment on human cognition on a global scale, and highlights the importance of urban design in human cognition and brain function.

Social behavior: Using visual cues to guide dancing on the fly

Quantitative behavioral analysis of Drosophila courtship reveals that visual cues of a female's body influence which actions a male performs during courtship. These actions in turn influence female actions, producing a mutual synchronization of courtship between male and female flies.

Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients

Impairment of navigation is one of the earliest symptoms of Alzheimer's disease (AD), but to date studies have involved proxy tests of navigation rather than studies of real life behaviour. Here we use GPS tracking to measure ecological outdoor behaviour in AD. The aim was to use data-driven machine learning approaches to explore spatial metrics within real life navigational traces that discriminate AD patients from controls. 15 AD patients and 18 controls underwent tracking of their outdoor navigation over two weeks. Three kinds of spatiotemporal features of segments were extracted, characterising the mobility domain (entropy, segment similarity, distance from home), spatial shape (total turning angle, segment complexity), and temporal characteristics (stop duration). Patients significantly differed from controls on entropy (p-value 0.008), segment similarity (p-value [Formula: see text]), and distance from home (p-value [Formula: see text]). Graph-based analyses yielded preliminary data indicating that topological features assessing the connectivity of visited locations may also differentiate patients from controls. In conclusion, our results show that specific outdoor navigation features discriminate AD patients from controls, which has significant implication for future AD diagnostics, outcome measures and interventions. Furthermore, this work illustrates how wearables-based sensing of everyday behaviour may be used to deliver ecologically-valid digital biomarkers of AD pathophysiology.

Behavioural thermal regulation explains pedestrian path choices in hot urban environments

Due to phenomena such as urban heat islands, outdoor thermal comfort of the cities' residents emerges as a growing concern. A major challenge for mega-cities in changing climate is the design of urban spaces that ensure and promote pedestrian thermal comfort. Understanding pedestrian behavioural adaptation to urban thermal environments is critically important to attain this goal. Current research in pedestrian behaviour lacks controlled experimentation, which limits the quantitative modelling of such complex behaviour. Combining well-controlled experiments with human participants and computational methods inspired by behavioural ecology and decision theory, we examine the effect of sun exposure on route choice in a tropical city. We find that the distance walked in the shade is discounted by a factor of 0.86 compared to the distance walked in the sun, and that shadows cast by buildings have a stronger effect than trees. The discounting effect is mathematically formalised and thus allows quantification of the behaviour that can be used in understanding pedestrian behaviour in changing urban climates. The results highlight the importance of assessment of climate through human responses to it and point the way forward to explore scenarios to mitigate pedestrian heat stress.

Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona’s Traffic with Empirical Data

Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. Yet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. This paper, therefore, presents a new large-scale agent-based traffic microsimulation for the Barcelona urban area using SUMO to show the possibilities and challenges of building these scenarios based on novel fine-grained empirical big data. It combines novel mobility data from real cell phone records with conventional surveys to calibrate the model comparing two different dynamic assignment methods for getting an operationally realistic and efficient simulation. Including through traffic and the use of a stochastic adaptive routing approach results in a larger 24-hour model closer to reality. Based on an extensive multi-scalar evaluation including traffic counts, hourly distribution of trips, and macroscopic metrics, this model expands and outperforms previous large-scale scenarios, which provides new operational opportunities in city co-creation and policy. The novelty of this work relies on the effective modelling approach using newly available data and the realistic robust evaluation. This allows the identification of the fundamental challenges of simulation to accurately capture real-world dynamical systems and to their predictive power at a large scale, even when fed by big data, as envisioned by the digital twin concept applied to smart cities.