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How an origin and a destination align with the street network may, anecdotally, be used as a heuristic to infer the length and complexity of routes from the origin to the destination. However, no method of measuring alignment with a street network exists, and furthermore, it is unclear whether and under what circumstances it is useful as a heuristic. In this paper, we propose a novel method for measuring alignment using the Earth Mover's Distance (EMD) between orientation distributions. We evaluated this metric using a dataset of 77,293 origin and destination pairs from 100 cities with different street networks, in order to test the hypothesis that an increasing degree of misalignment is predictive of a longer and more complex route from origin to destination. Our evaluation shows that our method for measuring alignment becomes more useful as a heuristic the more grid-like the street network surrounding the origin and destination is. To conclude, the results obtained indicate that alignment is an important factor for route properties, especially in grid-like street networks, a subclass of routes within an environment where the configuration of the street network makes predicting the length and complexity easier.

The routes displayed on maps by navigation support systems are intended to help users to orient themselves towards reaching the destination and to infer information related to their navigation. Inferring how complex a route is, including how well you think you can remember it and the likelihood of getting lost, may influence expectations on how it is navigated. However, it is not well understood when and where a route displayed on a map is perceived as complex and why someone perceives it this way. Current methods for assessing complexity tend to focus either on (i) the complexity of the route or on (ii) the complexity of the environment as a static and global property. By taking inspiration from navigational map reading and how routes and street networks are perceived on a map, this paper investigates how environmental complexity influences route complexity and length.We developed a new approach to gauge the alignment between the orientation of a route’s origin and destination with respect to the orientation of the streets within the network, and we investigated how this measure relates to route complexity and length.

Today, anyone feeling lost in a city or unsure about how to navigate can use navigation services to look up routes to where they want to go. Current research investigating these services has primarily focused on how to find an appropriate route and how to best support navigation along it, and not how routes and the maps they are presented on are perceived. What makes one route look more difficult to navigate than another? And how does experience with using navigation services and maps in daily life influence how difficult a route is perceived to be? We explored these questions in a survey study where participants rated the perceived difficulty of pedestrian routes in ten different cities. The results show that routes in more complex urban environments were perceived as more complex than routes in easier environments. At least partly, perceived difficulty seems to follow earlier conceptualizations of route complexity, but open questions remain regarding the interplay of environmental structure, route properties, and the map representation.

Despite being understated, anxiety is a critical factor affecting all levels of society, directly impacting individual decisions and with well-identified ramifications on social play, social constructs, and collective outcomes, as well as being a significant direct social toll tied to yearly trillion-USD social cost. Through a systematic literature review of social simulation research featuring models of anxiety, this paper frames the state of the art on anxiety modelling, and identifies trends and patterns in bibliographic indicators, aspects of anxiety that are modelled, how they are modelled, and their purpose and integration within agent based models. Based on these findings, this paper proposes a way forward as to structure the field as to enable the social simulation community as a whole to cover this critical aspect.

Driving is a highly social activity with various interactions between the different actors involved in traffic. Autonomous vehicles will pose several challenges to the social fabric of traffic and, as most new technology, will lead to changes in human behavior. This will surely hold for interactions between autonomous vehicles and actors \emph{outside} the vehicle. However and importantly, autonomous vehicles will also alter existing and introduce new social situations and interactions for those \emph{inside} the vehicle, which appears to be an under-researched topic. This paper will focus on the passengers of autonomous vehicles. We will discuss some of the implications and expected changes in the relationship between a car and those inside it, and highlight some of the open issues of being enclosed and driven by a highly complex, largely black-box AI system on wheels.

Anxiety is one of the most critical sources of harm to psychological wellbeing, tied to an array of issues, from discomfort and maladaptive coping to severe pathological disorders -making of anxiety one of the largest economic and social healthcare expenses. AI systems are not neutral to the exposure of individuals and societies to anxiety, and the current emphasis on performance-optimization of current AI systems arguably sets a pathway for a systemic rise of anxiety. As a response to this trend, towards further increasing the human-centeredness of existing applications, this paper is dedicated to depicting the landscape of open challenges, high-impact applications, and promising solutions for designing anxiety-sensitive agents. This paper first circumvents the key components of anxiety through a summary of the extensive psychology literature on anxiety; then shows the feasibility of building agent-based models by putting forward an example of a logical model of anxiety; and last, examines current research fields through the lens of anxiety, highlighting categories of prospective applications and techniques which stand to benefit from anxiety-sensitive agents.