Computational Streetscapes

Abstract

Abstract:Streetscapes have presented a long-standing interest in many fields. Recently, there has been a resurgence of attention on streetscape issues, catalyzed in large part by computing. Because of computing, there is more understanding, vistas, data, and analysis of and on streetscape phenomena than ever before. This diversity of lenses trained on streetscapes permits us to address long-standing questions, such as how people use information while mobile, how interactions with people and things occur on streets, how we might safeguard crowds, how we can design services to assist pedestrians, and how we could better support special populations as they traverse cities. Amid each of these avenues of inquiry, computing is facilitating new ways of posing these questions, particularly by expanding the scope of what-if exploration that is possible. With assistance from computing, consideration of streetscapes now reaches across scales, from the neurological interactions that form among place cells in the brain up to informatics that afford real-time views of activity over whole urban spaces. For some streetscape phenomena, computing allows us to build realistic but synthetic facsimiles in computation, which can function as artificial laboratories for testing ideas. In this paper, I review the domain science for studying streetscapes from vantages in physics, urban studies, animation and the visual arts, psychology, biology, and behavioral geography. I also review the computational developments shaping streetscape science, with particular emphasis on modeling and simulation as informed by data acquisition and generation, data models, path-planning heuristics, artificial intelligence for navigation and way-finding, timing, synthetic vision, steering routines, kinematics, and geometrical treatment of collision detection and avoidance. I also discuss the implications that the advances in computing streetscapes might have on emerging developments in cyber-physical systems and new developments in urban computing and mobile computing.

Agent models of customer journeys on retail high streets

“…There are natural affinities between typology-type approaches to customer journeys and agent-based modeling, where significant work already presents on classes and ontologies of agent characteristics and agent behaviors relative to urban processes and phenomena (Benenson and Torrens 2004 ), often at street-level (Torrens 2016a ). Batty et al ( 2003 ) discussed how the categorization of pedestrians must necessarily shift as the scale of observation of their behavior shifts.…”

“…Millonig and Gartner ( 2011 ) articulated this point well in their examination of wayfinding behavior among shoppers: “For pedestrians, the shortest path does not always represent the optimal route for an individual’s purposes, as studies have revealed that people often forgo to take the shortest path and prefer the ‘most beautiful’, ‘most convenient’ or ‘safest’ path.” (p. 3). Moreover, pedestrians have many degrees of freedom in their movement through urban streetscapes and their paths may often become highly dynamic, reactive, and adaptive, bucking the simple drivers that computational heuristics may suggest as proxies for their planning behavior (Torrens 2016a ).…”

“…However, data constraints on agent-based simulation are loosening, due in large part to the emergence of sensing technology and related information systems for building knowledge through automated means (Torrens 2016a ). Indeed, retailing and data analytics for customer experience—sometimes referred to as “retaillance” (a portmanteau of retail and surveillance)—have been at the forefront of these developments (Elnahla and Neilson 2021 ).…”

Kinetic Values, Mobility (in)equalities, and Ageing in Smart Urban Environments

“…One of the commercially available robots, the My Spoon Robot, can aid in eating for those who are unable to coordinate their dexterity (Bedaf et al 2015 ). Typically, ageing bodies have been approached at the level of physical bodies, and their relations to other bodies and things have been calculated using agent-based movement models to depict patterns of behaviour (Torrens 2016 ).…”

Perception of urban subdivisions in pedestrian movement simulation

“…While plenty of ABMs for pedestrian movement in indoor contexts have been advanced [see: [121][122][123], models dealing with large number of people in urban contexts are rarer [35,36,82]. In this context, our model is specifically intended to understand pedestrian dynamics at the urban scale.…”