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Compared to other self-propelled particles, pedestrians are able toanticipate, which gives them an edge in avoiding collisions and navigating incluttered spaces. These capabilities are impaired by digital distractionthrough smartphones, a growing safety concern. To capture these features, weput forward a continuous agent-based model (dubbed ANDA) hinging on atransparent delineation of a decision-making process and a mechanical layerthat handles contacts and collisions. In the decisional layer, each agentautonomously selects their desired velocity as the optimum of a perceived cost,notably balancing the will to move forward (described by a floor field) withthe bio-mechanical cost of walking and the risk of collision, assessed by ananticipated time-to-collision. Altogether, the model includes less than a dozenparameters, most of which are fit using independent experimental data.Numerical simulations demonstrate the versatility of the approach, whichsucceeds in reproducing empirical observations in extremely diverse scenarios,often quantitatively, with a single set of parameters. These scenarios rangefrom collision avoidance involving one, two, or more agents to collective flowproperties in unidirectional and bidirectional settings and to the dynamics ofevacuation through a bottleneck, where contact forces are directly accessible.Remarkably, a straightforward transcription of digital distraction into themodel, by reducing the frequency of decisional updates, suffices to replicatethe enhanced chaoticity of the flow, with more frequent sudden turns, observedexperimentally when 'smartphone-walking' pedestrians are brought in. Finally,the conceptual transparency of the model makes it easy to pinpoint the originof some deficiencies, notably its shortsighted account of anticipation (whenagents have to cross a group of people) and the disk-like pedestrian shape(when very dense crowds are considered). Our work thus clarifies the singularposition of pedestrian crowds in the midst of active-matter systems.

Anticipating Collisions, Navigating in Complex Environments, Elbowing,  Pushing, and Smartphone-Walking: A Versatile Agent-Based Model for Pedestrian  Dynamics