  Nonreciprocal interaction crowd systems, such as human-human, human-vehicle,
and human-robot systems, often have serious impacts on pedestrian safety and
social order. A more comprehensive understanding of these systems is needed to
optimize system stability and efficiency. Despite the importance of these
interactions, empirical research in this area remains limited. Thus, in our
study we explore this underresearched area, focusing on scenarios where
nonreciprocity plays a critical role, such as mass stabbings, which pose a
substantial risk to public safety. We conducted the first experiments on this
system and analysed high-accuracy data obtained from these experiments. The
extent of the direct threat zone is determined by the speed of the moving
threat and the radius of danger occurrence. We further categorize potential
threats into direct, adjacent, and rear-view zones, quantifying the level of
threat for pedestrians. Our study revealed that a pedestrian's desired velocity
correlated positively with potential threat intensity, increasing until near
the direct threat zone. An emerging steady state is observed when escape routes
are blocked by moving threats. This deviation affects the density-velocity
relationship, making it distinct from the general relationship. This deviation
signifies unique pedestrian behaviour in the presence of moving threats.
Additionally, the rate of change in the angle for pedestrian motion in various
desired directions is synchronized. This indicates the emergence of collective
intelligence in nonreciprocal interaction crowd systems. As a result, our study
may constitute a pioneering step towards understanding nonreciprocal
interactions in crowd systems through laboratory experiments. These findings
may enhance pedestrian safety and inform not only government crowd management
strategies but also individual self-protection measures.
