Agent-based simulator of dynamic flood-people interactions

This article presents a simulator for the modelling of the two‐way interactions between flooding and people. The simulator links a hydrodynamic model to a pedestrian model in a single agent‐based modelling platform, Flexible Large‐scale Agent Modelling Environment for the Graphical Processing Unit (FLAMEGPU). Dynamic coupling is achieved by the simultaneous update and exchange of information across multiple agent types. Behavioural rules and states for the pedestrian agents are proposed to account for the pedestrians' presence/actions in/to floodwater. These are based on a commonly used hazard rate (HR) metric to evaluate the risk states of people in floodwater, and by considering two roles for the pedestrians: evacuees or responders for action during or before the flood event, respectively. The potential of the simulator is demonstrated in a case study of a flooded and busy shopping centre for two scenarios: (a) during a flood evacuation and (b) pre‐flood intervention to deploy a sandbag barrier. The evacuation scenario points to changes in floodwater hydrodynamics around congested areas, which either worsen (by 5–8%) or lessen (by 25%) the HR. The intervention scenario demonstrates the utility of the simulator to select an optimal barrier height and number of responders for safe and effective deployment. Accompanying details for software accessibility are provided

Fast simulation of crowd collision avoidance

Real-time large-scale crowd simulations with realistic behavior, are important for many application areas. On CPUs, the ORCA pedestrian steering model is often used for agent-based pedestrian simulations. This paper introduces a technique for running the ORCA pedestrian steering model on the GPU. Performance improvements of up to 30 times greater than a multi-core CPU model are demonstrated. This improvement is achieved through a specialized linear program solver on the GPU and spatial partitioning of information sharing. This allows over 100,000 people to be simulated in real time (60 frames per second)

RateSetter: roadmap for faster, safer, and better platform train interface design and operation using evolutionary optimisation

There is a challenge ahead in the rail industry to accommodate increased demand. Time spent at the platform train interface (PTI) as passengers board and alight, rather than on the move, represents a limitation on system capacity. To overcome this, we propose RateSetter: an evolutionary optimiser that for the first time provides more effective PTI design choices based on passenger flow time and safety. An agent based passenger simulation model validated with CCTV footage is employed for fitness evaluation. The initial results provide guidelines not only for future PTI designs but also for retrofit designs to existing infrastructure, evaluating the effectiveness and diminishing returns of PTI features for the considered scenarios. Furthermore, it is observed that the proposed optimal PTI designs could significantly reduce the flow time for the cases examined. Results show that retro-fit designs could reduce the flow time in the range of 10%-35%

Simulating crowds and autonomous vehicles

Understanding how people view and interact with autonomous vehicles is important to guide future directions of research. One such way of aiding understanding is through simulations of virtual environments involving people and autonomous vehicles. We present a simulation model that incorporates people and autonomous vehicles in a shared urban space. The model is able to simulate many thousands of people and vehicles in real-time. This is achieved by use of GPU hardware, and through a novel linear program solver optimized for large numbers of problems on the GPU. The model is up to 30 times faster than the equivalent multi-core CPU model