A STATIC-DYNAMIC NETWORK MODEL FOR CROWD FLOW SIMULATION

The existing crowd flow simulation models fall into two major categories: those based on space syntax theories, and those based on the vehicular micro-simulation models incorporating origin-destination matrix. The space-syntax based models have a unique strength in their ability to analyse the spatial geometry quickly and generating valuable information about the configuration of space. However it suffers from the exclusion of dynamic effects of flow that are driven by the needs of people to go from A to B. The origin-destination based models are fundamentally micro-simulation models that provide for these dynamic effects. These models provide the valuable information on interaction of agents and densities as a function of time. However these models tend to be complex, both in pre-processing and execution, and reliable information on space effectiveness is only available at the end of a number of simulation runs. The present work brings these two techniques together by integrating graph-theory based network analysis with an origin-destination matrix model. The resulting model can be analysed in a static as well as dynamic state. In the static state, the model analyses space based on connectivity of nodes, superimposed with the origin-destination matrix of population to provide valuable information such as footfalls, density maps, as well as quasi-static parameters such as mean flow rates. In the dynamic state, the model allows time-dependent analysis of flow using a detailed agent based simulation that also incorporates dynamic route-choice modelling, agent behaviours and interaction, and stochastic variations. The paper presents the modelling technique and its implementation into simulation software SmartMove. The space is represented as a 3D network of nodes and links, with each link modelled as “1.5D” (width information is used for lateral positioning of people on the links). This enables rapid dynamic simulation of multiple scenarios without major computational overheads. The model is very effective in rapid design optimisation of spaces within and outside buildings. The static state model allows testing of various configurations quickly. The dynamic state model provides detailed investigation of such as maximum flow rates, queue lengths, and design parameters to attain a required level of service. The model is easily scalable and applicable to a range of scenarios. The paper illustrates this with reference to the circulation design of a 2,200+ capacity school, covering the pre-processing (population and origin-destination data), static analysis, dynamic simulation results, and (design and management) optimisation strategies.