Pedestrian Simulation in Transit Stations Using Agent-Based Analysis
The research discusses experiential outcome in the application of crowd simulation technology to analyze the pedestrian circulation in public spaces to facilitate design and planning decisions. The paper describes how to connect spatial design with agent-based simulation (ABS) for various design and planning scenarios. It describes the process of visualizing and representing pedestrian movement, as well as pathfinding and crowd behavior study. An ABS consists of a large number of agents, which are controlled by simple localized rules to interact with each other within a virtual environment, thereby formulating a bottom-up system. The concept of the ABS has been widely used in computer science, biology, and social science to simulate swarm intelligence, dynamic social behavior, and fire evacuation. The simulation consists of interacting agents which can create various complexities. This paper describes research on using local interactions to generate passenger flow analysis. An ABS is used to optimize the pedestrian flow and construct the micro-level complexity within a simulated environment. We focus on how agent-driven emergent patterns can evolve during the simulation in response to various design iterations. The research extends to the agents’ interactions driven by a set of rules and external environment. Our research method includes data collection, quantitative analysis, and crowd simulation on two train stations and surrounding areas in Sihui train station in Beijing, and Xuzhou, China. By proposing a mix-use program with the local public transportation system, the new development is integrated with the existing urban infrastructure and public space. Through the multi-agent simulation, we evaluate the crowd flow, total travel time, density, and public accessibility. Based on the result of ABS, we discussed whether various space design methods can improve pedestrian flow efficiency and passenger experience, as well as shortening transfer time, and reducing congestion.
KeywordsAgent-based simulation Pedestrian flow analysis Self-organizing
There were many computational methods applied to simulate agents involving movement, including “the simple statistical regression, spatial interaction theory, accessibility approach, space syntax approach and fluid flow analysis” . Michael Batty described the property of “Autonomy” and “the embedding of the agent into the environment” as two key properties of agents in an agent-based system (ABS). An ABS consists of numerous agents, which follow localized rules to interact with a simulated environment, thereby formulating a bottom-up system. Since Craig Reynolds’ artificial “bodies” and flock simulation, the concept of ABS has been widely used to study decentralized systems that include human social interaction. In urban modeling, agents can be defined as autonomous “physical or social” entities or objects that act independently of one another . ABS focuses on agents’ properties and processes, responsible for responding to external changes, specifically how agents can “sense” and “act” to form a complex system. The movements are usually based on simple rules such as separation, alignment, and cohesion. Computer scripts can be used to control agent’s velocity, maximum force, range of vision, and other properties.
In the early research phase, we compared the bottom-up ABS with cellular automation (CA) methods, as well as space syntax, to examine agents’ generation, spatial properties, and interaction with the environment.
1.1 Comparing ABS with Cellular Automation
Cellular automation (CA) calculates cells’ changing state through time, based on the state of neighboring cells and context. As two famous bottom-up systems, both CA and ABS compute the status of a changing object over time. However, it is important to understand the distinction between cells and agents. Batty describes agent as “mobile cells, which—objects or events that located with respect to cells but can move between cells” . However, the behaviors of CA are often unpredictable and lack purposive planning goals. It is difficult to use CA to add rules and other “purposive goals” to the system beyond context awareness. Similar to Betty’s global attraction surface in his study on the agent’s movement, we need a system to introduce external force rules to influence the agents’ behavior.
1.2 Comparing ABS with Space Syntax
Space syntax is another method to study movement pattern and accessibility of a network based on lines, nodes, and connections. With its own “agent analysis” tool, space syntax does not actually measure the interactions among agents, but provides fast feedback between geometric elements and their accessibility value within a grid of cells.
We studied space syntax as a reference tool for ABS. Through importing the College of Design Architecture Art and Planning (DAAP) building floor plan into space syntax analysis tool, we produced heat map to represent accessibility and spatial integration. Warmer colors represent higher spatial integration values. We computed the integration value of each cell by using the analysis tools in space syntax and visualized the values with colors. The qualitative values extracted from the space syntax analysis are imported into Grasshopper for further computing. In order to convert the space syntax results into a heat map representation, we created a data processing method to expand the color values automatically from paths to zones. It became obvious that even though space syntax provided a fast way to visualize interactions between agents and environment, however it cannot simulate the interactions among agents such as complex social behavior.
We also researched several other commercial agent-based tools in the entertainment industry. Mass animation tool has been widely used to simulate the behavior of crowds, where the agents’ movements are computed based on the interaction among themselves, as well as the interaction with the environment. We explored A* pathfinding an algorithm used to create the cognitive agents, which can populate a spatial model and navigate through a “cell”-based map. Different from the “reactive” agent in Reynolds’ flock simulation, these cognitive agents have their artificial intelligence (AI). The agents have the ability to respond to the changing environment and other agents’ movement in real time and adjust their behavioral parameters. The AI agents can make decisions while evaluating the results generated in a real-time environment.
2 ABS for Crowd Simulation
2.1 Path Visualization
With ABS, the autonomous “action” of each agent lies within modifying its movement based on the repulsion or attraction to neighboring agents, as well as the environment itself. Over a period, a crowd behavior is automatically formed as agents stop and remain equilibrium.
3 Urban Design Projects with Agent-based Simulation
3.1 Pengcheng Square, Xuzhou, China
Pengcheng Square is a proposed urban design project in Xuzhou, China. Designed by Beijing Jiaotong University, the goal is to create a mixed-use urban cluster, which includes residential, commercial, cultural, and public spaces. The existing subway system is integrated with the new proposed program to form a new urban center. Researchers from the University of Cincinnati and Beijing Jiaotong University collected the existing and projected pedestrian flow, in order to generate various scenarios for crowd simulation. A complex crowd movement pattern emerged based on the microscale interactions among agents. Multiple paths and crowd movement automatically adopted a set of rules based on both bottom-up movements, as well as the top-down planning methods.
3.2 Sihui Train Station, Beijing, China
When designing a public space outside a subway station, designers usually give priority to the issues of passengers’ flow during peak hours. However, besides this basic function, designers should also consider public space to facilitate other urban functions. For example, users’ all three levels of physiological, psychological, and emotional needs should be considered in the planning stage. In many Chinese cities, the scale of public space outside the subway station is large and often appears to be empty. As a consequence, the walking distances are long, and the environment quality is poor. Other problems include lacking service facilities, lacking public gathering places, and eventually lacking spatial identity.
Conventionally, the public space should be organized based on its purpose. It should serve for pedestrian flow, vehicular traffic, as well as spontaneous and social activities. However, in the planning process, it is often possible that planners cannot clearly define the needs of different functions. Sometimes environment usage deviates from the designer’s original plan . This is known as the concept of “adapted use.” For example, in the Sihui station, there are unplanned car parking lots on both sides of the public space, which should be defined as the adapted use. We borrowed the concept of “adapted use” and “bucketed space” to discuss the human behavior based on time and space. By ABS, we analyzed different pedestrian behaviors under various conditions. First, we revealed the circulation needs in different periods of time based on humane design. Second, we analyzed how to meet different pedestrian behaviors in time and space distribution. Third, we explored several ways to improve the efficiency and comprehensive use of the public space outside the train station.
The research compared several crowd simulation systems such as agent-based simulation, Cellular Automation, Space Syntax and investigated how to integrate ABS into design phase in two urban design projects. Different from the traditional top-down planning method, this crowd simulation method relies on the emergent properties and local interactions among agents. Within the process of ABS, design can be improved by observing the interaction between simulated crowd and the surrounding environment. Designers can observe agents’ changing behavior by proposing different spatial features. The crowd simulation could produce measurable improvement in the design. The ABS can predict certain “bottleneck” areas with potential congestion issues near train station entrances. Together with traditional humanistic evaluation and ABS, a new relationship of designer and design agent has been forged.
In the two urban design projects in China, we applied ABS for crowd analysis. The benefit is evident for analyzing alternative design scenarios. The result of simulation was used to suggest the pedestrian paths, as well as comparing different spatial organization of building programs. ABS can provide an invaluable analysis through simulating clearly defined rationales, such as choose the shortest route between spatial points, choose the less congested route by evaluating “cost” of each route, and select between elevator, escalator, and stairs by comparing the waiting time. However, these rationales will be easily overridden by the unpredicted crowd behaviors during the emergency evacuation such as panic, preferring the previous entrance as the emergency exit, and following a crowd. These uncertainty and complex variables make the ABS became subjective and lost the quantitate strength. In another word, an agent’s behavior would not be “realistic” enough for the emergency exits and fire evacuation analysis due to the lacking of complex social behaviors. Because the ABS is generated as a highly abstract in the micro-level, designers should consider to combine ABS with other empirical methods, and building codes to construct a realistic crowd movement model for the panic and extreme conditions. To study these extreme scenarios with complex behaviors, the research team is investigating virtual reality method using real human subjects to replace the ABS method.
5 Future Research
We are currently investigating crowd behavior using immersive virtual reality and augmented reality technology. The goal is to create an immersive virtual environment which allows a real person to react to different environmental conditions and behaviors of artificial agents, and test various design theories in both micro- and macro-levels. We are adapting various processes learned from previous ABS methods. Oculus Rift and HoloLens devices will be applied to the study of crowd behavior, to ultimately facilitate and enhance design decisions such as spatial organization. We are modeling various human–computer inferences to test the influence of smoke, fire, signage, lighting, and architectural features for egress. In an ideal situation, these immersive experiences should be integrated with ABS and serve as a feedback loop for crowd simulation.
We thank Communication Urban Environment (CUE) Grant, FDC Department and Interdisciplinary Grant from the University of Cincinnati; Haishan Xia, Qiang Sheng, Chuna Zhang, and Zijia Wang from the Beijing Jiaotong University, and Laura Kennedy and Niloufar Kioumarsi from the University of Cincinnati for their contribution and support. More information on the research is available at http://ming3d.com/VR.
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