Pedestrian Simulation in Transit Stations Using Agent-Based Analysis

Urban Rail Transit DOI 10.1007/s40864-017-0053-5 http://www.urt.cn/ ORIGINAL RESEARCH PAPERS Pedestrian Simulation in Transit Stations Using Agent-Based Analysis Ming Tang1 • Yingdong Hu2 Received: 23 December 2016 / Revised: 7 March 2017 / Accepted: 25 March 2017 Ó The Author(s) 2017. This article is an open access publication Abstract 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 & Ming Tang 1 School of Architecture and Interior Design, University of Cincinnati, Cincinnati, OH, USA 2 School of Architecture and Design, Beijing Jiaotong University, Beijing, China Editors: Haishan Xia and Chun Zhang 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. Keywords Agent-based simulation
Pedestrian flow analysis
Self-organizing 1 Introduction 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’’ [1]. Michael Batty described the property of ‘‘Autonomy’’ and ‘‘the embedding of the agent into the environment’’ as two key properties of agents in an agentbased 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 [1]. ABS focuses on agents’ properties and processes, responsible for responding to 123 Urban Rail Transit 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’’ [1]. 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 123 environment, however it cannot simulate the interactions among agents such as complex social behavior. We also researched several other commercial agentbased 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 en (...truncated)