Extracting Agent-Based Models of Human Transportation Patterns

Abstract

Abstract-Due to their cheap development costs and ease of deployment, surveys and questionnaires are useful tools for gathering information about the activity patterns of a large group and can serve as a valuable supplement to tracking studies done with mobile devices. However in raw form, general survey data is not necessarily useful for answering predictive questions about the behavior of a large social system. In this paper, we describe a method for generating agent activity profiles from survey data for an agent-based model (ABM) of transportation patterns of 47,000 students on a university campus. We compare the performance of our agent-based model against a Markov Chain Monte Carlo (MCMC) simulation based directly on the distributions fitted from the survey data. A comparison of our simulation results against an independently collected dataset reveals that our ABM can be used to accurately forecast parking behavior over the semester and is significantly more accurate than the MCMC estimator.I. INTRODUCTION Agent-based simulations have been used successfully for modeling human social systems in diverse fields including economics, sociology, anthropology, and archaeology [1]. A perennial question that arises in the development of an agentbased simulation is how to initialize the models to create a realistic population of agents. In simple models with few parameters, it is feasible to perform a sensitivity analysis to explore the effects of the parameters on the performance of the simulation. However in more complicated agent decisionmaking models, creating a realistic population of agents can be challenging due to the larger range of parameters governing the behavior of the simulated entities.Surveys and questionnaires can be used to collect an accurate static snapshot of the behavior of large social systems but lack the predictive power of simulations. It is more difficult to explore "what-if" questions with a survey since posing questions to participants about hypothetical scenarios can be problematic due to human cognitive biases such as anchoring or risk-aversion. In this paper, we show how both methodologies, surveying and agent-based simulation, can be combined to model human social systems with higher verisimilitude and to explore the ramifications of different behavior patterns and trends.This paper specifically addresses the problem of creating individual agent profiles for an activity-based microsimulation model of transportation, dining, parking, and building

Analyzing Agent-Based Models Using Category Theory

“…This case study extends our earlier work described in depth in [23] and [20]. The code for the urban ABM can be freely accessed online 3 .…”

Improving Markov Chain Monte Carlo Estimation with Agent-Based Models

“…The questions on the survey were grouped into different categories, related to possible places that could be visited on the main campus, and students were specifically queried about their visitation frequencies. Based on this data, we created the activity-based microsimulation described in [16].…”

Modeling social norms in real-world agent-based simulations

“…In this section, we describe the development process for our activity-based microsimulation, including the agent-based model, survey data collection, activity profile generation, path planning, and simulation system; see [24] for additional details on the data collection and model fitting procedures. For our urban region, we selected the University of Central Florida main campus, which is one the largest academic institutions in the US with almost 59,000 students and 10,567 staff.…”