Abstract
Pedestrian movement is woven into the fabric of urban regions. With more people living in cities than ever before, there is an increased need to understand and model how pedestrians utilize and move through space for a variety of applications, ranging from urban planning and architecture to security. Pedestrian modeling has been traditionally faced with the challenge of collecting data to calibrate and validate such models of pedestrian movement. With the increased availability of mobility datasets from video surveillance and enhanced geolocation capabilities in consumer mobile devices we are now presented with the opportunity to change the way we build pedestrian models. Within this paper we explore the potential that such information offers for the improvement of agent-based pedestrian models. We introduce a Scene- and Activity-Aware Agent-Based Model (SA2-ABM), a method for harvesting scene activity information in the form of spatiotemporal trajectories, and incorporate this information into our models. In order to assess and evaluate the improvement offered by such information, we carry out a range of experiments using real-world datasets. We demonstrate that the use of real scene information allows us to better inform our model and enhance its predictive capabilities.
Highlights
Pedestrian movement is woven into the fabric of urban regions
In this paper we present an approach to enhance simple path-based pedestrian models through the use of real trajectory data in order to improve their accuracy for simulating and predicting pedestrian movement
The motivation for our approach stems from the rapid growth in the availability of mobility information in the form of spatiotemporal trajectories [28]
Summary
Pedestrian movement is woven into the fabric of urban regions. From private indoor to public outdoor spaces, pedestrians are constantly utilizing their surroundings to reach destinations, exploring their environment, and to achieve specific goals. Pedestrian models describe the patterns of movement of individuals or groups in a scene over space and time, whereby a person’s current position is based on its old position, desired destination, and surroundings, including the physical environment and other people [2] Such models are widely used to simulate activities of crowds for planning and evaluation from an individual’s perspective (see [3] for a review). This allows us to test numerous scenarios in order to gauge the effects of a real-life events and improve our planning capabilities, assessing for example, how various building layouts and room configurations can impact evacuation time [18] The quality of these simulations depends heavily on the model used, as well as the physical (e.g., walking speed) and behavioral attributes (e.g., distribution of traffic volume within a scene and the agents’ cognitive models) that are used to describe pedestrian movement. We move on to show a number of experiments using the data from the activity scene models and the ABM (Section 5) and in Section 6 we provide a summary of the paper and an outlook for further work
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