J. Jessurun

A Multi-Agent Cellular Automata System for Visualising Simulated Pedestrian Activity

This paper describes the first impressions of the development of a multi-agent system that can be used for visualising simulated pedestrian activity and behaviour to support the assessment of design performance. This system is based on cellular automata and agent technology. Agents represent objects or people with their own behaviour, moving over a pedestrian network. Each agent is located in a simulated space, based on the cellular automata grid. Each iteration of the simulation is based on a parallel update of the agents conforming local rules. Agents positioned within an environment have sensors to perceive their local neighbourhood and affect their environment. In this manner, autonomous individuals and the interaction between them can be simulated by the system.

Semiautomatic Imputation of Activity Travel Diaries: Use of Global Positioning System Traces, Prompted Recall, and Context-Sensitive Learning Algorithms

The new generation of dynamic activity-based models requires multiday or multiweek activity–travel data. Global Positioning System (GPS) tracers may be a powerful technology to collect such data, but previous applications of this technology to collect data of full activity travel patterns (not just time, route, and location) still required a substantial amount of manual data imputation and processing and hence are still demanding for both respondent and researcher. A semiautomatic data imputation system would be a major breakthrough and would involve less respondent burden. This paper reports and illustrates the design of a system called TraceAnnotator that processes multiday GPS traces semiautomatically. The process of imputing transportation modes, activity episodes, and other facets of activity travel patterns is based on a learning Bayesian belief network (BBN), which represents the multiple relationships between spatial, temporal, and other factors, including errors in the technology itself. Activity type is identified by fusing GPS data with geographic information system land use data and personalized land use data. Land use data are built during the data collection process using reverse geocoding and an Internet-based prompted recall survey, which also allows checking and correction of any imputation errors. The prompted recall data are used to update the conditional probabilities of the BBN. Consequently, that the system can learn over time implies that imputation accuracy will improve over time, reducing respondent and researcher burden. A pilot study is presented and potential improvements of the learning algorithm are discussed.

Motivate: context aware mobile application for activity recommendation

This paper presents the design, implementation and evaluation of a context-aware recommendation system that promotes the adoption of a healthy and active lifestyle. A Smartphone application that provides personalized and contextualized advice based on geo information, weather, user location and agenda was developed and evaluated by a user study. The results show the potential of this mobile application in triggering behavior change by suggesting simple daily activities.

A FRAMEWORK FOR PROCESSING AGENT-BASED PEDESTRIAN ACTIVITY SIMULATIONS IN SHOPPING ENVIRONMENTS

Agent-based modeling is a computational methodology that allows the analyst to create, analyze, and experiment with artificial worlds populated by agents. A specific research area is microscale agent-based modeling, which can be used for the simulation of pedestrian movement for low- and high-density scenarios and for the effect of changes in an environment. Such models can also be used for pedestrian dynamics in city centers to show the design effects in the shopping environment. The main contribution of this article is an agent-based model that provides an activity agenda for pedestrian agents that guides their shopping behavior in terms of destination and time spent in shopping areas. This model involves choice mechanisms including where to stop, in what order, and which route to take. The article describes a framework for processing agent-based pedestrian activity simulations within a shopping environment. The main achievement of this research is a validation of the approach leading to a working system. Preliminary findings are reported here.

Modeling Time Duration of Planned and Unplanned Store Visits in a Multi-Agent Simulation of Pedestrian Activity in City Centers

Micro-scale agent-based modeling can be used for the simulation of pedestrian movement for low and high density scenarios and for the effect of changes in an environment. Such models can also be used for pedestrian dynamics in city centers to show the design effects in the shopping environment. The main contribution of this paper is to introduce the implication of time duration of a visit to a store within a simulation framework for pedestrian movement simulation. The paper reports findings of time spent in a store.