Towards Simulating Cognitive Agents in Public Transport Systems

Towards Simulating Cognitive Agents in Public Transport Systems

Sabine Timpf (University of Augsburg, Germany)
Copyright: © 2009 |Pages: 16
DOI: 10.4018/978-1-60566-226-8.ch008
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In this chapter, the authors present a methodology for simulating human navigation within the context of public, multi-modal transport. They show that cognitive agents, that is, agents that can reason about the navigation process and learn from and navigate through the (simulated physical) environment, require the provision of a rich spatial environment. From a cognitive standpoint, human navigation and wayfinding rely on a combination of spatial models (“knowledge in the head”), (default) reasoning processes, and knowledge in the world. Spatial models have been studied extensively, whereas the reasoning processes and especially the role of the “knowledge in the world” have been neglected. The authors first present an overview of research in wayfinding and then envision a model that integrates existing concepts and models for multi-modal public transport illustrated by a case study.
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In transport planning, simulation is an established tool and traditionally comprises four sequential steps: trip generation, trip distribution, modal choice, and traffic assignment (Ortuzar, 2001). These macro-models were critiqued, mainly for the strict sequence of the steps (re-planning is not possible) and the strong focus on individual motor car traffic (Meier, 1997). In contrast, simulations of public transport systems require an integration of different modes of transport, where each mode has specific properties and peculiarities.

The current trend in traffic simulation is towards activity oriented micro simulations and the inclusion of other modes of transport besides private cars (Widmer, 2000; Nagel, 2001; Raney et al., 2002a). Raney et al. (2002b) simulate the navigation of many travelers at once, which is needed to forecast the load on the transportation networks (Nagel, 2002). The focus in these simulations is on properties of the whole system (traffic loads, traffic flows), not on the individual traveler. However, when more than one mode of transport is involved, the cognitive processes of the individual traveler clearly matter. Hence, there is a need for models that handle the details of transfers between different modes of transport and provide agents with minimal cognitive processing capabilities.

We simulate the navigation process from the perspective of the user of the public transport system. The focus is on the user as a cognitive agent, i.e., an agent who can reason about the navigation process and navigate through and learn from the environment. From a cognitive standpoint, human navigation and wayfinding rely on a combination of spatial models (“knowledge in the head”), (default) reasoning processes, and knowledge in the world. There is a consensus in the spatial cognition community that many different models exist, but that there is a need to integrate them before the whole navigation process can be adequately described. Up to date, no integration effort has been undertaken because of the diversity of the existing models and theories.

Our goal is to design a modular system/framework in which different theories (and their resulting models) can be tested. The idea is that the system will allow the exchange of modules (implemented models) according to which theory currently prevails and for the purpose of comparing different approaches. Currently, many theories of spatio-temporal knowledge processing are known from research in psychology, geography and robotics, but they exist in separate models or even separate research communities. There are computational models for navigation or aspects thereof, which were built for the purposes of either proving psychological theories or for the purpose of robot navigation. As we will discuss in section 3, none of these models can be used for our specific case, although we build on insights from the TOUR (Kuipers, 1979) and NAVIGATOR models (Gopal et al. 1989). A multi-agent simulation system where each agent has cognitive and spatial processing capabilities seems to be an ideal basis for integrating models of navigation and test their effectiveness.

We will thus use a multi-agent methodology to simulate the complete navigation process, i.e. the wayfinding and the locomotion processes for a public transport system. This is different from other approaches where either the wayfinding aspect alone is modeled for a single agent (Raubal, 2001; Frank, 2001; Pontikakis, 2006) or the research is focused on pure locomotion described as physical models (e.g., Helbing & Molnar, 1995; Raney et al., 2002a). The integration of existing models will be a challenge in itself – thus we will work with a case study of navigation in a public transport system and especially with a study of the transfer process. The need to transfer from one means of transport to another at a specific time and place is mentioned as stressful by 70% of public transport users in our case study; 50% admitted to being annoyed by the need to transfer and 63% would rather travel a longer route than transferring (Heye, 2002).

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List of Reviewers
Table of Contents
Ana Bazzan, Franziska Klügl
Ana Bazzan, Franziska Klügl
Chapter 1
Takeshi Takama
This study discusses adaptation effects and congestion in a multi-agent system (MAS) to analyse real transport and traffic problems. Both... Sample PDF
Adaptation and Congestion in a Multi-Agent System to Analyse Empirical Traffic Problems: Concepts and a Case Study of the Road User Charging Scheme at the Upper Derwent
Chapter 2
Qi Han, Theo Arentze, Harry Timmermans, Davy Janssens, Geert Wets
Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling... Sample PDF
A Multi-Agent Modeling Approach to Simulate Dynamic Activity-Travel Patterns
Chapter 3
Michael Balmer, Marcel Rieser, Konrad Meister, David Charypar, Nicolas Lefebvre, Kai Nagel
Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In... Sample PDF
MATSim-T: Architecture and Simulation Times
Chapter 4
Ulf Lotzmann
In this chapter an agent-based traffic simulation approach is presented which sees agents as individual traffic participants moving in an artificial... Sample PDF
TRASS: A Multi-Purpose Agent-Based Simulation Framework for Complex Traffic Simulation Applications
Chapter 5
Paulo A.F. Ferreira, Edgar F. Esteves, Rosaldo J.F. Rossetti, Eugénio C. Oliveira
Trading off between realism and too much abstraction is an important issue to address in microscopic traffic simulation. In this chapter the authors... Sample PDF
Applying Situated Agents to Microscopic Traffic Modelling
Chapter 6
Andreas Schadschneider, Hubert Klüpfel, Tobias Kretz, Christian Rogsch, Armin Seyfried
Multi-Agent Simulation is a general and powerful framework for understanding and predicting the behaviour of social systems. Here the authors... Sample PDF
Fundamentals of Pedestrian and Evacuation Dynamics
Chapter 7
Rex Oleson, D. J. Kaup, Thomas L. Clarke, Linda C. Malone, Ladislau Bölöni
The “Social Potential”, which the authors will refer to as the SP, is the name given to a technique of implementing multi-agent movement in... Sample PDF
"Social Potential" Models for Modeling Traffic and Transportation
Chapter 8
Sabine Timpf
In this chapter, the authors present a methodology for simulating human navigation within the context of public, multi-modal transport. They show... Sample PDF
Towards Simulating Cognitive Agents in Public Transport Systems
Chapter 9
Kurt Dresner, Peter Stone, Mark Van Middlesworth
Fully autonomous vehicles promise enormous gains in safety, efficiency, and economy for transportation. In previous work, the authors of this... Sample PDF
An Unmanaged Intersection Protocol and Improved Intersection Safety for Autonomous Vehicles
Chapter 10
Heiko Schepperle, Klemens Böhm
Current intersection-control systems lack one important feature: They are unaware of the different valuations of reduced waiting time of the... Sample PDF
Valuation-Aware Traffic Control: The Notion and the Issues
Chapter 11
Charles Desjardins, Julien Laumônier, Brahim Chaib-draa
This chapter studies the use of agent technology in the domain of vehicle control. More specifically, it illustrates how agents can address the... Sample PDF
Learning Agents for Collaborative Driving
Chapter 12
Kagan Tumer, Zachary T. Welch, Adrian Agogino
Traffic management problems provide a unique environment to study how multi-agent systems promote desired system level behavior. In particular, they... Sample PDF
Traffic Congestion Management as a Learning Agent Coordination Problem
Chapter 13
Matteo Vasirani, Sascha Ossowski
The problem of advanced intersection control is being discovered as a promising application field for multiagent technology. In this context... Sample PDF
Exploring the Potential of Multiagent Learning for Autonomous Intersection Control
Chapter 14
Tomohisa Yamashita, Koichi Kurumatani
With maturation of ubiquitous computing technology, it has become feasible to design new systems to improve our urban life. In this chapter, the... Sample PDF
New Approach to Smooth Traffic Flow with Route Information Sharing
Chapter 15
Denise de Oliveira, Ana L.C. Bazzan
In a complex multiagent system, agents may have different partial information about the system’s state and the information held by other agents in... Sample PDF
Multiagent Learning on Traffic Lights Control: Effects of Using Shared Information
Chapter 16
Tamás Máhr, F. Jordan Srour, Mathijs de Weerdt, Rob Zuidwijk
While intermodal freight transport has the potential to introduce efficiency to the transport network,this transport method also suffers from... Sample PDF
The Merit of Agents in Freight Transport
Chapter 17
Lawrence Henesey, Jan A. Persson
In analyzing freight transportation systems, such as the intermodal transport of containers, often direct monetary costs associated with... Sample PDF
Analyzing Transactions Costs in Transport Corridors Using Multi Agent-Based Simulation
Chapter 18
Shawn R. Wolfe, Peter A. Jarvis, Francis Y. Enomoto, Maarten Sierhuis, Bart-Jan van Putten
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