Large-Scale Multi-Agent Simulations for Transportation Applications

Balmer, Michael; Nagel, Kai; Raney, Bryan

doi:10.1080/15472450490523892

Cited by 60 publications

(30 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These computational modeling approaches include cellular automata, network and agent-based modeling, neural networks, genetic algorithms, Monte Carlo simulations, and so on that are generally used in conjunction with scientific visualization techniques. Examples of complex systems that have been investigated with advanced computational modeling techniques include climate change (West & Dowlatabadi, 1999), urban transportation models (Balmer, Nagel, & Raney, 2004;Helbing & Nagel, 2004;Noth, Borning, & Waddell, 2000), and economics (Anderson et al, 1988;Arthur, Durlauf, & Lane, 1997;Axelrod, 1997;Epstein & Axtell, 1996). New communities of scientific practice have also emerged in which computational modeling techniques, in particular agent-based models and genetic algorithms, are being used to create synthetic worlds such as artificial life (Langton, 1989(Langton, , 1995 and artificial societies (Epstein & Axtell, 1996) that allow tremendous flexibility to explore theoretical and research questions in the physical, biological, and social sciences that would be difficult or impossible in "real" or nonsynthetic settings.…”

Section: Implications Of the Sciences Of Complex Systems For The Learmentioning

confidence: 99%

Complex Systems in Education: Scientific and Educational Importance and Implications for the Learning Sciences

Jacobson¹,

Wilensky²

2006

Journal of the Learning Sciences

470

322

View full text Add to dashboard Cite

The multidisciplinary study of complex systems in the physical and social sciences over the past quarter of a century has led to the articulation of important new conceptual perspectives and methodologies that are of value both to researchers in these fields as well as to professionals, policymakers, and citizens who must deal with challenging social and global problems in the 21st century. The main goals of this article are to (a) argue for the importance of learning these ideas at the precollege and college levels; (b) discuss the significant challenges inherent in learning complex systems knowledge from the standpoint of learning sciences theory and research; (c) discuss the "learnability issue" of complex systems conceptual perspectives and review a body of literature that has been exploring how learning sciences pedagogical approaches can lead to student learning of important dimensions of complex systems knowledge; (d) argue that the cognitive and sociocultural factors related to learning complex systems knowledge are relevant and challenging areas for learning sciences research; and (e) consider ways that concepts and methodologies from the study of complex systems raise important issues of theoretical and methodological centrality in the field of the learning sciences itself.

show abstract

Section: Implications Of the Sciences Of Complex Systems For The Learmentioning

confidence: 99%

Complex Systems in Education: Scientific and Educational Importance and Implications for the Learning Sciences

Jacobson¹,

Wilensky²

2006

Journal of the Learning Sciences

470

322

View full text Add to dashboard Cite

show abstract

“…However, only systems with a reduced number of decision-making entities have been represented in terms of intelligent transportation systems vol. to larger data sets (Balmer et al, 2003;Gloor et al, 2004). Thus, a coupling of both behaviors within the layered architecture we have proposed could be the basis for profiting from the qualities of the reactive and the cognitive approaches.…”

Section: Discussionmentioning

confidence: 98%

An Agent-Based Approach to Assess Drivers' Interaction with Pre-Trip Information Systems

Rossetti

Liu

2005

Journal of Intelligent Transportation Systems

View full text Add to dashboard Cite

Article:Rossetti, R. and Lui, R. (2005) An agent-based approach to assess drivers' interaction with pre-trip information systems. Journal of Intelligent Transportation Systems., 9 (1). pp. This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers' responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards

show abstract

“…The users' behavior is often described by their capabilities, their goal(s), and their behavior patterns, such as acceleration patterns. Abilities and scalabilities of multi-agent traffic simulation have been discussed in Balmer, Nagel, and Raney (2004). In this work, we focus on routing methods.…”

Section: Traffic Simulationmentioning

confidence: 99%

Don't Go With the Ant Flow: Ant-Inspired Traffic Routing in Urban Environments

Dallmeyer

Schumann

Lattner

et al. 2014

Journal of Intelligent Transportation Systems

View full text Add to dashboard Cite

Traffic routing is a well-established optimization problem in traffic management. Here, we address dynamic routing problems where the load of roads is taken into account dynamically, aiming at the optimization of required travel times. We investigate ant-based algorithms that can handle dynamic routing problems, but suffer from negative emergent effects like road congestions. These negative effects are inherent in the design of ant-based algorithms. In this article we propose an inverse ant-based routing algorithm to (a) maintain the positive features of ant-based algorithms for dynamic routing problems, while (b) avoiding the occurrence of negative emerging effects, like road congestion. We evaluated the performance of the proposed algorithm by comparing its results with two alternative routing algorithms, namely, A * , which is a static routing algorithm, and an iterative approach. In particular, the iterative approach is used for providing an upper bound, as it uses routing knowledge in a number of calibration runs, to determine the actual load, before the effective routing is done. For the evaluation we used the agent-based traffic simulation system MAINSIM. The evaluation was done with one synthetic and two real-world scenarios, to outline the practical relevance of our findings. Based on these evaluations, we can conclude that the inverse ant-based routing approach is particularly suited for a scenario with a high traffic density, as it can adapt the routing of each vehicle, while avoiding the negative emerging effects of conventional ant-based routing algorithms.

show abstract

Large-Scale Multi-Agent Simulations for Transportation Applications

Cited by 60 publications

References 20 publications

Complex Systems in Education: Scientific and Educational Importance and Implications for the Learning Sciences

Complex Systems in Education: Scientific and Educational Importance and Implications for the Learning Sciences

An Agent-Based Approach to Assess Drivers' Interaction with Pre-Trip Information Systems

Don't Go With the Ant Flow: Ant-Inspired Traffic Routing in Urban Environments

Contact Info

Product

Resources

About