Large-Scale Multi-Agent Simulations for Transportation Applications

In many transportation simulation applications including intelligent transportation systems (ITS), behavioral responses of individual travelers are important. This implies that simulating individual travelers directly may be useful. Such a microscopic simulation, consisting of many intelligent particles (= agents), is an example of a multi-agent simulation. For ITS applications, it would be useful to simulate large metropolitan areas, with ten million travelers or more. Indeed, when using parallel computing and efficient implementations, multi-agent simulations of transportation systems of that size are feasible, with computational speeds of up to 300 times faster than real time. It is also possible to efficiently implement the simulation of day-to-day agent-based learning, and it is possible to make this implementation modular and essentially “plug-and-play.” Unfortunately, these techniques are not immediately applicable for within-day replanning, which would be paramount for ITS. Alternative techniques, which allow within-day replanning also for large scenarios, are discussed

Towards truly agent-based traffic and mobility simulations

Traveling is necessary and desirable; yet, it imposes external costs on other people. Quantitative methods help finding a balance. Multi-agent simulations seem an obvious possibility here. A real world traffic simulation consists of many modules, all requiring different expertise. The paper discusses how such modules can be coupled to a complete simulation system, how such a system can be made fast enough to deal with real-world sizes (several millions of travelers), and how agent memory can be introduced. A real-world case study is presented, which says that multi-agent methods for traffic are mature enough to be used alongside existing methods. Finally, some outlook into the near future is given

The use of a simple cellular automata model as a testbed for kinetic theories of vehicular traffic flow

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 60-63).Issued also on microfiche from Lange Micrographics.The broad objective of this thesis is to explore the potential for the use of Cellular Automata (CA) models to provide a testbed for comparison of different kinetic models of vehicular traffic. We intend to develop a quantitative technique for comparing various kinetic equations that model a given CA-based microscopic model of vehicular traffic. We also plan to apply this technique to two instances of kinetic models, which are differentiated only by their assumptions regarding inter-vehicular correlation and are intended to statistically predict the behavior of ensembles of instances of the underlying CA. We apply different versions of the L₁ norm as a metric to compare two kinetic models within two scenarios that are representative of typical vehicular traffic problems. The kinetic models are both derived from the same simple CA traffic model, known as CA-184-CC, but they are based on different models of vehicle correlation: the modified vehicular chaos model; and the Nelson and Raney model, an ad-hoc model developed in an earlier paper by P. Nelson and the author. The traffic scenarios are "near-jam," in which a large traffic jam has a high probability of occurring in a particular location; and "pseudo-free-flow," in which vehicles have a high probability of being spaced out from one another enough to allow driving at the maximum desired speed. Results show that the L[distribution][superscript]₁₃̦[w][subscript] norm produces slightly smaller values for the near-jam scenario and significantly smaller values for the pseudo-free-flow scenario. Also, the L[distribution][superscript]₁₃̦[w][subscript] norm comparing the CA model to the modified vehicular chaos correlation model is smaller than that of the CA to the Nelson and Raney ad-hoc correlation model, indicating that the modified vehicular chaos model is a better fit to the ensembled CA model

An improved framework for large-scale multi-agent simulations of travel behavior

We describe a framework for running large-scale multi-agent sim-ulations of travel behavior. The framework represents each trav-eler as an individual “Agent ” that makes independent decisions about its desired use of the transportation system during a typical day. An Agent keeps a record of its decisions in a “Plan. ” A Plan contains the Agent’s schedule of activities it wants to perform dur-ing the day, including times and locations, along with the travel modes and routes it intends to utilize to travel between activities. An Agent database gives every Agent a memory where it can store several possible Plans, as well as performance information it uses to compare how well different Plans meet its needs. Agents score a Plan’s performance based on the output of the micro-simulator. The Agent database also allows Agents to periodically generate new Plans by connecting them to behavioral Modules that model the different kinds of decisions that affect an Agent’s Plan. For example, one Module chooses routes, another chooses activity du-rations. This paper describes the design and our current imple-mentation of this framework, plus the results of some verification scenarios.

An Agent-Based Simulation Model of Swiss Travel: First Results

In a multi-agent transportation simulation, travelers are represented as individual “agents,” who make independent decisions about their actions. We are implementing such a simulation for all of Switzerland, which is composed of modules that model those decisions for each agent, such as: (i) Activities generator, which generates a complete 24-hour day-plan, with each major activity (sleep, eat, work, shop, drink beer), their times, and their locations. (ii) Route planner, which determines the mode of transportation, as well as the actual route plan taken, for each leg of the agent’s chosen activity plan. (iii) Mobility simulation, which executes all plans simultaneously and in consequence computes the interaction between different travelers, leading e.g. to congestion. (iv) Feedback and learning, which resolves the interdependence between the above modules. For example, plans depend on congestion but congestion depends on plans. This is resolved via an iterative method, where an initial plans set is slowly adapted until it is consistent with the resulting travel conditions. This technique has similarities to day-to-day human learning and can also be interpreted that way. – Besides these modules, one also needs input data, such as the road network, or (synthetic) populations. In the future, further modules need to be added