Adjustments of Activity Timing and Duration in an Agent-Based Traffic Flow Simulation

Balmer, Michael; Raney, Bryan; Nagel, Kai

doi:10.1016/b978-008044581-6/50008-9

Cited by 42 publications

(28 citation statements)

References 7 publications

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“…Because of unavoidable aggregation errors, such an approach can fail rather badly, in the sense that the performance information derived from the aggregated information may be rather different from the performance that the agent in fact experienced (Raney/Nagel 2004). The procedure of the feedback and learning mechanism is described in detail by Balmer et al (2005). For better understanding, the key points are restated here.…”

Section: Agent Database -Feedbackmentioning

confidence: 99%

“…Yet, also the selection of the decision modules should, as long as they are able to scan the search space in a meaningful way, not be absolutely critical, since it is the scoring function described above that ultimately determines the agents' behavior. There is indeed some indication that simple mental modules, together with many iterations (¼ very long computing times), can lead to plausible results , Balmer et al 2005). Yet, the issue of computing times remains important (Meister et al 2006, Charypar et al 2006.…”

Section: Computational Modulesmentioning

confidence: 99%

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Multi-Agent Transport Simulations and Economic Evaluation

Nagel¹,

Grether²,

Beuck³

et al. 2008

Agent Based Models for Economic Policy Advice

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SummaryTolls are frequently discussed policies to reduce traffic in cities. However, road pricing measures are seldom implemented due to high investments and unpopularity. Transportation planning tools can support planning authorities by solving those problems if they take into account the following aspects: -Demographic attributes like income and time constraints -Time reactions to the policy -Schedule changes of population's individuals during the whole day Our approach uses multi-agent simulations to model and simulate full daily plans. Each of our agents has a utility function that appraises the performance of a typical, microscopically simulated day. The sum of all utility changes to a policy change can be interpreted as the change in the system's welfare thus the economic evaluation of a measure straightforward. The approach is tested with travel behavior of the Zurich metropolitan region in Switzerland. Several tolling schemes are investigated. It is shown that the simulation can be used to model travelers' reactions to time-dependent tolls in a way most existing transportation planning tools are not able to do. It is demonstrated that route adjustment only, as is done in many traditional transport planning packages, results in no economic gains from the tolls. As time-dependent tolls are a much-debated subject in transportation politics, the ability to fully model such tolls and the reactions of travelers may help to find better toll schemes. In a world where individuals have more and more freedom to schedule their daily plans, agent-based simulations offer an intuitive way to research complex topics with lots of interdependencies.

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Section: Agent Database -Feedbackmentioning

confidence: 99%

Section: Computational Modulesmentioning

confidence: 99%

Multi-Agent Transport Simulations and Economic Evaluation

Nagel¹,

Grether²,

Beuck³

et al. 2008

Agent Based Models for Economic Policy Advice

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“…Several iterations of planning and simulation lead to stable traffic patterns in the network. For more information see for example Balmer et al (2005).…”

Section: Matsim Freightmentioning

confidence: 99%

Modelling change in supply-chain-structures and its effect on freight transport demand

Ottemöller

Friedrich

2019

Transportation Research Part E: Logistics and Transportation Re

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Economic activity, especially in the manufacturing industries, is a major determinant of freight transport. Since the actual manufacturing processes are bound to specialised business establishments, which are only partially dispersed across space, commodity flows are required that connect locations of excess supply with locations that show unfulfilled demand for goods. In this context, economic reasoning of the involved actors leads to the formation of industry specific supply chain structures. Various authors emphasise the interrelation of these continuously evolving supply chain structures and freight transport demand. However, so far no disaggregate model exists that explicitly captures this interdependence.The study at hand addresses this gap by developing a disaggregate model for simulating the impact of change in supply chain structures on the corresponding freight transport demand. The proposed model covers centralisation and vertical disintegration as examples of structural change. For this purpose, the model quantitatively describes spatially disaggregate supply chain structures, consisting of business establishments and commodity flows, on the level of entire sectors. The model development is accompanied by an interdisciplinary literature review that gives an overview of existing research on supply chain structures and freight transport demand.The developed model consists of two phases. A first phase generates an artificial industry landscape of business establishments and commodity flows according to available aggregate statistics. The generation relies on elements of stochastic simulation and directed choice procedures. The model's second phase simulates change in the supply chain structures from the first phase. Using linear programming, a maximum solution range regarding the impact on freight transport is calculated. Increasing the degree of assumptions, the solution space can be narrowed. Here, the model applies a combination of stochastic simulation, linear programming, and fitting procedures.The model is applied for analysing centralisation in the poultry industry and vertical disintegration in the automotive industry of Germany. For both cases, a broad range of data sources is used, e.g. common public statistics on establishment sizes and spatial distribution of employment but also sectoral data, e.g. from industry associations or case studies. The real-world consistency of spatial flow patterns is ensured by assigning commodity flows according to statistical macroscopic flows.Overall, the simulation results show that an increase in freight transport performance is to be expected for the case of centralisation as well as vertical disintegration. However, the maximum solution ranges also indicate that assuming suitable location choice and flow assignment a reduction in freight transport performance is mathematically possible. The analysis also addresses the suitability of state measures for mitigating the impact of changes in the supply chain structure on freight transport demand.In summ...

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“…To find better routes, they make use of the events to calculate actual travel times and thus recognize jammed links. Using time adjustment, the departure times and activity durations are modified with the goal of optimizing the individuals' plan score (30). Additional behavioral modules are conceptually clear but not yet implemented: activity resequencing would change the order of activities (e.g., shopping after work instead of before work), and activity dropping would remove certain activities in an overloaded plan.…”

Section: Simulation and Scoringmentioning

confidence: 99%

Agent-Oriented Coupling of Activity-Based Demand Generation with Multiagent Traffic Simulation

Rieser

Nagel

Beuck

et al. 2007

Transportation Research Record: Journal of the Transportation R

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The typical method to couple activity-based demand generation (ABDG) and dynamic traffic assignment (DTA) is time-dependent origin-destination (O-D) matrices. With that coupling method, the individual traveler's information gets lost. Delays at one trip do not affect later trips. However, it is possible to retain the full agent information from the ABDG by writing out all agents' plans, instead of the O-D matrix. A plan is a sequence of activities, connected by trips. Because that information typically is already available inside the ABDG, this is fairly easy to achieve. Multiagent simulation (MATSim) takes such plans as input. It iterates between the traffic flow simulation (sometimes called network loading) and the behavioral modules. The currently implemented behavioral modules are route finding and time adjustment. Activity resequencing or activity dropping are conceptually clear but not yet implemented. Such a system will react to a time-dependent toll by possibly rearranging the complete day; in consequence, it goes far beyond DTA (which just does route adaptation). This paper reports on the status of the current Berlin implementation. The initial plans are taken from an ABDG, originally developed by Kutter; to the authors' knowledge, this is the first time traveler-based information (and not just O-D matrices) is taken from an ABDG and used in a MATSim. The simulation results are compared with real-world traffic counts from about 100 measurement stations.

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Adjustments of Activity Timing and Duration in an Agent-Based Traffic Flow Simulation

Cited by 42 publications

References 7 publications

Multi-Agent Transport Simulations and Economic Evaluation

Multi-Agent Transport Simulations and Economic Evaluation

Modelling change in supply-chain-structures and its effect on freight transport demand

Agent-Oriented Coupling of Activity-Based Demand Generation with Multiagent Traffic Simulation

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