Dynamic Network Simulation with AIMSUN

Barceló, Jaume; Casas, Jordi

doi:10.1007/0-387-24109-4_3

Cited by 171 publications

(104 citation statements)

References 14 publications

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“…Over the past decade, different microscopic traffic tools (e.g. VISSIM (Fellendorf and Vortisch, 2010), AIMSUN (Barceló and Casas, 2005), and SUMO (Krajzewicz et al, 2012)) have been applied for optimization of the traffic control systems when detailed vehicle information is required. For instance, Khondaker and Kattan (2015) presented a variable speed limit (VSL) control system in a connected vehicle environment, which focused on individual driver's behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

Jin

Kosonen

2017

Advances in Engineering Software

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A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation. Advances in Engineering SoftwareAccess to the published version may require subscription. N.B. When citing this work, cite the original published paper. AbstractTraffic flow is considered as a stochastic process in road traffic modeling. Computer simulation is a widely used tool to represent traffic system in engineering applications. The increased traffic congestion in urban areas and their impacts require more efficient controls and management. While the effectiveness of control schemes highly depends on accurate traffic model and appropriate control settings, optimization techniques play a central role for determining the control parameters in traffic planning and management applications. However, there is still a lack of research effort on the scientific computing framework for optimizing traffic control and operations and facilitating real planning and management applications. To this end, the present study proposes a model-based optimization framework to integrate essential components for solving road traffic control problems in general. In particular, the framework is based on traffic simulation models, while the solution needs extensive computation during the engineering optimization process. In this study, an advanced genetic algorithm, extended by an external archive for storing globally elite genes, governs the computing framework, and it shows superior performance than the ordinary genetic algorithm because of the reduced number of fitness function evaluations in engineering applications. To evaluate the optimization algorithm and validate the whole software framework, this paper illustrates a detailed application for optimization of traffic light controls. The study optimizes a simple road network of two intersections in Stockholm to demonstrate the model-based optimization processes as well as to evaluate the presented algorithm and software performance.

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Section: Literature Reviewmentioning

confidence: 99%

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

Jin

Kosonen

2017

Advances in Engineering Software

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“…The main advantages of a simulation-based DTA Although traffic simulations are originally intended for evaluating operational strategies, with the fast development of computer hardware, simulation-based DTA models have gained popularity for transportation planning applications. Examples of simulation-based DTA models that have been applied to real-life networks include DynaMIT Wen et al, 2006b;Balakrishna et al, 2008), DYNASMART (Mahmassani and Hawas, 1997;Mahmassani et al, 2004), VISTA (Ziliaskopoulos et al, 2004), DynusT , Dynameq (Florian et al, 2005(Florian et al, , 2006, AIMSUN (Barcelo and Casas, 2002a, 2002b, TRANSCAD (Caliper Corporation, 2009), INTEGRATION (Aerde et al, 1996) and METROPOLIS (de Palma and Marchal, 2002). …”

Section: Simulation-based Dtamentioning

confidence: 99%

A dynamic traffic assignment model for highly congested urban networks

Ben‐Akiva

Gao

Wei

et al. 2012

Transportation Research Part C: Emerging Technologies

126

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The management of severe congestion in complex urban networks calls for dynamic traffic assignment (DTA) models that can replicate real traffic situations with long queues and spillbacks. DynaMIT-P, a mesoscopic traffic simulation system, was enhanced and calibrated to capture the traffic characteristics in the city of Beijing, China. All demand and supply parameters were calibrated simultaneously using sensor counts and floating car travel time data. Successful calibration was achieved with the Path-size Logit route choice model, which accounted for overlapping routes. Furthermore, explicit representations of lane groups were required to properly model traffic delays and queues. A modified treatment of acceptance capacity was required to model the large number of short links in the transportation network (close to the length of one vehicle). In addition, even though bicycles and pedestrians were not explicitly modeled, their impacts on auto traffic were captured by dynamic road segment capacities.

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“…Outputs of the mCF model are xi(t), vi(t) and ai(t). The mCF model used in this study is described in [22]. It is representative of the car-following models classically used within traffic microsimulation tools.…”

Section: Microscopic Car-following Model (Mcf Model)mentioning

confidence: 99%

Selecting Noise Source and Traffic Representations that Capture Road Traffic Noise Dynamics Near Traffic Signals

Can¹,

Leclercq²,

Lelong³

2009

Acta Acustica united with Acustica

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Considering traffic dynamics greatly improves noise estimation in urban area. This can be achieved by coupling a dynamic traffic model with both noise emission laws and sound propagation calculation. Determining the relevant noise source and traffic representations to estimate classical noise descriptors (LAeq and statistical descriptors) near traffic signals has been recently studied. This research topic is extended in this paper to more specific descriptors which are able to capture noise dynamics at the traffic signal scale, for usual urban traffic situations (upstream, in front of, and downstream a traffic signal) and different distances from the road (5.5, 10 and 15 m). It appears that 14m-line sources ensure an estimation of all descriptors with errors below 2 dB(A) if traffic dynamics is precisely described. Macroscopic and microscopic car-following models are both relevant to highlight noise dynamics triggered by the traffic signal, but some differences between those traffic representations are observed.

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Dynamic Network Simulation with AIMSUN

Cited by 171 publications

References 14 publications

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

A dynamic traffic assignment model for highly congested urban networks

Selecting Noise Source and Traffic Representations that Capture Road Traffic Noise Dynamics Near Traffic Signals

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