State-of-the-art of vehicular traffic flow modelling

Hoogendoorn, Serge P.; Bovy, Piet H. L.

doi:10.1177/095965180121500402

Cited by 365 publications

(239 citation statements)

References 44 publications

(55 reference statements)

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“…In order for eqn. (1) to be solved, the relationship between flow and density must be explicitly considered. In the LWR theory it is given in the form q(x, t) = ρ(x, t)V [ρ(x, t)]…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis paper is concerned with the problem of macroscopic road traffic flow model calibration and verification. Thoroughly validated models are necessary for both control system design and scenario evaluation purposes. Here, the second order traffic flow model METANET was calibrated and verified using real data.A powerful optimisation problem formulation is proposed for identifying a set of model parameters that makes the model fit to measurements. For the macroscopic traffic flow model validation problem, this set of parameters characterise the aggregate traffic flow features over a road network. In traffic engineering, one of the most important relationships whose parameters need to be determined is the fundamental diagram of traffic, which models the non-linear relationship between vehicular flow and density. Typically, a real network does not exhibit the same traffic flow aggregate behaviour everywhere and different fundamental diagrams are used for covering different network areas. As a result, one of the initial steps of the validation process rests on expert engineering opinion assigning the spatial extension of fundamental diagrams. The proposed optimisation problem formulation allows for automatically determining the number of different fundamental diagrams to be used and their corresponding spatial extension over the road network, simplifying this initial step. Although the optimisation problem suffers from local minima, good solutions which generalise well were obtained.The design of the system used is highly generic and allows for a number of evolutionary and swarm intelligence algorithms to be used. Two UK sites have been used for testing it. Calibration and verification results are Preprint submitted to Applied Soft Computing September 18, 2015 discussed in detail. The resulting models are able to capture the dynamics of traffic flow and replicate shockwave propagation. A total of ten different algorithms were considered and compared with respect to their ability to converge to a solution, which remains valid for different sets of data. Particle Swarm Optimisation (PSO) algorithms have proven to be particularly effective and provide the best results both in terms of speed of convergence and solution generalisation. An interesting result reported is that more recently proposed PSO algorithms were outperformed by older variants, both in terms of speed of convergence and model error minimisation.

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“…In order for eqn. (1) to be solved, the relationship between flow and density must be explicitly considered. In the LWR theory it is given in the form q(x, t) = ρ(x, t)V [ρ(x, t)]…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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“…There exists a wide range of traffic models [15], [16]. An important factor that determines the choice of the prediction model to be used in MPC is the trade-off between accuracy of the model and the computational complexity since at each time step k the model will be simulated repeatedly within the on-line optimization algorithm.…”

Section: B Iv-based Mpcmentioning

confidence: 99%

Model-based predictive traffic control for intelligent vehicles: Dynamic speed limits and dynamic lane allocation

Baskar

Schutter

Hellendoorn

2008

2008 IEEE Intelligent Vehicles Symposium

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Abstract-We consider traffic management and control approaches for automated highway systems with a combination of intelligent vehicles and roadside controllers. The vehicles are organized in platoons with short intraplatoon distances, and larger distances between platoons. Moreover, all vehicles are assumed to be automated, i.e., throttle, braking, and steering commands are determined by an automated on-board controller. Within a platoon the vehicles coordinate their actions so as to maintain a small but safe intervehicle distance, using adaptive cruise control methods. Platoon leaders receive speed set-points and lane change commands from the roadside controller. We propose a model-based predictive control (MPC) approach to determine appropriate speed limits and lane allocations for the platoons. In general, this results in mixed-integer optimization problems. We discuss some methods to solve these problems suboptimally on-line. The proposed approach is then applied to a simple simulation example in which the aim is to minimize the total time all vehicles spend in the network by optimally assigning dynamic speed limits and lane changes.

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“…We noticed that most of the existing model predictive control strategies are based on the second order traffic flow model such as METANET. This kind of model describe the traffic flow in a more specific way, so as to describes the traffic flow process accurately [3]. However, this feature of the model increases the complexity of computation, consequently the computation speed is low and it is not suitable for controlling severe congestion areas.…”

Section: Introductionmentioning

confidence: 99%

Integrated network Linear-Quadratic Model Predictive Control for disturbance on freeways

Han

Yuan

Hoogendoorn

2015

2015 IEEE 12th International Conference on Networking, Sensing and Control

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Abstract-We propose an integrated approach for dynamic route guidance and ramp metering using Linear Quadratic Model Predictive Control (LQMPC). The main objective is to maximize the throughput of the network by guiding traffic flow towards to insufficiently utilized infrastructures. Due to the linear property of the LQMPC, it is fast enough for real-time online traffic control with a short control step (30 second for example). Simulation results from a case study demonstrate that the proposed traffic control approach has great potential to solve congestion when disturbances occur on freeways.

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State-of-the-art of vehicular traffic flow modelling

Cited by 365 publications

References 44 publications

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Model-based predictive traffic control for intelligent vehicles: Dynamic speed limits and dynamic lane allocation

Integrated network Linear-Quadratic Model Predictive Control for disturbance on freeways

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