Distributed Particle Filter for Urban Traffic Networks Using a Platoon-Based Model

Marinică, Nicolae; Sarlette, Alain; Boel, René

doi:10.1109/tits.2013.2271326

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…Next to this methodological challenge, the computational burden will increase tremendously since the required number of particles will increase exponentially (see Section 3.4) to ensure good coverage of the increased size of the state space. In this case, a parallel and distributed version of our framework needs to be developed to deal with large urban networks, and this is certainly possible (e.g., Marinică et al, 2013). The network topology can be a very useful heuristic to help partition the large urban network to achieve load balancing.…”

Section: Assumptions and Limitationsmentioning

confidence: 99%

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

Xie

Lint

Verbraeck

2018

Transportation Research Part C: Emerging Technologies

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With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions-particularly in urban contexts-due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss-and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.

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Section: Assumptions and Limitationsmentioning

confidence: 99%

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

Xie

Lint

Verbraeck

2018

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

show abstract

“…During the past few years, various short-term traffic forecast and estimation models have been proposed [5], whereas the most recent efforts have been focused on improving prediction accuracy and computational efficiency [6], [20], [21] and reducing computational complexity [7]. However, the majority of these models has been developed under a common assumption that traffic variables can be directly obtained at the location of interest.…”

Section: Related Workmentioning

confidence: 99%

Spatial Inference of Traffic Transition Using Micro–Macro Traffic Variables

Thajchayapong

Barria

2015

IEEE Trans. Intell. Transport. Syst.

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“…In order to explain the leader/follower . CA i receives measurements from local sensors which we assume to be sufficient to provide the approximate information on the arrival times of vehicles at intersection i in the near future (see [1] for a state estimator that can help to achieve this).…”

Section: Urban Traffic: Case Study For Coordination Controlmentioning

confidence: 99%

Leader–Follower Coordination Control for Urban Traffic

Boel

Marinică

2014

Coordination Control of Distributed Systems

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Open-loop control of traffic lights in an urban environment is typically tuned so as to generate green waves, which ideally should ensure that vehicles never have to stop, avoiding waste of capacity of the intersections. A green wave minimizes average delay under ideal, noiseless assumptions, by selecting the phase shift between the switching times of successive traffic lights, so that this phase shift corresponds to the travel time between intersections. Achieving green waves is easier if the vehicles travel together in large platoons along major axes of traffic flow. In a large network with dispersed two-way traffic and with different time delays along different roads connecting successive intersections, it becomes difficult to find a perfect green wave switching schedule. Moreover vehicles entering from uncontrolled side streets or parking lots, unexpected delays along connecting link roads, and all other sources of noise also lead to a deterioration of the performance of the open-loop strategy.Local feedback control tries to avoid this deterioration by adjusting the switching times of the traffic lights to currently available data on the expected arrival times of vehicles. This feedback control tends to destroy the green wave synchronization. Combining a green wave with feedback control thus requires active coordination among the neighboring feedback control agents. This problem is not critical when the traffic load is very light, since then all specifications are very easy to meet. It is also not relevant under very heavy load since then the effect of the random perturbations is small compared to the average load, and no green wave solution can exist anyway. However, under intermediate load, when there are a few critical intersections that

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Distributed Particle Filter for Urban Traffic Networks Using a Platoon-Based Model

Cited by 11 publications

References 9 publications

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

Spatial Inference of Traffic Transition Using Micro–Macro Traffic Variables

Leader–Follower Coordination Control for Urban Traffic

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