Data assimilation in agent based simulation of smart environments using particle filters

Wang, Minghao; Hu, Xiaolin

doi:10.1016/j.simpat.2015.05.001

Cited by 39 publications

(73 citation statements)

References 25 publications

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“…This, in itself, is a novel and important contribution. Few previous efforts have attempted to incorporate data assimilation with agent-based models [for example see 46,47], and it is unclear how DA methods, that have typically been created for linear models [16], can be adapted for non-linear ABMs.…”

Section: Research Problem and Related Workmentioning

confidence: 99%

Dealing with uncertainty in agent-based models for short-term predictions

2020

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Agent-based models (ABM) are gaining traction as one of the most powerful modelling tools within the social sciences. They are particularly suited to simulating complex systems. Despite many methodological advances within ABM, one of the major drawbacks is their inability to incorporate real-time data to make accurate short-term predictions. This paper presents an approach that allows ABMs to be dynamically optimised. Through a combination of parameter calibration and data assimilation (DA), the accuracy of model-based predictions using ABM in real time is increased. We use the exemplar of a bus route system to explore these methods. The bus route ABMs developed in this research are examples of ABMs that can be dynamically optimised by a combination of parameter calibration and DA. The proposed model and framework can also be used in an passenger information system, or in an Intelligent Transport Systems to provide forecasts of bus locations and arrival times.A PREPRINT -AUGUST 23, 2019 varies over the route and with at off-peak to peak periods. There are methods to incorporate streaming data into models, such as data assimilation (DA) routines [26,45]. Broadly, DA refers to a suite of techniques that allow observational data to be incorporated into models [45] to provide an optimal estimate of the evolving state of the system. Performing DA increases the probability of having an accurate representation of the current state of the system, thereby reducing the uncertainty of future predictions. This is a technique that has been widely applied in fields such as meteorology, hydrology and oceanography [21].There are, however, two methodological challenges that must be overcome to apply DA in ABM. First, DA methods are often intrinsic to their underlying models which are typically systems of partial differential equations with functions linearised mathematically. Hence DA methods typically rely on linearising the underlying model [16]. One of the most appealing aspects of agent-based models is that they are inherently non-linear, so it is not clear whether the assumptions of traditional DA methods will hold. Second, it is still unknown how much uncertainty DA can effectively deal with when implemented within ABM. Assimilation of real-time data into ABMs has only been attempted a few times and these examples are limited by their simplicity [28,46,47]. This paper is part of a wider programme of work 2 that is focused on developing DA methods to be readily used in ABM. This paper focuses on one particular model that aims to make predictions of bus locations in real time. Bus route operation has been chosen due to its inherent uncertainties -for example a model will need to account for uncertain factors affecting how buses travel on the roads [22] -but also for its tractability -there are many fewer interactions than present in, say, a model of a crowd. We also focus on one particular DA algorithm -the Particle Filter (PF). This method is chosen due to its ability to incorporate data into non-linear models such as ABM...

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Section: Research Problem and Related Workmentioning

confidence: 99%

Dealing with uncertainty in agent-based models for short-term predictions

2020

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“…, are miss-counted, and which passages in E k j are over-counts. To this end, a match procedure is applied, which is essentially the same with that in Wang and Hu (2015), where the authors use it to match two groups of agents (without identity information) based on their locations. The match procedure works in the following way.…”

Section: Weight Computation: Utilizing the Error Modelsmentioning

confidence: 99%

“…The OAT approach is very simple, and has the benefits that first, any change observed in the output will unambiguously be due to the single variable changed; second, the results can be easily compared, since when we change one variable at a time, we can keep all other variables fixed to their baseline values. Therefore, the OAT approach has been used in many data assimilation research, e.g., Xue et al (2012), Wang and Hu (2015). However, the OAT approach does not fully explore the input space, since it does not take into account the simultaneous variation of input variables, and more importantly, the nonlinear effects that occur due to the interaction of these input variables.…”

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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“…A few examples of the latter can be found e.g. in wildfire and transport simulations [5][6][7]26], and in agent-based simulations that predict the behavior of residents in buildings [21,22]. For DA in discrete systems simulations, the Sequential Monte Carlo (SMC) methods, a.k.a.…”

Section: Introductionmentioning

confidence: 99%

Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Cho

Huang

Verbraeck

2020

Lecture Notes in Computer Science

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Dynamic data-driven simulation (DDDS) incorporates realtime measurement data to improve simulation models during model runtime. Data assimilation (DA) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical conditions of DA using PFs: (1) the time interval of iterations, (2) the number of particles and (3) the level of actual and perceived measurement errors (or noises), and provide recommendations on how to strategically use data assimilation for DDDS considering these conditions. The results show that the estimation accuracy in DA is more constrained by the choice of time intervals than the number of particles. Good accuracy can be achieved without many particles if the time interval is sufficiently short. An over estimation of the level of measurement errors has advantages over an under estimation. Moreover, a slight over estimation has better estimation accuracy and is more responsive to system changes than an accurate perceived level of measurement errors.

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Data assimilation in agent based simulation of smart environments using particle filters

Cited by 39 publications

References 25 publications

Dealing with uncertainty in agent-based models for short-term predictions

Dealing with uncertainty in agent-based models for short-term predictions

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

Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

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