Validation of stochastic traffic flow model with microscopic traffic simulation

Chu, Kang-Ching; Yang, Li; Saigal, Romesh; Saitou, Kazuhiro

doi:10.1109/case.2011.6042479

Cited by 24 publications

(15 citation statements)

References 12 publications

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“…Given that we did not have access to manufacturer's specifications of the loop detectors, we use a value within the guidelines of the specification. The error for the evolution equation was chosen to be consistent with the results reported in Chu et al (2011), where authors report that standard deviation of the LWR model forecast error is usually under 3%, but higher for congested flow when compared with observation data from motorway sensors. In our numerical example, we use 4%.…”

Section: Particle Filtering Of the Lwr Modelmentioning

confidence: 98%

Bayesian analysis of traffic flow on interstate I-55: The LWR model

Polson¹,

Sokolov²

2015

Ann. Appl. Stat.

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Transportation departments take actions to manage traffic flow and reduce travel times based on estimated current and projected traffic conditions. Travel time estimates and forecasts require information on traffic density which are combined with a model to project traffic flow such as the Lighthill-Whitham-Richards (LWR) model. We develop a particle filtering and learning algorithm to estimate the current traffic density state and the LWR parameters. These inputs are related to the so-called fundamental diagram, which describes the relationship between traffic flow and density. We build on existing methodology by allowing real-time updating of the posterior uncertainty for the critical density and capacity parameters. Our methodology is applied to traffic flow data from interstate highway I-55 in Chicago. We provide a real-time data analysis of how to learn the drop in capacity as a result of a major traffic accident. Our algorithm allows us to accurately assess the uncertainty of the current traffic state at shock waves, where the uncertainty is a mixture distribution. We show that Bayesian learning can correct the estimation bias that is present in the model with fixed parameters.Comment: Published at http://dx.doi.org/10.1214/15-AOAS853 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

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Section: Particle Filtering Of the Lwr Modelmentioning

confidence: 98%

Bayesian analysis of traffic flow on interstate I-55: The LWR model

Polson¹,

Sokolov²

2015

Ann. Appl. Stat.

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“…However, it requires data fitting with the field data to decide a better functional form, and the result may vary for field data of different dates/times and locations. We adopt the log piecewise linear model in Equation (3) as the speeddensity function, which has been examined to have the best fit using traffic data from highway I-95 in Virginia [7].…”

Section: B the Speed-density Functionmentioning

confidence: 99%

“…The validation result using microscopic traffic simulation suggest that the SPDE model is capable of capturing the stochastic nature of the traffic flow evolution. Therefore, SPDE can be used as a primary building block within an ITS to provide traffic status prediction [7].…”

Section: Introductionmentioning

confidence: 99%

Optimization of probe vehicle deployment for traffic status estimation

Chu

2013

2013 IEEE International Conference on Automation Science and Engineering (CASE)

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Traffic congestion in urban areas is posing many challenges, and traffic flow model provides accurate traffic status estimation and prediction can be beneficial for congestion management. With the limitation of infrastructure, probe data from individual vehicles is an attractive alternative to inductive loop detectors as a mean to collect traffic data for traffic flow modelling. This paper investigates the optimal deployment strategy of probe vehicles. Data assimilation technique, Newtonian relaxation method, is used to incorporate probe data into macroscopic traffic flow model, and synthetic traffic is used to study the optimization problem. The tradeoff between the quality of traffic density estimation and operation cost of probing are investigating using multi-objective genetic algorithm. The results indicates that it is possible to decrease probe data for congested traffic with negligible degradation on the quality of traffic status estimation.

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“…To date, there are some OD estimation tools e.g. Estimator of PARAMICS, but their capabilities and potentials for a corridor network are still unclear [39]. The time-dependent OD matrix was estimated on the basis of zone-based percentile trip distribution.…”

Section: Preliminary Testingmentioning

confidence: 99%

Calibration of a rule-based intelligent network simulation model

Memon¹,

Meng²,

Wong

et al. 2016

J. Mod. Transport.

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This paper is focused on calibration of an intelligent network simulation model (INSIM) with reallife transportation network to analyse the INSIM's feasibility in simulating commuters' travel choice behaviour under the influence of real-time integrated multimodal traveller information (IMTI). A transportation network model for the central and western areas of Singapore was simulated in PARAMICS and integrated with INSIM expert system by means of an application programming interface to form the INSIM. Upon calibration, INSIM was able to realistically present complicated scenarios in which real-time IMTI was provided to commuters and the network performance measures being recorded.

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Validation of stochastic traffic flow model with microscopic traffic simulation

Cited by 24 publications

References 12 publications

Bayesian analysis of traffic flow on interstate I-55: The LWR model

Bayesian analysis of traffic flow on interstate I-55: The LWR model

Optimization of probe vehicle deployment for traffic status estimation

Calibration of a rule-based intelligent network simulation model

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