Hybrid bayesian networks for traffic load models from weigh-in-motion data

Hybrid bayesian networks for traffic load models from weigh-in-motion data

Morales-Nápoles, O.
Steenbergen, R.D.J.M.

2012

The Weigh-in-Motion (WIM) systems are used, among other applications, in pavement and bridge engineering, in infrastructure monitoring and assessment and inspection and reinforcement strategies. In the Netherlands and some other countries, the video-WIM system was implemented for pre-selection, and for continuously monitoring overloads. The systems record pictures of vehicles analyzed in the system together with measurements for number of axles, inter axle separation, vehicle length, weight per axle vehicular speed and total vehicular weight. In one month the WIM system in the Netherlands stores approximately 150,000 registrations of trucks. Currently, the WIM system operates in eight locations in the Netherlands. Because of the nature of the traffic, the quantities measured are regarded as random variables. The dependence structure of the data of such complex systems as the traffic systems are also very complex. Thus, the aim would be to be able to represent the complex multidimensional-distribution with a model where the dependence may be explained in a clear way and different locations where the system operates may be treated simultaneously. Bayesian Networks (BNs) offer an alternative for a model with the characteristics listed above. In this paper we discuss the construction of a large scale BN and results concerning its ability to adequately represent the data. The paper places special attention in the construction of the model. Furthermore, ideas as to how the model may be extended to include locations where the WIM system does not operate are given. These ideas benefit from structured expert judgment techniques previously used to quantify large scale Hybrid Bayesian networks (HBNs) with success. Copyright © (2012) by IAPSAM & ESRA.

Building Engineering & Civil Engineering
SR - Structural Reliability
TS - Technical Sciences
Buildings and Infrastructure
Traffic
Built Environment
Bayesian networks
Bridge reliability
Design loads
Weigh-in-motion
Bayesian Networks (bns)
Bridge engineering
Bridge reliability
Design load
Expert judgment technique
Hybrid Bayesian networks
Infrastructure monitoring
Netherlands
Pre-selection
Reinforcement strategies
Traffic load model
Traffic systems
Weigh-in-motion datum
Weigh-in-motion systems
Axles
Bayesian networks
Hybrid sensors
Safety engineering
Weigh-in-motion (WIM)

http://resolver.tudelft.nl/uuid:365258dc-da65-4532-bab5-700c5d18e390

470043

9781622764365

11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012, PSAM11 ESREL 2012, 25 June 2012 through 29 June 2012, Helsinki, 701-710

Sponsors: Radiation and Nuclear Safety Authority (STUK); VTT Technical Research Centre of Finland; Aalto University; Fortum Corporation; Teollisuuden Voima Oyj (TVO)

conference paper