Vehicular loads hazard mapping through a Bayesian Network in the State of Mexico

Abstract

Traffic counts collect information that is valuable, for example, in bridge and road design or maintenance processes. The average daily traffic volume is often the most collected measure of vehicular traffic, which is used in the design or assessment of major highways. Permanent control stations, situated in key locations of the highway network, gather data the entire year. However, one of the disadvantages of traffic count data is that most counters used, do not measure total vehicle weight and axle load data. Traffic counts display only the classification of vehicles, traffic volume, average daily traffic, and annual average daily traffic. Axle loads on the other hand are required, for example, as input in the design of pavement and new bridges, and the reliability assessment of existing ones. Weigh-in-motion (WIM) systems are usually used to collect vehicle load data. The State of Mexico (in central Mexico) has 115 permanent vehicle counting stations with 745 traffic counting points in its federally administered road network. However, due to the lack of WIM stations, it is not possible to obtain axle load data. In this paper, a Bayesian Network (BN) quantified with data from WIM stations in the Netherlands is used to describe the weight and length distribution of heavy vehicles registered in the permanent vehicle counting stations of the State of Mexico federal highways. The Dutch and Mexican vehicle types are matched according to similar characteristics. Later, synthetic WIM observations from the BN model are analysed through extreme value theory and vehicle loads with selected return periods are computed for all study counting points. The outcome is a mapping methodology with a linked database. The traffic volumes and extreme loads can then be easily found and compared with other highways in the network. This work shows that hazard maps can be implemented to provide importantly and summarized information to understand the risks of extreme traffic loads and to help in the reliability assessment and maintenance strategies of pavements and bridges.