Use of regression trees to predict overweight trucks from historical weigh-in-motion data

The traffic of overloaded trucks is a critical problem in highways.It affects pavement performance life,reduces the service life of bridges,and has a negative impact on road safety,average speed and level of service.There are several practices to prevent the truck overloading issue,i.e.,enforcement activities to verify the truck’s compliance with the legal weight limits.This paper investigates the development of a method that uses available weigh-in-motion(WIM)data to identify overloaded truck weight and travel patterns.The proposed approach is based on regression trees method,a simple and easily understandable analytic tool used to build prediction models from a large set of data.An overall analysis of the overloaded truck regression tree model shows that the most important variable to classify and predict overloading is the truck type.Regarding the axle overloading,the most significant variable is the time of the day(most of the overloaded trucks travel at late night or early morning).The regression tree results can be used to optimize the efficiency of administration activities by planning truck enforcement operations based on the more critical scenarios.Also,the results improve the knowledge about the load characteristics of trucks,which can lead to more effective pavement management systems and more assertive pavement structure designs.

Pavement markings:identification of relevant covariates and controllable factors of retroreflectivity performance as a road safety measureáã

In this paper we present relevant contributions and important features related to the study of the retroreflectivity performance of pavement markings.The contribution of this paper is threefold.First,we propose an artificial scheme to allow some randomization of the treatments owing to several restrictions imposed on the choice of the experimental units.It is an experiment involving one fixed factor(three types of materials)in a randomized block design executed on a high-traffic-volume highway.Under this condition,the traffic volume works as a stress factor and the degradation of the retroreflectivity of pavement markings is faster than the degradation on rural roads or streets.This is related to the second contribution:the possibility of a reduction of experimental time.The current experiment spent 20 weeks to collect the data.And finally a mixed linear model considering three random effects and several fixed effects is fitted and the most relevant effects pointed out.This study can help highway managers to improve road safety by scheduling the maintenance of pavement marks at the appropriate time,choosing adequate material for the pavement markings and applying the proposed artificial scheme in future studies.