Bridges are amongst the most vulnerable elements of road networks. Since vehicles with permits and that are illegally overweight can pose a threat to bridge safety, implementation of monitoring systems for these vehicles has become mandatory. Weigh-In-Motion (WIM) systems are an efficient weight-enforcement solution because they collect data on vehicular mass and other parameters in real time. However, installation and maintenance costs frequently hinder a widespread operational use of WIM systems. Thus, while WIM devices are widespread in America and Asia, they have not yet been applied broadly in Europe. Additionally, the relationships among Gross Vehicle Mass (GVM) and other vehicular characteristics have only been investigated in a fragmentary manner. This real-world case study adopted a data set of 14,800+ overweight vehicles in heavily industrialised northern Italy based on two-months of raw data from WIM devices to: (i) investigate the probability density function on the main characteristics of overweight vehicles, and (ii) to provide Road Authorities (RAs) with a Multiple Linear Regression (MLR) model to predict overweight vehicles’ GVM in a more cost-effective way than placing WIM systems on each road segment with bridges. Probability density functions revealed the existence of different vehicle typologies, including lorries with permits and that are illegally overweight, whereas inferential analysis showed MLR's high performance in predicting vehicles’ GVM. Minimum axle distance and total axle number were factors that had greater positive effects on the predicted GVM respectively. Conversely, by increasing vehicles’ width and length, a reduction in GVM was predicted. These findings could help support practitioners, RAs, and governments to implement rational and less resource consuming administration policies for their bridge assets.
Monitoring vehicles with permits and that are illegally overweight on bridges using Weigh-In-Motion (WIM) devices: A case study from Brescia
Ventura R.;Barabino B.;Vetturi D.;Maternini G.
2023-01-01
Abstract
Bridges are amongst the most vulnerable elements of road networks. Since vehicles with permits and that are illegally overweight can pose a threat to bridge safety, implementation of monitoring systems for these vehicles has become mandatory. Weigh-In-Motion (WIM) systems are an efficient weight-enforcement solution because they collect data on vehicular mass and other parameters in real time. However, installation and maintenance costs frequently hinder a widespread operational use of WIM systems. Thus, while WIM devices are widespread in America and Asia, they have not yet been applied broadly in Europe. Additionally, the relationships among Gross Vehicle Mass (GVM) and other vehicular characteristics have only been investigated in a fragmentary manner. This real-world case study adopted a data set of 14,800+ overweight vehicles in heavily industrialised northern Italy based on two-months of raw data from WIM devices to: (i) investigate the probability density function on the main characteristics of overweight vehicles, and (ii) to provide Road Authorities (RAs) with a Multiple Linear Regression (MLR) model to predict overweight vehicles’ GVM in a more cost-effective way than placing WIM systems on each road segment with bridges. Probability density functions revealed the existence of different vehicle typologies, including lorries with permits and that are illegally overweight, whereas inferential analysis showed MLR's high performance in predicting vehicles’ GVM. Minimum axle distance and total axle number were factors that had greater positive effects on the predicted GVM respectively. Conversely, by increasing vehicles’ width and length, a reduction in GVM was predicted. These findings could help support practitioners, RAs, and governments to implement rational and less resource consuming administration policies for their bridge assets.File | Dimensione | Formato | |
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