1,721,079 research outputs found
Estimating the frequency of traffic overloading on road bridges
Full text linkLoad limits, which appear to be routinely exceeded by trucks, occasionally result in road bridge failures. Therefore, predicting failures is crucial for safeguarding road safety. Past studies have largely focused on forecasting bridge failure event probability using the reliability analysis method, whilst occasionally accounting for vehicular overloading effects. Only recently, a study has investigated design traffic overloading event frequency using generalised linear regression models (GLRMs), including a power component and negative binomial regressions (NBRs). However, as far as the authors know, artificial neural network models (ANNMs) have never been applied to this field. This paper is an attempt to fill in these gaps. First a frequency-based metric of traffic overloading was adopted as a driver of failure probability. Second, two alternative ‘frequency’ models were specified, calibrated, and validated. The former was based on a GLRM, the latter on ANNMs. Then, these models were compared using regression plots (RPs), measures of errors (MoEs) and the ratio between the number of observed vs predicted design load overcoming events to evaluate their performance. The models analysed more than 2 million weigh-in-motion (WIM) data records from a pilot station on a bridge on a heavily used ring road in Brescia (Italy). Results showed that ANNMs outperformed GLRMs. ANNMs have a higher correlation coefficient (between predicted and target frequencies), lower MoEs, and a closer-to-unity ratio (between predicted and target frequencies). These findings may increase prediction accuracy of design traffic overloading events and give road authorities more effective traffic management to protect bridges from load hazards
Traffic Hazards on Main Road’s Bridges: Real-Time Estimating and Managing the Overload Risk
No full textThe risk associated with extreme traffic loads on bridges has seldom been explored, with State-of-the-art evaluation methods being time-consuming and unsuitable for fast risk management. Traditional risk management advocates optimizing offline bridge maintenance plans. In contrast, novel approaches that can assess and manage this risk live through Intelligent Transportation Systems (ITSs) are lacking. This study addresses these gaps with a three-block framework. It utilizes Weigh-In-Motion (WIM) systems for collecting bridge-specific traffic load data, develops a probabilistic Risk Prediction Model for estimating the frequency and severity of overloading events drawing on current Structural Design Codes (SDCs), and simulates an ITS-based architecture for implementing management actions. The framework was tested on 2.5M WIM raw data records gathered from the ring road of Brescia, Italy. Results showed that bridge design loads were overcome more frequently than SDCs prescriptions, and violations of the Traffic Code mass limit significantly affected risk predictions. These findings underscore the need for increased attention when issuing permits for extremely overweighted vehicles and encourage enforcement strategies implemented by ITS-based architectures for real-time risk management
Colorimetric and photometric characterisation of clear and coloured pavements for urban spaces
No full textA widespread use of clear and coloured pavements was internationally encouraged in recent years for different urban spaces. Unlike traditional "black" pavements, the design of the appropriate colour and visibility represent key factors for these wearing courses in environmental and monumental valuable contexts, where the aesthetic and the appearance of the finished layer represent essential characteristics. Operational methodologies and experimental measurements for the colorimetric and photometric characterisation of coloured pavements were presented. Due to cost consideration, thin pigmented slurry seals produced with two different clear synthetic emulsions and three inorganic pigments (red, green and blue) were taken into account. Physical parameters were monitored both at the construction phase and after an accelerated laboratory weathering, to evaluate the UV photo-oxidative effects. The obtained results highlighted three main findings: importance of a correct mix design, progressive colour change over UV exposition time and possible conversion of photometric parameters measured with different instruments
Road Network Safety Screening of County Wide Road Network. The Case of the Province of Brescia (Northern Italy)
No full textAlthough EU roads are the safest in the world, the target of halving the road deaths by 2020 was not achieved. Road Infrastructure Safety Management procedures are key to improve road safety performances, and their implementation is required for primary road networks. Specifically, Road Network Screening enables to apply a wide-level analysis to identify the most critical segments of the network, and direct in-depth investigations more efficiently. Crash prediction models (CPMs) are extremely useful tools for quantitative road safety analysis, and road network screening can greatly benefit from their application. The Highway Safety Manual (HSM) is one of the main references worldwide, but the reported models are subjected to transferability issues due to their site-specific formulation. Most of previous studies on CPMs focused on HSM calibration models or investigated the effect of several factors over the crash frequency on specific road type. However, to our knowledge, Europewide few attempts were performed to develop a road network screening by mean of CPM. This paper covers these gaps by developing a specific CPMs to screen county-road network and identify most critical segment. The model was applied to the main road network of the Province of Brescia (Northern Italy). Few, but significant variables were identified in the model and maps were produced to rank the road network based on the crash frequency values. This model can serve as a relevant decision support tool for all bodies responsible in the definition of road safety interventions and related resources allocation, prior than crashes occur
Estimating operating speed for county road segments – Evidence from Italy
Full text linkVehicle operating speed is a crucial factor for road safety, as it strictly affects occurrence and severity of crashes. Usually, 85th percentile of the operating speed distributions (i.e., V85) in free-flow traffic condition is widely accepted as a base value of consistency evaluation for homogenous portion of existing roads. Although the computation of V85 is simple, many road authorities cannot collect speed data for each road. Therefore, providing prediction models could be a useful tool to investigate the relationship between V85 and road characteristics. The literature proposed several models to account it. However, to the best of our knowledge, the effects of some road geometric characteristics, road markings and signs, traffic data, type of terrain and the simultaneous consideration of different road categories on the V85 prediction were not completely analyzed. This paper fills this gap by isolating key variables that mostly affect V85. In doing so, 60 000+ car spot speed data were collected along the county road network of the province of Brescia (Italy), and then processed by multiple regression models. The main findings show that V85 increases owing to the presence of a wider or paved shoulder, visible road median markings, a higher number of lanes and a higher percentage of cars with respect to the total traffic flow. Conversely, V85 decreases as the road axis curvature, the number of accesses and rate of forbidden overtaking increase. In addition, the presence of visible road external markings and the surrounding mountainous terrain contribute to decreasing V85. The overall findings may support road authorities to verify roads’ operating conditions and, possibly, adjust the speed limits, especially for existing roads
Bus crash risk evaluation: An adjusted framework and its application in a real network
Full text linkGreater attention to bus safety can lead to relevant benefits for public transport companies in terms of higher service performance, reliability, and lower insurance costs. Therefore, measuring the crash risk on bus routes provides an opportunity to improve the safety performance of transit operators. Previous research has explored the effects of many factors regarding the frequency and severity of bus crashes, whereas only a handful of studies have defined some crash risk indexes. Conversely, to the best of our knowledge, almost no research has been done regarding the crash risk in the bus transit network that integrates frequency, severity, and the exposure factors. This paper proposes a new framework to assess the crash risk for each transit bus route by the integration of safety factors, prediction models and risk methods. More precisely, this framework identifies several safety factors and specifies the risk components in terms of frequency, severity and exposure factors that may affect bus crashes. Then, it models their relationships to build a bus crash risk function. Lastly, according to the values returned by the previous function, the crash risk for each route is computed and a safety performance ranking for each route is provided. The feasibility of this framework is demonstrated in a real case study by using bus crash data provided by a mid-sized Italian bus operator. The findings show that transit managers could implement this framework in a road traffic safety management system to evaluate the risk of crashes on routes, monitor the safety performance of each route and qualify each route according to recent safety norms
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