Search Results (6)

Building resilient infrastructure is at the core of sustainable development, as evidenced by the UN Sustainable Development Goal 9. In fact, the effective operation of road networks is crucial and strategic for the smooth functioning of a nation’s economy. This is also fundamental from a sustainability perspective, as efficient transportation networks reduce traffic, and thus, their environmental impact. However, road networks are constantly at risk of traffic closure and/or limitations due to a plurality of natural hazards. These environmental stressors, among other factors like aging and degradation of structural materials, negatively affect the disaster resilience of both single components and the system of road networks. However, the estimation of such resilience indices requires a broad multidisciplinary vision. In this work, a framework for application to large road networks is delineated. In the proposed methodology, seismic hazard is considered, and its corresponding impacts on road networks are evaluated. The assessment encompasses not only the road network system (including squares, roads, bridges, and viaducts) but also the buildings that are located in the urban area and interact with the network. In this context, the probability that buildings will suffer seismic-induced collapse and produce partial or total obstruction of roads is considered. This scheme is designed for implementation in different geographical contexts using geo-referenced data that include information about specific risks and alternative rerouting options. The proposed methodology is expected to support the mitigation of functionality loss in road networks after disasters, contributing to both the economic and social dimensions of sustainability. To evaluate the methodology, two case studies focusing specifically on hospital-to-hospital connections were conducted in Naples and Turin, Italy. However, the proposed approach is versatile and can be extended to other critical infrastructures, such as theatres, stadiums, and educational facilities. Full article

The constant evolution of many urban areas ultimately reaches a point where the current infrastructure cannot further serve the needs of citizens. In the case of transport networks, congested roads, increased delay, and low level of service are among the indicators of a need for road infrastructure upgrade. Thessaloniki is the second-largest city in Greece with a population of over 1 million inhabitants in its metropolitan area. Currently, a significant share of the city’s traffic demand is served via its ring road, whose capacity is set to be enhanced through the construction of a flyover highway with the simultaneous upgrade of the existing ring road. The current study aims at investigating the key factors determining the final route choice of drivers between the two road axes. To that end, data from a combined revealed and stated preference survey targeting car drivers were collected, which were later exploited as the basis for the development of binary route choice regression and machine learning models. The results reveal that drivers’ choice is affected by criteria such as total travel time, the probability of accident occurrence, and closure time due to accident. The results of this paper could prove beneficial to transport researchers in forecasting drivers’ behavior in terms of route choice and to practitioners during the planning phase of similar infrastructure projects. Full article

High-precision and robust localization in GNSS-denied areas is crucial for autonomous vehicles and robots. Most state-of-the-art localization methods are based on simultaneous localization and mapping (SLAM) with a camera or light detection and ranging (LiDAR). However, SLAM will suffer from drift during long-term running without loop closure or prior constraints. Lightweight, high-precision environmental maps have gradually become an indispensable part of future autonomous driving. In order to solve the problem of real-time global localization for autonomous vehicles and robots, we propose a precise and robust LiDAR localization system based on a pre-built, occupied high-definition (HD) map called the Extended QuadTree (EQT) map. It makes use of a planar quadtree for block division and a Z-sequence index structure within the block cells. Then, a four-level occupancy probability cell value model is adopted. It will save about eight times the storage space compared with Google Cartographer, and the EQT map can be extended to store other information. For efficient scan-to-map matching with our specialized EQT map, the Bursa linearized model is used in the Gauss–Newton iteration of our algorithm, which makes the calculation of partial derivatives fast. All the above improvements lead to optimal storage and efficient querying for real-time scan-to-map matching localization. Field tests in an industrial park and road environment prove that positioning accuracy of about 6–13 cm and attitude accuracy of about 0.15° were achieved using a VLP-16 LiDAR. They also show that the method proposed in this paper is significantly better than the NDT method. For the long and narrow environment of an underground mine tunnel, high-resolution maps are also helpful for accurate and robust localization. Full article

In the former quarry near Spitz an der Donau (Austria), many rockfalls have occurred during operation as well as after closure. They have endangered a railway, the Wachau bicycle route, an important road, and the left Danube riverbank. Thus, future detachment scenarios were investigated and, in addition, weather and occurrence statistics were analyzed to determine the occurrence probabilities of these scenarios. Simulations of possible future rockslides were performed using the Distinct Element Code 3DEC in order to estimate the damage caused by these events. Based on these results, the risks of the scenarios were calculated according to the definition of risk as the product of damage and occurrence probability. By this means, the profitability of mitigation measures (e.g., a massive retaining structure fixed to the ground by bored piles) can be evaluated. Full article

The impact that trucks have on crash severity has long been a concern in crash analysis literature. Furthermore, if a truck crash happens in a tunnel, this would result in more serious casualties due to closure and the complexity of the tunnel. However, no studies have been reported to analyze traffic crashes that happened in tunnels and develop crash databases and statistical models to explore the influence of contributing factors on tunnel truck crashes. This paper summarizes a study that aims to examine the impact of risk factors such as driver factor, environmental factor, vehicle factor, and tunnel factor on truck crashes injury propensity based on tunnel crashes data obtained from Shanghai, China. An ordered logit model was developed to analyze injury crashes and property damage only crashes. The driver factor, environmental factor, vehicle factor, and tunnel factor were explored to identify the relationship between these factors and crashes and the severity of crashes. Results show that increased injury severity is associated with driver factors, such as male drivers, older drivers, fatigue driving, drunkenness, safety belt used improperly, and unfamiliarity with vehicles. Late night (00:00–06:59) and afternoon rushing hours (16:30–18:59), weekdays, snow or icy road conditions, combination truck, overload, and single vehicle were also found to significantly increase the probability of injury severity. In addition, tunnel factors including two lanes, high speed limits (≥80 km/h), zone 3, extra-long tunnels (over 3000 m) are also significantly associated with a higher risk of severe injury. So, the gender, age of driver, mid-night to dawn and afternoon peak hours, weekdays, snowy or icy road conditions, the interior zone of a tunnel, the combination truck, overloaded trucks, and extra-long tunnels are associated with higher crash severity. Identification of these contributing factors for tunnel truck crashes can provide valuable information to help with new and improved tunnel safety control measures. Full article

Fragility curves are essential tools to quantitatively assess the physical vulnerability of structures and infrastructures at risk for a given seismic hazard. They describe the probability of exceeding a given performance level under earthquake excitation, and are usually defined by a lognormal probability distribution function. Although debris from damaged buildings adjacent to road edges is the main cause of urban mobility disruption, studies on the fragility curves development for infrastructures subject to seismic actions focus on geotechnical effects, and do not analyze this type of road blockage. The article proposes an analytical procedure to construct fragility curves for urban road networks. It is based on the construction of debris graphs and the use of an appropriate fitting technique. For a given seismic intensity measure level, the developed fragility curves express the probability that the road is open or closed to the transit of emergency vehicles after debris fall. Therefore, the performance level is defined in terms of the width of the road pavement that remains free after the debris fall, or the width of the debris heap on the road pavement. Finally, the proposed framework is tested with real data of the main street in Amatrice, and the results are presented and discussed. Full article