Transportation Engineering最新文献

Catastrophes like landslides have the potential to impair critical transportation infrastructure, particularly road networks. The hilly regions in the state of Kerala in India are particularly prone to hazards and changing climate conditions. During the monsoon season, landslides are common in the Western Ghats, and the intensity of adverse impacts is more severe due to its densely populated regions. The study area of this research is in Idukki district of Kerala, where over 60 % of the land is prone to landslides. The monsoon rains bring with them a slew of disastrous landslides in the region. Most of the roadways in the study area are often disrupted due to landslides. Landslide risk assessment (LRA) is a crucial component of research on adverse impacts of landslide. The use of Geographical information System (GIS) and Multi Criteria Decision Analysis (MCDA) using Analytical Hierarchical Process (AHP) for susceptibility mapping and hazard risk assessment assists in the identification of disaster-prone areas. The data collected by field surveys were used to confirm the study results and the high-risk zones of the region were identified and the risk maps are prepared for the region as well as for the road network in the region. The risk of landslides may be described as the possibility of negative repercussions on road network thereby adversely impacting the inhabitants of the region. The landslide risk assessment of the road network in a hilly region is carried out which gives important insights for risk management and for future planning of resilient development in the region. This study enhances the knowledge for management of road network risk and vulnerabilities in intricate hilly region settings by developing a thorough vulnerability analysis framework. The research advances by giving transportation engineers a useful quantitative tool for identifying the risk and vulnerabilities of road network and directing design strategies and mitigation measures to lessen the possible negative effects of disruptive events like landslides on road infrastructure. The research findings could be an input for policy makers to plan for alternative resilient strategies for landslide risk management in road networks. The rational methodology adopted here could be replicated for carrying out risk and vulnerability assessment in other landslide prone areas.

Assessing the condition of roads is crucial to the maintenance and rehabilitation process as a country's progress is closely linked to its transport infrastructure. Therefore, it is essential to have well-maintained roads and to be able to control and monitor them properly. Technological advancements have transformed the way pavement inspections are carried out. This study presents an innovative approach that combines stereo cameras and a GPS module for efficient and accurate data collection. This integration of low-cost technologies provides a detailed three-dimensional view of pavements, complemented by accurate geospatial information. The experimental results showed that the 3D images of pavement damage had a relative volume measurement error of 0.80 %. Unlike traditional systems such as LIDAR and ground-penetrating radar, which involve more expensive technologies, the proposed method offers a cost-effective solution. This methodology not only simplifies the inspection process but also improves the planning and execution of road maintenance and repair activities. Its low cost makes it a viable option for various projects and applications in road infrastructure.

In metropolitan areas, traffic congestion has become a prevalent challenge due to rapid urbanization and increased vehicle usage, adversely impacting mobility, productivity, and quality of life. Identifying and mitigating persistent traffic bottlenecks is crucial for developing efficient transportation systems and guiding infrastructure planning decisions. This research proposes an innovative data-driven methodology to pinpoint recurrent traffic bottlenecks in Tehran's extensive highway network, addressing the limitations of traditional traffic monitoring methods. Through data mining and image processing techniques applied to 16 months of traffic flow maps from Google Maps, diverse information is extracted, including traffic nodes, congestion hotspots, and locations with the longest queue lengths. The image processing approach involves color-based segmentation, pixel-level analysis, and machine learning algorithms to determine congestion levels across the highway network. The identified bottlenecks are validated against ground truth data from CCTV cameras, demonstrating a remarkable 92 % correlation for key identified points. The proposed approach leverages the power of advanced analytics to comprehensively analyze all major highways, including areas lacking CCTV infrastructure. The robust validation process reinforces the reliability of this data-driven solution in capturing real-world traffic dynamics. As urban mobility challenges escalate globally, the integration of diverse data sources and cutting-edge techniques will be instrumental in guiding intelligent transportation planning and policy decisions.

Travel-time prediction is a critical component of Intelligent Transportation Systems (ITS), offering vital information for tasks such as accident detection, congestion management, and traffic flow optimisation. Accurate predictions are highly dependent on the selection of relevant features. In this study, a two-stage methodology is proposed which consists of two layers of Optimisation Algorithm and one Data-Driven method (OA2DD) to enhance the accuracy and efficiency of travel-time prediction. The first stage involves an offline process where interconnected optimisation algorithms are employed to identify the optimal set of features and determine the most effective machine learning model architecture. In the second stage, the real-time process utilises the optimised model to predict travel times using new data from previously unseen parts of the dataset. The proposed OA2DD method was applied to a case study on the M50 motorway in Dublin. Results show that OA2DD improves the convergence curve and reduces the number of selected features by up to 50 %, leading to a 56 % reduction in computational costs. Furthermore, using the selected features from OA2DD, reduced the prediction error by up to 29 % compared to the full feature set and other feature selection methods, demonstrating the method's effectiveness and robustness.

This paper presents a detailed accident investigation into incidents involving high-speed vehicles, particularly supersonic and hypersonic platforms. Examining challenges related to engine performance, structural integrity, and economic aspects in military aerospace, the study emphasizes the importance of real-time health monitoring systems. A key highlight is the exploration of how these systems support the autonomy of hypersonic vehicle. While recognizing challenges related to sensitive technologies and data, the paper outlines research directions encompassing human factors, simulation and training programs, and policy advocacy for integrating high-speed aircraft into existing airspaces. In summary, this research contributes valuable insights to the understanding of high-speed aviation accidents, with implications for improving safety and efficiency in the future and ultimately shows that high-speed aviation safety cannot be treated in the same manner as its subsonic counterpart.