Transportation Engineering最新文献

Nowadays, the development of technologies that improve airspace operation in many aspects is essential since the importance of air transportation for society is increasing. The airspace, although, may become more complex considering the integration of these aircraft due to the issues regarding the social acceptance of autonomous systems (e.g., familiarity between Air Traffic Controller - ATCo - and Unmanned Aircraft System - UAS) and the uncertainty in terms of operation (e.g., hardware failures, software failures, interfaces failures, and misunderstanding of instructions). However, standard procedures (e.g., landing procedures) may not be followed in complex situations due to safety constraints (e.g., loss of minimum aircraft separation). As a result, ATCos play an essential role in maintaining appropriate levels of safety and efficiency by conducting aircraft using Vectoring Points (VPs). Hence, ATCos must be trained to deal with such challenging scenarios, especially in resource-constrained regions, e.g., in the final sector of the Terminal Control Area (TMA), where the aircraft are guided to the landing phase. The primary goal of this research is to propose a framework for training Air Traffic Controllers (ATCos) to deal with complex situations (e.g., considering many aircraft as well as severe weather conditions) in the final sector considering the UAS integration into the National Airspace System (NAS). This approach is divided into a set of modules for (1) proposing the training scenarios, (2) proposing solutions, and (3) evaluating the quality and feasibility of the solutions proposed. The aspects evaluated in the solutions provided for the proposed scenarios are ATCo workload and efficiency.

Various numerical methods are commonly used to model the loading response of traffic infrastructures. However, the broader implementation of these methods into practice is typically limited by the high computing cost or technical complexity of the models. Computationally lighter methods are especially needed when a substantial number of calculations must be performed. Hence, this paper introduces a new simplified approach to estimating the loading response of different traffic infrastructures, e.g. roads and railways. This approach is based on the novel approximate solution for the vertical and shear stress fields caused by a uniformly distributed circular surface load. The presented theory has been validated using versatile field measurement data from different types of road structures under varied loading conditions. In the verification calculations, both the vertical stresses within the structure and the deflection of the road surface were examined. The findings indicate that the model closely aligns with the behavior observed in actual structures. The approach presented in this paper enables a quick and easy determination of the loading response of multi-layer traffic infrastructures, providing a new practical calculation tool for researchers and practitioners.

The main goal of this article is to present a method for estimating accidents in all types of level crossings in the city by using a type of statistical model. This study was conducted on 140 3-line and 4-line level intersections, both with and without traffic lights, in Tehran, whose accident information was collected in a period of three years (2019–2021) from police accident system reports and other required data. The data has been collected by field methods. To build the required model, the multiplicative form of the Riha equation, which is a comprehensive equation for predicting traffic accidents, has been used. In this equation, descriptive variables are selected in two continuous and binary forms, which are pre-determined by a functional form. They are obtained using the integral-differential method and are introduced to the mentioned equation. The results showed that in urban level intersections, the volume of traffic entering the intersection has the greatest effect on the amount of accidents, and for a certain volume of traffic, the effects of the geometric shape of the intersection (3-lane and 4-lane) and the type of traffic control (with or without lights) in the amount accidents are insignificant and negligible. According to the obtained results, the level of traffic safety can be significantly increased by controlling the volume of traffic entering intersections.

Steel structures are exposed to multiple environmental impacts that lead to reduced durability. Organic paint systems are a resilient mechanism to protect hydraulic steel structures from corrosion. Coatings lead to a decreased corrosion progress at damaged areas. In this work, we investigated the time dependency of undercoating corrosion in the neutral salt spray test in detail by conducting a long-term experiment. Thereby, the fundamental model of corrosion progress was investigated, leading to an increased reliability in approval procedures of coatings and in the choice of test parameters.

Additionally, repair materials for on-site repair of damaged coatings were investigated in weathering tests to compare their performance with conventional coatings. These materials are used for short-term repairs of small damaged areas in order to postpone replacement of ailing structures, thus enhancing the durability of steel structures.

The integration of intelligent transportation systems (ITS) in urban infrastructure has increased significantly, and one of the most notable examples is the development of autonomous vehicles (AVs). AVs have become a solution to various driving problems, such as performing complete overtaking maneuvers (OM). These maneuvers are considered one of the most difficult to carry out. Although there are many papers on OM maneuvers with AVs, not all of these studies focus on the performance of complete OM. Therefore, a comprehensive and scientific exploration of the analysis of complete OM with AVs is lacking. This study aims to address this gap through a systematic review following the PRISMA protocol as methodology, examining 51 articles published between 2008 and 2024 in the Science Direct, Scopus, and Web of Science (WOS) databases. The results showed that methodologies such as Model Predictive Control (MPC), Fuzzy Control (FC), and sigmoidal functions are used most to perform complete OM with AVs. MPC is the most relevant methodology due to its capability to be combined with other control systems and its predictive ability. FC and sigmoidal functions are also appropriate for dealing with inaccuracies and non-linear features associated with overtaking maneuvers. However, there are still complications related to computational complexity and sensor limitations. Future studies should consider and integrate the development of comprehensive systems that combine multiple real-time control methodologies and offer a robust combination of sensors. This review contributes to teaching studies that reveal promising opportunities for complete OM with AVs research and provide access to methodologies that could be optimized based on technological advances and emerging needs of the ITS sector. Addressing these knowledge gaps is essential to achieving safer and more efficient overtaking maneuvers by AVs.

Disruptions in road networks can significantly impact traffic flow, necessitating the identification and mitigation of vulnerable components. This study presents a methodology to assess the vulnerability and robustness of road networks, addressing the research gap in comprehensive approaches that effectively evaluate both controlled intersections and uncontrolled/free-flow highway segments. The proposed framework employs traffic simulation tools to model road closures within designated study areas, focusing on networks in Qatar. Performance measures such as average control delay and volume to capacity ratios are utilized to calculate vulnerability indices for network components. The two-pronged approach quantifies the impact of simulated closures at both the component and network levels, revealing critical intersections and road segments that markedly affect network performance during disruptions. The findings contribute to the field by providing transportation engineers with an effective quantitative tool to detect vulnerabilities in their road network, guiding design plans and actions to mitigate the potential harmful impacts of disruptive events on transportation infrastructure. The methodology's scalability ensures its applicability to various operational contexts, offering significant insights for transportation planning and infrastructure resilience. By establishing a comprehensive vulnerability analysis framework, this study enhances the understanding and management of road network vulnerabilities in complex urban environments. Further research on integrating dynamic traffic assignment and real-time data is recommended to further enhance the predictive accuracy and applicability of the proposed framework, ultimately improving transportation infrastructure resilience.

Driver distraction is one of the main causes of road crashes worldwide, with a relevant number of collisions, some fatal, especially among middle-aged drivers. In 2019, the European Regulation EU 2019/2144 mandated the installation of Advanced Driver Distraction Warning (ADDW) devices on all new vehicles by 2026. After the confirmation of its effectiveness in motorway driving, this driving simulation study evaluated the effectiveness of an auditory ADDW device in mitigating distraction on urban roads. Thirty middle-aged participants (fifteen females, aged between 25 and 35) were asked to read and respond to text messages on their cell phones, regardless of the device alert. They all drove (i) without being distracted (baseline condition), (ii) while distracted, and (iii) while distracted but supported by the ADDW device. Each participant faced three scenarios: (i) interaction with pedestrians at a mid-block crosswalk, (ii) driving in free-flow conditions on a dual-carriageway arterial, and (iii) driving behind another vehicle.

The results revealed a degradation in driving performance and worsening safety conditions. In contrast to what was demonstrated for motorway driving, the ADDW device did not produce significant improvements in driving performance along urban roads. For drivers who continue to text while driving, no positive effect of the ADDW auditory device was observed. Unlike the motorway scenario, the urban environment produces a variety of stimuli that render distracted driving dangerous irrespective of the use of an ADDW.

Road traffic crashes and fatalities pose a significant global challenge, particularly in low- and middle-income countries (LMICs), where a major cause is consistently linked to speeding (i.e., excessive or inappropriate speeds). However, the existing body of evidence regarding the estimated contribution of speeding to crashes and fatalities, remains limited and is considered outdated. This paper bridges this knowledge gap by reviewing evidence on the contribution of speeding to crashes and fatalities. The review draws on a wide range of sources including peer-reviewed studies on the subject, road safety monitoring-reports and available data summaries (104 sources). Data reliability was confirmed by including studies based on or linked to primary sources like police records, and excluding those with poor quality, implausible results, or lacked references to primary sources. The included sources contained 37 estimates from high-income countries (HICs), and 67 estimates from LMICs. Globally, HIC and LMIC estimates of contribution were calculated by assigning weights based on the proportion of fatalities in each country under this study. The results indicated that speeding contributes to approximately 54 % of fatalities worldwide, 57 % in LMICs, and 28 % in HICs. This translates to a speeding-related death every 49 s, with a 95 % likelihood of occurring in LMICs. These findings carry significant implications for policymakers emphasizing the urgent need to prioritize interventions that reduce speeding and improve road safety. Investigating gaps in LMICs data sources is a critical priority. In-depth studies and speeding intervention evaluations will enhance our current understanding of speeding contribution to crashes and fatalities.

Roundabouts are widely embraced for their perceived safety advantages over other types of unsignalized intersections. However, there has been an observed increase in crash rates at roundabouts over time in Jordan. This paper delves into modeling traffic crash severity at roundabouts, considering various factors such as weather, lighting, vehicle characteristics, geometric features, and driver age and gender. To comprehensively analyze roundabout crashes in Jordan, we constructed rule-based classifiers and Random Forest models after balancing the dataset. Rule-based models offer interpretability, albeit with some simplicity trade-off, while Random Forest models provide deeper analysis but require additional explanation. Presenting both outputs to subject matter experts and policymakers facilitates a holistic understanding of factors contributing to roundabout crashes in Jordan. Subsequently, CN2 results revealed that injury severity crashes are influenced by the time of the day, driver age, day of the week, speed, and number of vehicles involved. On the other hand, property damage-only crashes are affected by the number of lanes, time of the day, type of driver fault, lighting conditions, speed, and day of the week. The RF model analysis unveiled crucial factors influencing crash severity in roundabouts, notably the varying impact of driver age, time of day, the number of vehicles involved, seasonality, and vehicle speed. This proposed approach is promising, comprehensive, and not only enhances the understanding of roundabout crashes but also informs the development of effective, localized safety interventions.

Over the last decades, significant progress has been made and new approaches have been proposed for efficient collection of road condition data. Gravel roads are crucial for connecting urban and rural areas in Sweden, constituting a significant portion of the road network. Therefore, this study addresses the use of a developed Unmanned Aerial Vehicle (UAV)-based digital imaging system focusing on efficient collection of surface condition data over gravel roads.

The study focuses on in-situ profile measurements of a gravel road located in Trosa, Sweden, using three different profiling methods: UAV drone with RTK technology, Road Surface Tester (RST), and Rotary Laser Level (RLL) to explore the agreement between these methods.

The UAV drone, equipped with Real-Time Kinematic (RTK) technology, captures high-resolution images to produce detailed 3D surface models, overcoming the challenges posed by adverse weather conditions. Notable outcomes reveal RTK technology's stability, maintaining a steady 3D position accuracy below 2 cm. To enhance synchronization and comparison between different profiling methods, efforts should be made to standardize coordinate systems and measurement analysis software.

Minimum average absolute differences of 1.1 cm, 1 cm, and 0.7 cm were recorded for all profiles (from 1 m left to 1 m right of the road centerline) in the comparisons between UAV drone – RST, UAV drone – RLL, and RST – RLL methods, respectively. This underlines the significant advancement in UAV drone technology, enabling remarkably accurate measurements of vertical offsets for profiling the tested gravel road despite the high altitude at which the UAV drone operates.