Transportation Engineering最新文献

Cold Recycled Material (CRM) has emerged as a highly innovative road construction material, offering numerous advantages over conventional Hot Mix Asphalt (HMA), such as reduced construction costs, decreased energy consumption, and enhanced resistance to reflective cracking. CRM is primarily composed of Reclaimed Asphalt (RA) and bitumen emulsion, with the addition of cement as a co-binder. However, the presence of cement in CRM can lead to increased susceptibility to cracking under heavy traffic loads. In this study, the fracture-fatigue behaviour of CRM modified with by-product fillers was investigated in order to improve the overall performance of CRM mixtures. The Cyclic Indirect Tensile Fatigue test (CIT-CY) and Semi-Circular Bending (SCB) fracture tests were conducted on the CRM mixtures and mortars, respectively. The findings reveal that CRM materials incorporating specific fillers demonstrated a balanced combination of fatigue and fracture resistance, indicating the potential for these modified CRMs to enhance road construction applications.

Air traffic is exhibiting the characteristics of large flow, strong coupling, and high time variation. Therefore, the complex network of air traffic is more vulnerable to disturbances. When it is disturbed, the failure of some nodes spreads through dependency relationships in the network, resulting in cascade failure. In the event of a cascade failure, the network may quickly collapse until it is paralyzed, with widespread delays and flight cancellations. The current flow management and deployment methods still remain in the control-oriented stage, which is mainly completed by air traffic controls (ATCs), and lack of accurate flow adjustment and effective utilization of capacity. The whole air traffic system and its peripheral factors are intricate, so human and social factors must be integrated into the control and decision-making of the system. Considering engineering and social factors such as operation environment, social environment, personnel, rules, equipment, and information processing, we analyse the air traffic in a cyber-physical-social system (CPSS). To reflect the actual system behaviour rules, dynamic response, limit state, and so on, the corresponding computational experiment and comprehensive evaluation system are established. Based on neural networks and other technologies, a resource prediction scheme based on task demand is proposed for multi-dimensional resources such as airports, air routes, and ATC, to reduce the cost of system resource scheduling and improve resource utilization through resource prediction and adjustment. Finally, the accuracy of the proposed resource prediction algorithm is verified by theoretical analysis and simulation.

Road Traffic Accidents (RTAs) provide a substantial risk to both public safety and infrastructure within severe earthquake zones. This research aims to provide a thorough analysis of the many elements that contribute to RTAs in the area. Although research on RTAs has been conducted in numerous settings, there is a lack of localized research specifically focused on developing countries. Therefore, this study is necessary to address this gap in literature. Our study focuses on examining the distinct issues and elements that are particular to this specific location. The objective is to provide customized solutions that effectively reduce the occurrence of accidents. The objective of this study is to examine the status of RTAs, ascertain the main factors contributing to RTAs, and provide viable approaches to improve road safety. A comprehensive study of data was undertaken, which included the examination of key highways, accident-prone areas, and causes contributing to accidents. Insights were derived by the use of statistical analysis, hotspot mapping, and the categorization of incidents. The results revealed a distressing roadway are afflicted by a significant frequency of accidents, even those occurring on prominent thoroughfares. Excessive speed has been revealed as the primary contributing factor, closely followed by negligence and recklessness. The common factor seen in these events was the insufficient enforcement of traffic regulations. The research highlights several practical consequences. Firstly, it is advised to implement urgent actions such as the upgrading of the traffic database, the adoption of contemporary traffic management software, and the enforcement of rigorous traffic laws. The establishment of a comprehensive centralized database system is crucial in order to enhance the documenting and management of accidents. Furthermore, the use of cost-benefit analysis provides justification for the adoption of traffic calming measures in locations with a high incidence of accidents. Furthermore, the implementation of continuous road safety awareness efforts and the establishment of clear legislative guidelines pertaining to fatal accidents are imperative measures in promoting enhanced road safety.

Transportation networks within a region are vital. They link individuals from their starting points to their destinations. Nonetheless, even highly effective transportation systems may not ensure optimal performance, service quality, or fairness for users unless they are extensively interconnected and accessible to as many people as possible. In this context, we present a planning framework for complementing public transit (PT) systems with other cost-effective mobility systems, such as car-sharing (CS) systems, to improve performance in terms of connectivity and accessibility. These metrics are evaluated by using the suitably introduced Connectivity and Accessibility Index (CAI). Specifically, in the present research, we first introduce a novel methodology for assessing the connectivity and accessibility values of existing PT systems. Then, the proposed approach provides an optimization-based design model for a hybrid CS system comprising both one-way (OW) and two-way (TW) models. To evaluate the capabilities of the proposed approach, a real-world case study of the PT system of the city of Trento (Italy) is evaluated.

Diesel engine parameters, such as fuel and its additives, play an essential role in minimising the effects of engine vibration. This study aimed to use artificial neural networks (ANN) to model and analyse diesel engine vibration characteristics at different engine speeds using NH₃ as an additive in hazelnut (HD), peanut (PD), and waste-cooking oil (WD) biodiesels. The results showed good correlations between the ANN models and experimental results using regression analysis methods. The ANN models for diesel engines showed high accuracy. The ANN models indicated that a 5 % NH₃ additive decreased engine vibration for HD and PD.

In comparison, 10 % and 15 % NH₃ additive ratios increased engine vibration for HD, PD, and WD due to low combustion quality. The lowest vibration levels occurred with P100, P95A5, P90A10, and P85A15 at 1200 rpm. H100 and H95A5 produced the highest diesel engine resultant vibration (DERV) values. All ANN models generated the lowest and highest DERV values at 1200 rpm and 2100 rpm, respectively. The RMS method showed that H95A5, P85A15, and W85A15 contributed the most to diesel engine vibration. Using a low amount of NH₃ additive positively affected DERV for HD and PD but not for WD.

We investigated the effectiveness of an LED-based smart mid-block crosswalk system in mitigating the detrimental effects of driver engagement in non-driving-related tasks (NDRTs) with behavioural, performance, and subjective measurements. We designed a 2 (Crosswalk: smart vs conventional) by 2 (Task complexity: low vs. high NDRT) within-subjects experiment. Thirty-six drivers drove along four urban scenarios in a static driving simulator. We collected data on driving behaviour (speed, reaction distance), and safety (minimum time-to-collision [MTTC]), as well as subjective driver ratings on the perceived task load and their trust in the technology used, and performance levels achieved while performing the NDRTs.

Behavioural and performance observations showed that the smart mid-block crosswalk resulted in greater reaction distances and MTTC values when drivers interacted with pedestrians, thus indicating improved safety. Remarkably, the results also revealed that increased NDRT complexity does not negatively affect the smart crosswalk effectiveness in terms of driver-pedestrian collision prevention (i.e., MTTC does not decrease significantly). However, the NDRT complexity influenced driving performance in terms of speed and reaction distance at brake pedal pressure, with drivers exhibiting lower speeds and lower reaction distances with higher task loads. Moreover, the subjective ratings and performance levels while performing a NDRT reflected the experimental manipulation, with drivers perceiving higher task loads and performing worse in the higher NDRT complexity condition. Overall, the smart mid-block crosswalk led to a safer driver-pedestrian interaction compared to conventional crosswalks and achieved a good acceptance level both of which augur well for the widespread future installation of this technology.

Safety studies have indicated strong correlation between road friction and accident risk, with a dramatic increase in accident when friction drops below certain threshold. For this reason, managing pavement skid resistance is an important mean to reduce crashes. Unfortunately, during the pavement lifespan, skid resistance undergoes to deterioration due to several factors (traffic wear, weathering and aging). The correct management of road pavements implies the knowledge of the performance evolution, obtained both with monitoring and degradation models, however, among those latter available in literature, very few explored the influence of traffic vehicles in terms of type and travel mode. In this paper a new methodology combining the use of road sectioning schemes with a traffic damage criterion based on the dissipated energy at the tire-road pavement contact, for the development of degradation curves from experimental data collected on roads with different traffic, in terms of volumes, vehicle composition and motion conditions, is presented. The methodology has been validated to an open graded bituminous surface course (OGSC) on urban motorway and obtained degradation models have been also compared with those provided by a traditional degradation modelling approach highlighting the superior performance of the proposed approach.

Intelligent Transportation Systems are rapidly expanding to meet the growing demand for safer, more efficient, and sustainable transportation solutions. These systems encompass various applications, from traffic management and control to autonomous vehicles, aiming to enhance mobility experiences while addressing urbanization challenges. This paper examines key components of Intelligent Transportation Systems, including Vehicular Ad-hoc Networks, Intelligent Traffic Lights, Virtual Traffic Lights, and Mobility Prediction, emphasizing their role in improving transportation efficiency, safety, and sustainability. It explores recent advancements in communication systems that enable real-time Intelligent Transportation Systems operations, contributing to the realization of environmentally friendly smart cities.Moreover, the paper addresses security challenges associated with Intelligent Transportation Systems deployment, particularly concerning public transit privacy, and presents case studies illustrating the benefits of Intelligent Transportation Systems integration in specific urban areas, emphasizing its role in fostering Sustainable Smart Cities. Additionally, it examines proactive initiatives by automotive manufacturers in adhering to Intelligent Transportation Systems standards, ensuring mutual benefits for drivers and urban centers.

The prediction of delays and their reduction in all modes of passenger transportation, especially rail transportation, is of great importance and annually attracts the attention of many researchers. Train delays can be anticipated by predicting the arrival times of trains at stations. In this paper, a train operated by Raja Company, which travels daily on the Tehran-Mashhad route, has been investigated. This train route consists of 50 stations, of which five main stations, including Tehran, Garmsar, Semnan, Shahrud, and Mashhad, have been selected to predict the train's arrival and departure times at each of these stations. For this purpose, data related to the train timetable and the operations carried out at these five main stations over three years from 2018 to 2020 have been collected. Then, modeling was conducted to predict real-time arrival and departure times for each of these stations. Artificial neural networks, random forest regression, gradient boosting regression, and extreme gradient boosting regression were used for prediction modeling. After evaluating these models, the approach that yielded the best results based on the experimental data was selected for predicting the arrival and departure times at each station.

Since driving while drowsy is a significant cause of vehicle accidents, road safety could be improved if more effective methods were available for improving driver alertness. The present paper investigated whether it is possible to improve alertness via a wearable device that applied somatosensory vibration to the driver's wrist. The vibration used modulation frequencies ranging from 12 Hz to 50 Hz and a carrier frequency of 250 Hz. Random on-and-off intervals and variable vibration amplitudes were used to minimise sensory adaptation. Fifteen participants undertook a sixty-minute simulated driving task that caused drowsiness. Karolinska Sleepiness Scale (KSS), Power Spectral Density (PSD) and Higuchi's Fractal Dimension (HFD) analyses of brainwave signals were used to identify variations in driver alertness. The vibration stimulus was found to significantly improve alertness when compared to a no-vibration condition. Participants experienced an immediate improvement in alertness that reached significance within 9 min and was then sustained at the level seen at the beginning of the experiment, indicating a full restoration of alertness. This result demonstrates that wearable vibration devices have the potential to improve alertness in drivers.