Search

The common use of plastics in daily life has engendered gigantic amounts of waste plastics, which have a detrimental influence on the environment. Researchers have proposed several strategies for the recycling of waste plastics and their products. Among these, one strategy is to use them in the construction industry, particularly in asphaltic pavements. This study thus focused on the utilization of waste plastics, specifically Low-Density Polyethylene (LDPE) and High-Density Polyethylene (HDPE), as an additive in Asphalt Concrete (AC) mixes. Indirect Tensile Strength (ITS), Flow Time (FT), and Dynamic Modulus (DM) tests were used to investigate the moisture damage, permanent deformation, fatigue performance, and DM of the LDPE and HDPE modified AC mixes. According to the laboratory test results, the LDPE and HDPE modified AC mixes offer more resistance to moisture damage compared to the Control AC mixes. It was also concluded that asphalt mixes modified with the LDPE and HDPE attained 2.07 times and 1.26 times superior resistance to permanent deformation than the Control mixes. Furthermore, the LDPE and HDPE modified AC mixes outperformed the DM values of the Control mixes by 2.06 times and 1.41 times, respectively. In addition, it was determined that the LDPE and HDPE modified AC mixes exhibit 1.72 times and 1.39 times greater fatigue parameter values than their counterpart, suggesting superior fatigue resistance performance. The statistical models developed using Response Surface Methodology (RSM) revealed the highest values of the Coefficient of Determination (>0.80), adequate precision (>4.0), and lack-of-fit tests, indicating the model’s adequacy for predicting the response.

Pilot operational behaviour directly affects aviation safety, and exploring pilot behaviour can help decode, model, and predict pilot behavioural decisions. Therefore, it is necessary to recognize pilots’ states and predict the behaviours responsible for state changes. The purpose of this study was to investigate the relationship between pilot operating behaviour and brain activity. Using a flight simulator (DA42) and noninvasive functional near-infrared spectroscopy (fNIRS), changes in physiological signals were measured in 25 pilot cadets during different turning tasks. Twenty-five pilot cadets completed left and right turning tasks in a flight simulator, and the changes in blood oxygen concentration during different turning behaviours were analysed and modelled using machine learning models. Feature samples with significant differences ( $\text{p}< 0.05$ ) were extracted from the changes in oxyhaemoglobin concentration and selected for input into the SVM-linear classification model, the best results were achieved for the recognition of the pilot’s turning behaviour. After stratification based on feature importance, the SVM-RBF classification model obtained the highest accuracy (92.6%) after training with inputs containing a subset of features (60%) with the highest importance. In addition, the different turning behaviours of the pilots were closely related to activity in Brodmann area 17 (BA 17), BA 18 and BA 46. These findings provide insight into the neuroscientific mechanisms of pilot behaviour and provide an important physiological reference for improving pilot training.

Recent studies on microencapsulated self-healing cementitious materials have primarily focused on the particle size and preparation methods of the microcapsules. However, there has been limited attention paid to the microscopic aspects, such as the selection of curing agents and the curing duration of these materials. In this study, urea-formaldehyde resin/epoxy resin E-51 microcapsules were synthesized through in situ polymerization. This research investigates the feasibility of self-healing from a molecular mechanism perspective and evaluates the repair performance of microencapsulated self-healing cement mortar with varying microcapsule concentrations, curing agent types, and curing ages. The findings demonstrate that the microcapsule shells bond effectively with the cementitious matrix, with radial distribution function peaks all located within 3.5 Å. The incorporation of microcapsules enhanced the tensile strength of the modified cement mortar by 116.83% and increased the failure strain by 110%, indicating improved adhesion and mechanical properties. The restorative agent released from the microcapsule core provided greater strength after curing compared to the uncured state. Although the overall strength of the microencapsulated self-healing cement mortar decreased with higher microcapsule concentrations, the repair efficiency improved. The strength recovery rate of 28-day aged modified cement mortar had a significant improvement with the addition of X and Y curing agents, respectively.

The resurgence of passenger flows after the pandemic poses a significant challenge to the safe operation of rail transit. Therefore, adopting the waiting hall of an ultralarge railway station hub as an example, thermal radiation and evacuation simulations were conducted by the Fire Dynamics Simulator and Pathfinder, respectively. Island-style shops, known for their high crowd density and fire load, were defined as fire sources, and the effectiveness of a 6 m wide fire isolation zone was validated via the adoption of the dual-validation model. By comparing the relationships between the total evacuation population after passenger flow recovery and various evacuation parameters, it was shown that passengers were not evenly distributed among the exits in the waiting hall during an emergency, leading to uneven utilization. Furthermore, to gain a comprehensive understanding of the evacuation process under simulated fire conditions, an evacuation simulation involving 10,000 evacuees over a duration of 324.8 s was conducted. This study provides a theoretical basis for optimizing fire emergency evacuation plans for ultralarge railway station hubs.

Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots’ real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system’s performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.

As a sustainable mode of transportation, subways bring great convenience to the society. Although there have been many studies examining the relationship between the built environment and the station-level ridership, those studies focused mainly on the ridership, which is defined as the number of trips for each station. While ridership is an important indicator for evaluating subway demand, passenger-distance is another critical indicator that incorporates distance into demand evaluation, which has not yet been fully explored. To fill this gap, this paper investigates the relationship between the built environment around stations and the station-level passenger-distance (SLPD). As noted in previous studies, the relationship between the built environment and travel demand can vary by space. Therefore, a geographically weighted regression (GWR) model and a mixed geographically weighted regression (MGWR) model have been used to explore this spatially varying relationship using Chengdu, China, as an example case. The results were compared with that of an ordinary least squares (OLS) model. The comparison shows that the MGWR model that considers both global and local variables has the best goodness of fit. Results also show that 11 of the 25 potential variables are significantly related to SLPD. The accessibility of the station, station type, such as transfer or terminal, number of bus stops, number of restaurants, density of building area, density of the national road network, and density of the provincial road network, all have a positive correlation with SLPD. Meanwhile, the variables, whether it is a newly opened subway station, density of living points of interest (POIs), and density of railroad network, are all negatively correlated with SLPD. Ten of the eleven significant variables (except accessibility) have spatially varying relationships with SLPD. These findings can serve a useful reference for transportation planners for the demand evaluation.

Considering that driving stress is a major contributor to traffic accidents, detecting drivers’ stress levels in time is helpful for ensuring driving safety. This paper attempts to investigate the ability of ultra-short-term (30-s, 1-min, 2-min, and 3-min) HRV analysis for driver stress detection under real driving circumstances. Specifically, the t-test was used to investigate whether there were significant differences in HRV features under different stress levels. Ultra-short-term HRV features were compared with the corresponding short-term (5-min) features during low-stress and high-stress phases by the Spearman rank correlation and Bland–Altman plots analysis. Furthermore, four different machine-learning classifiers, including a support vector machine (SVM), random forests (RFs), K-nearest neighbor (KNN), and Adaboost, were evaluated for stress detection. The results show that the HRV features extracted from ultra-short-term epochs were able to detect binary drivers’ stress levels accurately. In particular, although the capability of HRV features in detecting driver stress also varied between different ultra-short-term epochs, MeanNN, SDNN, NN20, and MeanHR were selected as valid surrogates of short-term features for driver stress detection across the different epochs. For drivers’ stress levels classification, the best performance was achieved with the SVM classifier, with an accuracy of 85.3% using 3-min HRV features. This study makes a contribution to building a robust and effective stress detection system using ultra-short-term HRV features under actual driving environments.

Pilot factor is worth considering when analyzing the causes of civil aviation accidents. This study introduces a data-driven Bayesian network (BN) approach to investigating the joint causal effects of pilot and other factors on civil aviation safety. A total number of 163 individual pilot-related accidents in the National Transportation Safety Board (NTSB) aviation accident database from 2008 to 2020 are analyzed, focusing on eliciting the causal effects of various potential risk factors, including pilot factors, on civil aviation accidents. The modeling of the interdependency among the risk influencing factors (RIFs) and their causal contributory effect on the accident outcome is structured by a tree augmented network (TAN) and validated by sensitivity analysis. The novelty of this study is to incorporate pilot factors derived from the civil aviation accident database into risk analysis, combined with other external factors. The results indicate that weather conditions and flight phases are more correlated with casualty types of civil aviation accidents than pilot action and decision, and three other pilot factors only contribute to fatal injury in civil aviation accidents.

Train dispatching (TD) is at the forefront of all rail operations that transport passengers or goods. Recent technological advances and the explosion of digital data have introduced data-driven methods (DDMs) in rail operations. In this study, DDMs on the TD problem are briefly explored, focusing on relevant studies on delay distribution, delay propagation, and timetable rescheduling. Data-driven TD methods, including statistical methods (SM), graphical models (GM), and machine learning (ML) methods are reviewed. Then, key issues in establishing different data-driven models for the TD problem are addressed. Subsequently, ML methods are considered to be among the most promising DDMs that lead to innovative TD methods, relying on rich data obtained from train operations. This study emphasizes the potentials for designing new alternatives in the three key fields of interest and provides directions for further research on TD. Future research, including the ML-driven TD and intelligent TD, were discussed in this study.

Speeding behavior, especially serious speeding, is more common in taxi driver than other driving population due to their high exposure under traffic environment, which increases the risk of being involved in crashes. In order to prevent the taxi and other road users from speed-related crash, previous studies have revealed contributors of demographic and driving operation affecting taxi speeding frequency. However, researches regarding road factors, and spatial effect are typically rare. For this sake, the current study explores the contributions of 10 types of road characteristics and two kinds of spatial effects (spatial correlation and spatial heterogeneity) on taxi total speeding and serious speeding frequency. Taxi GPS trajectory data in a Chinese metropolis were used to identify speeding event. The study then established four kinds of Bayesian hierarchical count models base on Poisson and negative binominal distribution to estimate the contributor impacts, respectively. Results show that Bayesian hierarchical spatial Poisson log-linear model is optimum for fitting both total and serious speeding frequency. For the analysis, it is found that drivers are more likely to commit speeding on long multilane road with median strip, and road with non-motorized vehicle lane, bus-only lane and viaduct or road tunnel. Roads with low speed limit, and work zone are associated with increasing speeding as well. In terms of serious speeding, bus-only lane is not a contributor, while road speed camera number and one-way organization are significantly positive to the speeding frequency. Furthermore, it reveals that two spatial effects significantly increase the occurrence of speeding events; the impact of spatial heterogeneity is more critical.

Accurately forecasting the influence of disturbances in High-Speed Railways (HSR) has great significance for improving real-time train dispatching and operation management. In this paper, we show how to use historical train operation records to estimate the influence of high-speed train disturbances (HSTD), including the number of affected trains (NAT) and total delayed time (TDT), considering the timetable and disturbance characteristics. We first extracted data about the disturbances and their affected train groups from historical train operation records of Wuhan-Guangzhou (W-G) HSR in China. Then, in order to recognize the concatenations and differences of disturbances, we used a K-Means clustering algorithm to classify them into four categories. Next, parametric and nonparametric density estimation approaches were applied to fit the distributions of NAT and TDT of each clustered category, and the goodness-of-fit testing results showed that Log-normal and Gamma distribution probability densities are the best functions to approximate the distribution of NAT and TDT of different disturbance clusters. Specifically, the validation results show that the proposed models accurately revealed the characteristics of HSTD and that these models can be used in real-time dispatch to predict the NAT and TDT, once the basic features of disturbances are known.

Microscopic simulation models need to be calibrated to represent realistic local traffic conditions. Traditional calibration methods are conducted by searching for the model parameter set that minimizes the discrepancies of certain macroscopic metrics between simulation results and field observations. However, this process could easily lead to inappropriate selection of calibration parameters and thus erroneous simulation results. This paper proposes a video-based approach to incorporate direct measurements of car-following parameters into the process of VISSIM model calibration. The proposed method applies automated video processing techniques to extract vehicle trajectory data and utilizes the trajectory data to determine values of certain car-following parameters in VISSIM. This paper first describes the calibration procedure step by step, and then applies the method to a case study of simulating traffic at a signalized intersection in VISSIM. From the field-collected video footage, trajectories of 1229 through-movement vehicles were extracted and analyzed to calibrate three car-following parameters regarding desired speed, desired acceleration, and safe following distance, respectively. The case study demonstrates the advantages and feasibility of the proposed approach.

As an important part of the electric locomotive in railway transportation, the sensing and inspection of the pantograph has a significant effect on the safe operation of the train. In general, the pantograph carbon slip detection items include slide wear detection, slip strip crack detection, carbon slip fall-block detection and slip strip wear detection. The emergence and development of structured light measurement technology with 3D sensors provide new technical means for the acquisition of spatial 3D information. The three-dimensional data can not only obtain more information but also reduce the data deviation, thereby improving the measurement accuracy and work efficiency. At present, few studies have been conducted on the slide block and partial wear of the carbon slide. Therefore, this paper studies the detection of the pantograph slide block based on 3D sensor measurement technology. The experimental results indicate that it is feasible to adopt 3D measurement technology to detect the fall-block of the pantograph slide. In addition, a sound detection effect can also be obtained.

Anti-runaway prevention of rolling stocks at a railway station is essential in railway safety management. The traditional track skates for anti-runaway prevention of rolling stocks have some disadvantages since they are operated and monitored completely manually. This paper describes an anti-runaway prevention system (ARPS) based on intelligent track skates equipped with sensors and real-time monitoring and management system. This system, which has been updated from the traditional track skates, comprises four parts: intelligent track skates, a signal reader, a database station, and a monitoring system. This system can monitor the real-time situation of track skates without changing their workflow for anti-runaway prevention, and thus realize the integration of anti-runaway prevention information management. This system was successfully tested and practiced at Sunjia station in Harbin Railway Bureau in 2014, and the results confirmed that the system showed 100% accuracy in reflecting the usage status of the track skates. The system could meet practical demands, as it is highly reliable and supports long-distance communication.