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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. [ABSTRACT FROM AUTHOR]

Previous studies have found that drivers' physiological conditions can deteriorate under noise conditions, which poses a potential hazard when driving. As a result, it is crucial to identify the status of drivers when exposed to different noises. However, such explorations are rarely discussed with short-term physiological indicators, especially for rail transit drivers. In this study, an experiment involving 42 railway transit drivers was conducted with a driving simulator to assess the impact of noise on drivers' physiological responses. Considering the individuals' heterogeneity, this study introduced drivers' noise annoyance to measure their self-noise-adaption. The variances of drivers' heart rate variability (HRV) along with different noise adaptions are explored when exposed to different noise conditions. Several machine learning approaches (support vector machine, K-nearest neighbour and random forest) were then used to classify their physiological status under different noise conditions according to the HRV and drivers' self-noise adaptions. Results indicate that the volume of traffic noise negatively affects drivers' performance in their routines. Drivers with different noise adaptions but exposed to a fixed noise were found with discrepant HRV, demonstrating that noise adaption is highly associated with drivers' physiological status under noises. It is also found that noise adaption inclusion could raise the accuracy of classifications. Overall, the random forests classifier performed the best in identifying the physiological status when exposed to noise conditions for drivers with different noise adaptions. [ABSTRACT FROM AUTHOR]

Because of its large capacity, high efficiency and energy savings, the subway has gradually become the primary mode of transportation for citizens. A high density of passengers exists within a large-passenger-flow subway station, and the number of casualties and injuries during a fire emergency is substantial. In this paper, Pathfinder software and on-site measured data of Pingzhou station in Shenzhen (China) were utilized to simulate a fire emergency evacuation in a large-passenger-flow subway station. The Required Safe Egress Time (RSET), number of passengers and flow rates of stairs and escalators were analysed for three fire evacuation scenarios: train fire, platform fire and hall fire. The evacuation time of the train fire, which was 1173 s, was the longest, and 3621 occupants needed to evacuate when the train was fully loaded. Occupants could not complete the evacuation within 6 mins in all three fire evacuation scenarios, which does not meet the current standard requirements and codes. By changing the number of passengers and the number of stairs for evacuation, the flow rate capacity and evacuation time were explored, which have reference values for safety management and emergency evacuation plan optimization during peak hours of subway operation. [ABSTRACT FROM AUTHOR]

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., Author(s): Hongtai Yang [1]; Zhihao Zhao (corresponding author) [1]; Chaozhe Jiang (corresponding author) [1]; Yi Wen [2]; Malik Muneeb Abid [3] 1. Introduction With urbanization, a growing number of cars [...]

Accurate prediction of recoverable train delay can support the train dispatchers' decision-making with timetable rescheduling and improving service reliability. In this paper, we present the results of an effort aimed to develop primary delay recovery (PDR) predictor model using train operation records from Wuhan-Guangzhou (W-G) high-speed railway. To this end, we first identified the main variables that contribute to delay, including dwell buffer time, running buffer time, magnitude of primary delay time, and individual sections' influence. Different models are applied and calibrated to predict the PDR. The validation results on test datasets indicate that the random forest regression (RFR) model outperforms the other three alternative models, namely, multiple linear regression (MLR), support vector machine (SVM), and artificial neural networks (ANN) regarding prediction accuracy measure. Specifically, the evaluation results show that when the prediction tolerance is less than 1 min, the RFR model can achieve up to 80.4% of prediction accuracy, while the accuracy level is 44.4%, 78.5%, and 78.5% for MLR, SVM, and ANN models, respectively. Key words: high-speed railway, primary delay recovery, buffer time, data-driven approaches, random forest model. La prediction precise de retard de train recuperable peut appuyer la prise de decisions des regulateurs de train en matiere de revision d'horaire et d'amelioration de la fiabilite du service. Dans le cadre de cette etude, nous presentons les resultats d'un effort ayant pour but de developper un modele predictif du retablissement de retard primaire (RRP) utilisant des donnees d'exploitation des trains de la liaison ferroviaire a grande vitesse du Wuhan-Guangzhou (W-G). A cette fin, nous avons d'abord identifie les principales variables qui contribuent aux retards, y compris la periode tampon inactive, la periode tampon de parcours, l'ampleur du retard primaire et l'incidence des sections individuelles. Differents modeles sont appliques et calibres en vue de prevoir le retablissement du retard primaire. Les resultats de validation faite sur des ensembles de donnees d'essai indiquent que le modele de regression par foret d'arbres decisionnels (RFAD) surpasse les trois autres modeles de rechange, a savoir la regression lineaire multiple (RLM), la machine a vecteurs de support (MVS) et les reseaux de neurones artificiels (RNA) quant a la mesure de l'exactitude de prediction. Specifiquement, les resultats de l'evaluation montrent que lorsque la tolerance de prediction est moins de 1 minute, le modele de regression par foret d'arbres decisionnels (RFAD) peut realiser une exactitude de prediction jusqu'a 80,4 %, tandis que le niveau d'exactitude est de 44,4, 78,5 et 78,5 % pour les modeles RLM, MVS et RNA, respectivement. [Traduit par la Redaction] Mots-cles : liaison ferroviaire a grande vitesse, retablissement du retard primaire, periode tampon, approches fondees sur les donnees, modele de foret d'arbres decisionnels., Introduction Despite the availability of advanced communication and control technologies, high-speed railway (HSR) lines could still be subject to significant delay in their daily operations due to various unexpected disruptions [...]

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. [ABSTRACT FROM AUTHOR]

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., 1. Introduction An operating train may encounter various unexpected disturbances such as bad weather, power outage, facility failures, and so on [1], which can lead to considerable losses for both [...]

This paper presents a cost-benefit based approach to the problem of finding the optimal location and density of road weather information system (RWIS) stations over a regional road network. The novelty of the proposed method lies in the models that can be used to estimate the benefits of RWIS information, including reduced maintenance costs and collisions, thus determining the optimal number and location of a RWIS network. A case study based on the existing RWIS network in Northern Minnesota in the US is used to show the application of the proposed approach. Linear regression models are developed for the annual maintenance costs and the expected number of collisions that could occur on two types of highways delineated by the existence of a RWIS station nearby. The calibrated models are then applied to individual highway segments defined on the basis of a uniform grid system to determine the expected benefit of having a RWIS station installed. These benefits along with RWIS installation and maintenance costs are converted into net benefits and then the net present value, which are then used in determining the optimal number of RWIS stations and prioritizing the candidate locations. It was found from the case study that a total of 45 stations would provide the best return of investment with a 25 year net benefit of approximately $6.5 million and a life-cycle benefit-to-cost ratio of 3.5. Key words: RWIS stations, cost-benefit, benefits of RWIS, RWIS optimal location and density. Cet article presente une approche couts-benefices a la question de trouver l'emplacement et le nombre optimal de stations meteo-routes (SMR) a installer dans un reseau routier regional. La nouveaute de l'approche proposee est dans le genre de modeles qui peuvent etre utilises pour estimer les avantages de l'information SMR, incluant la reduction des couts d'entretien et des collisions, determinant ainsi le nombre optimal de stations et l'emplacement d'un reseau SMR. Une etude de cas basee sur un reseau SMR existant du nord du Minnesota, aux Etats-Unis, est utilisee pour demontrer l'utilisation de l'approche proposee. Des modeles de regression lineaire ont ete developpes pour les couts d'entretien annuels et le nombre prevu de collisions qui pourraient survenir sur deux types d'autoroutes delimitees par la proximite d'une station SMR. Les modeles etalonnes sont ensuite appliques aux segments d'autoroute individuels definis par un systeme de quadrillage uniforme afin de definir l'avantage d'installer une station SMR. Ces avantages, combines aux couts d'installation et d'entretien des SMR, sont convertis en benefices nets puis en valeur actualisee nette; ces donnees sont ensuite utilisees pour determiner le nombre optimal de stations SMR et classer les endroits possibles d'installation en ordre de priorite. L'etude de cas a permis de determiner qu'un total de 45 stations pourraient fournir le meilleur retour sur l'investissement avec un benefice net sur 25 ans d'environ 6,5 M$ et un rapport couts-benefices sur la duree de la vie utile de 3,5. [Traduit par le Redaction] Mots-des: stations SMR, couts-benefices, avantages des SMR, emplacements et nombres optimaux des SMR., Introduction Effective winter road maintenance (WRM) is indispensable in countries with severe winter weather events. Winter weather could cause significant increases in road collisions and traffic delay. Litigation has become [...]

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. [ABSTRACT FROM AUTHOR]

Combining traffic micro-simulation models and emission models is a primary method to estimate traffic emissions. However, there has been limited research on the impact of traffic micro-simulation model parameters on simulator outputs, which are critical to emissions calculations. Based on combining VISSIM and MOVES, this study uses the Morris and Sobol methods to explore the impact of parameters in VISSIM on operating mode distribution and travel time distribution. Taking an urban signal control intersection with three traffic scenarios in Chengdu, China as an example, this study verifies the methods' feasibility. Apart from these parameters, which have been proved to be necessary calibration parameters, including the desired speed distribution, the desired acceleration function, and the desired deceleration function, an additional 24 parameters related to simulation setting and driving behaviour models are selected as the initial parameters. The number of interaction objects, maximum look-ahead distance, average standstill distance, additive part of safety distance, and safety distance reduction factor close to a stop line, are considered to be the important parameters for this case study. The impact of these five parameters on the bins of operating mode distribution and travel time distribution are further analyzed with One-at-a-time, and these parameters are compared with those reported in previous studies. It is concluded that the important parameters selected in this study are reasonable and can support the calibration of VISSIM parameters for this case's traffic emissions. [ABSTRACT FROM AUTHOR]