The available test methods for optimal moisture content of cold recycled mixture(CRM)as well as its bulk specific gravity,and theoretical maximum relative density were analyzed in this work.Some test improvements were...The available test methods for optimal moisture content of cold recycled mixture(CRM)as well as its bulk specific gravity,and theoretical maximum relative density were analyzed in this work.Some test improvements were suggested to improve test control of the CRM road performance based on the discovered flaws.Besides,the properties of reclaimed asphalt pavement(RAP),including the content of old asphalt,penetration index,passing rate of 4.75 mm sieve,and gradation change rate after extraction,were examined.The effects of RAP characteristics on splitting tensile strength,water stability,the high-and low-temperature performance of emulsified asphalt CRM were studied.The results show that the optimum moisture content of CRM should be determined when the compaction work matches the specimen’s molding work.Among the analyzed methods of bulk specific gravity assessment,the dry-surface and CoreLok methods provide more robust and accurate results than the wax-sealing method,while the dry-surface method is the most cost-efficient.The modified theoretical maximum relative density test method is proposed,which can reduce the systematic error of the vacuum test method.The following RAP-CRM trends can be observed.The lower the content of old asphalt and the smaller the change rate of gradation,the smaller the voids and the better the water stability of CRM.The greater the penetration of old asphalt,the higher the fracture work and low-temperature splitting strength.The greater the penetration,the higher the passing rate of 4.75 mm sieve after extraction,and the worse the high-temperature performance of CRM.展开更多
Fatigue performance evaluation on asphalt mixture provides an essential reference for asphalt mixture design and pavement structure design.Laboratory fatigue test has been widely used by researchers and engineers to a...Fatigue performance evaluation on asphalt mixture provides an essential reference for asphalt mixture design and pavement structure design.Laboratory fatigue test has been widely used by researchers and engineers to assess the fatigue performance of asphalt mixture,due to its low cost,high efficiency,and strong operability.To ensure performance assessment reliability,fatigue test operated in the laboratory needs to simulate the loading and environmental conditions occurring in the field asphalt layer.As a result,loading setups in laboratory fatigue tests,including loading mode,loading frequency,loading waveform,temperature,and the controlled mode,have been carefully devised by researchers.This paper reviews the above common setups in fatigue tests,which aims to illustrate the classical and latest considerations as well as ideas proposed by researchers for improving fatigue test procedures.As fatigue response data of asphalt mixture is measured from fatigue test,an analysis method is required to process the data and identify fatigue behaviors of the material.Presently,the frequently-used fatigue data analysis methods include the fatigue life model,stiffness-modulus-based method,energy-based method,and viscoelastic continuum damage(VECD)method.This paper also provides a review survey on the main concepts,indicators,and models included in those methods to facilitate their applications.Meanwhile,the latest research progress and outputs related to the four methods are also introduced and discussed to reveal the state-of-the-art developments in this research area.展开更多
Currently,model tests are increasingly being used to simulate the construction of mountain tunnels,but the support structure of the model tests does not show the composite lining,and the force laws of the composite li...Currently,model tests are increasingly being used to simulate the construction of mountain tunnels,but the support structure of the model tests does not show the composite lining,and the force laws of the composite lining are not yet clear.In this research,the force variation of composite lining under three cases in model tests of deep-buried tunnels were carried out with the surrounding rock grade and installation time as the variation factors.The test results reveal that:(1)The suitable method to reduce the contact load between the secondary lining and primary support is to enhance the primary support in the soft and weak surrounding rock.Correspondingly,for ClassⅢsurrounding rock and better quality of surrounding rock,the primary support can lag behind the excavation face a certain distance.(2)The axial forces of the bolts tend to rise with concentration of 0.4 kN-0.7 kN after the secondary lining was installed.(3)With or without two to three excavation cycles delayed,the load sharing ratio of the secondary lining of the Class III surrounding rock is less than 10%.Finally,the numerical simulation verifies the feasibility of the model tests.展开更多
We optimized the gradation of cold recycled mixture(CRM)based on low-temperature performance.Firstly,the low-temperature crack resistance of CRM with different gradation and emulsified asphalt content was studied by i...We optimized the gradation of cold recycled mixture(CRM)based on low-temperature performance.Firstly,the low-temperature crack resistance of CRM with different gradation and emulsified asphalt content was studied by indirect tension(IDT)and semi-circular bending(SCB)test.Thereafter,the low-temperature performance evaluation index suitable for CRM was put forward.Then,the triangular coordinate statistical chart was used to analyze the optimal proportion of three grades of aggregate which are 2.36-4.75 mm,0.075-2.36 mm and below 0.075 mm.The results showed that the W_(f) and G_(f) could distinguish the low-temperature performance of CRM with different mixtures and emulsified asphalt dosage.For cold recycled fine aggregate,2.36-4.75 mm,0.075-2.36 mm and less than 0.075 mm account for 20%-25%,74.3%-80%and 5%-8%,respectively.The CRM with lower void fraction,higher W_(f) and G_(f) could be obtained.Based on the reported findings,it was suggested that the sieve passing percentage of 4.75,2.36,and 0.075 mm of CRM is 45%-55%,27%-52%and 1.5%-5%,respectively.展开更多
Employing flow space theory and multi-source data,this study examines the spatial net-work structure and factors influencing railway passenger flow,which is crucial for rail planning in densely populated megalopolises...Employing flow space theory and multi-source data,this study examines the spatial net-work structure and factors influencing railway passenger flow,which is crucial for rail planning in densely populated megalopolises.Focusing on China’s Yangtze River Delta(YRD)megalopolis,we utilize social network analysis(SNA)to explore the characteristics of various flow networks and their interactions with the railway passenger flow network.Key findings include:(1)a pronounced polarization effect and core-periphery structure exist in the YRD,notably within industry and railway flow networks;(2)industry and cor-poration flow significantly contributes to rail passenger flow,with corporation networks in commerce,technical services,and finance showing higher similarity to the railway passen-ger flow network;(3)there is significant heterogeneity in the correlation between rail pas-senger flow and other flows within sub-networks formed by connections among nodes of different levels;(4)enhancing railway services at lower-level nodes is essential to mitigate the disparity between population mobility and rail passenger flow and to promote rail transportation equity.This research offers valuable insights for policymakers in developing countries to strategically plan railroad networks in megalopolises.展开更多
Asphalt pavement is a key component of highway infrastructures in China and worldwide.In asphalt pavement design and condition assessment,the modulus of the asphalt mixture layer is a crucial parameter.However,this pa...Asphalt pavement is a key component of highway infrastructures in China and worldwide.In asphalt pavement design and condition assessment,the modulus of the asphalt mixture layer is a crucial parameter.However,this parameter varies between the laboratory and field loading modes(i.e.,loading frequency,compressive or tensile loading pattern),due to the viscoelastic property and composite structure of the asphalt mixture.The present study proposes a comprehensive frequency-based approach to correlate the asphalt layer moduli obtained under two field and three laboratory loading modes.The field modes are vehicular and falling weight deflectometer(FWD)loading modes,and the laboratory ones are uniaxial compressive(UC),indirect tensile(IDT),and four-point bending(4PB)loading modes.The loading frequency is used as an intermediary parameter for correlating the asphalt layer moduli under different loading modes.The observations made at two field large-scale experimental pavements facilitate the correlation analysis.It is found that the moduli obtained via laboratory 4PB tests are pretty close to those of vehicular loading schemes,in contrast to those derived in UC,IDT,and FWD modes,which need to be adjusted.The corresponding adjustment factors are experimentally assessed.The applications of those adjustment factors are expected to ensure that the moduli measured under different loading modes are appropriately used in asphalt mixture pavement design and assessment.展开更多
With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driv...With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.展开更多
Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under V...Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under Vehicle-to-Vehicle(V2V)communications is critical.There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones:(1)the CV environment provides high-resolution and multi-dimensional data,e.g.,vehicle trajectory data,(2)Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment,e.g.,the multiple interacting objects and the time-series variability.Hence,this study proposes a driving risk assessment framework under the CV environment.Specifically,first,a set of time-series top views was proposed to describe the CV environment data,expressing the detailed information on the vehicles surrounding the subject vehicle.Then,a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment.It is proved that this model can reach an AUC of 0.997,outperforming the existing machine learning algorithms.This study contributes to the improvement of driving risk assessment under the CV environment.展开更多
Distributed matrix-scaled consensus is a kind of generalized cooperative control problem and has broad applications in the field of social network and engineering.This paper addresses the robust distributed matrix-sca...Distributed matrix-scaled consensus is a kind of generalized cooperative control problem and has broad applications in the field of social network and engineering.This paper addresses the robust distributed matrix-scaled consensus of perturbed multi-agent systems suffering from unknown disturbances.Distributed discontinuous protocols are first proposed to drive agents to achieve cluster consensus and suppress the effect of disturbances.Adaptive protocols with time-varying gains obeying differential equations are also designed,which are completely distributed and rely on no global information.Using the boundary layer technique,smooth protocols are proposed to avoid the unexpected chattering effect due to discontinuous functions.As a cost,under the designed smooth protocols,the defined matrix-scaled consensus error tends to a residual set rather than zero,in which the residual bound is arbitrary small by choosing proper parameters.Moreover,distributed dynamic event-based matrix-scalar consensus controllers are also proposed to avoid continuous communications.Simulation examples are provided to further verify the designed algorithms.展开更多
Conflict severity results from the complex interactions between the roadway and environ-mental characteristics and the vehicle motion.Understanding how and to what extent a vehicle is influenced by roadway and surroun...Conflict severity results from the complex interactions between the roadway and environ-mental characteristics and the vehicle motion.Understanding how and to what extent a vehicle is influenced by roadway and surrounding road users during a conflict is helpful in analyzing the causal mechanisms of collisions,thus providing insights into roadway safety improvement countermeasures.This study utilized the NGSIM vehicle trajectory datasets to investigate the causal factors in conflicts at intersections by analyzing roadway-to-vehicle and vehicle-to-vehicle interactions.In order to remove the outliers and white noise existing in the raw data,vehicle trajectories were reconstructed by apply-ing discrete wavelet transform and Kalman filtering(KF).Generalized time-to-collision was adopted to detect and measure the severity of conflicts,by which 1127 conflict events were extracted.Path analysis(PA)models were then established to determine in exactly which ways the roadway-to-vehicle and vehicle-to-vehicle interactions were related to conflict severity.Various roadway and environmental characteristics such as traffic flow average speed,percentage of trucks,and intersection skew angle were included in the models.The results indicate that the roadway and environmental characteristics have both direct and indirect effects on conflict severity.In the indirect effects,the kinematics of conflicting vehicles such as the average and standard deviation of speed,plays an intermediate role in linking roadway factors and conflict outcome.The framework of this study can be used to assess roadway readiness for both human-driven and automated vehicles.展开更多
Passenger flow is the foundation for urban rail transit(URT)operations.However,its cal-culated results from assignment models may deviate from the actual situation in both spa-tial and temporal dimensions,which arouse...Passenger flow is the foundation for urban rail transit(URT)operations.However,its cal-culated results from assignment models may deviate from the actual situation in both spa-tial and temporal dimensions,which arouses more attention and needs to be evaluated in particular.On the other hand,onboard video data from URT trains provides a potential way for model evaluation.This study defines the evaluation problem,and proposes a method-ological solution for evaluating rail transit assignment models in the temporal dimension,which includes qualitative validation and difference quantification.A suitable time granu-larity is determined for the best effectiveness,and onboard video data are used for actual passenger flow extraction.The gap between the actual and calculated data by the model is identified with nonparametric statistical techniques(NPSTs)and quantified with time ser-ies similarity measurement(TSSM)methods.A case study on the Shanghai metro demon-strates the performance of the proposed approach,and several practice implications for URT operation agencies are discussed.展开更多
The high risk of injury resulting from non-motorized vehicle(NMV)crashes has created the goal of using the 3E strategy to comprehensively improve NMV safety.Traditional safety improvement methods identify hot zones ge...The high risk of injury resulting from non-motorized vehicle(NMV)crashes has created the goal of using the 3E strategy to comprehensively improve NMV safety.Traditional safety improvement methods identify hot zones generally by crash frequency or density,which is effective for roadway engineering improvements but neglects characteristics related to other improvements such as safety education.As safety education would be more effective if targeted at the residences of crash-involved road users,the traditional approach to hot zones may therefore provide biased results for such alternative countermeasures.After confirming that 77.17%of NMV crashes occurred outside the involved riders’areas of residence,this study compared the differences between the locations of crashes and the residences of NMV crash-involved riders in safety influencing factors and hot zone identification.A Poisson lognormal bivariate conditional autoregressive(PLN-BCAR)model was developed to account for potential correlations between crashes and involved riders.The model was compared with the univariate Poisson lognormal conditional autoregressive(UPLN-CAR)model and the bivariate Poisson lognormal conditional autoregressive(BPLNCAR)model;the PLN-BCAR model outperformed the other two models in its better interpretation of the influencing factors and its more efficient parameter estimation.Model results indicated that crashes were mainly associated with roadway and land use characteristics,while involved road users were mainly associated with socioeconomic and land use characteristics.The potential for safety improvement method was adopted to identify hot zones for countermeasure implementation.Results showed over 60%of the identified hot zones were inconsistent:they needed improvement in either engineering or education but not both.These findings can help target the type of improvement to better utilize resources for NMV safety.展开更多
Purpose–The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers.Existing studies have found that driver’s collision avoidance per...Purpose–The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers.Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload.However,whether moderation and mediation effects exist among warning information,driver’s cognition,behavior and risky avoidance performance is unclear.Design/methodology/approach–This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior.A driving simulator experiment was conducted with waring and command information.Findings–Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario.The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk.At the same time,command information can weaken this negative effect.Moreover,improved collision avoidance behavior can be achieved through increasing drivers’mental workload.Practical implications–The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior,thus contributing to improved in-vehicle information system design.Originality/value–The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’collision avoidance ability.Through further calibration with larger sample size,the proposed structural model can be used to predict the effect of invehicle warnings in different risky driving scenarios.展开更多
The widely-existed uncertainty of origin-destination(OD)demand in transportation networks has attracted extensive attention.Most characterizations or models of stochastic OD demands in networks assume a homogenous pro...The widely-existed uncertainty of origin-destination(OD)demand in transportation networks has attracted extensive attention.Most characterizations or models of stochastic OD demands in networks assume a homogenous probability distribution,though empirical studies are lacking of large-scale networks to justify this assumption.Given that the longterm continuous automatic fare collection(AFC)data of metro networks can provide complete OD passenger demand information,this study takes the Shanghai metro network as an example to empirically examine the stochasticity characteristics of OD passenger demands of metro network.Based on the morning peak OD demand data for 250 weekdays,a local outlier factor(LOF)method is used to identify and remove outliers in the data.A clustering method is used to cluster the OD pairs,study the fluctuation and distribution characteristics of the OD passenger demands,and select the optimal distribution type through goodness-of-fit indices.The results show that 1)the coefficients of variation of morning peak OD demands in the network are mainly distributed in the range of 0.2-0.6,different OD pairs have different fluctuations,and the degree of demand fluctuation decreases as the mean increases;2)the probability distribution types of OD demands based on statistical characteristics are heterogeneous;and 3)the optimal distribution type of OD demands is Poisson,lognormal/Gamma,and normal for OD pairs characterized by a small mean and right-skewness,a small mean and skewness close to 0,and a large mean,respectively.In contrast to the simple average-based data processing of OD passenger demand in metro networks,this paper presents a new perspective of mining long-term continuous data to understand the inherent stochasticity of OD passenger demands.The results can provide more realistic and practical inputs and assumptions for theoretical research on stochastic OD demands in metro networks.展开更多
Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on...Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use.Thanks to the advances in data collection and processing techniques,multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns.In this paper,based on a week’s operation data of 6600 passenger cars in Shanghai,we develop a systematic approach for identifying trips and travel purposes,and classify vehicles into four categories using a Gaussian-Mixed-Model.A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles.Wefind that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles.The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction,but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.展开更多
Through and right-turn shared lanes are widely designed to increase the capacity of through traffic,but they can also cause delays for right-turn vehicles.This study presents a dynamic control method for a shared lane...Through and right-turn shared lanes are widely designed to increase the capacity of through traffic,but they can also cause delays for right-turn vehicles.This study presents a dynamic control method for a shared lane that prioritizes right-turn vehicles at the beginning of the cycle and subsequently allows through traffic to queue in the shared lane for saturated discharge.The traffic wave model is employed to reveal the dynamics of the traffic flow under this control and to derive the relationships among major traffic parameters.Constrained by the major relationship,a linear programming approach to minimize the total queue length is developed to determine the proper values of control parameters,including the shared area length,subordinate signal time lag,and shared or exclusive duration.A sensitivity analysis of the control parameters for different arrival rates and flow ratios is performed.Comparisons are conducted among the dynamic shared lane,the fixed exclusive lane,and the fixed shared lane.The results show that the dynamic control method results in a lower delay for both through and total traffic.展开更多
This paper presented a real-time millimeter wave radar-based system for tracking vehicle trajectories in a wide area (continuously along a roadway with essentially no length limit in practice). The trajectory tracking...This paper presented a real-time millimeter wave radar-based system for tracking vehicle trajectories in a wide area (continuously along a roadway with essentially no length limit in practice). The trajectory tracking results were first validated for single vehicle trajectory tracking using the Real-Time Kinematic positioning technology based on the Beidou satellite navigation systems. The validation showed that the vehicle positions were captured with a mean lateral offset of −0.284 m and a mean longitudinal offset of −0.352 m. The mean estimated speeds were found to have a difference of only −0.048 km/h from the ground truths. The trajectory tracking results were also validated through multi-object tracking using video data from an unmanned aerial vehicle (UAV). Compared with the UAV video footage, the millimeter wave-based system was found to correctly capture around 92% of the total number of vehicles. For the correctly captured vehicles, their positions were found to be within 0.99 m (about a quarter of the width of a regular traffic lane) of the ground truth. In this paper, we also would like to share openly the entire validated datasets which has been recently published online as the TJRD TS platform. Additionally, a demonstration in using the dataset to detect aggressive driving behaviors, such as speeding was presented. It is expected that the open dataset will help enable researchers and practitioners to further explore the behaviors of road users, track the dynamics of safety risks and congestion formation, and evaluate incident impacts in a more microscopic but comprehensive way.展开更多
Select link analysis provides information of where traffic comes from and goes to at selected links.This disaggregate information has wide applications in practice.The state-of-the-art planning software packages often...Select link analysis provides information of where traffic comes from and goes to at selected links.This disaggregate information has wide applications in practice.The state-of-the-art planning software packages often adopt the user equilibrium(UE) model for select link analysis.However,empirical studies have repeatedly revealed that the stochastic user equilibrium model more accurately predicts observed mean and variance of choices than the UE model.This paper proposes an alternative select link analysis method by making use of the recently developed logit–weibit hybrid model,to alleviate the drawbacks of both logit and weibit models while keeping a closed-form route choice probability expression.To enhance the applicability in large-scale networks,Bell’s stochastic loading method originally developed for logit model is adapted to the hybrid model.The features of the proposed method are twofold:(1) unique O–D-specific link flow pattern and more plausible behavioral realism attributed to the hybrid route choice model and(2) applicability in large-scale networks due to the link-based stochastic loading method.An illustrative network example and a case study in a large-scale network are conducted to demonstrate the efficiency and effectiveness of the proposed select link analysis method as well as applications of O–D-specific link flow information.A visualizationmethod is also proposed to enhance the understanding of O–D-specific link flow originally in the form of a matrix.展开更多
Masks modified asphalt(MMA)provides a potential solution to pollution from discarded medical masks.Mixing temperature significantly affects storage stability and rheological performance of MMA.Traditional selection me...Masks modified asphalt(MMA)provides a potential solution to pollution from discarded medical masks.Mixing temperature significantly affects storage stability and rheological performance of MMA.Traditional selection method overly relies on trial-and-error experiment,neglecting the convenience offered by computational chemistry.Furthermore,previous literature lacks precise elucidation of MMA’s physical modification mechanism,especially concerning the binding mode and energy composition.To address these issues,the optimal mixing temperature for MMA was recommended based on molecular dynamics(MD).The rationality of recommended temperature was validated through laboratory tests,simultaneously investigating the impact of heating time.Fluorescence microscopy and multi-band spectroscopy were employed to acquire the microstructure.Binding modes in MMA were determined using binding sites exploration,evaluating the energy composition of each binding mode through quantum chemistry(QC).The interaction mechanism was explained based on surface properties of isolated molecules.Results indicated that 170℃was the recommended optimal mixing temperature derived from mixing free energy and Flory-Huggins interaction parameter.The fluctuations in softening point difference(DTR&B)and separation ratio(R_(S))concurrently tended towards stability,thereby validating the reliability of recommended temperature.Moreover,even after 72 h heating,MMA prepared at recommended temperature remained within a reasonable range concerning DTR&B,RS,and microscopic structure.Perpendicular,parallel,toroidal,and spherical modes emerged in MMA.Perpendicular and parallel modes exhibited the highest binding energy,while circular mode demonstrated the lowest.Binding energy is primarily governed by van der Waals interaction,attributed to the dominance of dispersion term on MMA’s molecular surface.Besides,due to the presence of polycyclic aromatic hydrocarbons in asphalt molecules,electrostatic interaction contributed to specific molecular bindings.展开更多
Aviation accidents,referring to unexpected and undesirable events involving aircraft,often cause great damage to property and human life.Learning from historical accidents is pivotal for improving safety in aviation.H...Aviation accidents,referring to unexpected and undesirable events involving aircraft,often cause great damage to property and human life.Learning from historical accidents is pivotal for improving safety in aviation.However,aviation accidents are typically documented and stored as unstructured or semi-structured free-text,rendering the ability to analyze such data a difficult task.This study presents a novel framework that combines text mining and network analytics techniques to provide the ability to analyze aviation accident reports automatically.The framework comprises a four-step modelling approach to:(1)the transformation of unstructured aviation safety report texts into structured numeric matrices using the TF-IDF matrix;(2)the identification of aviation accident topics using a structural topic model(STM);(3)the production of a word co-occurrence network(WCN)to determine the interrelations between aviation safety risk factors;and(4)quantitative analysis by technology of keywords to pinpoint key causal factors in aviation safety events.The proposed framework is validated by analyzing aviation accident reports collected by the National Transportation Safety Board(NTSB).The results indicate that STM provides a more granular partitioning of topics and better distinguishes between similar events compared to traditional latent dirichlet allocation(LDA).Among the identified topics,“Fuel and Power”and“En-route Phase”have the highest occurrence rate according to STM.Additionally,“Aircraft Crash”is the most prevalent topic in aviation accidents that resulted in fatal injuries,whereas the“Landing phase”is the most prevalent topic in nonfatal injuries on accidents.Based on the WCN,three centrality measures highlight“inspection of equipment”and“take off”as the most important risk factors in aviation safety.The proposed framework provides a comprehensive solution for in-depth analysis of aviation safety reports,offering decision support for aviation safety management and accident prevention,thereby reducing risks and strengthening safety measures.展开更多
文摘The available test methods for optimal moisture content of cold recycled mixture(CRM)as well as its bulk specific gravity,and theoretical maximum relative density were analyzed in this work.Some test improvements were suggested to improve test control of the CRM road performance based on the discovered flaws.Besides,the properties of reclaimed asphalt pavement(RAP),including the content of old asphalt,penetration index,passing rate of 4.75 mm sieve,and gradation change rate after extraction,were examined.The effects of RAP characteristics on splitting tensile strength,water stability,the high-and low-temperature performance of emulsified asphalt CRM were studied.The results show that the optimum moisture content of CRM should be determined when the compaction work matches the specimen’s molding work.Among the analyzed methods of bulk specific gravity assessment,the dry-surface and CoreLok methods provide more robust and accurate results than the wax-sealing method,while the dry-surface method is the most cost-efficient.The modified theoretical maximum relative density test method is proposed,which can reduce the systematic error of the vacuum test method.The following RAP-CRM trends can be observed.The lower the content of old asphalt and the smaller the change rate of gradation,the smaller the voids and the better the water stability of CRM.The greater the penetration of old asphalt,the higher the fracture work and low-temperature splitting strength.The greater the penetration,the higher the passing rate of 4.75 mm sieve after extraction,and the worse the high-temperature performance of CRM.
基金The study is supported by the National Natural Science Foundation of China(Grant No.52108412)the Postdoctoral Science Foundation of China(Grant Nos.BX2021216,2021M702479).
文摘Fatigue performance evaluation on asphalt mixture provides an essential reference for asphalt mixture design and pavement structure design.Laboratory fatigue test has been widely used by researchers and engineers to assess the fatigue performance of asphalt mixture,due to its low cost,high efficiency,and strong operability.To ensure performance assessment reliability,fatigue test operated in the laboratory needs to simulate the loading and environmental conditions occurring in the field asphalt layer.As a result,loading setups in laboratory fatigue tests,including loading mode,loading frequency,loading waveform,temperature,and the controlled mode,have been carefully devised by researchers.This paper reviews the above common setups in fatigue tests,which aims to illustrate the classical and latest considerations as well as ideas proposed by researchers for improving fatigue test procedures.As fatigue response data of asphalt mixture is measured from fatigue test,an analysis method is required to process the data and identify fatigue behaviors of the material.Presently,the frequently-used fatigue data analysis methods include the fatigue life model,stiffness-modulus-based method,energy-based method,and viscoelastic continuum damage(VECD)method.This paper also provides a review survey on the main concepts,indicators,and models included in those methods to facilitate their applications.Meanwhile,the latest research progress and outputs related to the four methods are also introduced and discussed to reveal the state-of-the-art developments in this research area.
基金the Scientific Research Project of Zhejiang Provincial Transportation Department(2021050)for the preparation of this manuscript。
文摘Currently,model tests are increasingly being used to simulate the construction of mountain tunnels,but the support structure of the model tests does not show the composite lining,and the force laws of the composite lining are not yet clear.In this research,the force variation of composite lining under three cases in model tests of deep-buried tunnels were carried out with the surrounding rock grade and installation time as the variation factors.The test results reveal that:(1)The suitable method to reduce the contact load between the secondary lining and primary support is to enhance the primary support in the soft and weak surrounding rock.Correspondingly,for ClassⅢsurrounding rock and better quality of surrounding rock,the primary support can lag behind the excavation face a certain distance.(2)The axial forces of the bolts tend to rise with concentration of 0.4 kN-0.7 kN after the secondary lining was installed.(3)With or without two to three excavation cycles delayed,the load sharing ratio of the secondary lining of the Class III surrounding rock is less than 10%.Finally,the numerical simulation verifies the feasibility of the model tests.
基金Funded by the Key Research and Development Plan of Jiangxi Province (No. 20223BBG74002)the Natural Science Foundation of China (Nos. 51778483, 51978521)the Fundamental Research Funds for the Central Universities (No. DUT24RC (3)100)。
文摘We optimized the gradation of cold recycled mixture(CRM)based on low-temperature performance.Firstly,the low-temperature crack resistance of CRM with different gradation and emulsified asphalt content was studied by indirect tension(IDT)and semi-circular bending(SCB)test.Thereafter,the low-temperature performance evaluation index suitable for CRM was put forward.Then,the triangular coordinate statistical chart was used to analyze the optimal proportion of three grades of aggregate which are 2.36-4.75 mm,0.075-2.36 mm and below 0.075 mm.The results showed that the W_(f) and G_(f) could distinguish the low-temperature performance of CRM with different mixtures and emulsified asphalt dosage.For cold recycled fine aggregate,2.36-4.75 mm,0.075-2.36 mm and less than 0.075 mm account for 20%-25%,74.3%-80%and 5%-8%,respectively.The CRM with lower void fraction,higher W_(f) and G_(f) could be obtained.Based on the reported findings,it was suggested that the sieve passing percentage of 4.75,2.36,and 0.075 mm of CRM is 45%-55%,27%-52%and 1.5%-5%,respectively.
基金funding support from NSFC grants(Grant No.52072263)Shanghai Collaborative Innovation Research Center for Multi-network&Multi-modal Rail Transit.
文摘Employing flow space theory and multi-source data,this study examines the spatial net-work structure and factors influencing railway passenger flow,which is crucial for rail planning in densely populated megalopolises.Focusing on China’s Yangtze River Delta(YRD)megalopolis,we utilize social network analysis(SNA)to explore the characteristics of various flow networks and their interactions with the railway passenger flow network.Key findings include:(1)a pronounced polarization effect and core-periphery structure exist in the YRD,notably within industry and railway flow networks;(2)industry and cor-poration flow significantly contributes to rail passenger flow,with corporation networks in commerce,technical services,and finance showing higher similarity to the railway passen-ger flow network;(3)there is significant heterogeneity in the correlation between rail pas-senger flow and other flows within sub-networks formed by connections among nodes of different levels;(4)enhancing railway services at lower-level nodes is essential to mitigate the disparity between population mobility and rail passenger flow and to promote rail transportation equity.This research offers valuable insights for policymakers in developing countries to strategically plan railroad networks in megalopolises.
基金The study was supported by the National Key R&D Program of China(No.2018YFB1600100)the National Natural Science Foundation of China(Grant No.5210081231)the Postdoctoral Science Foundation of China(Nos.BX2021216 and 2021M702479).
文摘Asphalt pavement is a key component of highway infrastructures in China and worldwide.In asphalt pavement design and condition assessment,the modulus of the asphalt mixture layer is a crucial parameter.However,this parameter varies between the laboratory and field loading modes(i.e.,loading frequency,compressive or tensile loading pattern),due to the viscoelastic property and composite structure of the asphalt mixture.The present study proposes a comprehensive frequency-based approach to correlate the asphalt layer moduli obtained under two field and three laboratory loading modes.The field modes are vehicular and falling weight deflectometer(FWD)loading modes,and the laboratory ones are uniaxial compressive(UC),indirect tensile(IDT),and four-point bending(4PB)loading modes.The loading frequency is used as an intermediary parameter for correlating the asphalt layer moduli under different loading modes.The observations made at two field large-scale experimental pavements facilitate the correlation analysis.It is found that the moduli obtained via laboratory 4PB tests are pretty close to those of vehicular loading schemes,in contrast to those derived in UC,IDT,and FWD modes,which need to be adjusted.The corresponding adjustment factors are experimentally assessed.The applications of those adjustment factors are expected to ensure that the moduli measured under different loading modes are appropriately used in asphalt mixture pavement design and assessment.
文摘With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.
基金sponsored by the Zhejiang Province Science and Technology Major Project of China(No.2021C01011)the National Natural Science Foundation of China(NSFC)(No.52172349)the China Scholarship Council(CSC).
文摘Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under Vehicle-to-Vehicle(V2V)communications is critical.There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones:(1)the CV environment provides high-resolution and multi-dimensional data,e.g.,vehicle trajectory data,(2)Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment,e.g.,the multiple interacting objects and the time-series variability.Hence,this study proposes a driving risk assessment framework under the CV environment.Specifically,first,a set of time-series top views was proposed to describe the CV environment data,expressing the detailed information on the vehicles surrounding the subject vehicle.Then,a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment.It is proved that this model can reach an AUC of 0.997,outperforming the existing machine learning algorithms.This study contributes to the improvement of driving risk assessment under the CV environment.
基金supported in part by the National Key Research and Development Program of China(No.2020AAA0108905)by the National Natural Science Foundation of China(Nos.62103302,62273262,62088101)+7 种基金by the Shanghai Sailing Program(No.21YF1450300)by the Shanghai Chenguang Program(No.22CGA19)by the Shanghai Municipal Science and Technology Major Project(No.2021SHZDZX0100)by the Shanghai Science and Technology Planning Project(Nos.21ZR1466400,22QA1408500)by the Shanghai Municipal Commission of Science and Technology Project(No.19511132101)by the Fundamental Research Funds for the Central Universities(No.2022-5-YB-05)by the Industry,Education and Research Innovation Foundation of Chinese University(Nos.2021ZYA02008,2021ZYA03004)by the Special Fund for Independent Innovation of Aero Engine Corporation of China(No.ZZCX-2021-007).
文摘Distributed matrix-scaled consensus is a kind of generalized cooperative control problem and has broad applications in the field of social network and engineering.This paper addresses the robust distributed matrix-scaled consensus of perturbed multi-agent systems suffering from unknown disturbances.Distributed discontinuous protocols are first proposed to drive agents to achieve cluster consensus and suppress the effect of disturbances.Adaptive protocols with time-varying gains obeying differential equations are also designed,which are completely distributed and rely on no global information.Using the boundary layer technique,smooth protocols are proposed to avoid the unexpected chattering effect due to discontinuous functions.As a cost,under the designed smooth protocols,the defined matrix-scaled consensus error tends to a residual set rather than zero,in which the residual bound is arbitrary small by choosing proper parameters.Moreover,distributed dynamic event-based matrix-scalar consensus controllers are also proposed to avoid continuous communications.Simulation examples are provided to further verify the designed algorithms.
基金sponsored by the National Natural Science Foundation of China(No.52372335)the Belt and Road Cooperation Program under the 2023 Shanghai Action Plan for Science,Technology and Innovation(No.23210750500).
文摘Conflict severity results from the complex interactions between the roadway and environ-mental characteristics and the vehicle motion.Understanding how and to what extent a vehicle is influenced by roadway and surrounding road users during a conflict is helpful in analyzing the causal mechanisms of collisions,thus providing insights into roadway safety improvement countermeasures.This study utilized the NGSIM vehicle trajectory datasets to investigate the causal factors in conflicts at intersections by analyzing roadway-to-vehicle and vehicle-to-vehicle interactions.In order to remove the outliers and white noise existing in the raw data,vehicle trajectories were reconstructed by apply-ing discrete wavelet transform and Kalman filtering(KF).Generalized time-to-collision was adopted to detect and measure the severity of conflicts,by which 1127 conflict events were extracted.Path analysis(PA)models were then established to determine in exactly which ways the roadway-to-vehicle and vehicle-to-vehicle interactions were related to conflict severity.Various roadway and environmental characteristics such as traffic flow average speed,percentage of trucks,and intersection skew angle were included in the models.The results indicate that the roadway and environmental characteristics have both direct and indirect effects on conflict severity.In the indirect effects,the kinematics of conflicting vehicles such as the average and standard deviation of speed,plays an intermediate role in linking roadway factors and conflict outcome.The framework of this study can be used to assess roadway readiness for both human-driven and automated vehicles.
基金supported by the National Natural Science Foundation of China(Nos.72071147 and 71701152)the Fundamental Research Funds for the Central Universities of China(No.22120220628).
文摘Passenger flow is the foundation for urban rail transit(URT)operations.However,its cal-culated results from assignment models may deviate from the actual situation in both spa-tial and temporal dimensions,which arouses more attention and needs to be evaluated in particular.On the other hand,onboard video data from URT trains provides a potential way for model evaluation.This study defines the evaluation problem,and proposes a method-ological solution for evaluating rail transit assignment models in the temporal dimension,which includes qualitative validation and difference quantification.A suitable time granu-larity is determined for the best effectiveness,and onboard video data are used for actual passenger flow extraction.The gap between the actual and calculated data by the model is identified with nonparametric statistical techniques(NPSTs)and quantified with time ser-ies similarity measurement(TSSM)methods.A case study on the Shanghai metro demon-strates the performance of the proposed approach,and several practice implications for URT operation agencies are discussed.
基金the International Science and Technology Cooperation Programme of China(2017YFE0134500)。
文摘The high risk of injury resulting from non-motorized vehicle(NMV)crashes has created the goal of using the 3E strategy to comprehensively improve NMV safety.Traditional safety improvement methods identify hot zones generally by crash frequency or density,which is effective for roadway engineering improvements but neglects characteristics related to other improvements such as safety education.As safety education would be more effective if targeted at the residences of crash-involved road users,the traditional approach to hot zones may therefore provide biased results for such alternative countermeasures.After confirming that 77.17%of NMV crashes occurred outside the involved riders’areas of residence,this study compared the differences between the locations of crashes and the residences of NMV crash-involved riders in safety influencing factors and hot zone identification.A Poisson lognormal bivariate conditional autoregressive(PLN-BCAR)model was developed to account for potential correlations between crashes and involved riders.The model was compared with the univariate Poisson lognormal conditional autoregressive(UPLN-CAR)model and the bivariate Poisson lognormal conditional autoregressive(BPLNCAR)model;the PLN-BCAR model outperformed the other two models in its better interpretation of the influencing factors and its more efficient parameter estimation.Model results indicated that crashes were mainly associated with roadway and land use characteristics,while involved road users were mainly associated with socioeconomic and land use characteristics.The potential for safety improvement method was adopted to identify hot zones for countermeasure implementation.Results showed over 60%of the identified hot zones were inconsistent:they needed improvement in either engineering or education but not both.These findings can help target the type of improvement to better utilize resources for NMV safety.
基金sponsored by the Chinese National Science Foundation(61803283)the“Chen Guang”project supported by ShanghaiMunicipal Education Commission and Shanghai Education Development Foundation(18CG17)the Shanghai Municipal Science and Technology Major Project(No.2021SHZDZX0100)and the Fundamental Research Funds for the Central Universities.
文摘Purpose–The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers.Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload.However,whether moderation and mediation effects exist among warning information,driver’s cognition,behavior and risky avoidance performance is unclear.Design/methodology/approach–This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior.A driving simulator experiment was conducted with waring and command information.Findings–Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario.The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk.At the same time,command information can weaken this negative effect.Moreover,improved collision avoidance behavior can be achieved through increasing drivers’mental workload.Practical implications–The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior,thus contributing to improved in-vehicle information system design.Originality/value–The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’collision avoidance ability.Through further calibration with larger sample size,the proposed structural model can be used to predict the effect of invehicle warnings in different risky driving scenarios.
基金sponsored by the National Natural Science Foundation of China(No.72021002)the Fundamental Research Funds for the Central Universities of China.
文摘The widely-existed uncertainty of origin-destination(OD)demand in transportation networks has attracted extensive attention.Most characterizations or models of stochastic OD demands in networks assume a homogenous probability distribution,though empirical studies are lacking of large-scale networks to justify this assumption.Given that the longterm continuous automatic fare collection(AFC)data of metro networks can provide complete OD passenger demand information,this study takes the Shanghai metro network as an example to empirically examine the stochasticity characteristics of OD passenger demands of metro network.Based on the morning peak OD demand data for 250 weekdays,a local outlier factor(LOF)method is used to identify and remove outliers in the data.A clustering method is used to cluster the OD pairs,study the fluctuation and distribution characteristics of the OD passenger demands,and select the optimal distribution type through goodness-of-fit indices.The results show that 1)the coefficients of variation of morning peak OD demands in the network are mainly distributed in the range of 0.2-0.6,different OD pairs have different fluctuations,and the degree of demand fluctuation decreases as the mean increases;2)the probability distribution types of OD demands based on statistical characteristics are heterogeneous;and 3)the optimal distribution type of OD demands is Poisson,lognormal/Gamma,and normal for OD pairs characterized by a small mean and right-skewness,a small mean and skewness close to 0,and a large mean,respectively.In contrast to the simple average-based data processing of OD passenger demand in metro networks,this paper presents a new perspective of mining long-term continuous data to understand the inherent stochasticity of OD passenger demands.The results can provide more realistic and practical inputs and assumptions for theoretical research on stochastic OD demands in metro networks.
基金supported by the project of the National Natural Science Foundation of China(No.71734004)。
文摘Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use.Thanks to the advances in data collection and processing techniques,multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns.In this paper,based on a week’s operation data of 6600 passenger cars in Shanghai,we develop a systematic approach for identifying trips and travel purposes,and classify vehicles into four categories using a Gaussian-Mixed-Model.A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles.Wefind that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles.The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction,but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.
基金supported by the National Natural Science Foundation of China(Nos.52325210,52131204,and 52302411)the Shanghai Science and Technology Innovation Action Plan Project(No.19DZ1209004)the Fundamental Research Funds for the Central Universities(No.22120220137).
文摘Through and right-turn shared lanes are widely designed to increase the capacity of through traffic,but they can also cause delays for right-turn vehicles.This study presents a dynamic control method for a shared lane that prioritizes right-turn vehicles at the beginning of the cycle and subsequently allows through traffic to queue in the shared lane for saturated discharge.The traffic wave model is employed to reveal the dynamics of the traffic flow under this control and to derive the relationships among major traffic parameters.Constrained by the major relationship,a linear programming approach to minimize the total queue length is developed to determine the proper values of control parameters,including the shared area length,subordinate signal time lag,and shared or exclusive duration.A sensitivity analysis of the control parameters for different arrival rates and flow ratios is performed.Comparisons are conducted among the dynamic shared lane,the fixed exclusive lane,and the fixed shared lane.The results show that the dynamic control method results in a lower delay for both through and total traffic.
基金supported by the National Key R&D Program of China(2019YFB1600703)the Chinese National Natural Science Foundation(Grant No.72001161 and 52172348)the Fundamental Research Funds for the Central Universities.
文摘This paper presented a real-time millimeter wave radar-based system for tracking vehicle trajectories in a wide area (continuously along a roadway with essentially no length limit in practice). The trajectory tracking results were first validated for single vehicle trajectory tracking using the Real-Time Kinematic positioning technology based on the Beidou satellite navigation systems. The validation showed that the vehicle positions were captured with a mean lateral offset of −0.284 m and a mean longitudinal offset of −0.352 m. The mean estimated speeds were found to have a difference of only −0.048 km/h from the ground truths. The trajectory tracking results were also validated through multi-object tracking using video data from an unmanned aerial vehicle (UAV). Compared with the UAV video footage, the millimeter wave-based system was found to correctly capture around 92% of the total number of vehicles. For the correctly captured vehicles, their positions were found to be within 0.99 m (about a quarter of the width of a regular traffic lane) of the ground truth. In this paper, we also would like to share openly the entire validated datasets which has been recently published online as the TJRD TS platform. Additionally, a demonstration in using the dataset to detect aggressive driving behaviors, such as speeding was presented. It is expected that the open dataset will help enable researchers and practitioners to further explore the behaviors of road users, track the dynamics of safety risks and congestion formation, and evaluate incident impacts in a more microscopic but comprehensive way.
基金supported by National Natural Science Foundation of China(51408433)Fundamental Research Funds for the Central Universities of Chinathe Chenguang Program sponsored by Shanghai Education Development Foundation and Shanghai Municipal Education Commission
文摘Select link analysis provides information of where traffic comes from and goes to at selected links.This disaggregate information has wide applications in practice.The state-of-the-art planning software packages often adopt the user equilibrium(UE) model for select link analysis.However,empirical studies have repeatedly revealed that the stochastic user equilibrium model more accurately predicts observed mean and variance of choices than the UE model.This paper proposes an alternative select link analysis method by making use of the recently developed logit–weibit hybrid model,to alleviate the drawbacks of both logit and weibit models while keeping a closed-form route choice probability expression.To enhance the applicability in large-scale networks,Bell’s stochastic loading method originally developed for logit model is adapted to the hybrid model.The features of the proposed method are twofold:(1) unique O–D-specific link flow pattern and more plausible behavioral realism attributed to the hybrid route choice model and(2) applicability in large-scale networks due to the link-based stochastic loading method.An illustrative network example and a case study in a large-scale network are conducted to demonstrate the efficiency and effectiveness of the proposed select link analysis method as well as applications of O–D-specific link flow information.A visualizationmethod is also proposed to enhance the understanding of O–D-specific link flow originally in the form of a matrix.
基金supported by the National Natural Science Foundation of China(No.52178433 and 51878499).
文摘Masks modified asphalt(MMA)provides a potential solution to pollution from discarded medical masks.Mixing temperature significantly affects storage stability and rheological performance of MMA.Traditional selection method overly relies on trial-and-error experiment,neglecting the convenience offered by computational chemistry.Furthermore,previous literature lacks precise elucidation of MMA’s physical modification mechanism,especially concerning the binding mode and energy composition.To address these issues,the optimal mixing temperature for MMA was recommended based on molecular dynamics(MD).The rationality of recommended temperature was validated through laboratory tests,simultaneously investigating the impact of heating time.Fluorescence microscopy and multi-band spectroscopy were employed to acquire the microstructure.Binding modes in MMA were determined using binding sites exploration,evaluating the energy composition of each binding mode through quantum chemistry(QC).The interaction mechanism was explained based on surface properties of isolated molecules.Results indicated that 170℃was the recommended optimal mixing temperature derived from mixing free energy and Flory-Huggins interaction parameter.The fluctuations in softening point difference(DTR&B)and separation ratio(R_(S))concurrently tended towards stability,thereby validating the reliability of recommended temperature.Moreover,even after 72 h heating,MMA prepared at recommended temperature remained within a reasonable range concerning DTR&B,RS,and microscopic structure.Perpendicular,parallel,toroidal,and spherical modes emerged in MMA.Perpendicular and parallel modes exhibited the highest binding energy,while circular mode demonstrated the lowest.Binding energy is primarily governed by van der Waals interaction,attributed to the dominance of dispersion term on MMA’s molecular surface.Besides,due to the presence of polycyclic aromatic hydrocarbons in asphalt molecules,electrostatic interaction contributed to specific molecular bindings.
文摘Aviation accidents,referring to unexpected and undesirable events involving aircraft,often cause great damage to property and human life.Learning from historical accidents is pivotal for improving safety in aviation.However,aviation accidents are typically documented and stored as unstructured or semi-structured free-text,rendering the ability to analyze such data a difficult task.This study presents a novel framework that combines text mining and network analytics techniques to provide the ability to analyze aviation accident reports automatically.The framework comprises a four-step modelling approach to:(1)the transformation of unstructured aviation safety report texts into structured numeric matrices using the TF-IDF matrix;(2)the identification of aviation accident topics using a structural topic model(STM);(3)the production of a word co-occurrence network(WCN)to determine the interrelations between aviation safety risk factors;and(4)quantitative analysis by technology of keywords to pinpoint key causal factors in aviation safety events.The proposed framework is validated by analyzing aviation accident reports collected by the National Transportation Safety Board(NTSB).The results indicate that STM provides a more granular partitioning of topics and better distinguishes between similar events compared to traditional latent dirichlet allocation(LDA).Among the identified topics,“Fuel and Power”and“En-route Phase”have the highest occurrence rate according to STM.Additionally,“Aircraft Crash”is the most prevalent topic in aviation accidents that resulted in fatal injuries,whereas the“Landing phase”is the most prevalent topic in nonfatal injuries on accidents.Based on the WCN,three centrality measures highlight“inspection of equipment”and“take off”as the most important risk factors in aviation safety.The proposed framework provides a comprehensive solution for in-depth analysis of aviation safety reports,offering decision support for aviation safety management and accident prevention,thereby reducing risks and strengthening safety measures.
