A new method for calculating the failure probabilityof structures with random parameters is proposed based onmultivariate power polynomial expansion, in which te uncertain quantities include material properties, struc...A new method for calculating the failure probabilityof structures with random parameters is proposed based onmultivariate power polynomial expansion, in which te uncertain quantities include material properties, structuralgeometric characteristics and static loads. The structuralresponse is first expressed as a multivariable power polynomialexpansion, of which the coefficients ae then determined by utilizing the higher-order perturbation technique and Galerkinprojection scheme. Then, the final performance function ofthe structure is determined. Due to the explicitness of theperformance function, a multifold integral of the structuralfailure probability can be calculated directly by the Monte Carlo simulation, which only requires a smal amount ofcomputation time. Two numerical examples ae presented toillustate te accuracy ad efficiency of te proposed metiod. It is shown that compaed with the widely used first-orderreliability method （ FORM） and second-order reliabilitymethod （ SORM）, te results of the proposed method are closer to that of the direct Monte Carlo metiod,and it requires much less computational time.展开更多
In order to analyze the risky factors that affect vehicle-cyclist crash injury severity at the intersection area,especially the factors relating to the road users behaviors,an empirical study was conducted by collecti...In order to analyze the risky factors that affect vehicle-cyclist crash injury severity at the intersection area,especially the factors relating to the road users behaviors,an empirical study was conducted by collecting accident records from 2011 to 2015 from the General Estimates System.After preliminary screening,the variables were classified into 5 main categories including cyclists characteristic and behavior,drivers characteristic and behavior,vehicle characteristic,intersection condition,and time.The random parameter ordinal probit(RPOP)was used to study the significant influencing factors and corresponding heterogeneity.The results show that failing to obey traffic signals,failing to yield to right-of-way,dash and drinking before cycling can increase the injury severity for cyclists,and the corresponding fatal injury likelihoods increase by 53.2%,40.0%,86.3%,and 211.5%,respectively.Moreover,drivers inattention,speeding,going straight and left turning increase the risk of crashing for cyclists.The corresponding fatal injury likelihoods increase by 134.5%,186.5%,69.3%,and 22.7%,respectively.Other indicators such as age,gender,vehicle type,traffic signal and intersection type can also affect injury severity.展开更多
This paper aims to study the stochastic period-doubling bifurcation of the three-dimensional Rossler system with an arch-like bounded random parameter. First, we transform the stochastic RSssler system into its equiva...This paper aims to study the stochastic period-doubling bifurcation of the three-dimensional Rossler system with an arch-like bounded random parameter. First, we transform the stochastic RSssler system into its equivalent deterministic one in the sense of minimal residual error by the Chebyshev polynomial approximation method. Then, we explore the dynamical behaviour of the stochastic RSssler system through its equivalent deterministic system by numerical simulations. The numerical results show that some stochastic period-doubling bifurcation, akin to the conventional one in the deterministic case, may also appear in the stochastic Rossler system. In addition, we also examine the influence of the random parameter intensity on bifurcation phenomena in the stochastic Rossler system.展开更多
Stochastic period-doubling bifurcation is explored in a forced Duffing system with a bounded random parameter as an additional weak harmonic perturbation added to the system. Firstly, the biharmonic driven Duffing sys...Stochastic period-doubling bifurcation is explored in a forced Duffing system with a bounded random parameter as an additional weak harmonic perturbation added to the system. Firstly, the biharmonic driven Duffing system with a random parameter is reduced to its equivalent deterministic one, and then the responses of the stochastic system can be obtained by available effective numerical methods. Finally, numerical simulations show that the phase of the additional weak harmonic perturbation has great influence on the stochastic period-doubling bifurcation in the biharmonic driven Duffing system. It is emphasized that, different from the deterministic biharmonic driven Duffing system, the intensity of random parameter in the Duffing system can also be taken as a bifurcation parameter, which can lead to the stochastic period-doubling bifurcations.展开更多
In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model e...In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model estimator in the Random Parameters Logit (RPL) model. The results of our Monte Carlo experiments show that the positive-part Stein-like estimator provides smaller MSE than the pretest estimator in the fully correlated RPL model. Both of them outperform the fully correlated RPL model estimator and provide more accurate information on the share of population putting a positive or negative value on the alternative attributes than the fully correlated RPL model estimates. The Monte Carlo mean estimates of direct elasticity with pretest and positive-part Stein-like estimators are closer to the true value and have smaller standard errors than those with fully correlated RPL model estimator.展开更多
Lane-changing is performed either to follow the route to a planned destination(i.e.,mandatory lane-changing)or to achieve better driving conditions(i.e.,discretionary lane-changing).A connected environment is expected...Lane-changing is performed either to follow the route to a planned destination(i.e.,mandatory lane-changing)or to achieve better driving conditions(i.e.,discretionary lane-changing).A connected environment is expected to assist during lane-changing manoeuvres,but it is not known well how driving aids in a connected environment assist lane-changing execution.As such,this study investigates the impact of a connected environment on lanechanging execution time during mandatory and discretionary lane-changing manoeuvres.To this end,this study designed an advanced driving simulator experiment where 78 drivers performed these manoeuvres on a simulated motorway in three randomised driving conditions.The conditions were baseline(without driving aids),a fully functioning connected environment with a perfect supply of driving aids,and an impaired connected environment with delayed driving aids.The lane-changing execution time has been modelled by a random parameters hazard-based duration modelling approach,which accounts for the panel nature of data and captures the unobserved heterogeneity.Results suggest that,compared to the baseline condition(i.e.,a non-connected environment),most of the drivers in the connected environment take more time to complete their lane-changing manoeuvres,indicating drivers’safer lane-changing execution behaviour in the connected environment.The communication delay driving condition has been found to have more deteriorating effects on mandatory lanechanging manoeuvres than discretionary lane-changing manoeuvres.This study concludes that(i)the connected environment increases safety margin during both lane-changing manoeuvres,and(ii)a higher magnitude of safety margin is observed during mandatory lane-changing manoeuvres whereby drivers have a higher need for assistance.展开更多
Purpose–This study aims to investigate the safety and liability of autonomous vehicles(AVs),and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.Desi...Purpose–This study aims to investigate the safety and liability of autonomous vehicles(AVs),and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.Design/methodology/approach–The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations.The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs,respectively,as well as accommodating the heterogeneity issue simultaneously.Findings–The findings show that day,location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.Originality/value–The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.展开更多
One of the critical areas of road safety is motorcycle safety. Motorcyclists are more vulnerable to injuries than occupants of other motor vehicles when involved in crashes.Researchers have studied the relationships b...One of the critical areas of road safety is motorcycle safety. Motorcyclists are more vulnerable to injuries than occupants of other motor vehicles when involved in crashes.Researchers have studied the relationships between motorcycle crash severity and crash contributing factors. They are crash characteristics, roadway geometric design features,traffic characteristics, socio-demographics and environmental conditions. However, few researchers considered unobserved heterogeneity effects when modeling motorcycle crash injury severities, let alone interaction effects. In this research, motorcycle crashes in Wyoming that occurred from 2008 to 2017 were analyzed. Specifically, the injury severities of single motorcycle crashes and multiple vehicle crashes involving motorcycles were modeled. The response was whether the motorcycle crash incurred an incapacitating injury or fatality or not. The binary logistic regression and mixed binary logistic regression modeling structures were implemented. The mixed models revealed effects that otherwise would have been undisclosed in the binary logistic regression models’ results. According to the results of single motorcycle crashes, the majority of motorcycle-animal crashes and of motorcycle-barrier crashes were likely to be severe relative to other single motorcycle crashes. It was also found that horizontal curves increased the risk of severe injuries.Young riders were found to be less at risk of being gravely injured in single motorcycle crashes than older riders as well. Furthermore, riding under the influence and high posted speed limits increased the odds of severe crashes regardless of whether the crashes were single motorcycle crashes or multiple vehicle crashes involving motorcycles. Additionally,the mixed models uncovered interaction effects and unobserved effects pertaining to speed limits.展开更多
This study investigated the impact of traffic violations on crash injury severity on Wyoming’s interstate highways.A random parameters multinomial logit(MNL)model with heterogeneity in means was estimated as an alter...This study investigated the impact of traffic violations on crash injury severity on Wyoming’s interstate highways.A random parameters multinomial logit(MNL)model with heterogeneity in means was estimated as an alternative to the mixed logit model.This was done to better account for unobserved heterogeneity in the crash data.As per the results,the random parameters model with heterogeneity in means not only exhibited a better fit but also uncovered more insights regarding the factors influencing crash injury severity.The advanced model showed that traffic violations,crash characteristics and environmental characteristics among other factors impact crash injury severity on Wyoming’s interstate highways.With regards to traffic violations,driving too fast for prevailing conditions and driving under the influence of alcohol and drugs were identified as the main violations that significantly influenced crash severity.Among other useful insights,the heterogeneity in mean specification indicated that the likelihood of severe injury crashes is increased by the interactive effect between non-trucks(vehicles not classified as trucks)and driving too fast for conditions.This is a significant implication that high speed behavior by non-truck drivers in adverse weather conditions is ranked as one of the hazardous traffic violations on Wyoming’s interstates.This study provided for the first time important information on the impact of traffic violations on crash severity of crashes that occurred on challenging roadways that characterized by mountainous terrain and severe weather conditions.Results from the study will help enforcement agencies in the state to better identify appropriate countermeasures to mitigate the impact of violations on crash severity.展开更多
This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and...This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features,vehicle profiles,roadway conditions and environment conditions.The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016,including 27 arterials with 16827 injury samples.By quantifying the driving conditions and driving actions,the random parameter structural equation model was built up with measurement variables and latent variables.Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively,while driving conditions and driving actions were quantified and reflected by vehicles,road environment and crash features correspondingly.The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.展开更多
Bicycling has been actively promoted as a clean and efficient mode of commute.Besides,due to the personal and societal benefits it provides,it has been adopted by many city dwellers for short-distance trips.Despite th...Bicycling has been actively promoted as a clean and efficient mode of commute.Besides,due to the personal and societal benefits it provides,it has been adopted by many city dwellers for short-distance trips.Despite the integral role this active transport mode plays,it is unfortunately associated with a high risk of fatalities in the event of a traffic crash as they are not protected.Many studies have been conducted in several jurisdictions to examine the factors contributing to crashes involving these vulnerable road users.In the case of Louisiana which is currently experiencing increased cases of severe and fatal bicycleinvolved crashes,less attention has been paid to investigating the critical factors influencing bicyclist injury severity outcomes using more detailed data and advanced econometric modeling frameworks to help propose adequate policies to improve the safety of riders.Against this background,this study examined the key contributing factors influencing bicyclist injuries by using more detailed roadway crash data spanning 2010-2016 obtained from the state of Louisiana.The study then applies an advanced random parameter logit modeling with heterogeneity in means and variances to address the unobserved heterogeneity issue associated with traffic crash data.To overcome the imbalanced data issue,three major crash injury levels were used instead of the conventional five crash injury levels.Besides,the data groups classified under each injury level were compared for the final variable selection.The study found that distracted drivers,elderly bicyclists,careless operations,and riding in dark conditions increase the probability of having severe injuries in vehicle-bicyclist crashes.Moreover,the variables for straight-level roadways and city streets decrease the odds of severe injuries.The straight-level roadway may provide better sight distance for both drivers and bicyclists,and complex environments like city streets discourage crashes with severe injuries.展开更多
The consequences of flight delay can significantly impact airports’ on‐time performance and airline operations, which have a strong positive correlation with passenger satisfaction. Thus, an accurate investigation o...The consequences of flight delay can significantly impact airports’ on‐time performance and airline operations, which have a strong positive correlation with passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of main importance in decision-making processes. Although statistical models have been traditionally used in flight delay analysis, the presence of unobserved heterogeneity in flight data has been less discussed. This study carried out an empirical analysis to investigate the potential unobserved heterogeneity and the impact of significant variables on flight delay using two modeling approaches. First, preliminary insight into potential significant variables was obtained through a random parameter logit model (also known as the mixed logit model). Then, a Support Vector Machines (SVM) model trained by the Artificial Bee Colony (ABC) algorithm, was employed to explore the non-linear relationship between flight delay outcomes and causal factors. The data-driven analysis was conducted using three-month flight arrival data from Miami International Airport (MIA). A variable impact analysis was also conducted considering the black-box characteristic of the SVM and compared to the effects of variables indented through the random parameter logit modeling framework. While a large unobserved heterogeneity was observed, the impacts of various explanatory variables were examined in terms of flight departure performance, geographical specification of the origin airport, day of month and day of week of the flight, cause of delay, and gate information. The comprehensive assessment of the contributing factors proposed in this study provides invaluable insights into flight delay modeling and analysis.展开更多
The effect of sealed or unsealed road pavements on motorist’s injury severities has not been extensively explored.This study collected a four-year crash dataset(2015–2018)from South Australia to explore this issue.T...The effect of sealed or unsealed road pavements on motorist’s injury severities has not been extensively explored.This study collected a four-year crash dataset(2015–2018)from South Australia to explore this issue.The data shows 3,812 and 1,086 crashes at sealed and unsealed pavement surfaces,respectively,during those years.This study examines the consequence of sealed and unsealed pavements on driver injury severity outcomes of motor vehicle crashes.A mixed logit model was developed by accounting for heterogeneity in means and variances of the random parameters.The variables were distributed among several categories:driver,temporal,spatial,roadway characteristics,crash type,vehicle type,and vehicle movement.Four random parameters were observed in the sealed model,whereas five parameters were in the unsealed one.Moreover,the sealed pavements model showed substantial heterogeneity in means of four of the random parameters,while the unsealed pavements model has some heterogeneity in both means and variances of some of the random parameters.Marginal effect results indicate that two indicator variables have enlarged the likelihood of driver severe injury consequences in sealed,alcohol involvement and posted speed limit>100 km/hr.Additionally,four other significant variables sustain the probability of severe injury outcomes at unsealed pavement like male drivers,middle-aged drivers,rollover crash types,and crashes at straight roads.Based on these variables,various countermeasures were recommended to enhance the safety of both types of pavements.展开更多
In this paper, dynamic simulation of a beam-like structure with a transverse open crack subjected to a random moving mass oscillator is investigated. The simultaneous effect of a crack and a random oscillator has not ...In this paper, dynamic simulation of a beam-like structure with a transverse open crack subjected to a random moving mass oscillator is investigated. The simultaneous effect of a crack and a random oscillator has not been addressed up to now. The crack in the beam at different locations and with different depths is considered as one group of damage, each as an individual imperfection. In addition, bearing immobility is considered as another type of problem in the beam. Mass, stiffness, damping and velocity of the oscillator are assumed to be random parameters. An improved perturbation technique is applied to reduce the simulation time. It was found that there is a maximum value of the variance of each uncertain parameter, in which the maximum reliability of the perturbation method can be achieved, and that this maximum value can be obtained by the Alpha-Hilber Monte-Carlo simulation method. The simulation results reveal that the mass and the velocity uncertainty cause high uncertainty in the deflection of the beam. Also, the pattern of the deflection is not affected by different random oscillator parameters, and as a result, the type of damage can be identified even with high uncertainty. Moreover, the deflection in the nodes around the mid-span of the beam provides the best information regarding the imperfections, and consequently leads to the best sensor locations in an actual experiment.展开更多
Interfacial transition zones （ITZs） between aggregates and mortar are the weakest parts in concrete. The random aggregate generation and packing algorithm was utilized to create a two-phase concrete model, and the z...Interfacial transition zones （ITZs） between aggregates and mortar are the weakest parts in concrete. The random aggregate generation and packing algorithm was utilized to create a two-phase concrete model, and the zero-thickness cohesive elements with different normal distribution parameters were used to model the ITZs with random mechanical properties. A number of uniaxial tension-induced fracture simulations were carried out, and the effects of the random parameters on the fracture behavior of concrete were statistically analyzed. The results show that, different from the dissipated fracture energy, the peak load of concrete does not always obey a normal distribution, when the elastic stiffness, tensile strength, or fracture energy of ITZs is normally distributed. The tensile strength of the ITZs has a significant effect on the fracture behavior of concrete, and its large standard deviation leads to obvious diversity of the fracture path in both location and shape.展开更多
To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural netw...To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.展开更多
In this paper, the Chebyshev polynomial approximation is applied to the problem of stochastic period-doubling bifurcation of a stochastic Bonhoeffer-van der Pol （BVP for short） system with a bounded random parameter...In this paper, the Chebyshev polynomial approximation is applied to the problem of stochastic period-doubling bifurcation of a stochastic Bonhoeffer-van der Pol （BVP for short） system with a bounded random parameter. In the analysis, the stochastic BVP system is transformed by the Chebyshev polynomial approximation into an equivalent deterministic system, whose response can be readily obtained by conventional numerical methods. In this way we have explored plenty of stochastic period-doubling bifurcation phenomena of the stochastic BVP system. The numerical simulations show that the behaviour of the stochastic period-doubling bifurcation in the stochastic BVP system is by and large similar to that in the deterministic mean-parameter BVP system, but there are still some featured differences between them. For example, in the stochastic dynamic system the period-doubling bifurcation point diffuses into a critical interval and the location of the critical interval shifts with the variation of intensity of the random parameter. The obtained results show that Chebyshev polynomial approximation is an effective approach to dynamical problems in some typical nonlinear systems with a bounded random parameter of an arch-like probability density function.展开更多
基金The National Natural Science Foundation of China(No.51378407,51578431)
文摘A new method for calculating the failure probabilityof structures with random parameters is proposed based onmultivariate power polynomial expansion, in which te uncertain quantities include material properties, structuralgeometric characteristics and static loads. The structuralresponse is first expressed as a multivariable power polynomialexpansion, of which the coefficients ae then determined by utilizing the higher-order perturbation technique and Galerkinprojection scheme. Then, the final performance function ofthe structure is determined. Due to the explicitness of theperformance function, a multifold integral of the structuralfailure probability can be calculated directly by the Monte Carlo simulation, which only requires a smal amount ofcomputation time. Two numerical examples ae presented toillustate te accuracy ad efficiency of te proposed metiod. It is shown that compaed with the widely used first-orderreliability method （ FORM） and second-order reliabilitymethod （ SORM）, te results of the proposed method are closer to that of the direct Monte Carlo metiod,and it requires much less computational time.
基金The National Key Research and Development Program of China(No.2017YFC0803902).
文摘In order to analyze the risky factors that affect vehicle-cyclist crash injury severity at the intersection area,especially the factors relating to the road users behaviors,an empirical study was conducted by collecting accident records from 2011 to 2015 from the General Estimates System.After preliminary screening,the variables were classified into 5 main categories including cyclists characteristic and behavior,drivers characteristic and behavior,vehicle characteristic,intersection condition,and time.The random parameter ordinal probit(RPOP)was used to study the significant influencing factors and corresponding heterogeneity.The results show that failing to obey traffic signals,failing to yield to right-of-way,dash and drinking before cycling can increase the injury severity for cyclists,and the corresponding fatal injury likelihoods increase by 53.2%,40.0%,86.3%,and 211.5%,respectively.Moreover,drivers inattention,speeding,going straight and left turning increase the risk of crashing for cyclists.The corresponding fatal injury likelihoods increase by 134.5%,186.5%,69.3%,and 22.7%,respectively.Other indicators such as age,gender,vehicle type,traffic signal and intersection type can also affect injury severity.
基金Project supported by the National Natural Science Foundation of China (Grant No. 10872165)
文摘This paper aims to study the stochastic period-doubling bifurcation of the three-dimensional Rossler system with an arch-like bounded random parameter. First, we transform the stochastic RSssler system into its equivalent deterministic one in the sense of minimal residual error by the Chebyshev polynomial approximation method. Then, we explore the dynamical behaviour of the stochastic RSssler system through its equivalent deterministic system by numerical simulations. The numerical results show that some stochastic period-doubling bifurcation, akin to the conventional one in the deterministic case, may also appear in the stochastic Rossler system. In addition, we also examine the influence of the random parameter intensity on bifurcation phenomena in the stochastic Rossler system.
基金Project supported by the National Natural Science Foundation of China(Grant Nos10472091and10332030)
文摘Stochastic period-doubling bifurcation is explored in a forced Duffing system with a bounded random parameter as an additional weak harmonic perturbation added to the system. Firstly, the biharmonic driven Duffing system with a random parameter is reduced to its equivalent deterministic one, and then the responses of the stochastic system can be obtained by available effective numerical methods. Finally, numerical simulations show that the phase of the additional weak harmonic perturbation has great influence on the stochastic period-doubling bifurcation in the biharmonic driven Duffing system. It is emphasized that, different from the deterministic biharmonic driven Duffing system, the intensity of random parameter in the Duffing system can also be taken as a bifurcation parameter, which can lead to the stochastic period-doubling bifurcations.
文摘In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model estimator in the Random Parameters Logit (RPL) model. The results of our Monte Carlo experiments show that the positive-part Stein-like estimator provides smaller MSE than the pretest estimator in the fully correlated RPL model. Both of them outperform the fully correlated RPL model estimator and provide more accurate information on the share of population putting a positive or negative value on the alternative attributes than the fully correlated RPL model estimates. The Monte Carlo mean estimates of direct elasticity with pretest and positive-part Stein-like estimators are closer to the true value and have smaller standard errors than those with fully correlated RPL model estimator.
基金partly funded by the Australian Research Council grant DP210102970.
文摘Lane-changing is performed either to follow the route to a planned destination(i.e.,mandatory lane-changing)or to achieve better driving conditions(i.e.,discretionary lane-changing).A connected environment is expected to assist during lane-changing manoeuvres,but it is not known well how driving aids in a connected environment assist lane-changing execution.As such,this study investigates the impact of a connected environment on lanechanging execution time during mandatory and discretionary lane-changing manoeuvres.To this end,this study designed an advanced driving simulator experiment where 78 drivers performed these manoeuvres on a simulated motorway in three randomised driving conditions.The conditions were baseline(without driving aids),a fully functioning connected environment with a perfect supply of driving aids,and an impaired connected environment with delayed driving aids.The lane-changing execution time has been modelled by a random parameters hazard-based duration modelling approach,which accounts for the panel nature of data and captures the unobserved heterogeneity.Results suggest that,compared to the baseline condition(i.e.,a non-connected environment),most of the drivers in the connected environment take more time to complete their lane-changing manoeuvres,indicating drivers’safer lane-changing execution behaviour in the connected environment.The communication delay driving condition has been found to have more deteriorating effects on mandatory lanechanging manoeuvres than discretionary lane-changing manoeuvres.This study concludes that(i)the connected environment increases safety margin during both lane-changing manoeuvres,and(ii)a higher magnitude of safety margin is observed during mandatory lane-changing manoeuvres whereby drivers have a higher need for assistance.
基金National Natural Science Foundation of China(No.52072214)the project of Tsinghua University-Toyota Joint Research Center for AI technology of Automated Vehicle(No.TTAD2021-10).
文摘Purpose–This study aims to investigate the safety and liability of autonomous vehicles(AVs),and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.Design/methodology/approach–The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations.The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs,respectively,as well as accommodating the heterogeneity issue simultaneously.Findings–The findings show that day,location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.Originality/value–The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.
文摘One of the critical areas of road safety is motorcycle safety. Motorcyclists are more vulnerable to injuries than occupants of other motor vehicles when involved in crashes.Researchers have studied the relationships between motorcycle crash severity and crash contributing factors. They are crash characteristics, roadway geometric design features,traffic characteristics, socio-demographics and environmental conditions. However, few researchers considered unobserved heterogeneity effects when modeling motorcycle crash injury severities, let alone interaction effects. In this research, motorcycle crashes in Wyoming that occurred from 2008 to 2017 were analyzed. Specifically, the injury severities of single motorcycle crashes and multiple vehicle crashes involving motorcycles were modeled. The response was whether the motorcycle crash incurred an incapacitating injury or fatality or not. The binary logistic regression and mixed binary logistic regression modeling structures were implemented. The mixed models revealed effects that otherwise would have been undisclosed in the binary logistic regression models’ results. According to the results of single motorcycle crashes, the majority of motorcycle-animal crashes and of motorcycle-barrier crashes were likely to be severe relative to other single motorcycle crashes. It was also found that horizontal curves increased the risk of severe injuries.Young riders were found to be less at risk of being gravely injured in single motorcycle crashes than older riders as well. Furthermore, riding under the influence and high posted speed limits increased the odds of severe crashes regardless of whether the crashes were single motorcycle crashes or multiple vehicle crashes involving motorcycles. Additionally,the mixed models uncovered interaction effects and unobserved effects pertaining to speed limits.
基金funded by the Wyoming Department of Transportation
文摘This study investigated the impact of traffic violations on crash injury severity on Wyoming’s interstate highways.A random parameters multinomial logit(MNL)model with heterogeneity in means was estimated as an alternative to the mixed logit model.This was done to better account for unobserved heterogeneity in the crash data.As per the results,the random parameters model with heterogeneity in means not only exhibited a better fit but also uncovered more insights regarding the factors influencing crash injury severity.The advanced model showed that traffic violations,crash characteristics and environmental characteristics among other factors impact crash injury severity on Wyoming’s interstate highways.With regards to traffic violations,driving too fast for prevailing conditions and driving under the influence of alcohol and drugs were identified as the main violations that significantly influenced crash severity.Among other useful insights,the heterogeneity in mean specification indicated that the likelihood of severe injury crashes is increased by the interactive effect between non-trucks(vehicles not classified as trucks)and driving too fast for conditions.This is a significant implication that high speed behavior by non-truck drivers in adverse weather conditions is ranked as one of the hazardous traffic violations on Wyoming’s interstates.This study provided for the first time important information on the impact of traffic violations on crash severity of crashes that occurred on challenging roadways that characterized by mountainous terrain and severe weather conditions.Results from the study will help enforcement agencies in the state to better identify appropriate countermeasures to mitigate the impact of violations on crash severity.
基金supported by National Natural Science Foundation of China(No.52072214).
文摘This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features,vehicle profiles,roadway conditions and environment conditions.The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016,including 27 arterials with 16827 injury samples.By quantifying the driving conditions and driving actions,the random parameter structural equation model was built up with measurement variables and latent variables.Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively,while driving conditions and driving actions were quantified and reflected by vehicles,road environment and crash features correspondingly.The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.
文摘Bicycling has been actively promoted as a clean and efficient mode of commute.Besides,due to the personal and societal benefits it provides,it has been adopted by many city dwellers for short-distance trips.Despite the integral role this active transport mode plays,it is unfortunately associated with a high risk of fatalities in the event of a traffic crash as they are not protected.Many studies have been conducted in several jurisdictions to examine the factors contributing to crashes involving these vulnerable road users.In the case of Louisiana which is currently experiencing increased cases of severe and fatal bicycleinvolved crashes,less attention has been paid to investigating the critical factors influencing bicyclist injury severity outcomes using more detailed data and advanced econometric modeling frameworks to help propose adequate policies to improve the safety of riders.Against this background,this study examined the key contributing factors influencing bicyclist injuries by using more detailed roadway crash data spanning 2010-2016 obtained from the state of Louisiana.The study then applies an advanced random parameter logit modeling with heterogeneity in means and variances to address the unobserved heterogeneity issue associated with traffic crash data.To overcome the imbalanced data issue,three major crash injury levels were used instead of the conventional five crash injury levels.Besides,the data groups classified under each injury level were compared for the final variable selection.The study found that distracted drivers,elderly bicyclists,careless operations,and riding in dark conditions increase the probability of having severe injuries in vehicle-bicyclist crashes.Moreover,the variables for straight-level roadways and city streets decrease the odds of severe injuries.The straight-level roadway may provide better sight distance for both drivers and bicyclists,and complex environments like city streets discourage crashes with severe injuries.
文摘The consequences of flight delay can significantly impact airports’ on‐time performance and airline operations, which have a strong positive correlation with passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of main importance in decision-making processes. Although statistical models have been traditionally used in flight delay analysis, the presence of unobserved heterogeneity in flight data has been less discussed. This study carried out an empirical analysis to investigate the potential unobserved heterogeneity and the impact of significant variables on flight delay using two modeling approaches. First, preliminary insight into potential significant variables was obtained through a random parameter logit model (also known as the mixed logit model). Then, a Support Vector Machines (SVM) model trained by the Artificial Bee Colony (ABC) algorithm, was employed to explore the non-linear relationship between flight delay outcomes and causal factors. The data-driven analysis was conducted using three-month flight arrival data from Miami International Airport (MIA). A variable impact analysis was also conducted considering the black-box characteristic of the SVM and compared to the effects of variables indented through the random parameter logit modeling framework. While a large unobserved heterogeneity was observed, the impacts of various explanatory variables were examined in terms of flight departure performance, geographical specification of the origin airport, day of month and day of week of the flight, cause of delay, and gate information. The comprehensive assessment of the contributing factors proposed in this study provides invaluable insights into flight delay modeling and analysis.
文摘The effect of sealed or unsealed road pavements on motorist’s injury severities has not been extensively explored.This study collected a four-year crash dataset(2015–2018)from South Australia to explore this issue.The data shows 3,812 and 1,086 crashes at sealed and unsealed pavement surfaces,respectively,during those years.This study examines the consequence of sealed and unsealed pavements on driver injury severity outcomes of motor vehicle crashes.A mixed logit model was developed by accounting for heterogeneity in means and variances of the random parameters.The variables were distributed among several categories:driver,temporal,spatial,roadway characteristics,crash type,vehicle type,and vehicle movement.Four random parameters were observed in the sealed model,whereas five parameters were in the unsealed one.Moreover,the sealed pavements model showed substantial heterogeneity in means of four of the random parameters,while the unsealed pavements model has some heterogeneity in both means and variances of some of the random parameters.Marginal effect results indicate that two indicator variables have enlarged the likelihood of driver severe injury consequences in sealed,alcohol involvement and posted speed limit>100 km/hr.Additionally,four other significant variables sustain the probability of severe injury outcomes at unsealed pavement like male drivers,middle-aged drivers,rollover crash types,and crashes at straight roads.Based on these variables,various countermeasures were recommended to enhance the safety of both types of pavements.
文摘In this paper, dynamic simulation of a beam-like structure with a transverse open crack subjected to a random moving mass oscillator is investigated. The simultaneous effect of a crack and a random oscillator has not been addressed up to now. The crack in the beam at different locations and with different depths is considered as one group of damage, each as an individual imperfection. In addition, bearing immobility is considered as another type of problem in the beam. Mass, stiffness, damping and velocity of the oscillator are assumed to be random parameters. An improved perturbation technique is applied to reduce the simulation time. It was found that there is a maximum value of the variance of each uncertain parameter, in which the maximum reliability of the perturbation method can be achieved, and that this maximum value can be obtained by the Alpha-Hilber Monte-Carlo simulation method. The simulation results reveal that the mass and the velocity uncertainty cause high uncertainty in the deflection of the beam. Also, the pattern of the deflection is not affected by different random oscillator parameters, and as a result, the type of damage can be identified even with high uncertainty. Moreover, the deflection in the nodes around the mid-span of the beam provides the best information regarding the imperfections, and consequently leads to the best sensor locations in an actual experiment.
基金supported by the National Basic Research Program of China (973 Program:2011CB013800)
文摘Interfacial transition zones （ITZs） between aggregates and mortar are the weakest parts in concrete. The random aggregate generation and packing algorithm was utilized to create a two-phase concrete model, and the zero-thickness cohesive elements with different normal distribution parameters were used to model the ITZs with random mechanical properties. A number of uniaxial tension-induced fracture simulations were carried out, and the effects of the random parameters on the fracture behavior of concrete were statistically analyzed. The results show that, different from the dissipated fracture energy, the peak load of concrete does not always obey a normal distribution, when the elastic stiffness, tensile strength, or fracture energy of ITZs is normally distributed. The tensile strength of the ITZs has a significant effect on the fracture behavior of concrete, and its large standard deviation leads to obvious diversity of the fracture path in both location and shape.
基金supported by the China State Railway Group Co.,Ltd.Science and Technology Research and Development Program Project(Grant No.L2024G007)the Natural Science Foundation of Hunan Province(Grant No.2024JJ5427)+1 种基金the National Natural Science Foundation of China(Grant No.52478321,52078485)the Science and Technology Research and Development Program Project of China Railway Group Limited(Grant No.2021-Special-08,2022-Key-06&2023-Key-22).
文摘To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.
基金Project supported by the Major Program of the National Natural Science Foundation of China, China （Grant No 10332030）, the National Natural Science Foundation of China （Grant No 10472091）, and the Graduate Starting Seed Fund of Northwestern Polytechnical University, China （Grant No Z200655）.
文摘In this paper, the Chebyshev polynomial approximation is applied to the problem of stochastic period-doubling bifurcation of a stochastic Bonhoeffer-van der Pol （BVP for short） system with a bounded random parameter. In the analysis, the stochastic BVP system is transformed by the Chebyshev polynomial approximation into an equivalent deterministic system, whose response can be readily obtained by conventional numerical methods. In this way we have explored plenty of stochastic period-doubling bifurcation phenomena of the stochastic BVP system. The numerical simulations show that the behaviour of the stochastic period-doubling bifurcation in the stochastic BVP system is by and large similar to that in the deterministic mean-parameter BVP system, but there are still some featured differences between them. For example, in the stochastic dynamic system the period-doubling bifurcation point diffuses into a critical interval and the location of the critical interval shifts with the variation of intensity of the random parameter. The obtained results show that Chebyshev polynomial approximation is an effective approach to dynamical problems in some typical nonlinear systems with a bounded random parameter of an arch-like probability density function.
