The location of U-turn bays is an important consideration in indirect driveway left-turn treatments.In order to improve the performance of right-turns followed by U-turns（RTUTs）,this study evaluates the impacts of t...The location of U-turn bays is an important consideration in indirect driveway left-turn treatments.In order to improve the performance of right-turns followed by U-turns（RTUTs）,this study evaluates the impacts of the separation distances between driveway exits and downstream U-turn locations on the safety and operational performance of vehicles making RTUTs.Crash data are investigated at 179 selected roadway segments,and travel time data are measured using video cameras at 29 locations in the state of Florida,USA.Crash rate models and travel time models are developed based on data collected in the field.It is found that the separation distance between driveway exits and downstream U-turn locations significantly impacts the safety and operational performance of vehicles making right turns followed by U-turns.Based on the research results,the minimum and optimal separation distances between driveways and U-turn locations under different roadway conditions are determined to facilitate driver use of RTUTs.The results of this study can be used for future intersection improvement projects in China.展开更多
Understanding the characteristics of time and distance gaps between the primary(PC)and secondary crashes(SC)is crucial for preventing SC ccurrences and improving road safety.Although previous studies have tried to ana...Understanding the characteristics of time and distance gaps between the primary(PC)and secondary crashes(SC)is crucial for preventing SC ccurrences and improving road safety.Although previous studies have tried to analyse the variation of gaps,there is limited evidence in quantifying the relationships between different gaps and various influential factors.This study proposed a two-layer stacking framework to discuss the time and distance gaps.Specifically,the framework took random forests(RF),gradient boosting decision tree(GBDT)and eXtreme gradient boosting as the base classifiers in the first layer and applied logistic regression(LR)as a combiner in the second layer.On this basis,the local interpretable model-agnostic explanations(LIME)technology was used to interpret the output of the stacking model from both local and global perspectives.Through SC dentification and feature selection,346 SCs and 22 crash-related factors were collected from California interstate freeways.The results showed that the stacking model outperformed base models evaluated by accuracy,precision,and recall indicators.The explanations based on LIME suggest that collision type,distance,speed and volume are the critical features that affect the time and distance gaps.Higher volume can prolong queue length and increase the distance gap from the SCs to PCs.And collision types,peak periods,workday,truck involved and tow away likely induce a long-distance gap.Conversely,there is a shorter distance gap when secondary roads run in the same direction and are close to the primary roads.Lower speed is a significant factor resulting in a long-time gap,while the higher speed is correlated with a short-time gap.These results are expected to provide insights into how contributory features affect the time and distance gaps and help decision-makers develop accurate decisions to prevent SCs.展开更多
Research into the impact of road accidents on drivers is essential to effective post-crash interventions.However,due to limited data and resources,the current research focus is mainly on those who have suffered severe...Research into the impact of road accidents on drivers is essential to effective post-crash interventions.However,due to limited data and resources,the current research focus is mainly on those who have suffered severe injuries.In this paper,we propose a novel approach to examining the impact that being involved in a crash has on drivers by using traffic surveillance data.In traffic video surveillance systems,the locations of vehicles at different moments in time are captured and their headway,which is an important indicator of driving behavior,can be calculated from this information.It was found that there was a sudden increase in headway when drivers return to the road after being involved in a crash,but that the headway returned to its pre-crash level over time.We further analyzed the duration of the decay using a Cox proportional hazards regression model,which revealed many significant factors(related to the driver,vehicle,and nature of the accident)behind the survival time of the increased headway.Our approach is able to reveal the crash impact on drivers in a convenient and economical way.It can enhance the understanding of the impact of a crash on drivers,and help to devise more effective re-education programs and other interventions to encourage drivers who are involved in crashes to drive more safely in the future.展开更多
Travel time reliability(TTR) modeling has gain attention among researchers’ due to its ability to represent road user satisfaction as well as providing a predictability of a trip travel time.Despite this significant ...Travel time reliability(TTR) modeling has gain attention among researchers’ due to its ability to represent road user satisfaction as well as providing a predictability of a trip travel time.Despite this significant effort,its impact on the severity of a crash is not well explored.This study analyzes the effect of TTR and other variables on the probability of the crash severity occurring on arterial roads.To address the unobserved heterogeneity problem,two random-effect regressions were applied;the Dirichlet random-effect(DRE)and the traditional random-effect(TRE) logistic regression.The difference between the two models is that the random-effect in the DRE is non-parametrically specified while in the TRE model is parametrically specified.The Markov Chain Monte Carlo simulations were adopted to infer the parameters’ posterior distributions of the two developed models.Using four-year police-reported crash data and travel speeds from Northeast Florida,the analysis of goodness-of-fit found the DRE model to best fit the data.Hence,it was used in studying the influence of TTR and other variables on crash severity.The DRE model findings suggest that TTR is statistically significant,at 95 percent credible intervals,influencing the severity level of a crash.A unit increases in TTR reduces the likelihood of a severe crash occurrence by 25 percent.Moreover,among the significant variables,alcohol/drug impairment was found to have the highest impact in influencing the occurrence of severe crashes.Other significant factors included traffic volume,weekends,speed,work-zone,land use,visibility,seatbelt usage,segment length,undivided/divided highway,and age.展开更多
Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with...Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.展开更多
现有的高速公路实时事故预测模型对高速公路信息化采集设备的布设密度和采集的数据粒度要求很高,在低信息化的高速公路管理工作上难以得到应用.结合国内高速公路信息化现状,使用单个检测器所采集的数据,对髙速公路追尾事故实时风险进行...现有的高速公路实时事故预测模型对高速公路信息化采集设备的布设密度和采集的数据粒度要求很高,在低信息化的高速公路管理工作上难以得到应用.结合国内高速公路信息化现状,使用单个检测器所采集的数据,对髙速公路追尾事故实时风险进行研究.基于江苏省扬州市启扬髙速公路上布设的超声波交通流检测器所采集的交通流数据,采用配对案例对照方法和二元逻辑回归,建立了双车道高速公路追尾事故实时预测模型.对事故前5-20 m in的交通流数据分别构建流量时空矩阵、速度时空矩阵、平均车头间距时空矩阵,通过引人矩阵特征值简化建模过程并避免了指标间的相关性过高问题.模型总体精度85.7%,事故预测精度33.3%,误报率低于2% ,相比已有模型总体预测精度较高,误报率较低,表明了该方法应用于追尾事故实时预测领域的可行性和有效性.展开更多
This paper presents an efficient recovery scheme suitable for real-time mainmemory database. In the recovery scheme, log records are stored in non-volatile RAM which is dividedinto four different partitions based on t...This paper presents an efficient recovery scheme suitable for real-time mainmemory database. In the recovery scheme, log records are stored in non-volatile RAM which is dividedinto four different partitions based on transaction types. Similarly, a main memory database isdivided into four partitions based data types. When the using ratio of log store area exceeds thethreshold value, checkpoint procedure is triggered. During executing checkpoint procedure, someuseless log records are deleted. During restart recovery after a crash, partition reloading policyis adopted to assure that critical data are reloaded and restored in advance, so that the databasesystem can be brought up before the entire database is reloaded into main memory. Therefore downtime is obvionsly reduced. Simulation experiments show our recovery scheme obviously improves thesystem performance, and does a favor to meet the dtadlints of real-time transactions.展开更多
文摘The location of U-turn bays is an important consideration in indirect driveway left-turn treatments.In order to improve the performance of right-turns followed by U-turns（RTUTs）,this study evaluates the impacts of the separation distances between driveway exits and downstream U-turn locations on the safety and operational performance of vehicles making RTUTs.Crash data are investigated at 179 selected roadway segments,and travel time data are measured using video cameras at 29 locations in the state of Florida,USA.Crash rate models and travel time models are developed based on data collected in the field.It is found that the separation distance between driveway exits and downstream U-turn locations significantly impacts the safety and operational performance of vehicles making right turns followed by U-turns.Based on the research results,the minimum and optimal separation distances between driveways and U-turn locations under different roadway conditions are determined to facilitate driver use of RTUTs.The results of this study can be used for future intersection improvement projects in China.
基金This research was funded in part by Innovation-Driven Project of Central South University(Grant No.2020CX041)the Fundamental Research Funds for the Central Universities of Central South University(Grant No.2022ZZTS0717)。
文摘Understanding the characteristics of time and distance gaps between the primary(PC)and secondary crashes(SC)is crucial for preventing SC ccurrences and improving road safety.Although previous studies have tried to analyse the variation of gaps,there is limited evidence in quantifying the relationships between different gaps and various influential factors.This study proposed a two-layer stacking framework to discuss the time and distance gaps.Specifically,the framework took random forests(RF),gradient boosting decision tree(GBDT)and eXtreme gradient boosting as the base classifiers in the first layer and applied logistic regression(LR)as a combiner in the second layer.On this basis,the local interpretable model-agnostic explanations(LIME)technology was used to interpret the output of the stacking model from both local and global perspectives.Through SC dentification and feature selection,346 SCs and 22 crash-related factors were collected from California interstate freeways.The results showed that the stacking model outperformed base models evaluated by accuracy,precision,and recall indicators.The explanations based on LIME suggest that collision type,distance,speed and volume are the critical features that affect the time and distance gaps.Higher volume can prolong queue length and increase the distance gap from the SCs to PCs.And collision types,peak periods,workday,truck involved and tow away likely induce a long-distance gap.Conversely,there is a shorter distance gap when secondary roads run in the same direction and are close to the primary roads.Lower speed is a significant factor resulting in a long-time gap,while the higher speed is correlated with a short-time gap.These results are expected to provide insights into how contributory features affect the time and distance gaps and help decision-makers develop accurate decisions to prevent SCs.
基金supported by the National Natural Science Foundation of China(No.71671100)the Joint Research Scheme of the National Natural Science Foundation of China/Research Grants Council of Hong Kong(Nos.71561167001 and N HKU707)+1 种基金the Director Foundation Project of National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data(PSRPC)the Research Funds of Tsinghua University(No.20151080412).
文摘Research into the impact of road accidents on drivers is essential to effective post-crash interventions.However,due to limited data and resources,the current research focus is mainly on those who have suffered severe injuries.In this paper,we propose a novel approach to examining the impact that being involved in a crash has on drivers by using traffic surveillance data.In traffic video surveillance systems,the locations of vehicles at different moments in time are captured and their headway,which is an important indicator of driving behavior,can be calculated from this information.It was found that there was a sudden increase in headway when drivers return to the road after being involved in a crash,but that the headway returned to its pre-crash level over time.We further analyzed the duration of the decay using a Cox proportional hazards regression model,which revealed many significant factors(related to the driver,vehicle,and nature of the accident)behind the survival time of the increased headway.Our approach is able to reveal the crash impact on drivers in a convenient and economical way.It can enhance the understanding of the impact of a crash on drivers,and help to devise more effective re-education programs and other interventions to encourage drivers who are involved in crashes to drive more safely in the future.
基金the Center for Accessibility and Safety for an Aging Population at Florida State UniversityFlorida A&M UniversityUniversity of North Florida for funding support in research
文摘Travel time reliability(TTR) modeling has gain attention among researchers’ due to its ability to represent road user satisfaction as well as providing a predictability of a trip travel time.Despite this significant effort,its impact on the severity of a crash is not well explored.This study analyzes the effect of TTR and other variables on the probability of the crash severity occurring on arterial roads.To address the unobserved heterogeneity problem,two random-effect regressions were applied;the Dirichlet random-effect(DRE)and the traditional random-effect(TRE) logistic regression.The difference between the two models is that the random-effect in the DRE is non-parametrically specified while in the TRE model is parametrically specified.The Markov Chain Monte Carlo simulations were adopted to infer the parameters’ posterior distributions of the two developed models.Using four-year police-reported crash data and travel speeds from Northeast Florida,the analysis of goodness-of-fit found the DRE model to best fit the data.Hence,it was used in studying the influence of TTR and other variables on crash severity.The DRE model findings suggest that TTR is statistically significant,at 95 percent credible intervals,influencing the severity level of a crash.A unit increases in TTR reduces the likelihood of a severe crash occurrence by 25 percent.Moreover,among the significant variables,alcohol/drug impairment was found to have the highest impact in influencing the occurrence of severe crashes.Other significant factors included traffic volume,weekends,speed,work-zone,land use,visibility,seatbelt usage,segment length,undivided/divided highway,and age.
基金supported by the National Natural Science Foundation (71301119)the Shanghai Natural Science Foundation (12ZR1434100)
文摘Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.
文摘现有的高速公路实时事故预测模型对高速公路信息化采集设备的布设密度和采集的数据粒度要求很高,在低信息化的高速公路管理工作上难以得到应用.结合国内高速公路信息化现状,使用单个检测器所采集的数据,对髙速公路追尾事故实时风险进行研究.基于江苏省扬州市启扬髙速公路上布设的超声波交通流检测器所采集的交通流数据,采用配对案例对照方法和二元逻辑回归,建立了双车道高速公路追尾事故实时预测模型.对事故前5-20 m in的交通流数据分别构建流量时空矩阵、速度时空矩阵、平均车头间距时空矩阵,通过引人矩阵特征值简化建模过程并避免了指标间的相关性过高问题.模型总体精度85.7%,事故预测精度33.3%,误报率低于2% ,相比已有模型总体预测精度较高,误报率较低,表明了该方法应用于追尾事故实时预测领域的可行性和有效性.
文摘This paper presents an efficient recovery scheme suitable for real-time mainmemory database. In the recovery scheme, log records are stored in non-volatile RAM which is dividedinto four different partitions based on transaction types. Similarly, a main memory database isdivided into four partitions based data types. When the using ratio of log store area exceeds thethreshold value, checkpoint procedure is triggered. During executing checkpoint procedure, someuseless log records are deleted. During restart recovery after a crash, partition reloading policyis adopted to assure that critical data are reloaded and restored in advance, so that the databasesystem can be brought up before the entire database is reloaded into main memory. Therefore downtime is obvionsly reduced. Simulation experiments show our recovery scheme obviously improves thesystem performance, and does a favor to meet the dtadlints of real-time transactions.
